AI promised to free us from the mundane so we could focus on more creative, meaningful, human work. But somewhere along the way, many of us started using it to do more, move faster, and produce endlessly, often without asking whether any of it was making us better.

At Synthesia Live in New York, I closed the conference with a different kind of AI conversation…what AI is doing to the way we think, learn, communicate, and lead. Of all things, this is our moment to design a future where people become more capable, experiences become more personal, and work becomes more human.

(Full talk at the end)

Let’s talk about what happens when AI changes us?

How we think, how we learn, how we communicate, how we create, how we lead, how we show up for customers, employees, students, and one another, how we perceive and how we’re perceived. We are and aren’t changing with how we use AI. Research shows that we’re starting to let AI do our thinking for us. We’re starting to sound and write like AI. We’re imposing an AI Tax on our friends, communities, and colleagues. We’re exchanging quality for quantity, speed for uniqueness, and content for context.

At the same time, the same is true in our work. We’re largely employing AI to do yesterday’s work. Most companies are still modernizing the past with AI. We’re doing the same in our day-to-day work, all in the name of productivity boosts.

Businesses are taking legacy processes, legacy training programs, legacy service models, legacy communications, and wrapping them in shiny new AI interfaces. They are making yesterday more efficient, cheaper, and scalable.

But faster old work is still old work.

And in what’s becoming an AI-first world, for better or worse, the opportunity isn’t to do what we already know more efficiently. The opportunity is to do what we couldn’t do before.

That was the real message of my keynote.

AI automation is only the beginning of a new AI status quo. The message I wanted to share is that the future belongs to those who use AI to augment people and help them grow, personalize experiences, and build entirely new forms of capability.

The Control-Alt-Delete Moment Facing All of Us

The night before Synthesia Live, I had flown in from Silicon Valley where I met with a group of executives from Australian universities and institutions. They were on the last stop of an AI tour and made time to visit us at ServiceNowHQ.

They had spent days meeting companies, founders, researchers, and AI leaders. They had seen enough to understand that education, work, skills, and institutions were not simply facing a technology upgrade. It was a test of vision and leadership.

By the end of our conversation, I asked them what they wanted to take back home. What was the one thing they wanted to change?

The answer was legacy.

Everything about how their institutions taught, measured, supported, and prepared people for the future was suddenly open for reconsideration. They didn’t want to go home as executives defending the institution. They wanted to go home as students, ready to learn and unlearn.

That is the mindshift AI requires.

It is not about asking, “How do we use AI to make our current model more efficient?”

It is asking, “What about yesterday deserves to live tomorrow?”

And then, with equal honesty, “What must we now do differently because the future is no longer waiting for us?”

The Student Who Built a Machine to Avoid Handwriting Homework

I shared a story during the keynote about a high school student in India. His school reportedly banned AI-generated homework and required assignments to be handwritten so students would have to think about what they were writing.

So, he built a machine that could write in his handwriting while AI generated the content.

@taylorlorenzI feel like this kid should never have to do homework again?! Also it does math! #tech #technology #ai #creator #creatoreconomy #success #artificialintelligence #siliconvalley #contentcreator #innovation #influencer

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Now, the point is not to celebrate cheating. The point is to notice what happens when human imagination collides with legacy constraints.

A student saw the system, understood the constraint, and redesigned around it. You could say, he understood the assignment. Ha!

That is creativity.

That is also a warning.

If students can reimagine the future of homework, leaders can reimagine the future of work, learning, service, and communication. But only if they’re willing to stop protecting the past.

Said No Customer Ever

One of my opening slides read:

“I hope I get to talk to a customer service AI chatbot today.” – Said no customer ever.

Another read:

“I hope I get to take an AI audio training course today.” – Said no employee ever.

We laugh because it is true.

While AI is advancing at an exponential pace, many experiences are still designed in ways that are counterintuitive to how people now think, learn, and behave.

As a digital anthropologist, I’ve long studied how technology reshapes behavior. Think about TikTok. When people scroll through endless short-form videos, they begin to process information differently. Attention changes. Expectations change. Learning patterns change. Then we hand that same person a 90-minute training video, a PDF, or a textbook and wonder why engagement collapses.

AI is doing something similar.

We are learning to work differently. Think differently. Ask differently. Create differently. But most enterprise systems still expect people to behave as if nothing has changed.

The future of learning, service, and communication is not more content at efficiency and scale. It is more intuitive, adaptive, and human-centered experiences.

And that’s the challenge. Productivity is not the summit, although it is touted as such. Personalization at scale, learning, service, marketing, is the real win.

Cognitive dAIrwinism

There is another side to this story.

AI did not arrive with an instruction manual. It didn’t come with a parent, a teacher, or a wise mentor telling us how to use it in ways that make us better.

So, we are already seeing new failure modes emerge.

Cognitive offloading. AI atrophy. Cognitive debt. Digital amnesia. AI sycophancy. AI slop. AI brain fry. Cognitive surrender.

This is what I call cognitive dAIrwinism.

How we use AI shapes how we think. If we use AI to think for us, we risk outsourcing the very capabilities we need to strengthen in an era of AI. If we use it to move faster without judgment, we scale mediocrity and miss new opportunities. If we use it to generate more content without care, we create AI slop.

And slop has a cost. It’s called an AI Tax.

It is the hidden labor required to review, correct, rewrite, validate, and tolerate low-quality AI output. Employees feel it every day. It shows up as rework and editing, frustration, lost trust, more questions and more meetings, more requests like, “can you make this sound less like AI?”

Speed is becomes the norm, but hopefully not the goal.

When quality gives way to quantity, trust becomes the casualty. And trust is priceless.

AI is Not Taking Jobs, It is Taking Tasks

The headlines tell us AI is coming for jobs. And yes, there will be disruption. There already is. But there is an important distinction leaders must understand. AI automates tasks. Jobs are bundles of tasks, judgment, relationships, context, accountability, empathy, creativity, and decision-making.

Agents can execute tasks at scale. But they still need human intention, supervision, standards, ethics, and human imagination. Whether humans are in the loop or above the loop, the future of work still needs people.

The question is whether companies use AI to automate people out of work or augment people into more valuable work.

The World Economic Forum estimates that 170 million new roles will be created this decade, even as 92 million are displaced. It also projects that 39% of core skills will change by 2030, and 63% of employers already cite skills gaps as the biggest barrier to transformation.

This is bigger than HR. This is a leadership mandate.

If skills are changing in real time, learning has to show up in real time. If AI is changing work, learning cannot remain a library of courses people visit when they have time. It has to become a living system that coaches, adapts, guides, and improves people in the flow of work at their pace of learning, closing the skills gaps that unique to each person.

Training has to change. We’re not aiming to check more boxes around training, the goal is to make people measurably better. Measurably happier. Measurably more capable.

From AI Fluency to Augmented Intelligence

A lot of organizations are talking about AI fluency right now. That is good. But basic fluency is not enough. The question is, what kind of fluency are we scaling? What are the skills needed to compete with AI and what are the gaps across the organization.

Are we teaching people how to use AI to create more slop? Or are we teaching them how to do what they couldn’t do without AI and what AI couldn’t do without them?

OpenAI has described a capability overhang: the widening gap between what frontier models can do and how most people actually use them. According to OpenAI, typical power users rely on roughly 7x more advanced thinking capabilities than typical users. That means there is already a gap among AI users.

The capability overhang doesn’t measure people who have AI and those who do not. It measures the capability and output in thinking and working with AI vs. those who only know how to prompt it.

This is why I believe we need to move from AIQ as an “Artificial Intelligence Quotient” to AIQ as “Augmented Intelligence Quotient.” It helps leaders and people balance all the critical “quotients” necessary to navigate a future that has yet to be charted, IQ, EQ (emotional intelligence), SQ (social quotient), AQ (adversity quotient)

Artificial intelligence quotient, as many describe it, is about tool use.

Augmented Intelligence Quotient is about human expansion.

AIQ is how we use AI to scale thinking, creativity, decision velocity, problem-solving, empathy, and imagination. It is the ability to work with AI in ways that make us more capable, not more dependent.

Otherwise, we are not using AI.AI is using us.

Iterative AI and Innovative AI

As Dave Wright and I outline in our new book, “Infinite: How Visionary Leaders Transform Today’s Businesses into AI-Forward Companies,” there are two forms of AI strategy every leader needs to understand.

The first is iterative AI.

Iterative AI improves what already exists. It removes routine work, reduces bottlenecks, accelerates workflows, scales scripting, localization, updating, distribution, and reporting. It helps make current learning more visual, engaging, personalized, and easier to retain. It helps managers coach more consistently. It helps teams work faster and more efficiently.

This is important work.

Some of yesterday deserves to live tomorrow. Iterative AI helps make that work better.

But then there is also innovative AI.

Innovative AI creates new value. It asks what becomes possible when expertise can scale visually, conversationally, and personally. It asks why learning is still treated as content delivery instead of capability building. It asks what happens when video is no longer passive, but interactive, adaptive, and human-like in every experience.

It asks:

What if every employee had access to an on-demand coach, tutor, role-play partner, or guide?

What if onboarding adapted to each person’s role, skill level, region, language, and confidence?

What if sales enablement became practice, not passive consumption?

What if leadership communication became interactive and measurable?

What if learning happened exactly when someone needed to decide, act, or improve?

Iterative AI makes the old model better.

Innovative AI makes new models possible.

And disruption happens when new things make old things obsolete.

The good news is that disruption doesn’t have to happen to you. It can happen because of you.

AI Video as the Adaptive Interface for Human Capability

For years, video was a broadcast format. One person to many…one message, one version, one language, one pace, one experience.

With AI, video becomes something else.

It becomes a scalable interface for human-centered, personalized experiences in learning, service, onboarding, research, enablement, and communication.

The first chapter of AI in learning was asset acceleration. We used it to create faster, translate faster, localize, update, reduce production costs, and increase reach.

The next chapter is capability acceleration…auditing and closing the gap causing the capability overhang.

This is where AI video, agents, data, and workflows converge to create adaptive experiences that don’t just deliver information, but help people become better at what they do and where they need to be.

At the Synthesia event, data showed that leaders expect AI to drive more personalized learning, wider internal reach, and better learner engagement. That is the right direction. But the opportunity is bigger than engagement.

Engagement is not a click.

Experience is not an NPS score.

An experience is how someone feels in a moment. What they remember. What they do next.

A great learning experience should build capability and confidence. A great service experience should reduce friction, create trust, and build relationships. A great communication experience should make people feel seen, informed, and capable.

AI video should be used to make learning and service more human.

The IKEA Lesson: Automate the Routine, Augment the Human

One of my favorite examples is IKEA.

IKEA introduced an AI chatbot named Billie, a nod to its famous bookcase. Billie was designed to handle routine customer inquiries and deflect cases that did not need to escalate to a human. It worked. Billie handled nearly half of customer inquiries.

A traditional automation mindset would stop there.

The board slide writes itself: AI deflects 47% of inquiries. Fewer calls = lower cost. Reduce headcount. And boom…ROI achieved.

But IKEA did something more interesting.

They studied the other engagements.

What couldn’t Billie solve?

Inside those unresolved inquiries, they found a signal. Customers were asking for help with interior design, room planning, and product decisions. They didn’t just need answers. They needed advice. Taste. Context. Judgment. You know, the things where humans thrive in their help.

So, IKEA reskilled thousands of customer service employees into remote interior design advisors. They turned a cost center into a growth channel that generated roughly €1.3 billion in sales in its first year.

This represented 4% toppling growth that year.

And that is the difference between automation and augmentation.

Automation asks: “How many people can we remove?”

Augmentation asks: “What new value can our people create now that AI has removed the routine?”

That is the future of work.

We’re talking about fewer people doing less work. We’re talking about more capable people doing more meaningful work creating and delivering new value, better experiences, and more meaningful relationships.

The Future OS of Learning

For learning and development leaders, this moment is profound.

The future of L&D is not a larger library of AI created content. It is a living system that powers the future of work. It’s a system that can sense skill gaps, personalize development, coach in real time, simulate scenarios, support managers, guide decisions, measure progress, and adapt as work changes.

In this future, L&D is where intelligence lives. L&D becomes a capability engine. HR becomes a reinvention partner. IT becomes the infrastructure for human and agent collaboration. Leaders become designers of new ways of working. And employees become active participants in their own augmentation.

The future of work is AI plus humans.

AI Is a Test of Leadership

At the end of my keynote, I shared that the real test of AI is leadership.

In my work with companies around the world, across industries, I see a lot of management. I do not always see a lot of leadership.

Management asks how to reduce cost. Leadership asks how to create value.

Management asks how to automate the process. Leadership asks whether the process deserves to exist and if not, they ask the questions that help identify better ways of working. In the process, they identify new opportunities for value creation.

Management asks how to scale content. Leadership asks how to scale capability.

Management asks how to use AI. Leadership asks what kind of future AI should help us build.

This is where the human quotients matter. IQ, EQ, SQ, AQ. Intelligence. Empathy. Social connection. Adversity. And now, AIQ. Augmented Intelligence Quotient is the means toward human expansion.

AI can scale thinking, creativity, decisions, and problem-solving. Agents can scale capabilities across workflows, teams, and organizations. But they need us to set direction, define standards, ask better questions, and imagine better outcomes.

The future is being built by those willing to learn, unlearn, experiment, and lead.

Nothing new happens without something new happening.

That “something” starts with us.

To view the the other sessions, and they’re amazing, please click here.

There was a moment last year when I found myself staring at a very different future.

I had just watched Geoffrey Hinton (watch the video here), one of the pioneers of artificial intelligence, discuss the possibility that AI could eventually surpass human intelligence. One line stayed with me, “if you want to understand what it feels like to no longer be the apex intelligence, consider the chicken.”

Hinton used this phrase to warn that artificial intelligence will soon become much smarter than you and me. Basically, he’s saying that if and when a machine gets smarter than us, we will lose control, and our fate will depend on a power much greater than ourselves.

It was sobering. And for a moment, it felt as though the future had already been decided.

Then I had what I can only describe as a stoic realization: there are things I cannot control. But there are also things I can.

That moment changed my relationship with AI.

I recently joined Cal Fussman on his Big Questions podcast to explore this tension—the growing fear and anger surrounding artificial intelligence, and the very different future that becomes possible when we reclaim our agency.

The AI backlash we are witnessing should surprise no one.

For years, influential voices have warned people that jobs will disappear, career ladders will collapse, and machines will outperform them. When young people push back against AI, they are responding to a future they were told to expect.

Leadership has to offer a more strategic and inclusive vision, empowerment, and agency.

The most important question may no longer be, What is AI going to do to my job?

Try asking:

What can I do now that I couldn’t do before?

And then go one step further:

What can AI do that it couldn’t do without me?

Those questions move us from automation to AI augmentation.

Automation asks AI to make yesterday faster, cheaper, and more efficient. Augmentation challenges us to imagine possibilities that did not exist yesterday.

That requires a different mindset. It requires curiosity, judgment, creativity, and what I call a beginner’s mind—the humility to acknowledge that our experience can sometimes become a boundary around what we believe is possible.

I often use a simple prompt before I even begin prompting AI:

WWAID: What Would AI Do?

Remember, we don’t want AI to do our thinking or decision making. That’s cognitive Darwinism right there. We want to augment our thinking with AI to do what we couldn’t do without AI and what AI couldn’t do without us.

The question forces us to challenge our own assumptions before we ask the machine to reinforce them. And, that is the opportunity in front of us.

The future of AI is uncertain. But uncertainty is not the same thing as inevitability. We still get to participate in what comes next.

And perhaps the most important work leaders can do right now is help people believe that they have a role in shaping it.

AI is everywhere right now, but I’m increasingly convinced that the most important conversation should be about what the technology is revealing about us.

When the future gets literally booed, we need to talk about it. Why? Because it reflects culturally where we lack leadership at work, university/school, and at home.

🚨NSFW

With that said…

I recently joined leaders from the American Supply Association in Newport, Rhode Island, for a conversation about artificial intelligence, the future of work, and the changing role of leadership. Although the presentation was designed for executives from supply, distribution, manufacturing, and construction, the discussion quickly moved beyond any one industry. The questions in the room were the same questions confronting leaders everywhere: What does AI actually mean for our businesses? How quickly should we move? What happens to our people? Where do we begin? How do we measure success (and what does it look like?). And perhaps most honestly, what are we supposed to know when no one has a complete playbook?

I have spent much of my career studying how technology changes behavior and how those behavioral shifts eventually reshape markets, organizations, and culture. AI is different from previous waves of innovation because it’s not confined to one device, platform, department, or customer experience. It is influencing how people search, write, learn, decide, communicate, create, shop, manage, and work. It is moving into every function at once, while its capabilities continue to improve at a pace most organizations are not designed to absorb let alone scale.

That does not mean the future is predetermined. Technology may introduce new capabilities, but people determine what those capabilities become. The most consequential question facing leaders is whether we will use AI primarily to optimize what we did yesterday or to imagine what could or should exist tomorrow.

Technology Changes People, and People Change Markets

Every major technology begins as something obscure, unnecessary, or even threatening. Over time, it becomes useful, then convenient, then expected. Eventually, the behavior it introduced becomes so normal that we forget how quickly or dramatically we changed.

For example, I told the sequential steps from the Internet to Ubers and Lyfts to Waymos and the autonomous flying vehicles that come next.

In 1999, parents warned their children not to meet strangers from the internet and certainly not to get into their cars. Less than two decades later, millions of people were summoning strangers from the internet and climbing into their vehicles without hesitation. Today, in cities such as San Francisco, Los Angeles, and Austin, people can summon a car with no driver at all. What once sounded reckless became innovative, then convenient, and finally ordinary.

The same progression is unfolding with AI. People are already giving intelligent systems access to their calendars, preferences, communications, documents, and decisions. AI agents are beginning to research, schedule, compare, recommend, purchase, and act on behalf of users. Some of these behaviors still feel experimental, but so did mobile banking, social media, streaming, and ride-sharing when they first appeared.

This matters for business because technology does more than improve convenience. It resets expectations. Once customers experience an intelligent, personalized, immediate response in one part of their lives, they begin to expect that level of service everywhere. They do not care whether your company operates with legacy systems, fragmented data, old approval structures, or entrenched processes. They simply know that another company made something feel easier.

Your customers are not comparing your business only with your closest competitor. They are comparing it with the best experience they have anywhere. Their expectations are being shaped by Amazon, Apple, Uber, Netflix, ChatGPT, and every other platform that teaches them what speed, personalization, simplicity, and intelligence can feel like.

That’s why AI isn more than only a technology initiative. It is also a customer-experience issue, a workforce issue, an operating-model issue, and a leadership issue.

AI Anxiety Is Often a Reflection of Leadership

Many employees, particularly younger workers, encounter AI through headlines about layoffs, automation, job loss, and displacement. Then they hear their own executives describe AI in terms of efficiency, productivity, cost reduction, and doing more with fewer resources. It isn’t difficult to understand why enthusiasm may be limited.

Employees are not necessarily resisting the technology itself. They may be resisting the future they believe leadership is designing around it.

When companies introduce AI without a clear human narrative, people are left to fill in the blanks. Executives say “transformation,” while employees hear “replacement.” Leaders say “productivity,” while teams hear “more work with fewer people.” Management says “efficiency,” while workers wonder whether their experience, judgment, and contribution are being reduced to a line item.

This is a communication problem. And it's also a trust problem, and trust cannot be repaired with a training module or a software demonstration.

Leaders have to explain why AI is being introduced, how work will change, which skills will become more valuable, what employees will be able to do that they couldn’t do before, and how the value created by AI will be reinvested. They must show that the goal is more than making work cheaper, but to make people more capable and the organization more valuable.

The most responsible leaders aren’t promising that roles won’t ever change. They will help people understand how roles evolve and give them the tools, support, and confidence to evolve with them. They are creating a future with their employees instead of announcing a future to them.

AI Still Needs Human Direction

For all the extraordinary claims made about artificial intelligence, AI does not understand your company in the way you do. It does not possess institutional memory, lived experience, moral responsibility, empathy, or a sense of purpose. It can identify patterns, generate options, synthesize information, and increasingly take action through AI agents, but it does not inherently know what matters.

That distinction is essential because business decisions are rarely based on information alone. They involve context, tradeoffs, consequences, timing, relationships, values, and judgment. A recommendation may be mathematically sound and still be strategically foolish. A decision may increase efficiency while damaging trust. A process may become faster or less expensive while producing a worse experience for employees or customers.

AI cannot resolve those tensions on its own. It needs people who can recognize what is missing, challenge the answer, understand the broader context, and decide what outcome is worth pursuing.

This is why leadership becomes more important in an AI-driven organization, not less. The companies that succeed will not simply be those that deploy the most models, copilots, or agents. They will be the companies that develop people who know how to think more expansively because these tools exist.

The Risk of Outsourcing Our Thinking

One of the easiest mistakes to make with AI is to confuse speed with intelligence. When a tool can write an email, summarize a report, build a presentation, or produce an answer in seconds, it feels as though we have become more productive. Sometimes we have. At other times, we’ve simply transferred our thinking to a system and accepted whatever came back. And that can create AI slop and impose an AI tax on others.

Convenience can become dependency. And with AI help, it might become the MO. If AI writes every message, distills every document, frames every argument, and proposes every decision, we may save time while gradually weakening the skills we need most: critical thinking, synthesis, creativity, judgment, and original point of view.

The more valuable use of AI is to not have it think for us, but to help us think beyond ourselves. It can compare opposing perspectives, uncover contradictions, challenge assumptions, model scenarios, simulate stakeholder reactions, and surface questions we may not have considered.

Consider the difference between asking AI to summarize ten reports so you do not have to read them and asking it to compare those reports, identify where they disagree, surface convergences or next layer implications, and reveal the questions no one appears to be asking. The first saves time. The second improves the quality of thought and your differentiated value.

From AI Fluency to Augmented Intelligence

Many organizations are now focused on AI adoption and fluency. Employees are being taught how to use chatbots, copilots, image generators, and other intelligent tools. This work is necessary, but fluency should be treated as the starting point rather than the destination.

Knowing how to operate AI does not mean knowing how to create meaningful value with it. A person can learn to prompt a system without learning to think more creatively, question assumptions, or make better decisions.

I have been exploring this distinction through the concept of AIQ. The term is often used to describe an Artificial Intelligence Quotient, or a person’s ability to understand and use AI. I believe we need a more ambitious definition: Augmented Intelligence Quotient.

The Augmented Intelligence Quotient is the ability to combine human imagination, judgment, empathy, creativity, and experience with machine speed, scale, and pattern recognition. We shouldn't ask AI to do our work for us. The real growth come from using AI to extend what we are capable of seeing, understanding, and creating.

Said another way, AIQ is the practice of exploring what we couldn’t produce without AI and what AI couldn’t create without us.

A leader with basic AI fluency may AI to write a strategy memo. A leader practicing augmented intelligence may ask AI to challenge that strategy from the perspective of a customer, competitor, regulator, employee, investor, and future market entrant. The value goes beyond producing more content. We have the ability to improve the quality of the leader’s judgment before a decision is made.

Leaders who use AI to ask more consequential questions will develop more original ideas and make better decisions.

You Compete With AI, Not Against It

The public narrative often positions humans and machines as opponents. It makes for an effective headline, but it isn’t the most useful way to think about the future of work.

In practice, the more immediate competition is between people who know how to work with AI and people who do not. A salesperson who uses AI to understand an account, prepare for a meeting, anticipate objections, and personalize recommendations will have an advantage over someone relying only on memory and instinct. A manager who uses AI to model scenarios, identify operational patterns, and challenge assumptions will have an advantage over someone relying exclusively on historical reports.

The same is true at the organizational level. A company that combines human expertise with machine intelligence will outperform one that treats AI only as an efficiency or cost-reduction tool.

This is also why the skills becoming more important in the AI era are deeply human. Curiosity, creative thinking, empathy, resilience, adaptability, critical judgment, and the ability to learn and unlearn will become more valuable because machines can scale execution, but humans must still determine what deserves to be scaled.

Iterative AI and Innovative AI

One of the most useful distinctions for leaders is the difference between iterative AI and innovative AI.

Iterative AI improves what the organization already does. It can make processes faster, reduce costs, remove friction, automate repetitive work, improve forecasting, and increase productivity. It asks familiar and necessary questions: How can we perform this task more efficiently? Where can we remove bottlenecks? How can we reduce errors? How can we serve customers more quickly?

Every organization should pursue these opportunities because there is no virtue in preserving unnecessary complexity or inefficient work.

The risk appears when optimization becomes the entire strategy.

Innovative AI asks a different set of questions. Why does this process exist in the first place? Are we solving the right problem, or simply improving an old solution? What customer need remains unmet? What new service, experience, or business model becomes possible now that intelligence is more scalable? What would we create if we were starting the company today?

Iterative AI improves the current business. Innovative AI helps build the next one.

The strongest companies will pursue both, using efficiency gains to create the capacity required for reinvention. That second step is where many organizations fall short. They automate work, reduce expense, and declare success without deciding how the liberated time, talent, and resources will be invested in growth.

Efficiency is valuable, but it just not innovation. You cannot automate your way to imagination, and you cannot cut your way to growth.

Dave Wright and I explore this topic and what to do differently in depth in our new book, “Infinite: How Visionary Leaders Transform Today’s Businesses into AI-Forward Companies.”

Every Business Has a Reinvention Opportunity

The most immediate applications of AI are already visible across sales, service, operations, finance, marketing, talent, and product development. AI can help teams prepare for customer conversations, personalize follow-up, analyze demand, clarify proposals, improve forecasting, detect risk, summarize service histories, and recommend next actions.

Yes. These use cases matter because they improve existing work. The larger opportunity, however, emerges when intelligence begins to connect workflows and functions across the organization.

Imagine a company that anticipates demand using a combination of customer behavior, market signals, weather, operational patterns, and real-time context. Imagine a service organization that does more than resolve issues quickly; it identifies the patterns behind those issues and helps redesign the experience so the problems occur less often. Imagine training that continuously adapts to an employee’s role, performance, knowledge gaps, and customer needs. Imagine turning years of institutional expertise into an intelligent service that customers can access whenever they need it.

This goes beyond productivity. These ideas represent new capabilities, better experiences, and potentially new sources of revenue.

This is where AI moves from optimization to reinvention.

Start with One Workflow, but Question the Workflow

Leaders often ask where to begin, and a practical answer is to select one meaningful workflow. Choose an area where friction is visible, the outcome matters, and employees or customers would notice the improvement. Establish clear governance, define what AI can access and do, and create a controlled environment for experimentation.

Governance is especially important as AI agents become more capable of interacting with systems and performing work. Organizations must define what requires human approval, what should be monitored, which actions are prohibited, and how mistakes will be contained.

At the same time, governance should enable responsible experimentation rather than become a sophisticated excuse for doing nothing.

Once a workflow has been selected, leaders should resist the temptation to automate it exactly as it exists. Many business processes were designed for another era, shaped by paper forms, functional silos, manual approvals, disconnected systems, and organizational politics. Automating a flawed process can make dysfunction move faster, which is an impressive technical achievement but a questionable business strategy.

Begin with the outcome instead. What is the customer or employee trying to accomplish? Where is information lost? Which steps add value and which survive only because no one has challenged them? Where is human judgment essential? What can AI handle? What should always be escalated? How would this workflow be designed if intelligence were available from the beginning?

That’s how business reinvention takes shape: not through one sweeping transformation program, but through the thoughtful redesign of value creation, one workflow at a time.

Innovation Requires a Beginner’s Mind

Experience helps leaders recognize patterns, avoid mistakes, and make decisions quickly. It can also become a filter that limits what they are willing to consider. Experts know what has worked. Innovators remain curious about what may work next.

A beginner’s mind does not require abandoning expertise. It means creating enough distance from what we know to explore what we may be missing.

Most leaders operate within two familiar categories: what they know and what they know they do not know. Together, these define the boundaries of the comfort zone. AI gives us a powerful way to explore a third category: what we do not know we do not know.

That territory is ambiguous and difficult to measure. It may not come with a benchmark, case study, or guaranteed return. It is also where new possibilities tend to emerge.

To explore it, organizations need more than technology. They need psychological safety. Employees must feel that they can ask questions that sound naïve, challenge assumptions that appear permanent, and test ideas that may not work. Leaders who demand certainty from the beginning will receive predictable ideas based on the past.

Innovation requires a willingness to be wrong before something more valuable can be discovered.

A More Useful Leadership Practice

Leaders do not need to become AI experts or engineers, but they do need to become more intentional about how they think with AI.

Instead of asking AI only to write the memo, ask it to find weaknesses in the argument. Instead of asking it to summarize the market, ask it to identify signals that do not fit the prevailing narrative. Instead of asking how AI can reduce the cost of service, ask what recurring service problems reveal about the design of the business. Instead of training employees only to prompt faster, redesign work so they can spend more time applying judgment, empathy, creativity, and synthesis.

A useful discipline is to begin each day with one exploratory question: What am I not seeing? Which assumption is limiting our options? What would customers expect if they knew nothing about how our industry traditionally operates? Which process would we eliminate if we were designing the company today? What new value becomes possible when human imagination and machine intelligence work together?

The goal shouldn’t be to accept every answer AI provides. The goal is to use those answers to stretch our own thinking and expand the range of options we are capable of considering.

The Future Remains a Leadership Choice

Honestly, AI shouldn’t be something employees are left to figure out alone, or read or hear about, and it isn’t a transformation that can be solely delegated to IT. The opportunity is for AI to become part of how organizations think, learn, decide, operate, and create value. That makes it a leadership responsibility.

The companies that thrive will be led by people who understand how technology changes behavior, how expectations evolve, how work must be redesigned, and how intelligence can be used to create better outcomes for customers, employees, partners, and communities.

Those leaders will not pretend to have every answer. They will be willing to learn publicly, experiment thoughtfully, protect trust, create room for curiosity, and ask questions worthy of the capabilities now available to us.

AI will continue to advance. Agents will become more capable. Workflows will become more autonomous, and established business models will be challenged. Yet AI does not get to decide what your organization becomes. That remains a human responsibility and, for leaders willing to think more ambitiously, an extraordinary opportunity.

AI is creating a dangerous illusion in the executive suite. If a task can be automated, the person behind it can be eliminated. Not so fast. Tasks are not jobs, and judgement isn’t software.

Ford just learned why that assumption can become very expensive.

After leaning more heavily on AI and automated quality systems, Ford brought back, hired, or promoted roughly 350 experienced technical specialists, including veteran engineers sometimes called “gray beards.” Their job wasn’t to compete with AI. It was to make AI, and the work surrounding it, better.

What happens when a company cuts people in the name of AI only to learn that AI couldn’t cut it, forcing executives to rehire the very people they cut? This is a story about rethinking the role people and AI play in business transformation and why executives need to think beyond efficiency gains and cost-cutting to remain competitive in an era of AI Darwinism.

AI has seduced too many executives into believing the wrong thing. But AI itself is not a strategy. The mistake many executives are making is believing that because AI can automate tasks, it can also replace the human judgment, context, empathy, and hard-earned expertise that make those tasks valuable in the first place.

This perception can lead to leadership and AI getting very expensive, or rather costly.

The early promise of generative AI was intoxicating. Faster service. Lower costs. Fewer people. More output. Less friction. Suddenly, “efficiency” became a proxy for strategy, and AI became a convenient answer to questions leaders had not fully asked.

But speed is not the same as quality. Automation is not the same as intelligence. And replacing people is not the same as reinventing work.

Ford is now offering one of the clearest lessons yet.

The Trap: Treating AI as Labor Substitution

Ford reportedly hired, promoted, or brought back about 350 experienced technical specialists after concluding that AI and automated quality systems alone were not delivering the results it needed. These veteran engineers, sometimes referred to internally as “gray beards,” are now helping mentor younger staff, lead design reviews, and improve the AI systems Ford uses to find defects earlier, according to Business Insider.

While this may sound like a story about AI failure, it is a story about leadership learning and trying again. And let that be the mantra of AI business reinvention. We can’t stigmatize mistakes. We have to celebrate companies that right their errs and lead the way by learning and growing in public.

In this case, Ford corrected its operating model around AI.

“We had been relying more and more on automated quality systems” and not getting the desired results, Ford COO Kumar Galhotra said. “We brought back technical specialists” and “they hunt for failure points before a part ever reaches the plant floor,” as published in Ford Authority.

This sentiment reframes AI from replacement to reinforcement.

These experts were brought back to raise quality standards and scale wisdom in ways that make humans matter, even with AI.

AI Is Only as Good as the Knowledge It Can Access

Charles Poon, Ford’s vice president of vehicle hardware engineering, was refreshingly direct.

“Artificial intelligence is a fantastic tool, but it’s only as good as the information you use to train it,” Poon toldBloomberg. “Mistakenly we thought that by just introducing artificial intelligence and ingesting the design requirements that we had, that that would produce a high-quality product.”

He added, “We recognized that for us to enhance some of our automation and machine learning and artificial intelligence tools we needed to ensure that they were trained by the most experienced individuals,” according to Ford Authority.

That should be required reading in every boardroom and conference room.

The issue was not AI. The issue was the assumption that AI could produce excellence from incomplete inputs, disconnected workflows and data, and institutional knowledge that lives in the heads of the company’s most experienced experts.

Every company has its version of this problem. It lives in the judgment of knowledge workers. In the exceptions that never make it into process documents. In the handoffs between departments. In the difference between what the workflow says happens and what happens.

When that knowledge leaves the organization, AI does not magically recover it. It simply automates around the absence.

Klarna Learned a Similar Lesson

Ford is not alone.

Klarna became one of the most cited examples of AI-driven efficiency after its AI assistant, built with OpenAI, handled 2.3 million conversations in its first month, managed two-thirds of customer service chats, and performed the equivalent work of 700 full-time agents, according to OpenAI.

Those results were impressive, and they still are.

But Klarna later shifted its posture, investing again in human customer service and emphasizing that customers should be able to reach a person when they need one. CEO Sebastian Siemiatkowski acknowledged that cost had become “a too predominant evaluation factor,” leading to “lower quality,” and said that “really investing in the quality of the human support is the way of the future,” according to Sifted.

A Klarna spokesperson put the lesson this way, “AI brings speed. Human talent brings empathy. Together, they let us deliver service that’s fast when it matters, and personal when it counts.”

Again, this is not a reason to ridicule Klarna. It is a moment to celebrate changing direction vs. doubling down on mistakes.

Klarna is learning in public what many companies still have to learn in private. AI can handle speed, volume, and repeatability. People handle ambiguity, emotion, trust, accountability, and the moments that define the brand.

The Infinite Lesson: Redesign Work, Don’t Just Automate It

This is the point Dave Wright and I make in our new book Infinite: How Visionary Leaders Transform Today’s Businesses into AI-Forward Companies.

AI adoption is not the same as becoming AI-forward.

AI adoption installs tools into the existing organization and into existing workflows. AI-forward leadership redesigns how value is created, how knowledge compounds, how humans and agents collaborate, and how the business learns faster than change itself.

The companies that win with AI will not be the ones that simply reduce headcount. They will be the ones that increase human-agent leverage.

That requires leaders to move from org charts to work charts.

Org charts show who reports to whom. Work charts show how value actually flows: across functions, systems, data, decisions, approvals, exceptions, agents, and humans. They reveal where AI can accelerate outcomes, where it can remove friction, where it should not act autonomously, and where human judgment must remain central.

Ford’s quality reset is a work-chart lesson. The breakdowns were not confined to one team. They lived where design, manufacturing, software, hardware, supply chain, and quality intersected. AI could help detect problems, but experienced people were needed to understand why those problems emerged and how to prevent them upstream.

The Measure is Value, not Fewer People

After Ford brought back the ‘gray beards,’ the results speak volumes, and are also worth studying carefully.

In the 2026 J.D. Power U.S. Initial Quality Study, Ford ranked highest among mass market brands, improving to 152 problems per 100 vehicles, according to Reuters. Ford also said it was the first time since 2010 that it ranked No. 1 among mainstream brands in the study, according to Ford.

Ford improved by pairing intelligent systems with experienced humans, cross-functional collaboration, and a prevention mindset.

That’s leadership…knowing when to right the ship even after a wrong turn was ordered.

What Leaders Should Do Now

First, stop asking where AI can replace people. Ask where AI can multiply expertise.

Second, redesign work before automating it. If the workflow is fragmented, AI will scale fragmentation. If the process is built around find-and-fix, AI will help you find and fix faster. But the real breakthrough is prevention, prediction, and reinvention.

Third, treat knowledge as infrastructure. Data is not enough. Prompts are not enough. Requirements are not enough. AI needs context, edge cases, feedback loops, institutional memory, and human judgment.

Fourth, measure AI by value creation, not activity or headcount reduction. The goal is better products, faster learning, stronger customer experiences, lower risk, and new capacity for growth.

Finally, build for human-agent leverage. The future is not human versus machine. It is people with agents, experts with systems, judgment with automation, wisdom with scale.

Ford and Klarna are leadership lessons because they learned how to use AI better.

The right people make AI worth scaling. Put AI to work for people.

This story was originally published in Forbes. Now I can share it here too…

For nearly three decades, we built the web for people.

We designed pages for people to browse. Search engines helped people find them. Marketers competed for clicks. Brands optimized funnels. Publishers chased attention. Commerce depended on getting a human being from discovery to decision to transaction.

That model is starting to shift.

We are entering a new era of the internet in which people are no longer the only audience navigating the web. AI systems are increasingly searching, synthesizing, comparing, recommending, deciding, and beginning to act on our behalf. Search is becoming synthesis. Browsing is becoming delegation. Interfaces are becoming conversations. And websites are evolving from digital destinations into intelligent business platforms that must communicate with humans and machines.

This isn’t Web 3.0. It is something potentially more consequential: the intelligent web.

And its arrival changes the fundamental rules of digital business.

When an AI agent can evaluate products before you ever see them, summarize a company without visiting its homepage, recommend one provider over another, negotiate choices against your preferences, or eventually complete a transaction on your behalf, the customer journey doesn’t disappear. It becomes mediated by intelligence.

That means your next customer may never “visit” you in the way we have traditionally understood a visit.

Your next customer may arrive through an AI answer, a recommendation, a citation, or an action taken by an agent that decided you were the most credible, relevant, trustworthy choice.

This creates an entirely new competitive question for every company on the web…

What does your business look like when AI becomes the interface between you and the people you hope to serve?

That was the larger idea I explored in my keynote at WP Engine’s DE{CODE} 2026. This year’s theme explored one of the most important shifts now reshaping business, marketing, commerce, publishing, and customer experience: the rise of the intelligent web. [FULL VIDEO AT THE END]

represents a redistribution of attention, influence, discovery, trust, and agency. And while AI is accelerating its arrival, we still have a choice in what this new web becomes.

We can fill it with infinitely generated content, synthetic sameness, and automated noise.

Or we can build something better.

We can become more useful. More original. More human. More trustworthy. More source-worthy.

In a web where machines can generate almost anything, meaning becomes the scarcity. Trust becomes the advantage. And becoming the signal becomes the strategy.

In my keynote, I explored how leaders, marketers, agencies, publishers, eCommerce brands, SMBs, and creatives can navigate this shift with intention.

The intelligent web will be shaped by those who become the signal: source-worthy, trustworthy, contextually relevant, emotionally resonant, and useful to both people and machines.

This article expands on the ideas I shared in the keynote. My hope is that it gives you a deeper way to think about what is changing, why it matters, and what to do next. Because the future of the web isn’t just being built by AI. It’s also being built by us.

Welcome to the Intelligent Web

We are entering an industry renAIssance.

Research shows that one-third of all new published websites are generated by AI. According to Wired, researches from Imperial College of London, Stanford University, and the Internet Archive found that not only are 35% of all new websites AI-generated, online writing is “increasingly sanitized and artificially cheerful,” which appears to be making the internet fake-happy. And according to projections from ARK Invest, AI-generated text has officially outpaced human-written content, symbolizing a shift in how information is created and consumed across the internet.

This is an inflection point.

Now, the web is being reimagined for two audiences at once: human beings and the AI agents increasingly acting on our behalf. Search is becoming synthesis. Browsing is becoming delegation. Content is becoming context. Websites are becoming intelligent business platforms. And every organization now has to ask a new question:

Are we building for synthetic traffic and transactions, or are we building for engagement and trust?

That was the heart of my keynote at WP Engine’s DE{CODE}, where this year’s event explored how AI is reshaping the web for both humans and agents. WP Engine framed the moment as a move from static digital delivery systems to “intelligent, context-rich experiences” that AI systems will seek and cite.

The intelligent web will not reward everyone equally. It will reward those who become useful, credible, discoverable, readable, actionable, and trusted. It will reward those who stop treating AI as a content machine and start treating it as a catalyst for better experiences, deeper relevance, and more meaningful relationships.

That is why I opened the keynote with a simple truth: none of this came with an instruction manual. There is no official playbook for how to build a web that serves humans and machines. There is no universal standard for agent-ready commerce. There is no mature operating model for trust in an AI-mediated customer journey. And yet, here we are, building the future in real time.

The question isn’t whether the web will become intelligent. It already is.

The question is whether we will be intelligent enough to shape it in a meaningful way from here.

Cognitive Darwinism Comes for the Web

I call this moment Cognitive Darwinism, which represents a human moment in how we choose to work with AI. Do we let AI do our thinking or do we expand our thinking with AI?

Cognitive Darwinism is the evolution of behavior, decision-making, creativity, and trust under the pressure of artificial intelligence. Some people will use AI to think better. Others will use it to avoid thinking altogether. Some companies will use AI to augment imagination. Others will use it to automate mediocrity at speed and scale.

AI can extend us. It can help us explore the unknown, challenge assumptions, synthesize complexity, and design possibilities we may not have seen otherwise. But when we simply ask AI to do the work for us, we risk collapsing into the known. We ask for answers instead of better questions. We accelerate output without deepening insight. We publish without learning. We create without meaning.

That is artificial intelligence. And because it offloaded thinking, it’s being studied as “cognitive surrender.” And surrender has consequences.

Researchers are already examining how AI use can encourage cognitive offloading and affect critical thinking. Gallup found that Gen Z’s sentiment toward AI has become more negative over the past year, with excitement and hope falling and anger rising.

Forty-eight percent of Gen Z workers say that the risks of AI in the workforce outweigh its benefits and a staggering 80% say that using it as a shortcut makes learning more difficult. Forty-four percent of Gen Z workers admit to sabotaging their employers’ AI deployments as a form of rebellion. They “hate” AI because of 1) fears of job displacement, 2) security holes, and 3) AI paradoxically adds more work (that’s the AI Tax!)

This does not mean AI is bad. It just means we need to think about how and why and when we use it. And honestly we have a lot of work to do.

The intelligent web cannot be built on cognitive surrender nor on through a heavy AI tax. It has to be built on cognitive augmentation and human empowerment.

That means AI should not replace our thinking, it should provoke it. It should not flatten our creativity. It should expand it. It should not remove the human from the equation. It should make the human more capable, more curious, more discerning, and more useful.

The future of the web does not belong to those who can produce the most.

It belongs to those who can matter the most.

AI Slop Is the New Spam

Before we continue, we have to talk about slop because it perfectly captures what happens when speed and scale divorce themselves from quality and care.

Merriam-Webster named “slop” its 2025 Word of the Year, defining it as low-quality digital content produced usually in quantity by artificial intelligence.

AI slop is what happens when organizations confuse publishing content with serving people. It is what happens when marketers use AI to meet quotas instead of fulfill needs. It is what happens when agencies sell volume instead of value. It is what happens when creators stop creating and start outsourcing their voice to the statistical average of the internet to hit arbitrary OKRs.

AI can create good content. But the problem is that too many people use AI to create content without purpose, empathy, originality, or accountability.

And when that happens, trust erodes (hello LinkedIn peers!)

In the keynote, I made the point that when we use AI to scale content without thinking about the person on the other side, we are not just producing more noise. We are creating more work for everyone else. We are increasing the burden required to find what is useful, true, relevant, and actionable. That burden is the AI tax.

Humans pay it when they have to wade through garbage. And the won’t for long.

Agents will pay it when they have to determine what is credible enough to summarize, recommend, or act upon.

Brands will pay it when they are no longer trusted.

As the web becomes more polluted with synthetic sameness, the value of signal increases. Originality becomes more valuable. Authority becomes more valuable. Empathy becomes more valuable. Taste becomes more valuable. Trust becomes more valuable.

Slop creates the opening for substance, and that window is only growing.

Content Is No Longer King. Context Is (It’s Always Been).

For years, we said content was king and for years, in every era of the web, I’ve said that context is king.

In the early web, that made sense. Content gave people reasons to visit. Content helped brands become visible. Content powered search. Content built audiences.

But in the intelligent web, content alone shouldn’t be enough.

Context is king.

Context means relevance. It means usefulness. It means the right information, in the right format, at the right moment, for the right human or agent trying to achieve a specific outcome. It isn’t just what you publish. It’s why it matters, who it serves, how it is understood, and what it helps someone do next.

People do not search because they want search results. They search because they want progress and outcomes. They want an answer. A recommendation. A decision. A purchase. A fix. A path. A moment of clarity.

Agents are no different. They will not browse like humans. They will parse, compare, evaluate, summarize, rank, recommend, and act. They will look for structured meaning. They will reward clarity. They will need evidence. They will need permission and governance. They will need signals of trust.

This is why the intelligent web is more than a publishing challenge. It’s also an experience design challenge.

We’re now designing more than pages. We need to design outcomes.

From Search Engines to Answer Engines to Action Engines

Search is changing faster than most organizations are prepared to admit.

Pew Research Center found that Google users who encountered an AI summary clicked a traditional search result in 8% of visits, compared with 15% for users who did not see an AI summary. Pew also found that users clicked links inside AI summaries in only 1% of visits.

This is the shift from search engines to answer engines.

But the next phase is already emerging: action engines.

OpenAI describes ChatGPT agent as a system that can reason, research, navigate websites, fill out forms, work with files, connect to tools, and take actions on a user’s behalf. ChatGPT Atlas extends that idea into the browser, where users can ask ChatGPT to take action directly on the web.

That changes the game.

When a person asks an agent to find a product, compare providers, book a reservation, evaluate software, renew a subscription, or resolve a service issue, your website is no longer simply being visited. It is being interpreted. It is being judged. It may be acted upon without the human ever experiencing your carefully designed funnel.

The customer journey does not disappear. It becomes mediated. And if discovery, comparison, and conversion are increasingly mediated by AI, then brands must design for both the human and the machine. Not one or the other. Both.

That means your content must be understandable. Your data must be structured. Your policies must be clear. Your value must be legible. Your trust signals must be visible. Your experience must be actionable.

SEO isn’t dead. But SEO is no longer enough.

We now need to think in terms of AI search visibility, generative engine optimization, answer engine optimization, and agentic optimization. WP Engine’s DE{CODE} agenda reflected this shift directly, with tracks covering agent-ready architecture, structured data, semantic HTML, AI citation strategies, and agentic commerce.

The old equation was:

Create content. Win search. Earn attention.

The new equation is:

Create meaning. Earn trust. Become the source. Enable action.

The Website Becomes an Intelligent Business Platform

A website used to be a destination.

Then it became a channel.

Now it is becoming something more dynamic: an intelligent business platform.

That means your site starts where people learn about you. But now it’s also where humans and agents understand what you stand for, what you offer, how credible you are, how to transact with you, how to resolve issues, and how to continue the relationship.

This requires a different mindset.

A site built for the intelligent web needs to be human-readable and machine-readable. It needs to be emotionally resonant and structurally clear. It needs story and schema. It needs brand and metadata. It needs originality and operational readiness.

This is where many organizations will get stuck. They will treat machine readability as a technical SEO project. They will add schema, update tags, chase citations, and drive links.

Important? Yes.

Sufficient? No.

The intelligent web is more than about being found by machines. It’s about being trusted by machines because you are useful to people.

That is a very different bar.

AI Becomes the UI

One of the most important ideas from the keynote is also one of the simplest:

AI becomes the UI.

As AI mediates more interactions, the interface between your brand and your customer may not be your homepage, your app, your landing page, or your ad. It may be a summary. A recommendation. A comparison table. A voice response. A video. A purchase agent. A service agent. A synthetic answer built from signals across the web.

Content gets indexed.

Context provides framing.

Your brand gets chosen.

That means brand is no longer just what you say about yourself. Brand is what systems can understand, verify, summarize, and recommend about you. It is what you produce. What you enable. What you help someone accomplish. It is your usefulness made visible.

In the keynote, I asked the question I keep coming back to: #WDYSF

WDYSF: What do you stand for?

If AI systems are going to interpret, summarize, rank, recommend, and act based on what they understand about you, then ambiguity becomes expensive. Generic positioning becomes dangerous. Slop becomes self-sabotage. Your brand must become a signal strong enough for people and machines to recognize.

The intelligent web rewards clarity.

So, what do you stand for?

What do you make easier?

What do you make possible?

What do you help people become?

Source-Worthy Is the New Search-Worthy

In a world of infinite output, being searchable is just the ante. You have to become source-worthy.

Source-worthy content is created to be relied upon. It has perspective. Evidence. Utility. Originality. Context. It helps someone or something (ahem, AI agents) make a better decision.

AI systems need sources to synthesize from. Agents need signals to act upon. Humans need confidence to trust what they are seeing.

In the keynote, I encouraged leaders, marketers, agencies, creatives, publishers, eCommerce brands, and SMBs to stop trying to outpublish everyone. That is the wrong race. The goal is to out-clarify, out-serve, out-story, out-experience, out-community, and ultimately out-trust them.

That is such an important distinction.

The intelligent web does not need more content. It needs better answers.

It needs better experiences. It needs better signals. It needs more humanity.

The Rise of Agent-Ready Commerce

Commerce is about to become more agentic.

Today, we search, compare, click, browse, filter, read reviews, abandon carts, return later, and maybe convert. Tomorrow, agents will do more of that work for us. They will search across providers, compare options, evaluate constraints, check preferences, manage timing, apply policies, and potentially initiate transactions.

I shared an example in the keynote of using OpenAI’s Atlas browser to help pursue hard-to-get restaurant reservations in New York. The point was not restaurants. The point was delegation. I wanted an outcome. The agent helped with research, availability, waitlists, and next steps. But the desire, the context, the judgment, and the meaning were still human.

This is the future of commerce.

Humans will still want things. Agents will help fulfill those wants.

That means businesses need to design journeys that agents can understand and navigate without losing the emotional, experiential, and trust-building elements that make people choose one brand over another.

Agent-ready commerce starts with transactions. It excels through continuity. It’s about service. It’s about permissions, security, identity, policy, and trust. It’s about knowing when an agent can act, when a human should be consulted, and how the experience should feel when the person re-enters the loop.

This is why the future of eCommerce is more than conversion optimization. Now, it’s also about relationship architecture.

What Leaders Must Do Now

Leaders have a simple but difficult job in this moment: decide what kind of company they are becoming as discovery, comparison, and conversion become increasingly mediated by AI.

That means asking harder questions.

Are we using AI to automate yesterday’s assumptions, or to imagine tomorrow’s possibilities?

Are we creating more content, or more clarity

Are we optimizing for traffic, or designing for trust?

Are we making our business easier for agents to understand and act upon?

Are we building experiences people can feel and machines can verify?

The intelligent web isn’t just an IT initiative, or a marketing initiative or an agency initiative. It’s a business reinvention initiative.

It belongs at the leadership table because it changes how customers discover you, how they evaluate you, how they buy from you, how they trust you, and how they come back.

What Marketers Must Do Now

Marketers are not just producing campaigns anymore.

They are shaping a knowledge layer that AI systems will use to describe, compare, and transact with the brand. That means every asset becomes part of a larger system of meaning.

Your messaging has to be consistent. Your claims have to be supportable. Your expertise has to be findable. Your content has to help people take the next step. Your brand has to show up with clarity across text, audio, video, community, documentation, reviews, commerce, and service.

Marketing becomes less about pushing messages and more about designing moments of trust.

What Creatives Must Do Now

Creatives have an extraordinary opportunity.

AI can generate. But it cannot care.

It can mimic style. But it cannot originate from lived experience.

It can remix patterns. But it does not possess intuition, empathy, taste, or conviction.

That is the creative advantage.

In an era of synthetic abundance, originality becomes premium. Voice becomes premium. Experience design becomes premium. The ability to make someone feel something becomes premium.

Creatives should not compete with AI at the level of output. They should use AI to expand their range, accelerate exploration, test possibilities, and then bring the human magic that

AI cannot manufacture.

The intelligent web needs more than information.

It needs imagination.

What Agencies Must Do Now

For agencies, this is a control-alt-delete moment.

The old model of building sites, producing content, running campaigns, and optimizing for search is being rewritten. Clients will still need those things, but they will need them in service of a larger mandate: becoming visible, credible, recommended, and actionable in an AI-mediated web.

That makes agencies more valuable, not less, if they evolve.

Agencies can become advisors of trust. Architects of agent-ready experiences. Designers of relevance. Builders of intelligent business platforms. Translators between brand, technology, data, content, commerce, and customer experience.

The best agencies will not sell more output.

They will sell better outcomes.

What SMBs Must Do Now

Small and mid-sized businesses should not try to outpublish bigger brands.

That isn’t the game.

The advantage for SMBs is closeness. Specificity. Community. Service. Story. Trust. The ability to be human in a web that is becoming increasingly synthetic.

Do not fall into the AI slop business.

Use AI to understand your customers better. Use it to answer real questions. Use it to improve service. Use it to make your expertise clearer. Use it to become easier to find, easier to trust, and easier to choose.

Bigger brands may have more content.

You can have more meaning.

No Matter Who You Are, Just Be the Signal

The intelligent web is already messy.

AI-generated writing is exploding. Whether one treats that as an exact measurement or a directional signal, the implication is impossible to ignore: when production becomes infinite, discernment becomes priceless.

We are also seeing the same pattern in software. Fast Company reported that Apple’s App Store saw 235,800 new apps in the first quarter of 2026, up 84% from the same period the prior year, as AI tools made app creation faster and easier. This is what happens when creation gets democratized before quality systems catch up.

More becomes meaningless.

Speed becomes slop.

Volume becomes noise.

And that is why this moment belongs to the signal makers.

Be the source. Be the standard. Be the reprieve. Be the thing someone finds and feels: finally, this is what I was looking for.

The Architects of the Intelligent Web

The intelligent web will not build itself.

Or, more accurately, it will build itself badly if we leave it to automation alone.

This is our moment to shape it with purpose, imagination, empathy, and responsibility. To design for humans and agents. To build for discovery and action. To create source-worthy content. To make sites machine-readable and machine-actionable. To protect people, data, identity, and trust. To turn brand into a signal that stands out in a sea of sameness.

That is why I hope you watch the full keynote.

Not because it has all the answers. It does not. None of us do yet.

But it does frame the questions every leader, marketer, creative, developer, agency, publisher, eCommerce company, and SMB needs to ask now.

The web is becoming intelligent.

The future is becoming agentic.

And we are not bystanders.

We are the architects of the intelligent web.

So let’s build something worthy of the humans it serves.

What should leadership become when intelligence is no longer scarce?

At the 17th Global Peter Drucker Forum, I joined Johan Roos and Deborah Perry Piscione for a conversation about what CEOs should actually do with AI. I opened with a provocation: many leaders are on a path to automating themselves out of a job—not because AI is destined to replace them, but because they are using it to make yesterday’s work faster, cheaper, and increasingly replaceable. The real opportunity is not to perfect the company we already know. It is to rethink how value is created, how work is designed, how people are augmented, and what leadership must become when intelligence is no longer scarce.

Johan is a respected management scholar, educator, author, and longtime contributor to the Drucker Forum. Deborah is a Silicon Valley entrepreneur, investor, and author studying how AI, robotics, and other disruptive technologies are reshaping employment and society. I joined them as a practicing futurist in my role at ServiceNow, and, as I introduced myself that morning, still very much a student of AI.

Anyone who tells you they have mastered this moment probably has not spent enough time understanding the technology and its impact on work, the workforce, and on ourselves.

Johan opened the session by asking each of us to make a strong claim, a provocation, something expressed to raise the collective heart rate of the room. A thoughtful discussion, after all, should do more than confirm what everyone already believes.

So I offered mine:

CEOs are on a path to automating themselves out of a job.

My point was not that an algorithm would soon occupy the C-Suite, though there are parts that could. My argument was that many leaders are using AI to do what their organizations already do, only faster, cheaper, and at greater scale.

They are automating tasks, accelerating processes, increasing output, reducing costs, and optimizing operating models built for another era. Those improvements and incremental gains are real. They may even generate impressive returns over the next quarter.

But a faster version of yesterday’s company is not necessarily prepared for tomorrow. This is a different moment in time.

The defining responsibility of a CEO is not to make the past run more efficiently. It is to imagine and build the future before someone else does.

That became the central tension of our conversation.

Deborah challenged CEOs to understand the economic and technological forces already reshaping work.

Johan challenged leaders to protect and expand human capacity as AI becomes more pervasive. I tried to connect those provocations to the choices CEOs and boards must make now.

Looking back, the session was less of a panel and more an intervention about leadership.

AI Can Help CEOs Manage, the Opportunity Is to Help Them Lead

There are certainly CEOs who are challenging the status quo. There are CEOs who recognize they’re not the right leaders to see AI business reinvention through. And there are CEOs already outsourcing their thinking to AI. In fact, a recent study showed 62% of CEOs reported using AI to make the majority of their decisions. 70 percent admitted to second-guessing their judgements when their choices conflict with AI’s recommendations. 65 percent felt decision-making had become less collaborative since adopting AI. And to top it off, 46% said they now rely on AI more than on the advice of colleagues.

One CEO in the audience offered a useful distinction. AI, he explained, could automate much of his work “below the line”: reviews, analysis, administration, reporting, and other activities that consume executive attention. That would give him more time “above the line” to think about the future of the product, the company, and the market.

I told him he was a blueprint for other CEOs.

But no one in the room asked what they weren’t thinking about, what they hadn’t considered, or what other CEOs or founders were doing differently. And when curiosity or humility aren’t given space to explore what we don’t know to know, any conversation will be limited to validation, agreement, incremental thinking, and reinforcing existing confirmation biases.

That’s exactly why for transformation, growth, and innovation to materialize, leaders must first recognize that there may be other questions, scenarios, and opportunities they’re not yet considering. This is the inception of a mindshift. And from there, an infinite journey can unfold.

His point captured the opportunity. AI can give leaders back time, but what they do with that time will determine whether it becomes a source of reinvention or merely another productivity tool.

Too many executives have gradually become managers of institutional complexity. Their days are filled with meetings about the business instead of time spent imagining the future of the business. They review dashboards, manage expectations, navigate politics, prepare for the next board meeting, and protect the operating model.

AI can make those activities more efficient. But leadership is not the administrative maintenance of the present. That’s ‘management.’ Leadership is the ability to see possibilities others do not yet see, articulate a future people can believe in, and create the conditions for the organization to bring it to life.

AI can help CEOs manage more effectively. The real opportunity is to help them lead more imaginatively.

Months after the Drucker Forum, I led a series of workshops with roughly 100 CEOs and chief operators in Arizona. The stated subject was AI, but the real conversation was leadership. Once we created a trusted setting, several executives admitted a version of something they rarely say publicly: “I know this matters. I know we have to move. But I’m not sure where to begin, and I’m not sure the organization is ready.”

That admission was not a failure of leadership. It was the beginning of it. And that’s the point. Humility and curiosity was in the room.

The greater danger is pretending to know, rushing into activity to project confidence, and allowing urgency to substitute for direction.

Deborah Perry Piscione: Understand What Is Coming, Then Decide How to Participate

Deborah brought urgency to the conversation from her vantage point in Silicon Valley. She described a world moving rapidly from generative AI to agentic systems and eventually to physical AI and humanoid robotics. Her message was not that every prediction from Silicon Valley should be accepted as destiny. It was that CEOs cannot afford to remain spectators while the economics of cognitive and physical work are being rewritten.

Her practical advice began with experimentation. Leaders need direct experience with the technology. They must understand what it can do, how quickly it is improving, where it fails, and how employees, customers, entrepreneurs, and competitors are already applying it.

This does not mean a CEO must become an AI engineer. It does mean AI literacy can no longer be delegated entirely to the CIO, a center of excellence, or the unusually enthusiastic person who discovered prompting last Tuesday.

Deborah also advanced an essential strategic choice...should the organization build its own AI capabilities, buy them from trusted providers, or blend internal and external systems?

Her answer was appropriately contextual. Build where proprietary intelligence is central to competitive advantage. Buy where established platforms can provide common capabilities faster and more effectively. Blend when enterprise knowledge, specialized models, external innovation, and human expertise must work together.

But before any of those choices can produce value, Deborah argued, leaders must fix the plumbing. Data has to be usable. Legacy systems must be capable of communicating. Information must move securely across the organization. The infrastructure underneath AI may not be glamorous, but neither is explaining to the board why the brilliant proprietary model cannot access the information required to do its job.

Her point has only become more relevant. In an article I later contributed to Future Economy, I described an emerging enterprise AI transformation gap: companies are producing more intelligence at the individual level, yet they still lack the architecture to let that intelligence act across workflows.

An intelligent model placed inside a fragmented enterprise does not make the enterprise intelligent. It may simply help dysfunction move faster.

Stop Piloting AI Around the Edges of the Business

When an audience member asked about reports showing widespread failure among generative AI pilots, I shared what I routinely see in my work with CEOs and boards.

The pilots are siloed. The data is siloed. The workflow is siloed. The ownership is siloed. The measurement is siloed.

The use case may perform perfectly within its narrow boundary and still fail to create meaningful enterprise value because it was never connected to the way value actually flows through the organization.

Siloed pilots produce siloed outcomes.

The better-performing AI transformations begin with an end-to-end workflow and a meaningful business result. That means looking beyond a single department or task to understand how people, data, systems, decisions, and policies come together to create an outcome for a customer, employee, or stakeholder.

Remember, AI is not a strategy.

The organization needs a strategy for competing and creating value in a world where intelligence is becoming abundant, inexpensive, embedded, and increasingly autonomous. AI is a critical part of that strategy, but it cannot be separated from decisions about customers, work, talent, operating models, growth, governance, and the future identity of the enterprise.

A budget, a timeline, a collection of pilots, and a slide featuring several concentric circles do not constitute transformation.

The more useful question of the moment is “What outcome should now be possible that was not possible before?”

The Real Danger Is Failing to Dream Big Enough

Near the end of the session, I returned to the opening provocation.

The danger is not that CEOs are experimenting with AI. The danger is that they are not dreaming big enough with it.

When leaders confront an unfamiliar technology, they naturally begin with what they know. They look at the present organization and ask where AI might reduce cost, eliminate effort, improve productivity, or replace labor.

Those are understandable starting points. They are also constrained by the assumptions of the existing business.

If you begin with yesterday’s processes, roles, metrics, and operating model, the most likely outcome is an AI-powered status quo.

You may become more efficient without becoming more innovative, more productive without becoming more valuable, or more automated without becoming truly transformed.

I later explored this at AI Week in Milan. Most organizations are using AI to improve the work they already perform. What they also need is a parallel growth curve dedicated to creating new capabilities, services, experiences, and business models. I describe these as iterative AI and innovative AI. The first improves today. The second invents tomorrow. Leaders need both, and they must prevent the efficiency agenda from consuming the reinvention agenda.

No CEO will be criticized for finding legitimate savings. But efficiency cannot become the outer boundary of imagination.

You cannot automate your way to innovation. You cannot cut your way to growth.

Innovation means creating new value. It means doing something tomorrow that the organization could not do yesterday.

Automate the Work, Augment the People

I shared the example of IKEA during the panel because it illustrates the difference between treating AI as a cost lever and using it as a platform for growth.

IKEA introduced an AI-powered customer service capability that reportedly handled nearly half of its inbound inquiries. Viewed through an automation lens, that success could have justified significant headcount reduction. If the objective is simply to deflect customer contacts and take out cost, the equation is straightforward.

But IKEA looked at what the AI could not resolve.

Many of the remaining customers wanted help with interior design. Rather than treating those interactions as failures, IKEA recognized an unmet need. Thousands of customer service employees were reskilled as remote design advisers, creating a new service and a significant source of revenue.

The technology absorbed one form of work. The people moved into a more valuable form of work.

Since the forum, I have encountered other examples that reinforce the same lesson.

Ford leaned heavily on automated quality systems and later recognized that technology could not fully replace the judgment and tacit knowledge of experienced specialists. The company brought back more than 350 veteran technical experts—affectionately known as “gray beards”—to find failure points, mentor teams, and improve the systems. Klarna, after becoming one of the most visible symbols of AI-driven efficiency, also began reinvesting in human customer service when it became clear that speed and scale could not entirely replace empathy and human connection.

These stories should not be used to ridicule companies for getting AI wrong. Quite the opposite. They demonstrate what learning looks like.

The mistake is not experimenting and discovering limitations. The mistake is refusing to adapt because the original automation narrative looked better in the investor presentation.

The CEO’s job is to pursue two questions at once:

Where can AI remove friction, repetition, delay, and unnecessary cost?

And where can AI help people become more creative, informed, capable, empathetic, and valuable?

Automation can fund the future. Augmentation helps create it.

Johan Roos: Use AI to Expand the Mind, Not Escape It

Johan brought another essential dimension to the conversation. He argued that professionals, leaders included, face a choice in how they use AI.

AI can amplify curiosity, creativity, critical thinking, communication, and collaboration. Or, it can gradually erode them. It’s a topic I explored in-depth at SXSW 2026.

The risk is cognitive offloading: allowing AI to perform so much of the mental work that our own capacity weakens. This erosion is unlikely to arrive through one dramatic decision. It will happen through hundreds of convenient prompts and outputs.

AI writes the message. AI summarizes the report. AI prepares the argument. AI recommends the restaurant. AI advises us how to handle a disagreement. Eventually, we stop asking AI to help us think and begin asking it what we should think.

The technology becomes more capable while the user becomes less so.

Johan challenged leaders to use it deliberately. His recommendation was to role-model active, thoughtful experimentation and remain alert to the warning signs of over dependence. Use AI before an important communication, he suggested, but then show up as yourself. Do not allow leadership to converge into the same optimized, synthetic voice.

I agree, and I would extend the idea.

CEOs should use AI to improve the quality of their thinking, not to avoid the responsibility of thinking. More so, use AI to expose biases and “expert mind-sets.” Ask it to challenge assumptions, reveal blind spots, argue against the preferred strategy, model alternative futures, and expose uncomfortable trade-offs.

Do not use it to manufacture confidence where none exists.

This is also the thinking behind a concept I later developed called WWAID: What Would AI Do?

WWAID is not an invitation to outsource judgment. It is a pre-prompt mindshift. Before asking AI to improve an existing process, leaders step back and ask whether the process should exist in its current form at all. Before automating a task, ask whether intelligence could eliminate the need for it. Before adding AI to an experience, ask how intelligence might redefine its purpose and value.

The goal is to help us escape the limitations of how we have been trained to think.

Embodied Leadership Will Become More Important

Johan also spoke about embodied leadership...the distinctly human experience of being present with another person, reading the room, looking someone in the eye, sensing hesitation, responding to emotion, and communicating with authenticity.

That idea came to life through an audience member who described receiving a question from her daughter. Unsure how to answer, she turned to AI and sent back the resulting response. Her daughter replied, in effect, “Mom, I wanted to talk to you, not AI.”

Ouch, but I’m sure it was a necessary lesson.

The daughter was not only looking for information. She was looking for connection.

Employees often want the same thing from leaders.

They do not only look to a CEO for an answer. They’re looking for judgment, reassurance, honesty, empathy, presence, and some indication that another human being understands what is at stake.

My answer was simple: humility, humanity, and empathy are among the most important leadership traits we can develop in the age of AI.

Then I added, “My goodness, be human.”

Johan connected that moment to the idea behind his work on “human magic”: the qualities we must hold onto and cultivate as algorithms become more capable.

The message has stayed with me.

As AI becomes more advanced, humanity does not become less relevant. It becomes more differentiated...and, augmented.

Leaders will need the humility to admit that they do not have all the answers. They will need the vulnerability to acknowledge uncertainty while still providing direction. They will need the courage to make decisions that balance economic performance with human consequences.

The CEO Guide to Using AI

Our conversation ranged across technology, work, employment, cognition, infrastructure, governance, accountability, and humanity. But the practical guidance for CEOs can be distilled into seven questions.

1. What future are we trying to create?

Do not begin with the model, tool, or vendor. Remember, AI is not the strategy. Begin with the customer, employee, business, or societal outcome the organization should now be capable of delivering.

2. Are we improving the existing company or inventing the next one?

Pursue efficiency and reinvention simultaneously. Improve today’s performance while creating the capabilities, experiences, and business models that could define tomorrow.

3. Which end-to-end workflows matter most?

Move beyond isolated tasks and departmental pilots. Examine how value actually moves across people, functions, systems, decisions, and data.

4. Is the organizational plumbing ready?

Address fragmented data, disconnected systems, siloed workflows, unclear ownership, permissions, security, governance, and technical debt. AI cannot orchestrate an enterprise that cannot communicate with itself.

5. Where should we automate, and where should we augment?

Use AI to eliminate mundane work, but reinvest the resulting capacity in people, learning, experimentation, service, creativity, and growth.

6. How will we protect and expand human judgment?

Define what AI can recommend, what it can decide, what it can execute, and where accountable human intervention remains essential.

7. How will I personally model the change?

A CEO cannot delegate curiosity. Use the tools. Ask better questions. Share what you are learning. Admit what you do not know. Encourage experimentation without pretending uncertainty has disappeared.

The Future Is Still a Leadership Decision

The session was titled “The CEO Guide to Using AI,” but the enduring lesson was not really about using AI.

It was about what kind of leader, and what kind of company, AI will reveal. This is a moment to invent the “infinite company.”

Deborah reminded us that the economics and capabilities of intelligent systems are moving quickly, whether organizations feel ready or not. Progress is not only a question of what technology can do, but also what we allow it to do to us. The audience brought the lived experience of executives navigating urgency, skepticism, responsibility, and uncertainty.

The leaders most vulnerable to AI are those who believe they have the least to learn, who confuse activity with progress, efficiency with growth, and those who use AI only to defend the business model that made them successful.

The defining work of leadership is not to preserve the company that exists. Yet so many are touting this as the next frontier. The real opportunity however is to build the company that deserves to exist next, and to help people become the best versions of themselves along the way.

Please watch the conversation here and share your thoughts! 🙏

Thank you for the opportunity to be part of this incredible opportunity, Richard Straub!

Also, thank you to the incredible Alexander Osterwalder for the clip!

After spending time in candid workshops with nearly 100 CEOs and senior operators, I came away with a realization that isn’t being discussed openly enough: leaders know AI will reshape their businesses, but many are still unsure how to lead through what comes next.

That uncertainty isn’t a weakness but pretending it that doesn’t exist…is.

In my latest Forbes column, I explore the questions CEOs are wrestling with behind closed doors, from the limits of today’s efficiency-first thinking to the AI slop-induced “AI tax” already showing up inside organizations. More importantly, I look at what it will take to move AI from a collection of tools and experiments to a true leadership agenda, one built around imagination, trust, growth, and the courage to become a beginner again.

The AI Conversation CEOs are not Having Out Loud

AI is now firmly on CEO and board agendas. But many leadership teams are still asking the wrong questions. Recently, I led several workshops with roughly 100 CEOs and Chief Operators at a leadership conference in Arizona.

The topic was AI. But the real conversation was leadership.

These were honest conversations with operators who are under daily pressure to grow revenue, protect margins, improve customer experience, support stretched teams, open better organizations, retain talent and make faster decisions in a more complex operating environment.

What became clear is that CEOs feel the pressure. Their boards are asking about it. Their teams are experimenting with it. Vendors are pitching it. Competitors are talking about it. The headlines only make everything more confusing.

But beneath the urgency is something much more revealing, but often kept quiet.

Many CEOs know AI is going to change their business, but they do not yet know how to lead through it. The recent CEO departures at Coca-Cola and Walmart signaled as much.

That is not something most CEOs are going to say out loud in a boardroom or at an all-hands meeting (unless they’re already on their way out). CEOs are expected to project clarity. They are expected to have conviction. They are expected to know where the business is going.

But in a trusted setting, the more honest version surfaced: “I know this matters. I know we need to move. But I’m not sure where to start, and I’m not sure my organization is ready.”

Executives don’t know what they don’t know. This was by far the most interesting part of each workshop. Leaders would share that they are the CEO because they know what to do. But they admitted that they don’t what to do right now. That scares them. And they’re not sharing that honesty with their teams. Anyone who appears as an AI expert is heard, regardless of their credentials or caliber of ideas.

Companies that survive and thrive will be the ones whose leaders are willing to rethink how the enterprise creates value.

Most CEOs Are Still Thinking Too Small

Any time AI came up, most leaders immediately went to the obvious places: customer service chatbots, productivity tools, automation, reporting, marketing content and operational efficiency.

Those are all valid starting points. They are also too narrow. The dominant mindset still is: “Where can we bolt AI onto the business?” Much less common is the more important question: “How should the business work differently now that AI exists?”

If AI is treated as a bolt-on, it will mostly produce incremental gains. Faster emails. Better summaries. More efficient customer service. Some cost savings. Some productivity improvements.

But if AI is treated as a catalyst, it forces a much deeper conversation about work, workflows, operating models, decision rights, talent, guest experience and growth.

Most companies are trying to use AI to optimize existing workflows. They aiming to reduce costs. But very few are asking whether those workflows should exist in their current form or whether there are new outcomes to be achieved not possible before AI.

Not having these conversations lead to missed opportunities.

The more advanced leaders in the room were already beginning to see AI differently. They were talking about AI not only as an efficiency lever, but as a growth catalyst. They named opportunities in customer experience, revenue generation, service models, locale, labor planning, team experience, training and better decision-making.

That is where the conversation needs to go. Not just, “How do we save time or money as we optimize yesterday?”

But, “What can we now do that we could not do before?”

The Real Bottleneck Is Imagination

IT came up a lot in the workshops, often as a roadblock as IT leaders were said to prioritize protecting their positions instead of getting closer to the business. Data came up constantly. Governance, risk, privacy and security were top of mind.

These are real issues. Most companies have fragmented systems, disconnected data, unclear ownership and legitimate concerns around how AI tools are being used. Those problems cannot be ignored.

But the deeper bottleneck is not technical. It is imaginative. And imagination is at war against those pushing agendas that make AI the new status quo.

Most leadership teams have not yet developed a shared picture of what AI could make possible across the organization. In the recent Microsoft Work Trends Index report, research found that only 1 in 4 AI users (26%) say their leadership is clearly and consistently aligned on AI. As a result, the conversation defaults to tools. The company starts with use cases before it has a point of view.

That is backwards. A leadership team should start with asking:

• “Where is our business constrained by old assumptions?”

• “Where are we too slow?”

• “Where are we too manual?”

• “Where are we overly dependent on tribal knowledge?”

• “Where are decisions being made with incomplete information?”

• “Where are teams spending time on work that does not create differentiated value?”

• ‘Where are guests experiencing friction?”

• “Where are operators not getting the support they need?”

Those questions open up a different kind of AI conversation. They move AI from a technology agenda to a business agenda.

How imagination takes shape is also tied to embracing a beginner’s mind.

CEOs are Learning That Fluency Cannot be Delegated

One of the clearest themes from the workshops was that AI fluency is becoming a leadership priority.

That does not mean every CEO needs to become technical. It does mean CEOs and senior teams need enough understanding to ask better questions, challenge shallow ideas, recognize real opportunities and make informed decisions about risk.

AI cannot live only with IT. It cannot live only with digital. It cannot live only with innovation teams or a few enthusiastic power users. Of course, those functions matter. But AI strategy has to be led by the business.

The risk is that, in the absence of senior-level fluency, whoever sounds most confident becomes the de facto AI expert. That could be a vendor. It could be a consultant. It could be an internal enthusiast. It could be someone with technical expertise but limited understanding of the company’s operating model.

Confidence is not the same as judgment. CEOs and leadership teams do not need to know everything about AI, but they do need to know enough to lead.

The ‘AI Tax’ Is Already Showing Up

There was another theme in the room that deserves more attention.

In many companies, employees are already using AI to improve personal productivity. That sounds positive, and in many cases it is. But the results are uneven.

A lot of organizations are now seeing more volume, but not always more value. More content, but not always better thinking. More drafts, more summaries, more decks, more emails, but also more generic work, more rework and more quality control issues.

This is the AI tax.

I read this quote in a recent article on Futurism, “I have never knowingly finished reading an email signed by a human but written by AI.”

AI generated content is turning people off.

It is the hidden cost of unmanaged adoption. It shows up as shallow analysis, off-brand communication, hallucinated facts, duplicated effort, and the quiet frustration of managers who now have to clean up AI-assisted work that was never thoughtfully reviewed.

But the answer is not to ban experimentation. That would be a mistake. The answer is to raise the floor, set standards, set norms at a higher-bar.

Organizations need to teach people not only how to use AI, but how to use it well and what good vs. great looks like. And on that note, leaders need to call out what slop looks like and why it’s not acceptable. They need to demonstrate how to prompt with context., how to pressure-test output, hw to protect sensitive data, how to preserve brand and personal voice, and how to know when human judgment matters most.

AI fluency is not just about access to tools. It is about quality of thinking and output.

Fear Is In The Room

Every CEO understands there is fear around AI and jobs.

Employees are wondering what this means for their roles, their teams, their relevance and their future. Some are excited. Some are anxious. Many are both.

Leaders feel this tension. They know they need to move. They also know that if AI is framed only as a cost-cutting exercise, the organization will resist it.

That is why change management came up repeatedly.

But “change management” may be too soft or “out of date” a phrase for what is required. This is not simply about communications, training, or adoption plans. This is about trust, vision, and leadership.

I don’t know anyone who enjoys “change” or “management.”

Employees need to understand the company’s intent, vision, direction…a future motivating state. And they need to believe they play a role in building that future.

Is AI here to eliminate people? To empower them? To change what great performance looks like? To create capacity for growth? To improve the customer’s experience? To make the business more resilient?

If leaders do not define the narrative, employees will create their own.

And in the absence of trust, the default narrative will be fear.

The Best Leaders Are Willing To Become Beginners Again

One of the most striking moments in the workshops was hearing several CEOs arrive at the same conclusion on their own: they need to approach AI with a beginner’s mind. Remember why you started.

That is easy to say and hard to do.

CEOs are rewarded for experience, pattern recognition, conviction, and decisiveness. They are usually in the room because they have seen a lot, solved a lot, and developed good instincts over time.

AI challenges that posture.

It is moving too quickly for any leader to pretend they have it all figured out. The old patterns still matter, but they may not be enough. In some cases, they may even get in the way.

A beginner’s mind does not mean abandoning judgment. It means being honest about where judgment needs to be rebuilt.

The leaders who navigate this well will not be the ones who perform certainty. They will be the ones who create the conditions for their organizations to learn faster than the market around them. And this mindset is where imagination comes to life.

What CEOs And Leadership Teams Should Do Next

The path forward is not to launch random pilots or chase every new tool. It is to build a leadership agenda for AI.

First, stop starting with use cases. Start with the work.

Look across the enterprise and identify where work is slow, repetitive, inconsistent, overly manual, or trapped in silos. Then ask how that work could be redesigned with AI. The goal is not to add AI to bad processes. The goal is to rethink the process.

Second, define the company’s AI ambition.

Every leadership team should be able to answer a simple question: What do we want AI to make true about our company?

Do we want to become more efficient? More predictive? More guest-obsessed? Better at supporting operators? Faster at opening restaurants? More personalized in how we serve customers? Better at growing revenue? A better place to work?

Without a clear ambition, AI becomes a collection of disconnected experiments. With a clear ambition, it becomes a strategic agenda.

Third, build executive fluency.

The CEO and leadership team need dedicated time to understand what AI can do, where it fails, what risks it creates, and how it may reshape the business. This cannot be a one-time inspiration session. It has to become part of how the senior team thinks about strategy, operations, talent, and growth.

Fourth, treat governance as an enabler.

Risk is real. Governance matters. But the purpose of governance should not be to slow the organization down. It should help people move faster with confidence.

Teams need clarity on what tools are approved, what data can be used, where human review is required, how brand standards are protected, and what behaviors are off limits.

No rules creates risk. Too many rules creates paralysis. Good governance creates trust.

Fifth, confront the data reality.

Many companies are not ready for the AI they say they want because their data is too fragmented. Guest data, labor data, transaction data, training data, operational data, marketing data, real estate data, and financial data often sit in disconnected systems.

Perfect data is not required to begin. But leadership teams need to be honest about which data domains matter most and where better integration would create the greatest leverage.

AI will not magically create enterprise intelligence from organizational fragmentation.

Sixth, redesign work before redesigning headcount.

If AI is introduced primarily as a labor reduction tool, employees will protect themselves. If it is introduced as a way to create capacity, improve work, and unlock growth, the conversation changes.

One of the most productive moments in the workshops came when I asked leaders what they would do with resources freed up by AI.

That question shifted the energy in the room.

Instead of talking only about efficiency, leaders began talking about growth. Better guest experiences. Stronger teams. New services. More support for operators. Faster innovation. Better decisions.

That is a more meaningful frame.

Freed-up capacity should not automatically become removed capacity. In the best companies, it becomes growth capacity. Reinvest efficiency gains back into the business and people.

Finally, build a culture of disciplined experimentation.

The companies best positioned for AI are not necessarily the ones with the most tools. They are the ones where people feel empowered to try new things, ask better questions, challenge old assumptions, and learn from failure.

But experimentation needs discipline. Otherwise, it becomes noise.

Leaders need to define what good AI-assisted work looks like. They need to create safe spaces for testing. They need to share what is working. They need to call out low-quality output. They need to make learning visible.

AI adoption cannot be a free-for-all. It has to become an organizational capability.

The Leadership Moment

The biggest takeaway from my time with these CEOs is that they are not behind because they lack interest. They are behind because AI requires a more honest leadership conversation than most companies are currently having.

This is not just about tools. It is not just about automation. It is not just about chatbots, productivity, or cost savings and efficiency gains.

It is about whether leaders are willing to admit that the old playbook may not be enough or even right.

For many CEOs, the hardest part of AI will be creating the space to learn before they feel ready, to ask questions they are not used to asking, and to lead through ambiguity without pretending it does not exist.

That is the conversation CEOs may not be having out loud. But it is the conversation they need to have with their leadership teams now.

• Where are we thinking too small?

• What do we not yet understand?

• What work would we redesign if we were building the company today?

• How do we turn efficiency into growth?

• And what kind of leadership does this moment require from us?

The CEOs who answer those questions honestly will do more than adopt AI. They will use it to build a different kind of company.

Infinite ∞ | Mindshift | Keynote Speaker

Something curious is happening in the age of infinite convenience: people are deliberately choosing less.

They are buying iPods, CDs, digital cameras, paper notebooks, and devices designed to do one thing instead of everything. On the surface, it looks like nostalgia. Look closer, and it feels more like resistance, a quiet attempt to reclaim attention, taste, presence, and control from technologies engineered to keep us continuously connected and perpetually distracted.

At the same time, AI is introducing a more consequential tradeoff. Digital platforms taught us to outsource our attention. Generative AI now makes it possible to outsource parts of our thinking, creativity, judgment, and even our voice. The risk is not that we use AI. It is that we use it so reflexively that we stop noticing which human capabilities we are no longer practicing.

These movements are connected. Both force us to ask what convenience is giving us, and what it may be quietly taking away. The challenge ahead is not to reject technology or retreat from the future. It is to build a more intentional relationship with it: one that expands our humanity rather than gradually automating it.

A Tale of Two Futures

On one side of the digital equation, we observe young people buying iPods, CD players, digital cameras, DVD players, first-generation iPhones, and even old Macintosh computers. They are intentionally replacing devices that can do everything with devices that do one thing, often slowly and even imperfectly. But that’s the point. As a single purpose device, it does what it needs to do without demanding their attention every eight seconds.

On the other side, The Atlantic explores whether we are entering a “postliterate age,” one in which fewer people possess the patience, desire, or cognitive stamina to read books and engage deeply with complex ideas. Think about it. In an era of AI, it’s so easy, even normalized, to take something complex and run it through ChatGPT or Claude to summarize it for you.

One movement is attempting to reclaim attention and to take back control.

The other shows what happens when we surrender it in the name of speed and scale.

Social media trained us to give away our attention. And now generative AI is raising the stakes, inviting us to give away the thinking itself. 🤯

The Analog Rebellion is not Really About Nostalgia, It’s About Agency

It’s easy to point to nostalgia when revisiting analog trends. Even I made the investment in a record player and vinyl. And I recently found my old iPod Nano 6th generation with a video screen that I was thinking about using again. I just don’t have wired earbuds lol! Oh, and while visiting my sister, she showed me that she still has the original iPhone. As I’m writing this, I think I’m going to ask her if she’d consider selling it to me.

The people “going analog” are not necessarily abandoning technology. Many of the devices they are rediscovering are in fact digital. The reference to analog, I guess, is intentionality. In theory, single-function devices keep you more in the moment. So, what people appear to be rejecting is not technology itself, but the conditions under which modern technology demands to be used.

The attraction of the old iPod is not merely its click wheel or its millennial aesthetic. It is that the device contains only the songs you deliberately placed there. There is no infinite library, no algorithmically generated playlist, no notification informing you that someone you barely remember has posted something you do not need to see.

In The Atlantic’s reporting, one woman described feeling paralyzed by the millions of songs available through streaming services. On her iPod Nano, she has only the music she chose. Others described buying CDs, waiting for them to arrive, and sitting down to listen to an album with intention. Interest in these older devices is substantial enough that eBay reported an average of 1,300 searches an hour for iPods last year, while prices for some models rose by more than 50 percent.

What looks like retro enthusiasm may actually be a quest for agency (a word that I am using more and more since the launch of ChatGPT in 2022).

People are rediscovering something modern product design spent years trying to eliminate…friction. Selecting an album, loading music onto a device, inserting a DVD, developing a photograph, or finding your way without GPS requires intent, focus, and effort. But that work creates participation. Participation creates memory. And memory helps transform consumption into experience.

We engineered friction out of our lives in the name of convenience and speed. Then we discovered that some of that friction was where life was happening.

This prompted me (get it!?) to think about that profound line from the series finale of the U.S. version of The Office when the character Andy Bernard observed, “I wish there was a way to know you’re in the good old days before you’ve actually left them.”

Convenience Became Connectivity, and Connectivity Became Captivity

There was a period when technology was useful without being omnipresent. As former Apple executive and “father of the iPod” Tony Fadell describes it in the article, devices were once “convenient, but not connected.” Your camera took pictures. Your music player played music. Your phone made calls.

Then those products converged into the “everything device.”

The smartphone became our camera, map, newspaper, television, office, shopping mall, social life, entertainment center, memory, and increasingly our emotional support system. Convenience and connectivity became inseparable. Every activity became an opportunity to capture attention, gather data, recommend something, or keep us engaged for just a little longer, and then a lot longer.

The problem with an everything device is that it eventually wants everything from us.

This was part of the journey I explored in Lifescale. At the time, I argued that we had not simply become distracted. Our behaviors were being deliberately shaped around interruption, validation, immediacy, and the endless scroll. Reclaiming our lives did not require abandoning technology. It required learning to use technology without allowing technology to use us, and then to scale our lives intentionally and directionally. In fact, I still use that book and wear the bracelet (I forgot I had Post-Its too!) to live a more creative, productive, and happy life.

Going analog is, in many ways, Lifescaling made physical. It is an attempt to place boundaries around infinity, to turn our attention away from everything we could be consuming and toward what we have consciously chosen to experience.

The Postliterate Future is Already Arriving

The parallel cultural movement is more troubling, and not just because I’m an author. It’s because I’m fighting to stay human.

In “The End of Reading Is Here,” author Rose Horowitch examines the possibility that the literate age may prove to be a relatively brief chapter in human history. Fewer than half of American adults reported reading a book of any kind in 2022, and only 38% said they had read a novel or short story. The proportion of Americans who read for pleasure on any given day fell from 28% in 2004 to 16% in 2023. The decline cuts across age, gender, and education. And here we are three years into generative AI, which makes me wonder where the stats sit today.

Reading isn’t a defense of the past or holding on to nostalgia. Long-form reading is a superpower. It boosts our cognitive stamina, strengthens critical thinking, enhances analytical skills, boosts empathy and social cognition, enhances our vocabulary and communication, and supercharges our creativity!

Reading also teaches how to remain present while thinking.

Deep reading combats distractions and lengthens our attention span and trains us to stay with an idea long enough for it to challenge us. It requires interpretation, imagination, patience, memory, and tolerance for ambiguity. It allows us to inhabit another person’s perspective without immediately responding, rating, sharing, or swiping away.

Digital culture rewards almost the opposite behavior. It compresses ideas into clips, headlines, summaries, reactions, and increasingly generated answers. We are becoming highly proficient at moving across information while becoming less practiced at moving through it.

We consume more while absorbing less. We encounter more voices while developing less of our own.

The decline of reading and the rise of analog culture aren’t separate trends. They are different responses to the same condition: a society that has optimized access to information while steadily eroding the human capacity to engage with it.

AI Is Moving Us from Attention Outsourcing to Cognitive Outsourcing

Social media competed for our attention. AI offers to complete the thought before we have one. And speaking of AI, we erode our authentic intelligence with every swipe, scroll, (and prompt).

People now turn to chatbots to draft emails, summarize information, prepare for conversations, make decisions, interpret emotions, generate ideas, and tell them what to say next. The Atlantic has documented heavy AI users who feel unable to begin meaningful work without consulting a chatbot, including one educator who realized his first instinct in solving a simple physical problem was to ask ChatGPT rather than think through it himself.

Let’s be clear. I’m not saying that AI is inherently harmful. Used intentionally, it can remove drudgery, democratize expertise, challenge assumptions, deepen analysis, and enable us to create things that were previously beyond our reach.

The danger though begins when assistance subconsciously becomes dependence.

AI produces polished answers so quickly that fluency can masquerade as understanding. We can appear more articulate while becoming less certain of what we actually believe. We can produce more ideas while exercising less imagination. We can respond faster while slowly losing the ability to sit with uncertainty long enough to develop an original point of view.

Philosopher Kwame Anthony Appiah describes the deepest risk as “constitutive de-skilling.” It’s the erosion of judgment, imagination, empathy, and our feel for meaning and proportion. Just what our society needs right now!

Before AI, digital tech was already interrupting our thinking. Now it can prevent our thinking in the first place. But hey, we can get more done, at speed at scale!

And the faster we move, the greater the risk of burnout. With the algorithm working in my favor, I then was presented with an article in Bloomberg that tracked the uptick in mental health leave. In fact, 67% of US employers report an increase in mental-health-related leaves of absence and accommodation requests over the past year, according to a survey by employment law specialists Littler Mendelson.

Think Outside of the Bot 🤖

We gave digital our attention. Are we about to give AI our minds?

Recently I shared some thoughts, and a nifty t-shirt concept! I’ll reshare some of the thinking here.

When we want people to be creative, we tell them to “think outside the box.” But unless we change the rules, create psychological safety, reward curiosity, and give people time to explore, we are not really changing the paradigm. We are merely redecorating the same box.

I feel the same way about AI.

We ask people to think bigger with it but then measure them by how much faster they complete yesterday’s work. We deploy AI to summarize, automate, respond, and produce more of what already exists. Then we wonder why it is not transforming the business, or the person using it.

This is why I have been advocating WWAID: What Would AI Do?

The question should come before the prompt. It challenges us to imagine possibilities we might otherwise miss.

But I am now embracing an additional mantra: Think outside of the bot.

And I’m not alone. The Economist launched a brilliant series of billboards shading AI, with one of them proclaiming just that, “think outside the bot.”

Thinking outside of the bot means bringing something original to the relationship before asking the machine to contribute. It means forming an opinion before requesting an answer. It means using AI to interrogate your assumptions rather than manufacture your convictions.

The aim for AI-human work is not to compete with AI at being a machine. It is to become more human because of what the machine makes possible.

What Are We Teaching the People Around Us?

These choices affect more than our individual productivity.

Children develop judgment by making imperfect decisions. They build creativity through boredom, play, reading, conversation, and the slow frustration of trying to make something work. When every question is immediately answered, every assignment summarized, and every uncomfortable moment filled with content, we may be removing the very conditions through which a self is formed.

The same is true in our families. Presence is not simply occupying the same room while everyone attends to a different screen. It is the increasingly rare experience of giving another person attention without simultaneously offering it to a platform.

And it is true at work. Leaders cannot ask teams for breakthrough thinking while surrounding them with back-to-back meetings, constant notifications, productivity surveillance, and AI systems designed primarily to increase output. Creativity needs space. Curiosity needs permission. Original thinking needs enough time to become inconvenient.

Perhaps every family and every organization should ask:

What human capacity are we strengthening through technology, and what capacity are we quietly allowing to disappear?

The Future May Need to Become a Little Less Convenient

The people buying iPods are not retreating from the future. Ideally, they’re prototyping a healthier version of it.

They are reminding us that more choice doesn’t always create more freedom. It can fuel fatigue. More connection does not guarantee more belonging. More information does not necessarily produce more wisdom. And more intelligence on demand does not automatically make us more intelligent.

The real question is not whether we should choose analog or digital, or humans over AI. The question for us to deeply consider is whether we remain intentional enough to choose at all. That requires a different kind of AI, authentic intelligence.

What parts of your life should remain deliberately slow? What decisions should still require your judgment? What conversations should never be summarized? What should your children learn to struggle through? What should your team be expected to imagine before asking a machine? And what are you willing to make a little harder, quieter, slower, or more human, so that it remains truly yours?

Use the technology. Explore its possibilities. Let it stretch what you can see, create, and become.

Just remember to think before you prompt.

Oh, and think outside of the bot. 🤖

Yours authentically,

Brian

People aren’t rejecting AI as a technology. They’re rejecting the future they were told to expect because of AI.

For the last few years, AI has been introduced less as a tool for human progress and more as a force of inevitability: fewer jobs, fewer opportunities, fewer entry points, fewer safeguards, fewer humans in the loop. This didn’t happen by accident. It is the result of systemic leadership failure across business, academia, government, technology, and media.

CEOs framed cuts as transformation. Investors rewarded efficiency over reinvention. Educators policed AI as cheating before teaching students how to think with it. Policymakers debated control before building safeguards. Frontier leaders warned of disruption without designing a more human path forward.

So yes, AI has a trust problem. But trust wasn’t lost by the technology alone. It was lost by the way leaders chose to wield it, explain it, and profit from it.

This cannot be solved by narrative or PR. It requires new leadership.

When the Future Gets Booed

Of course graduates boo commencement speakers.

At the University of Arizona, former Google CEO Eric Schmidt was met with boos when he raised AI and its effects during a commencement address. He was speaking to a generation that has heard, again and again, that the future they are entering may have less room for them.

At the University of Central Florida, Gloria Caulfield told arts, humanities, communications, and media graduates that “artificial intelligence is the next industrial revolution.” Students booed. One graduate explained the response plainly: “I don’t think that kids are having a hard time accepting it because we know that AI exists. I think we’re just having a hard time acknowledging that it’s taking away job opportunities from us.”

Axios called it a new graduation ritual: booing AI. The same report noted a Glendale Community College moment where an AI system skipped students’ names and the president blamed the technology.

Then came Ronny Chieng at Harvard. In a Class Day speech, he exclaimed, “F AI,” and told graduates their mission was to “destroy AI.” The crowd roared its approval. But beneath the joke was a deeper truth. Chieng wasn’t rejecting medical breakthroughs or scientific progress. He was pushing back on a world where AI is used to smooth away the messy, meaningful, human work of creativity, learning, and effort.

The Signal Beneath the Backlash; Trust is Falling

The pushback is not anti-technology. It is anti-erasure and anti-irrelevance.

And the data says the same thing.

Pew Research Center research found that AI usage is rising, but trust is falling. About half of U.S. adults now use AI chatbots, and 38% of employed adults say they use chatbots for work tasks. ChatGPT alone has reached 44% of U.S. adults.

Yet the same study found that Americans are deeply skeptical of AI’s broader impact. Forty percent say AI will have a negative impact on society over the next 20 years, compared with just 16% who expect a positive impact. For their own lives, 31% expect a negative effect versus 23% positive.

The concern cuts even deeper. Sixty-three percent of Americans say AI is advancing too quickly. Seventy-one percent believe the increased use of AI will make their personal information less secure. Sixty-seven percent have little to no confidence in the U.S. government to regulate AI effectively, and about six in ten are not confident that U.S. companies will develop and use AI responsibly.

It’s hard not to feel that if you don’t already.

Adoption Without Consent Becomes Resistance

The Wall Street Journal framed the backlash as an “American rebellion against AI,” connecting booed commencement speakers, blocked data centers, plummeting poll numbers, protests, political fights, energy concerns, job fears, and growing community resistance. Its reporting noted that voters in Festus, Missouri, ousted four city council members shortly after they approved a $6 billion data center, that roughly 360,000 Americans were in Facebook groups opposing data centers, and that local opposition blocked or delayed at least 48 data center projects valued at about $156 billion last year.

This is what happens when leaders sell inevitability instead of agency at a speed no one was ready for.

This is what happens when the public hears, over and over again, that AI will be used to reduce headcount, automate jobs, cut costs, and accelerate productivity, but rarely hears a credible plan to create new value, new roles, new mobility, and new futures.

You reap what you sow.

And for too long, the AI story has been sown in fear.

You Reap the Narrative You Sow

A growing number of companies have publicly cited AI efficiencies as a rationale for layoffs. Business Insider recently tracked 16 companies that have announced AI-related staff cuts or restructurings, while also noting the possibility that some companies may be using AI as a convenient explanation for cuts they would have made anyway (or at least to cover the costs of AI tech and infrastructure, not because AI could take over jobs.)

AI does not walk into a company and eliminate a job.

Leaders do.

Boards do.

Investors do.

AI can perform tasks. It can summarize, analyze, classify, generate, recommend, simulate, reason, and increasingly act within boundaries. But a job is more than a bundle of tasks. A job is context, judgment, accountability, relationship, craft, care, ethics, taste, presence, and trust. A job is also agency, identity, mobility, contribution, belonging, and hope.

When leaders over rely on AI investments and reduce work to tasks, people become costs.

When people become costs, the only ROI for AI becomes cutting costs.

You’ve heard the saying, “when you’re a hammer, everything looks like a nail.” When it comes to AI automation, that phrase can be adapted, “When you’re an axe, everything looks like headcount.”

This is why trust collapses. And this is when leaders fail. Failures mount when leaders use an exponential technology with an elimination mindset.

The Old Playbook is Still the Problem

Executives, managers, teachers, parents, mentors, we need to become the leaders we are waiting for.

You can’t automate your way to innovation.

You can’t cut your way to growth.

Yet so much of the AI conversation remains trapped in yesterday’s business logic: reduce costs, increase efficiency, do more with less, focus on the quarter, win over the street. While it looks good on a spreadsheet or an executive dashboard, it does not build belief or instill trust over the long term. It does not inspire people to learn. It does not invite or empower workers, students, customers, or communities into the future.

It does however, create resistance. And that’s where we are.

Researchers are seeing early evidence that AI is starting to hurt entry-level jobs, especially in roles where AI can perform the same tasks junior employees used to do. Stanford Digital Economy Lab researchers found results “consistent with the hypothesis that generative AI has begun to affect entry-level employment.”

AI is not affecting all jobs equally. It is putting the most pressure on the first rung of the career ladder, especially in work where companies can use AI to do what junior employees used to learn by doing. If leaders automate the entry level without redesigning how people learn, grow, and contribute, they do not just cut costs. They break the talent pipeline.

Cengage describes the emerging experience gap painfully and honestly clear: entry-level jobs have long been the training ground where people build judgment, communication, professional confidence, and practical skills. But as AI absorbs routine early-career tasks, employers increasingly expect junior workers to perform more analytical and judgment-based work earlier, sometimes asking them to evaluate AI outputs before they have learned the underlying work themselves.

At the same time, employers are rapidly shifting expectations. NACE reports that more than one-third of entry-level jobs now require AI skills, nearly triple the share from fall 2025, and 28% of employers say they are seeking early-career talent who can use AI in their work.

No Wonder People are Angry (and Scared)!

So imagine being a student.

Your professors told you not to use AI because it was cheating. Employers now expect you to use AI because it is essential. Executives tell you AI will create opportunity, while headlines tell you companies are cutting jobs because of AI. Then commencement speakers tell you to embrace the revolution.

The boos are feedback.

They are saying: You have not earned our trust. And this isn’t only a student problem. It is an institutional problem.

AI Should Expand Thinking and Capacity with People

Futurism recently highlighted a small UK executive survey that found 62% of executive respondents said they use AI to make “most decisions,” while 46% said they rely on advice from AI more than from their own colleagues.

The piece also referenced research from Carnegie Mellon and Microsoft suggesting that knowledge workers who trust generative AI’s accuracy may show lower propensity for critical thought. I mean…WTF (what’s the future!?)

The sample is limited and the tone is pointed, but the irony is important: some of the same leaders asking workers to trust AI may already be outsourcing too much of their own thinking to it.

That isn’t leadership. This is cognitive surrender and AI abdication.

AI should expand our thinking, not replace it. It should challenge assumptions, not launder them. It should help leaders explore more possibilities, not avoid responsibility for the choices they make.

And that takes…leadership. From above…from the side…from within.

People are Still the Strategy

Jensen Huang made an important distinction on Memos to the President. He argued that AI automates specific tasks, such as coding or reading scans, but does not replace the human purpose of a job: innovation and problem-solving. In his framing, AI can increase demand for software engineers and radiologists by supercharging productivity, not by erasing the need for human expertise.

That is the narrative we should have been building all along.

AI is not the strategy. People are still the strategy.

AI is a capability. For leaders who lead it, it’s a force multiplier, a new layer of intelligence and agency that can help people do what was not possible yesterday. But this is only true if leaders choose augmentation over automation…only if they redesign work before they redesign the workforce…only if they build new ladders before they remove the first rung.

This is where the mindshift has to begin.

From Finite Automation to Infinite Possibility

A finite company uses AI to make yesterday cheaper.

An infinite company uses AI to make tomorrow possible.

The difference is not technology. It is leadership. And it’s leadership at every level…as parents, teachers, mentors, managers, leaders.

So, it’s not just “finite leaders,” it’s everyone who hold back progress, whether they realize it or not.

What’s the opposite in this case? It’s infinite…

An infinite company does not start by asking, “How many people can we replace?” It asks, “What new value can we create?” It asks, “What work should humans no longer have to do?” It asks, “What can people become capable of when routine work is offloaded and human imagination is amplified?” It asks, “What outcomes were impossible before AI that are now within reach?”

That is how we change the conversation from fear to agency.

And we need to change it now.

How Leaders Can Earn Back Trust

First, leaders have to stop using AI as a euphemism for layoffs.

If AI is part of a restructuring, say exactly how. Explain what work is changing, what value is being created, what skills are needed next, what investments are being made in people, and how displaced capacity will be reinvested. Do not hide behind inevitability. If the choice is cost reduction, own it. If the strategy is growth, prove it.

Second, redesign work around outcomes, not org charts. Think automation + augmentation. You have to redesign work before you redesign the workforce.

When AI automates work, it replaces tasks that entry-level workers often handle: drafting, coding basic features, research, customer support, analysis, admin work, etc. In those areas, employment for younger or less-experienced workers appears to be declining.

When AI augments work, it helps people do their jobs better rather than replacing the work outright. In those cases, the negative employment effect is weaker or less obvious.

The org chart tells us who reports to whom. It does not tell us how value is created. Leaders need work charts that map outcomes, tasks, handoffs, data, decisions, human judgment, AI agents, risk controls, and feedback loops. AI transformation is not about sprinkling tools across departments. It is about reimagining how work flows.

Third, rebuild the entry-level ladder.

If AI absorbs the routine work that once trained people, then organizations need new apprenticeships. Early-career workers should learn how to collaborate with AI, interrogate its outputs, understand the underlying work, develop judgment, and contribute to real outcomes faster. Do not eliminate the training ground. Reinvent it.

Fourth, make agency and augmentation measurable.

Every AI initiative should answer four questions: What human capacity did we unlock? What customer or employee outcome improved? What new capability did we build? What did we reinvest in people, products, services, or growth? Cost savings alone is not transformation. It is accounting.

Fifth, teach students how to think with AI, not surrender thinking to it.

Education cannot treat AI only as a cheating problem. Students need to learn how to question AI, use it as a collaborator, detect errors, challenge bias, build original ideas, and protect their own critical thinking. The future does not belong to students who outsource their minds. It belongs to those who augment them.

Sixth, include more voices in AI decisions.

Workers. Students. Customers. Artists. Teachers. Communities. Local officials. Privacy experts. Labor leaders. People who will live with the consequences. When AI is designed in closed rooms and deployed into open society, backlash is not a bug. It is the system responding.

Finally, leaders have to earn trust in public.

Prove it in public.

It’s ok if you don’t have the answers. There is no playbook. The problems compound when you try to use yesterday’s playbook to navigate an uncharted future. You have to ask different questions and do the work to answer them.

That’s leadership. So, lead.

Show the jobs created. Show the roles redesigned. Show the people reskilled. Show the guardrails. Show the impact. Show the harms prevented. Show how AI is helping humans become more capable, not just making organizations more efficient.

This is the leadership audition of our time. No pressure! 😉

The leaders of tomorrow will not be remembered for how quickly they cut.They will be remembered for how courageously they imagined.

They will be remembered for turning fear into fluency, fluency into capability, capability into creativity, and creativity into new value.

The AI backlash is real. But it is not the end of the story. It is an invitation to write a better one.

Remember, people are not asking us to stop the future. But they are asking to be included in it. They are asking for a future where AI does not diminish human potential, instead, it expands it. It’s a future where students are not told their degrees are obsolete before they even begin, a future where workers are not treated as costs to be optimized away, a future where leaders do not confuse automation with innovation or efficiency with progress.

This is our moment to wonder again.

Ask better questions.

Design better systems.

Build better companies.

Become better leaders.

Let’s stop asking people to accept a future they were never invited to help shape.

Let’s build it together.

This is a topic that is near and dear to my heart, more so in an era of AI.

I had the privilege of keynoting UserTesting conferences in San Francisco and New York. It was my third and fourth UserTesting event respectively, which I guess makes this a situationship now. 😍

The theme was experience innovation in an era of AI. Naturally, I opened with PowerPoint slides, because nothing says “experience innovation” like a 79-slide deck projected in a conference ballroom.

You’re welcome.

OK, so the real message wasn’t about slides. It wasn’t even really about AI. It was about people. It was about how human beings are changing faster than the experiences designed for them. It was about why, in this new era of increasingly ‘intelligent’ machines, the most important competitive advantage may be the most human one of all…empathy.

What follows is a summary of my presentation. The team over at UserTesting was also kind enough to publish the San Francisco session online and is free to view (I hope you love it!)

Let’s go...

We are living in a moment when every company is trying to figure out how to use AI to move faster, produce more, spend less, automate more, and impress Wall Street along the way. And yes, AI will absolutely change how we work. But what I see in my work, is most organizations are limiting AI’s potential to do what they did yesterday, only faster, cheaper, and at scale tomorrow.

That’s not transformation though. That is operational déjà vu (I had to look up where the accents go) with more intelligent software.

The innovators do and will use AI differently. Yes. They use it to automate the parts of yesterday that deserve a place in the evolved enterprise. They also use it to do what they could not do yesterday. They use it to discover what they did not know was possible. They use it to augment, not automate or replace, human creativity, intuition, and capacity.

AI = Augmented Intelligence

I asked the audience to think about AI not just as artificial intelligence, and certainly not just as generative automation, but as “augmented intelligence.” AI can summarize. AI can synthesize. AI can identify patterns. AI can speed up insight. But AI cannot care. AI cannot sit with a customer’s frustration and feel the heat rising in its own chest. AI cannot watch a user (aka a human being) struggle with a workflow and suddenly feel responsible for making it better.

Remember…

AI cannot build intuition.

AI cannot feel understanding.

AI cannot internalize empathy.

And intuition, understanding, and empathy is where true experience design begins.

This is why X: The Experience When Business Meets Design feels even more relevant today than when it published in 2015. Back then, I was trying to make the case that experience wasn’t a department, a metric, a journey map, or a design sprint. Experience was something much more human. It was a memory. A visceral memory. Something you feel. Something you recall. Something you share.

And there are really only two types of experiences people remember: the ones that are wonderful and the ones that, well, suck.

Everything in between is forgotten. And yet, that’s exactly what we design for…transactional moments. Unfortunately, most businesses design for the middle. Not intentionally, of course. I don’t believe teams sit in conference rooms saying, “How can we make this forgettable?” Although, to be honest, some customer journeys do make you wonder.

Forgettable is what happens when companies optimize for transactions instead of feelings. It happens when they measure completion rates but not emotion or memories. It happens when they design for internal processes instead of human moments. It happens when executives believe they’re delivering “great experiences” while customers quietly, and sometimes not so quietly, disagree.

The Customer’s Experience Begins with an Apostrophe

We are not our customers.

That was one of the central ideas in the keynote, and it may be more important now than ever. We say “customer experience,” “user experience,” “brand experience,” and “employee experience” as if the phrase itself makes us customer-centered. It doesn’t. So I asked the audience to add one small thing.

An apostrophe S.

‘s

It’s not customer experience.

It’s the customer’s experience.

It’s the user’s experience.

It’s the employee’s experience.

The experience is theirs. And it’s yours to design, not leave to chance.

That tiny grammatical shift changes everything. It moves the center of gravity from us to them. It reminds us that the experience does not belong to the company. It belongs to the person living it.

And that person has changed.

Modern Consumers are Digital Narcissists

The modern customer is not just digital-first anymore. They are mobile-first, app-shaped, AI-curious, algorithmically trained, emotionally overloaded, and increasingly impatient. I call them Generation Novel, not because of age, but because of shared interests, behaviors, empowerment, and impatience. They are the product of the attention economy, the introvert economy, the AI economy, and a world that has made convenience and personalization feel like a birthright.

We did not set out to become digital narcissists. It just happened. Originally, I referred to us as “accidental narcissists” because every device, every app, every feed, every notification quietly taught us that we are the most important person in the world.

Just ask any influencer or creator.

Our phones learned us. Apps anticipated us. Algorithms programmed us. Maps reassured us. Delivery apps showed us the little car creeping toward our house so we wouldn’t contact customer support demanding a refund because our tacos were four minutes late.

That map wasn’t just a feature. It was empathy disguised as UI.

Years ago, when working around companies like Uber, the real competitor wasn’t always another ride-sharing company. The real competitor was impatience. How long is too long before someone opens another app? That is the experience question. That is the user psychology question. That is the business question.

The same thing happens with drone delivery. The first time a drone drops something at your house, it feels like science fiction. By the second or third time, if it’s late, you’re looking at your watch. “Excuse me, my autonomous flying robot is 90 seconds behind schedule.”

That’s the world we live in.

People move from magic to entitlement much faster than most organizations move from committee to pilot.

This is why experience design has to evolve. Customers don’t compare you only to your direct competitors. They compare you to the best, most intuitive, most personalized, most invisible, most extraordinary experiences in their lives.

TikTok changes how people expect content to move. DoorDash changes how people expect transparency to feel. Apple changes how people expect objects to behave. Disney Parks change how people expect physical environments to create wonder. And now AI is changing how people expect everything to respond, predict, assist, and adapt.

You Compete with AI

You compete with AI.

Read that again.

You don’t just compete with people or companies using AI. You compete with the expectations AI creates. You compete with every experience that teaches your customers that life should be more relevant, immediate, intuitive, personal, predictive, and maybe even a little magical.

But magic doesn’t happen by accident.

In X, I wrote about experience as intentional design. Not decoration. Experience is the intentional shaping of moments that become memories.

Now, you can compete with AI, not against it, but with it…augmented with AI to do what you couldn’t do yesterday to unlock experiences not possible or imaginable before.

This is a Call for an Experience, not Just a Brand, Style Guide

At UserTesting, I asked a simple question: Do you have an experience style guide?

Not a brand style guide. Not a UI kit. Not a design system. Those are important, but they don’t go far enough. I mean a guide that defines how people should feel in each moment of truth. What should they feel when they first land on your site or when they walk in the front door? What should they feel when they open your app or when they interact with your AI or when they navigate your IVR or when a representative greets them? What should they feel when something goes wrong? What should they feel when they need help? What should they feel after they leave?

What should they remember?

What should they tell someone else?

Most organizations have customer journey maps. Fewer define moments of truth. Fewer still have designed feelings for those moments. Yet, as Cesare Pavese famously wrote, “We do not remember days, we remember moments.”

So why do we keep prioritizing the forgettable moments?

Design for IGNITE MOMENTS

This is where I introduced the idea of ignite moments. With the mobile revolution, I worked with Google to define micro-moments. Ignite moments build on that lineage. Micro-moments helped us understand intent in a mobile-first world. Ignite moments help us design meaning in an AI-first, emotionally fatigued world.

In addition to becoming an accidental narcissist, people today are stressed. They are anxious. They are overwhelmed. They are rethinking success, happiness, identity, work, relationships, and the future. They are staying in more. They are planning less. Even dating apps now feel like part-time jobs. Imagine looking for love and needing a dashboard, a profile strategy, a messaging funnel, and emotional recovery time.

What does this have to do with design?

Everything.

Because people bring all of that into the experiences they have with you. They don’t arrive as clean personas or synthetic beings. They arrive as human beings. Distracted. Hopeful. Impatient. Skeptical. Sometimes exhausted. And when your experience adds friction, confusion, or indifference, they feel it more than you realize.

Ignite moments are designed to say, “I see you,” and, “I got you.”

They are thoughtful and intentional. They convey care. They reduce anxiety. They create clarity. They add warmth. They help someone feel special, because someone designed the the moment to prove it.

This is why I believe we need more digital warmth.

Design for Digital Warmth

I used vinyl as an analogy in the keynote. Anyone who loves vinyl will happily corner you at a party and explain why it sounds better. And, in fairness, there is a warmth to analog sound that compressed files often lack. That’s what digital experiences are missing. Warmth. Texture. Feeling. Humanity.

We have engineered too many experiences to be efficient, to convert, and too few to be felt or memorable and shareable.

Digital warmth is the sixth love language. I’m sure Gary Chapman will be thrilled to learn that I’ve taken the liberty of updating his work for the digital and AI economy.

But it matters.

The old golden rule says, “Treat others the way you want to be treated.” But the idea of digital empathy, to truly design meaningful ignite moments, says, “Treat others the way they want to be treated.”

That changes the purpose and the work. It changes the research. It changes user testing. It changes what you look for when you watch the videos, study the patterns, and listen to what people are really saying underneath the words.

The Golden Rule of Digital Empathy

The goal is not just to get to insights faster.

I love speed. Every executive loves speed. Every team is under pressure to move faster. AI can absolutely help us get to insights faster. But don’t let speed strip away the very thing that makes insight valuable. Watch the videos. See the hesitation. Hear the sigh. Notice the workaround. Feel the frustration. Build intuition. Build understanding. Build empathy.

The best ideas don’t come only from looking backward at data. They come from imagining people’s future needs.

That’s the difference between optimization and innovation.

Optimization asks, “How do we make this process faster?”

Innovation asks, “Should this process exist at all?”

Optimization made websites responsive in a mobile world.

Innovation made TikTok feel native to the phone.

Optimization made ketchup bottles squeezable.

Innovation asked why we were still attacking glass bottles like they owed us money.

Yes, I talked about ketchup. The original glass ketchup bottle was designed in the 1800s and it was and still is a terrible delivery vessel. We all developed rituals to get ketchup out of it. The palm slap. The knife. The shake. The prayer. Then came squeezable bottles. Better, but still imperfect. Then came the upside-down gravity bottle. Suddenly, someone had empathy for the condiment experience.

WATCH THIS 🎥

That is innovation!

Everything can be reimagined when you feel the friction.

Everything is up for reimagination now…even parking signs…especially parking signs. Anyone who has tried to decipher a city parking sign while cars are honking behind them understands that design is not an aesthetic exercise. In New York, if you stop too long to read a sign, your life may be physically threatened. That’s not a customer journey. That is not an ignite moment. That’s a hostage situation!

Yet so much of the world is designed this way. Technically complete, accurate, and functional, but emotionally hostile.

This is why human-centered design must be more than a workshop. It must become a leadership practice. It must become how companies make decisions. It must become how teams define value. It must become how organizations connect brand promise to operational reality.

Design for People, not for the Spreadsheet

Brand is not what you say it is. Brand is the culmination of the experiences people have and share. It’s what people feel and share when you’re not in the room.

That was true when Yelp first gave customers a public voice and business owners reacted as if the villagers had stormed the castle. “How dare you tell people about the horrible experience I bestowed upon you!”

It is even more true now.

Customers don’t need your permission to define your brand. They define it every day through reviews, screenshots, group chats, social posts, TikToks, and increasingly, through AI agents that may one day summarize whether you are worth someone’s time.

Remember, there are only two experiences people have and remember…experiences that suck and experiences that are extraordinary. And, in both extremes, that is what people share. Which are you designing for?

The brand is the memory expressed to others.

BX, CX, UX, and service experience are not separate planets. They are concentric circles of experience innovation. Brand experience asks how someone feels before, during, and after engagement. Customer experience brings the promise to life across the journey. User experience shapes interaction across products, services, devices, and touchpoints. Service experience orchestrates the roles, props, systems, and processes that deliver it all.

Together, they determine whether someone feels seen or sold to.

The Concentric Circles of Experience Innovation

Experience has to be measured differently.

We know ROI. We worship ROI. We tattoo ROI on quarterly planning decks. But experience needs a champion. It needs a metric. It needs something more meaningful. At the conference we explored the concept of RoX: Return on Experience. RoX captures more than a moment, a product, or a service interaction. It measures how someone feels overall throughout their relationship with you, and how those feelings connect to outcomes. It measures against the stated experience you said you would design and deliver in ignite moments, how they’re felt in those moments, what they sparked (or ignited) as a result, and how they’re shared.

Experience is not separate from growth. It is the source of growth.

Measure for Experiences and Outcomes

When people feel good, they stay. When they stay, they buy again. When they buy again, they tell others. When they tell others, they create advocacy. When they trust you, they give you permission to innovate with them.

The opposite is also true. When companies ignore experience, the “I” in ROI changes meaning.

It becomes return on ignorance.

It’s the cost of not knowing your customer. It’s the cost of treating those ignite moments, those moments of truth, as cost centers. So, what is the cost of designing for assumptions? What is the cost of friction? What is the cost of indifference? What is the cost of forgettable moments? What is the cost of a customer who leaves quietly because nothing was bad enough to complain about, but nothing was good enough to remember?

That is the silent killer of growth and innovation.

This is why experience leaders must change the narrative inside the organization. Designers, researchers, product leaders, marketers, and CX teams have to speak the language of business without losing the language of humanity. Think like a venture capitalist. Think like an investment banker. Make the case for experience not as a cost center, but as a growth engine. Connect UX and CX to acceleration, loyalty, premium pricing, advocacy, retention, and innovation.

But don’t stop there. Humanize the customer.

Bring executives into their world. Show them the moments that matter. Let them feel the friction. Let them watch the struggle. Let them hear the language customers use. Then translate that empathy into economic value.

That is how experience earns its seat at the table.

Change the Narrative: How to Elevate Experience to the C-Suite

In the AI era, having a seat at the table becomes even more important. As more teams use AI to generate content, build interfaces, synthesize research, and automate engagement, sameness will flood the market. AI will make average easier. It will make mediocrity scalable. It will make “good enough” dangerously abundant.

While helpful, synthetic persona engagement offers no opportunity for empathy.

Human-centered experience design is how you escape the sea of sameness.

This is the renewed relevance of X. The book was never just about design. It was about business meeting design at the intersection of behavior, emotion, technology, and memory. It was about the idea that experience is not what happens when someone clicks. It is what happens when someone feels.

When I wrote X, I even designed the book itself as an experience. I asked what a book might look like for someone whose brain had been rewired by mobile devices. What if a printed book had to behave a little more like an app? What if readers needed to jump in and out, move nonlinearly, follow color cues, and experience the ideas visually, emotionally, and spatially?

That was the point. The medium had to embody the message.

Today, that message matters even more.

AI is going to accelerate everything. But acceleration without empathy only gets you to the wrong destination faster. Automation without imagination only scales yesterday. Intelligence without humanity becomes forgettable.

So maybe this isn’t about artificial intelligence at all.

Remember Why You Started

Maybe it’s about remembering what purpose intelligence serves here…and I don’t mean artificial intelligence. I mean human and emotional intelligence (EQ).

It is for understanding. It is for creating. It is for caring. It is for seeing what others miss. It is for designing something better because you felt the problem deeply enough to no longer tolerate it.

If you’re waiting for someone to tell you what to do, you’re on the wrong side of innovation.

Start with people.

Watch them. Listen to them. Learn from them. Ask what they value now. Ask what makes them anxious. Ask what gives them relief. Ask what makes them feel special. Ask what they avoid. Ask how they define success, happiness, premium, trust, and worth. Ask what they wish worked differently. Ask what they have stopped expecting because disappointment trained them to lower their standards. Observe them. Feel where they struggle or hit friction or feel under-appreciated or overlooked. See where they hit deadends.

Then raise those standards for them. Innovate.

Design the ignite moments. Add digital warmth. Build trust. Deliver wow. Make the experience native, personal, intuitive, and meaningful…because I promise you, someone on the other side will feel it. And they will remember. And they will share it.

How Do You Make People Feel?

In the end, Maya Angelou said it best:

“People may forget what you said.

People will forget what you did.

But they will never forget how you made them feel.”

That is experience.

That is design.

That is innovation.

And in an age of AI, it may be the most human advantage we have left.

I’ll leave you with this...

Earlier this year, we launched ServiceNow Futures, a future-forward team of futurists, innovation officers, and futures engineers inside ServiceNow. Our mission is simple, but urgent: help leaders see further and act sooner.

As part of that work, we translate emerging signals, market shifts, and technology trends into actionable insights for the C-suite. Our research, thinking, and customer-facing work become executive frameworks, reports, webinars, and workshops — practical tools to help business and technology leaders make better decisions today for a world that does not fully exist yet.

Today, I’m proud to share the release of my latest ServiceNow Futures Report, “AI Cultures and Agentic Darwinism: Why Survival of the Fittest Starts With a Leadership Mindshift.” It is available to download immediately, with no email gate and no paywall. The other news is that it was developed with advisory from my old friend and former Altimeter colleague, now General Partner at Blitzscaling Ventures, Jeremiah Owyang. 🙌

I wanted to share it here with this community because the report explores something I believe every leader needs to confront now: AI transformation is not just a technology strategy. It is a cultural one.

The companies that will thrive in an era of AI Darwinism will not simply be the ones that deploy the most AI tools, launch the most pilots, or chase the biggest productivity targets. That’s becoming status quo. They will be the organizations with the leadership capacity, cultural readiness, and operating imagination to evolve.

So, let’s dive in.

The question we sought to answer is among the companies navigating AI, what kind of culture was taking shape as these companies navigated their AI journey.

We explored four AI cultures, each with four different survival odds. Technically, Jeremiah’s research identified five cultures. In this report, we explored the following…

1. AI Followers

2. AI Forward

3. AI First

4. AI Native

Understanding the Four AI Cultures

AI Followers wait. They look for proven ROI, best practices, vendor maturity, regulatory comfort, and consensus before moving with conviction. They value stability and compliance. There is wisdom in caution, but in a world moving this quickly, waiting can become a strategy for falling behind. Followers use AI to optimize what already exists. They are trying to play the old game better.

AI Forward organizations move deliberately. They integrate AI alongside people and often emphasize augmentation to bring employees into the journey. They create centers of excellence, pilot high-value use cases, scale what works, and design human-in-the-loop workflows. Forward cultures understand that AI transformation is not a technology rollout. It is a change in behavior, belief, and operating rhythm.

AI First companies invert the default. Instead of asking, “Who should do this work?” they ask, “Can AI do this first?” The operating assumption changes. AI becomes the first resort, not the afterthought. Humans do not disappear. They move up the value chain toward judgment, creativity, empathy, trust, exception handling, and orchestration.

AI Native companies are built, or rebuilt, with AI at the core. For startups, AI is not a feature. It is the foundation. They scale with intelligence, not headcount. Tiny teams can produce the output of much larger organizations because AI performs operational work and humans orchestrate outcomes. But AI Native is not only a startup phenomenon. Established companies can also rethink functions, workflows, and business models from first principles by asking, “If we started over today with AI at the center, what would this become?”

The AI Mirror Reflects Your Future Prospects

These cultures are not merely labels. They are a reflection in the AI mirror.

Where are you really?

And where are your competitors, customers, employees, and AI-native challengers going?

Once leaders can answer honestly, they can evolve intentionally.

From Iteration to Innovation

In the report, we introduce a bimodal strategy for doing so. Mode 1 is optimized AI. It improves existing workflows, reduces manual work, lowers costs, accelerates cycle times, and creates visible wins. This matters because credibility matters. CFOs need proof. Employees need momentum. Leaders need confidence.

Mode 1 is not the destination. It is rooted in meaningful progress.

Mode 2 is innovative AI. It uses the capacity created by optimization to explore new products, services, experiences, revenue streams, and business models. This is where AI stops being a productivity story and becomes a growth story.

The magic is in the cycle. Mode 1 frees resources: cash, time, and talent. Those resources should not disappear into more meetings, messages, and dashboards. They must be deliberately reinvested into Mode 2. Savings become fuel. Efficiency becomes capacity. Capacity becomes reinvention. Reinvention creates advantage. Advantage invites further investment.

That is how evolution compounds.

From Optimize to Reinvent

The AI Evolution Arc maps the journey from Optimize to Integrate to Reimagine to Reinvent. Or, culturally, from AI Follower to AI Forward to AI First to AI Native.

This progression is not guaranteed. Organizations can get stuck optimizing indefinitely. They can celebrate productivity while competitors redefine the market. They can become more efficient at work that matters less.

Darwin taught us that adaptation determines survival.

In the era of AI Darwinism, adaptation is now a leadership choice.

Please read the report. 🙏

There is a moment in every book journey that I never take for granted.

It is the moment a box arrives, and an idea that once lived only in your imagination is suddenly real enough to hold.

That is the moment I wanted to share with you.

Today, I’m proud, grateful, and honestly a little emotional to share that my new book with Dave Wright, Chief Innovation at ServiceNow, Infinite: How Visionary Leaders Transform Today’s Businesses into AI-Forward Companies, is now available.

This book began with a question that kept following us into every executive conversation, every AI workshop, every boardroom discussion, and every moment of uncertainty leaders are facing right now:

What does it really mean to build a company for an age when intelligence is no longer scarce?

There are so many books, courses, talks, and articles that explore intelligence as a tool, as a co-pilot, as a solution for automation, cost-cutting and efficiency gains.

We set out to share our work in how AI is also a catalyst to rethink the company itself.

This moment is bigger than tech. It is asking us to reconsider how work flows, how value is created, how people and agents collaborate, how leaders measure progress, and how organizations evolve when change will no longer arrive in waves, but instead continuously.

That is why we wrote Infinite.

It is a book for leaders who feel the tension between the urgency of this moment and the responsibility to lead what comes next with humanity, imagination, and courage.

An Infinite Company is infinite because it is designed for boundless capability and possibility. It learns. It adapts. It grows smarter with every decision. It uses AI to elevate human value.

We believe AI should be used to reimagine work, to free people from the repetitive and the routine, to create new capacity, to unlock new forms of creativity, judgment, empathy, and invention, to help leaders build organizations that are more efficient and more plugged-in to what is possible.

This is a moment to stop optimizing the past and start designing what comes next. It is a moment to ask better questions:

• Are we using AI to improve yesterday, or invent tomorrow?

• Are we building faster versions of old companies, or new companies worthy of this moment?

• Are we treating AI as a feature, or as a foundation for business reinvention?

• Are we making people more replaceable, or more capable?

From here on out, the future will be designed.

And Infinite is here to help leaders design it.

Thank you for being part of this journey…for reading, sharing, challenging, and encouraging the ideas that eventually became this book.

We hope it helps you see this moment differently. We hope it gives you language for what you already feel is changing. Most of all, we hope it gives you the confidence and those around you to lead what comes next.

The Infinite future begins with us, right now.

With gratitude,

Brian

♾️

Surprise! 🎉

After writing Mindshift, I took time to listen, learn, and rethink what leadership now requires. I’m excited, humbled, and honestly grateful to share what comes next.

Introducing, Infinite: How Visionary Leaders Transform Businesses into AI-Forward Companies.

Releasing on June 23rd, Infinite introduces the concept of the “Infinite Company,” a new model for organizations built to thrive in the age of AI. Please pre-order on Amazon.

Three years ago, I joined ServiceNow as Head of Global Innovation. More recently, I’ve been working with our Innovation Office to build ServiceNow Futures, a thought lab for research, reports, and executive advisory. I’m doing work that I love, studying disruption from the inside, working with leaders in real time, and helping organizations see around corners.

As part of this work, my friend, colleague, and ServiceNow Chief Innovation Officer, Dave Wright, and I have spent a lot of time with executives, boards, AI-native founders, and analysts who are actively redesigning the future of business.

What we’ve seen is clear. AI is not just another technology cycle. It is not merely a tool for automation, productivity, efficiency, or cost reduction. It is forcing every organization to ask a much bigger question: What kind of company do we need to become when intelligence itself becomes abundant?

That question became the foundation for our new book: Infinite. And it features a powerful foreword by ServiceNow Chairman and CEO Bill McDermott.

AI-native companies are revealing a new model for speed, scale, learning, and human-agent collaboration. Many of them are growing with tiny teams, intelligent agents, and operating models that look nothing like the companies most of us grew up building, leading, or advising. That is both the warning and the invitation.

Incumbent organizations are not powerless. But they must learn to move beyond using AI to optimize yesterday’s work. They must reimagine workflows, roles, culture, leadership, governance, and value creation itself.

Dave and I wrote this book to help leaders evolve from finite operating models, built for control, silos, and linear growth, into infinite companies designed for adaptability, intelligence, human potential, and exponential outcomes.

This book is for executives, boards, founders, strategists, builders, and anyone who feels the gravity of this moment and wants to do more than react to it.

It is for leaders who believe the future should not happen to us. It should happen because of us.

Pre-order a copy for yourself, your team, or the executives in your life who are ready to lead what comes next.

Thank you for being part of this next chapter with me.

With deep gratitude,

Brian

p.s. Pre-orders help signal momentum, support the launch, and most importantly, get the book into the hands of leaders who need a more courageous, human, and practical blueprint for the age of AI.

p.p.s. Thank you for your support and sponsorship, Nick Tzitzon. 🙏

AI is forcing every organization to reveal what it really believes about people.

Do leaders see AI as a way to shrink the company into a more efficient version of yesterday? Or do they see it as a way to expand the organization’s capacity to learn, imagine, decide, create, and grow?

That is the fork in the road.

I call this moment AI Darwinism: the new competitive reality where advantage goes to the most adaptive, not the most automated. The winners will not be the companies that replace people fastest. They will be the companies that learn faster, augment better, and use human + artificial intelligence to create new value before the market forces them to.

That is why the real ROI of AI is no longer just Return on Investment. It is Return on Intelligence.

Return on Investment asks: What did we spend, and what did we save?

Return on Intelligence asks: What did we learn? What did we unlock? What new capacity did we create? What new value can we now deliver that was impossible, impractical, or invisible before?

You cannot automate your way to innovation and growth.

Automation can make yesterday more efficient.

Augmentation is how we invent what comes next.

Automation is Useful, Automation as Strategy is a Trap

Let’s get this out of the way: automation is not the enemy. It is necessary. It is just not absolute.

Some work should absolutely be automated. Manual drudgery should be reduced. As ServiceNow CEO Bill McDermett says, we need to take the soul crushing work out of our day-to-day potential.

Employees should not spend their best cognitive hours copying, pasting, reconciling systems, searching for information, formatting decks, summarizing meetings, or navigating workflows that should have been redesigned years ago. Customers should not wait in a queue for answers a system can responsibly deliver in seconds. AI can and should remove friction from work.

I think we can all agree here.

But automation becomes dangerous when it graduates from tactic to strategy.

That is when the boardroom conversation narrows, and as a result, the strategy and what good looks like narrows. The imagination collapses into a spreadsheet. AI becomes a cost-cutting mechanism: fewer people, fewer hours, fewer costs, fewer steps, fewer “inefficiencies.” The organization looks more productive on paper, but it may also become less capable, and more so, less competitive in practice.

Harvard Business Review recently framed the choice directly: CEOs are deciding whether their AI strategy is primarily about improving the bottom line through automation and headcount reduction, or growing the top line through augmentation and innovation. The authors argue that automation may produce faster early gains, but augmentation has greater long-term potential because it builds capability, trust, learning, and new productive capacity.

Yet, the 2025 Indeed Workforce Insights Report, which surveyed ~80,000 workers across eight countries, found that the time saved with AI was largely spread across “more of same tasks.” Any potential for augmentation, for example, innovation/creative work and relationship management, didn’t even break the top five use cases.

As the others of the Havard Business Review observed,” It’s easier for executives to imagine using AI to streamline what people already do than to reimagine how it might be used to produce entirely new value.”

That is the strategic conversation leaders should be having.

Not “How many jobs can AI replace?” But “How much more value can our people create because AI exists?”

Not “How do we use AI to do the same work with fewer people?” But “What work should exist now that intelligence is becoming abundant?”

That is the difference between using AI to optimize the past and using AI to invent the future. The challenge is that executives may not know the difference.

Jensen Huang’s Lesson: Tasks Are Not Jobs

Jensen Huang offered one of the clearest executive explanations of this distinction on SCSP’s Memos to the President podcast.

He warned against the kind of fear-based AI rhetoric that tells young people not to become radiologists, software engineers, or knowledge workers because AI will allegedly erase those professions. His point was that if society convinces people not to enter fields where we will actually need more human capability, not less, that pessimism becomes self-defeating.

The task of a job and the purpose of a job are related, but they are not the same.

If you define a software engineer as someone who types code, then yes, AI looks like a replacement technology. If you define a software engineer as someone who solves problems, designs systems, translates possibility into functionality, and turns imagination into infrastructure, then AI becomes a force multiplier.

Huang used the example of code. If we assume the economy only needs a fixed amount of software, then AI writing code seems to imply fewer engineers. But Huang challenged the premise itself. Why assume we only need the same amount of software? Why assume the world only needs one billion lines of code when we could need a trillion…across healthcare, science, manufacturing, retail, energy, education, and entirely new domains of human possibility?

At NVIDIA, Huang said AI now does much of the coding, yet the company is hiring more engineers than ever. The reason is not paradoxical. Well, it may seem counterintuitive in this

It is the point. When AI takes on more tasks, the ceiling for ambition rises. More problems become addressable. More experiments become feasible. More ideas become buildable.

That is Return on Intelligence.

The goal is not to preserve or even optimize every task. The goal is to elevate the purpose of the work.

The Hidden Cost of a Replacement Signal

AI strategy is not just an operating decision. It is a signal.

Employees are watching how leadership talks about AI. They are listening for whether AI is being introduced to help them become more valuable or to make them more disposable. They are reading between the lines of every pilot, every reorg, every “efficiency initiative,” every platitude that promises AI will “free people up” without explaining what they will be freed to do.

HBR’s research found that only 44% of surveyed employees said their organization had formally announced AI plans. Overall, 62% believed their organization was using AI to augment employee capabilities, while 34% believed AI was being used to automate work and reduce costs. The gap grows more revealing by level: 81% of senior leaders believed their organization was all-in on augmentation, while only 53% of individual contributors perceived augmentation and 40% suspected automation.

The perception gap is breathtaking. To be honest, it’s business as usual. And, I call it the iteration gap. It’s the gap between maximizing the past and imagining the future. Coming from Silicon Valley, the past wasn’t even in the conversation. We weren’t ever asking, what are we doing that can be better tomorrow? We were always saying, not really asking, “WTF!?” aka “What’s the future…!?” It was always a directive.

We weren’t ever asking, what are we doing that can be better tomorrow? We were always saying, not really asking, “WTF!?” aka “What’s the future…!?” It was always a directive.

Leaders may think they are driving transformation, but they are instead using AI to optimize the past…without first assessing whether or not the past was worthy of bringing forward to the future. It’s the difference between iteration and innovation. Iteration makes yesterday less expensive, faster, more efficient. Innovation is doing what you didn’t do yesterday, to create new value.

In that same vein, automation is using AI to optimize yesterday. Augmentation is collaborating with AI to do what was not possible without AI and what AI couldn’t do without people. Simple said, using AI to do yesterday’s work is not augmentation. But in both cases, iteration and automation, can appear as innovation and augmentation because you’re using new technology to work in new ways. If the workflow and outcome are optimized, it’s not innovation or augmentation. And to be clear, that’s cool. You need iteration. You need automation. You just can’t innovate or create new value without using AI or any technology and/or mindset to explore the unknown and unlock what wasn’t visible or possible before.

If you do not communicate this vision or intent and purpose or goals, you do not achieve anything other than a better yesterday. Then there’s the narrative that AI is a replacement engine, that people matter less than AI’s capacity to replace headcount, salaries, and all other employee costs. For the record, that is an unproductive, and basic, narrative. Come on. Are we talking status quo here or leadership?

When employees believe AI is being deployed to replace them, trust erodes. Adoption becomes a metric. People may comply, but they do not commit. They use AI because they are told to, not because they believe it according to a vision. But they believe it will help them grow. HBR describes this as the difference between employees becoming “pilots” of AI and becoming passengers along for the ride. When people are passengers, engagement remains shallow, guidance is unclear, and AI output can devolve into “workslop,” low-effort, low-quality AI-generated work that adds noise instead of value.

This is one of the great overlooked risks in enterprise AI. Poor AI strategy does not just produce bad outputs. It produces defensive humans. And defensive humans do not transform companies.

The same HBR article points to three dynamics that CEOs and boards need to hear...

First, the threat of layoffs undermines well-being, which in turn affects productivity, retention, and talent attraction. Second, poorly integrated AI workflows create confusion, shallow adoption, and more workslop. Third, cost-cutting AI strategies can hollow out junior roles and future innovation.

A company can look efficient in the short term while becoming less capable over time.

The Leadership Mandate: Turn Automation Savings into Augmentation Capital

This is where AI strategy has to graduate from pilots, dashboards, and efficiency conversations into a new operating discipline.

If automation creates savings, time, money, resources, leaders have to decide where those savings go. Do they fall straight to the bottom line? Do they disappear into the machinery of more meetings, more output, more dashboards, more pressure, and fewer people? Or are they intentionally reinvested into new capabilities, new customer value, new roles, new skills, new services, and new business models?

Think of it this way…if you invest in automation, which frees up people’s time, where will the ROI of AI come from? The easy answer is to cut costs. In a world where AI automation threatens to become the new status quo, companies can hinder competitiveness. Augmentation, on the other hand, represents an opportunity to reinvest resources into new competitiveness that creates new value?

Are you taking away jobs or are you creating them?

That is the question every CEO, board, CIO, CFO, COO, and CHRO should be asking now. Because the true measure of AI is not just what it removes from the business. It is what the business becomes capable of because AI is now part of the work.

This is the missing metric in most AI strategies: reinvested capacity.

Companies are already tracking hours saved, tickets deflected, cycle time reduced, costs avoided, code generated, content produced, and tasks automated. Those numbers matter. But they mostly tell us what AI took out of the system. They do not tell us what intelligence, creativity, judgment, trust, and value were put back in.

That is the difference between efficiency and innovation, productivity and progress.

A company can save 100,000 hours and become no more innovative. It can generate more content and say less. It can move faster and still move in the wrong direction. It can automate customer service and still never understand the customer. It can eliminate junior work and accidentally eliminate the apprenticeship layer where future leaders learn judgment, context, taste, and accountability.

This is the part of the AI conversation that remains dangerously underdeveloped or under. We are measuring activity because activity is easy to count. But in AI Darwinism, activity is not the advantage...it is adaptability.

Harvard Business Review’s research shows why augmentation is not just a nicer version of automation. It is a different performance path. Automation tends to create faster, more visible gains because it substitutes AI for well-defined work. Augmentation requires deeper upfront investment in workflow redesign, capability-building, data infrastructure, management practices, and human-AI coordination. But that investment is what shifts the organization’s productive frontier.

It’s the difference between automating the past and inventing the future.

That is the strategic discipline leaders need now. Do not just ask, “How much time did AI save?” Ask, “What can we do with the time?”

Did we use it to get closer to customers?

Did we use it to experiment with new offerings?

Did we use it to improve decisions?

Did we use it to reskill people into higher-value roles?

Did we use it to redesign workflows around outcomes rather than departments?

Did we use it to make work more meaningful, or simply more demanding?

IKEA gives us an important operating example of what reframing looks like. When the company deployed it’s AI chatbot Billie to serve customers and deflect escalations, it was so successful, that it handled 47% of initial engagements. The company could have fallen into an automation trap. Deflecting almost 50% of service inquires demands a review of resources. After all, it represented 3.2 million interactions and nearly €13 million in savings. That alone would have been enough for most companies to declare victory and move on.

So the question becomes, do you cut headcount for short-term ROI gains? It’s easy to stop there. But IKEAs move went beyond efficiency. IKEA turned that efficiency into capacity.

The company did not just ask, “How many inquiries can AI handle?” It asked, implicitly, “What can our people now do that creates more value?” Ingka Group says 8,500 call-center co-workers were reskilled into areas such as remote interior design, digital retail sales, relationship-building, and complex customer inquiries; Reuters also reported that Ingka’s remote interior design channel generated €1.3 billion, or 3.3% of total revenue, with a target to reach 10% by 2028.

That is Return on Intelligence in practice.

Brian Solis Keynote at Avasant’s Leadership Conference, Huntington Beach, Calif.

Human + AI Collaboration = Augmented Capacity

The AI chatbot was not the transformation. The transformation was the redeployment of human capability into work that was more consultative, more relational, more creative, and more commercially meaningful.

This leadership toward augmentation: automate the work that no longer deserves human limitation, then move people toward the work where human capability matters more.

That sounds simple, but it requires a very different kind of executive vision and courage. It requires leaders to stop treating technology and people as costs trapped inside workflows and start treating them as capacity waiting to be amplified. It requires leaders to ask whether the current operating model deserves optimization or reinvention. It requires leaders to redesign work before they reduce people. It requires the CFO to see savings as investment capital, the CIO to see AI as workflow architecture, the CHRO to see skills, roles, and trust as strategic infrastructure, and the CEO to make the intent unmistakable.

Remember, employees are being todld to adopt AI. They’re trained to increase AI fluency, and that fluency is measured. So they are not just adopting AI, there are also interpreting it.

They’re asking: Is this here to help me grow, or to make me disappear? Am I being invited to become a pilot of the future, or am I a passenger in someone else’s cost-reduction plan? Is leadership using AI to build a better company, or just a smaller one?

Trust is now part of the AI stack.

HBR found that when employees believe AI is being introduced to make their work better, adoption rises through curiosity and agency rather than compliance. The organization cultivates pilots over passengers. The opposite path is just as instructive: when AI is associated with replacement, layoffs, and forced adoption, well-being declines, workslop rises, attrition increases, employer brand suffers, and leadership pipelines erode.

This is why “AI adoption” is the wrong finish line. Adoption without belief is compliance. Compliance without trust becomes status quo. Status quo at scale becomes a loss of competitiveness. And no company can transform with a workforce that is quietly defending itself from its own strategy.

The alternative is to make augmentation visible.

Like IKEA, show employees where AI is helping people move into better roles. Show how automation savings are funding training, experimentation, customer innovation, and new services. Show how exceptions are being studied as signals. Show how frontline teams are helping redesign work. Show how junior employees will still build judgment in an AI-shaped workplace. Show how leadership is protecting the learning architecture of the company even as tasks change.

This is especially important for early-career talent. If AI drafts the first analysis, writes the first version, summarizes the meeting, generates the code, answers the routine customer question, or prepares the report, then leaders have to redesign how people learn. Apprenticeship has to be built into the new workflow.

The next generation of talent should not be trained to compete with AI at the task level. They should be trained to think with AI, challenge AI, supervise AI, improve AI, and use AI to get closer to problems that matter. They need to learn judgment, context, taste, ethics, empathy, and systems thinking in new ways. If leaders fail here, they may not feel the damage immediately. But years from now, they will wonder why the organization has fewer people capable of leading through ambiguity.

That is the danger of short-term automation thinking…you’re essentially mortgaging the future.

Improve the Past While Visualizing a New Future

Automate and optimize what deserves to live in the future, not because it’s the way that things have always been done. Explore where to augment work to compete for the future, today.

That’s the mindshift.

For example, starting with pain points can trap people in a “loss minimization mindset,” where teams look for time lost, money lost, and friction reduced. The better frame is to use AI to reason, reframe, and reinvent work itself.

In our Business Transformation Mindset report, Dave Wright, Alexis Walker, and I explored distinction between the iteration mindset and the transformation mindset.

The iteration mindset asks how to do today’s work faster, cheaper, with less effort, and with greater efficiency.

The transformation mindset asks why we operate this way, what problems remain unsolved, what data and workflows are missing, and what could transform the business rather than simply fix it.

This is the bridge from automation to reinvention.

The goal is not to build a giant inventory of AI use cases that solidify status quo. That is useful, but insufficient. The work is to build a portfolio of new value cases: places where AI helps reveal unmet demand, redesign workflows, expand judgment, accelerate learning, improve customer outcomes, and create capacity that did not exist before.

This is also why Return on Intelligence needs to sit next to Return on Investment.

Return on Investment asks whether AI produced financial efficiency. Return on Intelligence asks whether AI made the organization more adaptive, more creative, more decisive, more trusted, and more valuable over time.

A useful way to think about it is:

ROI∞ = (Realized Value × Learning Velocity) ÷ (Time-to-Outcome × Resistance to Change)

We introduce this model in my new book with Dave Wright, “Infinite: How Visionary Leaders Transform Today’s Businesses into AI-Forward Companies.”

Realized value matters because AI is everywhere. Pilots, demos, copilots, agents, dashboards, and innovation labs can create the appearance of progress without changing the economics or experience of the business.

Learning velocity matters because AI Darwinism rewards organizations that learn faster than their environments change. The advantage is not simply adopting a model. It is building a company that can sense new signals, test new assumptions, absorb feedback, and improve continuously.

Time-to-outcome matters because speed without direction is just motion. AI should compress the distance between noticing a problem, understanding it, deciding what to do, acting on it, and learning from the result.

Resistance to change matters because culture can neutralize technology. Legacy incentives, fear, fragmented systems, poor data quality, unclear governance, and organizational politics can drag down Return on Intelligence faster than any model can lift it.

The AI Layoff Trap and Self-Inflicted Disruption

In their paper published in April 2026, Brett Hemenway Falk and Gerry Tsoukals explore the “AI Layoff Trap,” which can debilitate a company’s competitiveness by doing the very thing businesses have done throughout history, cut costs through automation to become more leaner and more efficient. The authors argue that “if AI displaces human workers faster than the economy can reabsorb them, it risks eroding the very consumer demand firms depend on.”

The paper’s model shows that firms can rationally over-automate because each company captures the full cost savings of replacing its own workers while bearing only a fraction of the demand destruction caused by lost wages. The result can become an automation arms race that harms workers and firm owners alike.

The paper’s “Red Queen” effect makes the warning even sharper. Better AI does not automatically solve the trap. In the model, higher AI productivity can widen the over-automation wedge because each firm perceives a market-share gain from automating beyond rivals, even though those gains cancel out when everyone does the same thing.

Think about it.

The more powerful AI becomes, the more dangerous unimaginative leadership becomes.

AI Darwinism will not punish companies for automating. It will punish companies for automating without imagination.

The winners will automate, yes. But they will also augment. They will use AI to remove friction and then reinvest the gains into intelligence. They will use agents to handle routine work and then elevate humans into higher-value judgment. They will use automation to expose unmet demand. They will use AI not simply to answer questions, but to help leaders ask better ones.

Take the time to ask different and better questions…if you don’t do it now, when will you have time to do it later?

What do our customers need that our current model cannot deliver?

What work exists only because our systems are fragmented?

What assumptions are embedded in this process?

What would we build if we were starting today?

What human capabilities become more important as AI becomes more capable?

What new value can we create now that time, intelligence, and agency are becoming more abundant?

That is why this moment is so consequential. AI is not just changing how work gets done. It is revealing the imagination, or the lack of imagination, inside the companies deploying it.

Some organizations will use AI to become faster versions of their past. They will celebrate efficiency, reduce costs, and call it transformation. Others will use AI to confront the deeper question: what should this company become now?

Automation will yield impressive savings.

Augmentation will shape the future.

That is the difference between return on investment and Return on Intelligence. And that is why you cannot automate your way to innovation and growth.

Automation can clear the runway.

Augmentation is how you learn to fly.

The evolution has begun.

There’s a strange tension inside companies right now and it’s creating an AI divide.

Employees are experimenting with AI faster than their organizations are evolving around them. Some are already using AI to think differently, work differently, and create value they couldn’t have imagined a year ago. Others are watching from the sidelines, unsure whether they have permission to try, fail, learn, or reinvent the way work gets done.

AI leadership is largely absent.

The deeper story is agency.

Who has it? Who’s developing it? Who’s blocked from using it? And which leaders are brave enough to redesign the organization so human potential and machine intelligence can finally meet in the work itself?

The numbers are revealing. OpenAI has described a “capability overhang,” where advanced AI users are extracting far more value from AI than typical users. Microsoft’s 2026 Work Trend Index points to a similar divide inside the enterprise: a small group of “Frontier Professionals” is already working in new ways, while many organizations still operate with old incentives, old workflows, and old assumptions.

In other words, the future is already showing up at work. It just isn’t evenly distributed across the org chart.

This is where leadership matters most.

AI Darwinism isn’t coming. It’s here. But this isn’t a warning about machines replacing people. It’s a call for leaders to close the gap between what people can now do and what their organizations are designed to allow.

That requires more than AI adoption. It requires a mindshift.

Agents, Human Agency, and the Opportunity for Every Organization

The Microsoft report finds that AI is expanding human agency as agents take on more execution, but the real question is whether organizations are built to capture that agency. Microsoft analyzed trillions of anonymized Microsoft 365 productivity signals and surveyed 20,000 workers using AI across 10 countries. The finding is both hopeful and uncomfortable: in many cases, people are ready ,but he systems around them are not.

Contrary to popular ideology, AI fluency is not the finish line.

AI fluency is a good start, but it is not the destination. Fluency is knowing the language. Like OpenAI, in its research, Microsoft identified an advanced category of work that they called Frontier work with those leading it, Frontier professionals.

For example, Microsoft found that 49% of Microsoft 365 Copilot conversations support cognitive work: analyzing information, solving problems, evaluating, and thinking creatively. Even more important, 58% of AI users say they are producing work they couldn’t have produced a year ago. That rises to 80% among Frontier workers. These workers use agents for multi-step workflows, rethink where AI should augment or automate work, and help create shared AI standards for their teams.

Said another way, the best AI users are not outsourcing their thinking. They’re upgrading it.

In fact, Microsoft found that 86% of AI users treat AI output as a starting point, not a final answer, and “stay responsible for the thinking.” Frontier Professionals are more likely than others to intentionally do some work without AI to keep their skills sharp and to pause before deciding what should be done by AI versus a human.

You could say that it’s the future of work in one sentence: the human who knows what not to delegate becomes more valuable than the human who delegates everything.

Human skills become the moat. AI does not eliminate judgment or critical thinking or creativity. It these skills the premium.

The Real AI Gap Is Coherence

Recently, Daniel Miessler, a cyber security AI engineer, recently published a post about how most companies are not remotely ready for AI. Miessler argues that legacy companies struggle with AI because they can’t clearly visualize or articulate what they want. “AI is about execution,” he writes, “and it’s quite powerless when it doesn’t know what to execute.” He points to unclear and constantly changing vision, goals, strategies, workstreams, metrics, and costs as the deeper problem.

Exactly.

AI is a mirror with an API. It reflects the past and connects it to efficiency.

This reminds me of something legendary venture capitalist Vinod Khosla once said. “Most businesses have no clue what is about to hit them in the next ten years when most rules of engagement will change,” he observed.

If your company is clear on direction, AI accelerates you. If your company is confused, AI scales the confusion. If your company is tied to the past, AI scales your past until your business can no longer compete in the present.

You can’t automate chaos into clarity.

You can’t agentically orchestrate a business that doesn’t understand its own music.

You can’t scale what you shouldn’t be doing in the first place.

This is why “use AI” is not a strategy. It’s a dare. And for many organizations, it’s a dare issued by leaders who have not mapped the work, clarified the outcomes, aligned incentives, or created the cultural conditions for people to experiment their way into the future.

Miessler’s most important question wasn’t, “What can AI do for us?” It is whether the company is in a state where AI can help at all. And maybe it should be a more direct question,” where can we go with AI that wasn’t possible before” or “what aren’t we doing that would make us more competitive?”

When executives hold up a mirror and honestly see themselves reflecting on these questions, it might help them realize something profounds… we’re not trying to do much more than what we’ve always done, we’re just evolving the past thinking that the market conditions will remain constant.

We’re implementing technology to free up human capacity, but we’re not repurposing that capacity toward value creation.

We’re moving faster but we may not be empowering our people to compete differently, meaning we’re running to stand still.

We’re driving AI adoption, fluency, and training, but to what end? Is AI helping us scale the past but not preparing for or building a new future not possible without AI? Are we pushing people to give their thinking and output to AI without reskilling them toward proper augmentation?

The Transformation Paradox

Microsoft calls this the Transformation Paradox: knowledge workers could be ready, but organizations aren’t.

In the 2026 Work Trends Index, Microsoft found that only 19% of AI users sit in the “Frontier” zone, where individual capability and organizational readiness reinforce each other. About 10% are blocked, representing skilled workers in companies that haven’t caught up. Half sit in the messy middle, where both individual practice and organizational conditions are still taking shape.

The misalignment gets worse at the top.

Only 26% of AI users say their leadership is clearly and consistently aligned on AI. Meanwhile, 65% fear falling behind if they don’t adapt quickly, 45% say it feels safer to focus on current goals than to redesign work with AI, and only 13% say they are rewarded for reinvention even when results aren’t immediate.

Leadership is not clear or aligned on AI. People fear falling behind. Employees focus current work instead of redesigning work because it feels safer. And employees aren’t incentivized or motivated to think differently, to innovate, or to improve outcomes.

Think about it this way…people are being told to transform while being measured, managed, and rewarded for not transforming.

That’s not a skills gap. That’s a leadership gap.

You cannot ask people to reinvent work and then grade them only on yesterday’s measures.

Psychological Safety Is AI Infrastructure

Work today is based on the verticalization of existing silos. Data, workflows, systems, and measures are all designed to optimize that work. Agentic AI, for example, represents an opportunity to connect disparate silos to make work flow horizontally across the enterprise. This means everything is up for reinvention…everything. Whatever past workflows remain moving forward should only be the result of deep thinking. Your next steps become intentional.

I have two words, “psychological safety.”

In the AI era, psychological safety is operational infrastructure. It is what allows people to say, “This process no longer makes sense.” It gives employees permission to challenge inherited workflows, surface broken handoffs, test new human-agent models, and admit when AI outputs are wrong before those errors compound at scale.

In its Work Trend Index research, Microsoft found that when managers created psychological safety around experimentation, employees reported up to 20 points higher AI readiness and value and were 1.4x more likely to be high-frequency users of agentic AI. When managers actively modeled AI use, employees reported a 17-point lift in AI value, a 22-point lift in critical thinking about their AI use, and a 30-point lift in trust in agentic AI.

That’s proper leverage.

Silence fosters technical debt.

Fear cultivates organizational latency.

And cultures that dissuade experimentation will lose to cultures that learn in public, correct quickly, and scale what works.

From Adoption to Absorption

AI adoption asks, “Are people using AI?”

AI absorption asks, “Is AI changing how value is created?”

Microsoft found that organizational factors such as culture, manager support, and talent practices account for more than 2x the reported AI impact of individual mindset and behavior, 67% vs. 32%. The constraint is no longer what people can do. It is how work is structured around them.

In other words, you can send everyone to prompt school, but if your culture doesn’t encourage and reward experimentation, your managers don’t model AI use, your workflows remain siloed, and your incentives worship yesterday’s KPIs, congratulations: you’re optimizing the past while your competitors are experimenting forward.

The future of work needs design.

The New Leadership Work

The job of leadership now is to rearchitect work, otherwise, we’re talking about management.

Start with outcomes and the work that supports them, not the org chart. Org charts tell you who reports to whom. Outcomes thinking necessitates “work charts” that show how people and tech move value.

Pick one critical flow: lead-to-cash, claim-to-settle, incident-to-resolution, employee onboarding, product launch, customer renewal.

Then ask: what outcome are we trying to create, where does judgment matter, where does execution slow down, where can agents help, where must humans stay accountable, and what should the work teach us as it runs?

Next, find your Frontier Professionals. They are already there, quietly building the future with Claude in the margins while the organization debates the past.

Study them. Ask how they decide what to delegate, how they evaluate outputs, how they document workflows, how they share what they learn, and where the system blocks them.

Then train managers to become AI work coaches. They must model AI use, set standards for AI-assisted work, create room for experimentation, and reward people for redesigning work, not just completing tasks faster.

Finally, build evaluation infrastructure. As agents execute more work, the cost of bad outputs compounds. Microsoft says every Frontier Firm needs to answer three questions:

1. who reviews agent performance,

2. who has authority to update the workflows agents run,

3. and how does a local win get captured and scaled across the organization?

The firms that answer those questions start building “Owned Intelligence,” institutional know-how that compounds over time and becomes hard to replicate.

That is a real moat.

The Leadership Test

AI Darwinism isn’t coming, it’s here. The evolution has already begun. The future isn’t waiting for the next offsite, the next strategy cycle, or the next perfectly aligned transformation deck. The gap is widening now between people who are learning how to think, create, decide, and lead with AI, and organizations still asking them to fit that potential into yesterday’s jobs, workflows, incentives, and permission structures. That gap will not close itself. Leaders close it.

Every executive team should ask seven questions now:

1. What outcomes are we trying to improve, not just what tasks are we trying to automate?

2. Which workflows create the most value, friction, delay, or customer pain?

3. Where are our frontier employees already using AI in advanced ways?

4. Where are they blocked by policies, metrics, incentives, or management habits?

5. Which work should be delegated, collaborated on, explored, or kept human-led?

6. What quality standards define excellent AI-assisted work?

7. How do we capture lessons from every experiment and scale them into the operating model?

That is how companies move from AI adoption to AI absorption.

That is how leaders close the agency gap.

That is how leading companies compete for the future.

That starts with a mindshift.

AI is not just changing work. It is challenging leaders to become worthy of the future their people are already beginning to imagine.

The evolution has begun. The question is whether you will manage the gap, or close it.

Welcome to my masterclass: AI Darwinism: How Leaders Move from Automation to Reinvention.

In all seriousness, I recently joined my friends on “The Meg & Amy Show,” and somewhere along the way, it felt less like an interview and more like a one-hour masterclass on how to survive AI Darwinism.

Admission is free. So, enroll, or rather, watch now 🙏

Course Insights

You’ll learn what AI is really asking of leaders now: why AI maturity is getting harder, why too many organizations are using AI to optimize yesterday, and how the next generation of leaders can use AI to reinvent workflows, augment people, unlock new value, and ask the question that changes everything: What if?

A ha Moments and Takeaways

1. AI maturity isn’t adoption. It’s the ability to reinvent how value is created.

2. The companies losing to AI Darwinism aren’t behind on tools. They’re trapped in yesterday’s operating model.

3. Automation makes the old business more efficient. Augmentation makes the next business possible.

4. The real AI breakthrough isn’t productivity. It’s discovering what your customers need that your company never thought to offer.

5. AI transformation starts with leaders willing to challenge sacred cows.

6. The future belongs to those who ask better questions because of AI.

7. Workflow reinvention is where AI moves from experimentation to transformation.

8. The most dangerous AI strategy is using tomorrow’s technology to protect yesterday’s business.

9. Curiosity, humility, and psychological safety are now competitive advantages.

10. The defining question of the AI era is, “What if?”

AI Darwinism

When every conversation seems to talk about clever prompts, or how people are using AI agents for personal productivity, or sharing automation use cases to drive efficiency, we went after the necessary shifts in leadership and strategy.

This conversation is about what happens when the rules of business, work, leadership, and value creation start changing at the same time.

Most organizations are still trying to fit AI into the shape of the business they already know. They’re using it to move faster, cut costs, summarize meetings, automate tasks, and make yesterday’s workflows more efficient.

That’s useful. But it’s not reinvention.

The real opportunity is to ask what AI now makes possible that wasn’t possible before. What work should be redesigned? What customer needs are hiding in plain sight? What roles can be elevated instead of eliminated? What sacred cows are standing in the way of growth?

This is why AI maturity is getting harder. The bar keeps moving from generative AI to agents to agentic workflows to entirely new operating models. Every step forward reveals that AI transformation is not a technology project. It’s a leadership test.

The companies that thrive won’t be the ones that simply automate the old model. The companies that thrive in an era of AI Darwinism will be those brave enough to imagine the next one.

Thank you, Meg Bear and Amy Wilson, for the opportunity!

My recent conversation with the one-and only Guy Kawasaki on his podcast, Remarkable People, was one of those rare moments that felt like a reunion, a reflection, and in many ways, a reckoning with the past, present, and future of innovation.

Guy and I covered a lot of ground, from old-school Silicon Valley to AI and the future of leadership, but these are the headlines I hope inspire you to listen/watch the conversation (it’s a rare moment for the two of us to be together!)

It’s really about the difference between reacting to change and building the future before it happens. I think the last time we were on camera together was 15 years ago! That can’t be right!

Anyway, we fixed that! It would mean a lot of you could join us…

Listen (Spotify). Watch (Youtube).

TL;DR

A Mindshift is not just open-mindedness. It’s the discipline of looking ahead and shaping the future instead of reacting to it.

Most leaders are trapped in the daily swirl of iteration, not innovation. Emails, meetings, urgency, and noise keep people busy, but not necessarily aligned with where they actually want to go.

Self-awareness is the real starting point for transformation. Most people think they’re self-aware. Very few actually are. And those who lack self-awareness don’t know they do!

AI exposes the difference between doing yesterday better and inventing tomorrow. That becomes the core tension in the conversation.

Automation and augmentation are not the same thing. Automation improves existing work. Augmentation unlocks work, outcomes, and possibilities that did not exist before. And “you can’t automate your way to innovation!”

Linear growth is not enough anymore. The bigger opportunity is exponential or “logarithmic” thinking, where leaders stop optimizing the past and start designing new value.

Positive disruption is the goal. Don’t wait to be disrupted. Invest in disrupting yourself first.

Storytelling is strategy. The hero in the story is not the company, product, or executive. The hero is the person you’re trying to help and the future they can achieve with you.

Movements are built, not wished into existence. Complaining is easy. Making excuses is commonplace. Manifesting a better future takes vision, action, and belief.

Culture determines whether mindshift is possible. In some organizations, possibility wins. In others, perpetuation wins. Culture is the shared beliefs, mindsets, mental models, and values that inform the behaviors that become the people operating system of the business. “People like us, do things like this,” as Seth Godin says.

Psychological safety matters. People won’t challenge assumptions or imagine new futures if they fear punishment or resentment for doing so. You can’t stigmatize asking questions or trying something new and expect different results.

The critical skills for the AI era are still deeply human. Curiosity, imagination, empathy, critical thinking, and openness become more valuable, not less. AI cannot feel empathy. AI cannot exercise judgement. AI does not possess intuition.

AI should be treated as a cognitive exoskeleton. The question is not only what it can automate, but what it can help humans do that was previously impossible.

The real subtext is bigger than the book. In the end, we’re just two friends, industry veterans, comparing notes on what still matters in leadership, innovation, optimism, and reinvention after decades in Silicon Valley.

This is your invitation to listen to or watch a rare moment between two geeks.

Our Relationship Goes Back to a Time Before Web 1.0

Guy and I go way back…back to the 1990s, back to a different Silicon Valley, back to a time when the future felt more handcrafted than automated, when belief systems were built in coffee shops, garages, in hallways and lobbies at conferences, in startup offices, and in long conversations about what technology could become if people were bold enough to imagine it differently.

That’s part of what made this conversation so special. It took me back to the early days of Silicon Valley.

The theme of our conversation was about my book, Mindshift. But more so, it was two old friends (as in long-time, not age!), two longtime students of innovation, two champions for change, comparing notes after decades of watching industries evolve, leaders rise and fall, technologies overpromise and underdeliver, and every so often, something genuinely world-changing emerge from the noise.

Recognizing Guy Kawasaki

For those who know Guy’s name but may not know the full scope of his legacy, he’s one of the people who helped define modern Silicon Valley storytelling. He was Apple’s chief evangelist and helped write the playbook for what technology evangelism would become. He later co-founded Garage.com, which evolved into Garage Technology Ventures, and has spent decades at the intersection of entrepreneurship, venture thinking, product conviction, and storytelling. Today, he’s chief evangelist at Canva and the creator of the Remarkable People podcast.

So, when Guy asks questions about innovation, he’s asking as someone who has lived through multiple eras of technological reinvention and helped shape how many of us learned to talk about change in the first place.

He is a truly remarkable person.

The Thread Beneath the Conversation

On the surface, we talked about Mindshift. We talked about what it means to stop reacting to change and start shaping the future. We talked about the need to ask better questions, not just work harder on old answers. We talked about the difference between automation and augmentation in AI, and why too many organizations are still using breakthrough technologies to do yesterday faster and efficiently instead of inventing tomorrow on purpose.

What we really explored though, is what happens when optimism enters the room. If it’s one thing you can say about my body of work, every vision, every report, every scenario, every prediction was rooted in optimism…not doom.

Both Guy and I came of age professionally in a version of Silicon Valley that believed deeply in possibility. It’s certainly not what we’re hearing today for the most part. But that belief of optimism and possibility still matters…maybe more than ever.

Time teaches you that innovation alone is not enough. Vision is not enough. Intelligence is not enough. You also need self-awareness. You need courage. You need discernment. You need the discipline to separate momentum from meaning.

That’s part of what made this exchange feel so personal to me. It wasn’t just about trends, tech, or futurism. It was about the human work required to navigate them.

Key Takeaways

You’re going to hear more than a conversation about a book.

1) You’ll hear two people wrestling with the same question from different angles: How do you build a future you actually want to live in?

2) Why self-awareness is the starting point for any real transformation. Because if you don’t understand where your time, energy, assumptions, and attention are going, you can’t meaningfully change direction.

3) Why so much business transformation stalls. It’s usually because leaders keep trying to fit profoundly new capabilities into old mental models. AI is not a strategy.

4) Automation matters, but augmentation matters more. One improves the familiar. The other unlocks what was previously impossible. One helps you optimize the past. The other helps you invent a different future.

5) Storytelling is not a soft skill no is it simply a way of telling stories. It’s one of the most powerful leadership tools we have. People do not move simply because the data is there. They move when they can see themselves in the story of what comes next. One of my favorite parts of the conversation was exploring the idea that the hero of the story is not the company, not the product, not the leader. The hero is the person whose life, work, or future changes because they chose to believe in a new possibility.

6) We hope you’ll experience the genuine warmth, candor, and shorthand that only comes from experience and living through and shaping Silicon Valley history and lore. The stories between the stories. The laughter that comes from having been around long enough to know how much of innovation is conviction, how much is timing, and how much is stumbling into the future before you fully understand what it is.

There’s something powerful about hearing two old friends and industry lifers talk honestly about what we got right, what we got wrong, what still matters, and what leadership requires now. Truthfully.

In a moment when so much conversation about AI, innovation, and transformation feels manufactured, overproduced, doomsday rhetoric, or agenda-driven, this one is sincerely human. Reflective. Curious. Real. We’re just two old friends who’ve spent decades trying to build the future.

And maybe that’s the point.

The future doesn’t just belong to the loudest people in the room. It belongs to the ones who can still learn, still imagine, still challenge themselves, and still invite others into building something better.

This is Your Invitation To Join Us

Please listen or watch and let us know what you think and feel after…

The transition to an AI-first economy demands a new playbook, yet too many leaders are leaning into an irresponsible narrative that weaponizes fear instead of inspiring innovation. The conversations dominating our industry today are hyper-focused on headcount reduction and apocalyptic job loss. They’re not just unhelpful, they are actively destructive and hinder AI optimism and possibility, which will drive business growth and innovation.

When executives adopt what Nvidia's Jensen Huang recently described as a "god complex" to peddle speculative AI doom, they fail their most basic mandate to lead.

Enterprise leaders are currently navigating a critical transition toward AI-native operating models, yet AI adoption is stalling due to a fundamentally flawed narrative. Instead of focusing on strategic growth, the leadership conversation is paralyzed by speculative doomsday prophecies, a paralyzing fear of change, and a narrow focus on task automation for cost-cutting. This alarmist approach to the future of work is a massive branding problem for artificial intelligence. It actively hinders innovation, limits business transformation, and limits human potential.

We need a course correction to explore ways AI becomes an engine for job creation, reindustrialization, and human capability. It is time to fundamentally rethink ROI away from return on ignorance. That mindset only fuels AI Darwinism. The future enterprise automate its way to innovation. It will drive growth and innovation by investing in “Return on Intelligence,” the exponential value we create when we stop fearing human replacement and start designing for human augmentation.

The True ROI of AI: Stop Fearing Task Elimination and Start Architecting Human Augmentation

Nvidia CEO Jensen Huang recently addressed this exact crisis of communication, pushing back against the industry’s “god complex” and re-centering the discussion on the factual realities of job creation, economic growth, and the future of work.

Jensen Huang recently joined Ylli Bajraktari, the CEO of the Special Competitive Studies Project (SCSP). The conversation took place on SCSP’s podcast, “Memos to the President.”

In the conversation, Jensen expertly, and sincerely, argues that alarmist claims about AI destroying jobs are fundamentally flawed and hurtful. He believes that AI is a powerful engine for job creation and reindustrialization. He challenges the leadership fear of change, reframing AI not as a mechanism for pure automation and cost-cutting, but as a tool for human augmentation that delivers a new ROI, “Return on Intelligence.”

As I was listening, I had to stop and notate meaningful quotes that I feel will benefit any leader navigating AI transformation…from AI native founders to AI-first and AI-forward leadership teams to academics and thought leaders.

“On the one hand, maybe a scientist thinks that by warning people that AI is going to completely permeate and proliferate across radiology and therefore radiologists are going to get wiped out…

…that might be considered warning and therefore helpful. But in fact, the counter would have been hurtful.

If we convinced everybody not to be radiologists, and we now need radiologists, that actually is hurtful to society.

It is hurtful if we convince all the young college graduates to not be software engineers and it turns out the United States need more software engineers than ever. That’s hurtful.”
“…we have to be mindful of how we communicate the importance of this technology and what it’s able to do, to advocate for policy and advocate for guardrails….

…scaring people with…nonsensical things, which are not going to happen…that this is an existential threat to humanity, there’s a 20% chance that it is existential. That’s ridiculous. That it’s going to wipe out 50% of new college grad jobs. That it’s going to completely destroy democracy. I mean, these kind of comments are not helpful.
…they’re made by, you know, people who are like me, CEOs…and somehow, because they became CEOs, you adopt a god complex and before you know it, you know everything.

…we have to be careful and really ground ourselves to talking about the facts.

The facts are AI has created more than half a million jobs in the last couple of years.

The facts are AI is our greatest, our best opportunity to reindustrialize the United States, to bring manufacturing jobs back to the United States.

The facts are that’s going to generate hundreds of thousands of jobs, trillions of dollars of new economy back into the United States.

The fact of the matter is companies that use AI have demonstrated the ability to grow faster. When they grow faster, they hire more people…AI creates jobs.”

The Mindshift from Return on Investment to Return on Intelligence

I was going to say, “louder, for the people in the back.” But instead, I’ll say “louder, for the people in the front!” Those who are the loudest are among the most influential. As the old saying goes, if you’re not part of the solution, you’re part of the problem.

By offloading mundane tasks, AI frees us to apply our imagination to more complex problems, expanding our ambitions and capabilities rather than shrinking our workforce. The narrative needs to shift from automating yesterday’s jobs toward yesterdays performance standards to visualizing tomorrow’s possibilities, making vision tangible, and articulating tomorrow’s roles and tasks that will get us there through human + AI collaboration.

That’s the mindshift from an automation mindset (cost-cutting and efficiency only) to augmentation (value creation, growth, and innovation).

The ultimate promise of an AI-first operating model is not found in a spreadsheet of slashed budgets and reduced headcount, but in the profound shift from task automation to human augmentation. By embracing this mindset, leaders can stop measuring success by what they save and start tracking their true “Return on Intelligence,” the exponential value created when human imagination is finally freed to tackle our greatest challenges. This is our mandate: to stop using AI merely to do less, and start deploying it to expand what we are fundamentally capable of achieving.

The future of AI does not need more doomsday prophets and clickbait headlines. The future needs vision and leaders people can trust.

Watch the video here 👇

When the loudest voices in AI keep selling disruption, panic, and inevitability, they should not be surprised when the next generation stops trusting the future. The real race is not AGI or super intelligence. The more important race is credibility, capability, and a future people actually want to join.

Fear is rising. Trust is falling. And the AI industry has no one to blame but itself.

It feels like in the headlines, feeds, and expert commentary, we’re being sold a false choice with AI. Either worship it or fear it.

I’m an optimist. And I believe that AI can help us unlock entirely new possibilities that make humans matter more in driving growth, better outcomes, and a more productive future. That’s why I’m not buying the sensationalism, nor should you.

What I believe is this: the AI we need in headlines and in our feeds has a message problem.

The Narrative is Backfiring

For the last two years, the public has been bombarded with a relentless narrative: AGI is near. Superintelligence is around the corner. White-collar jobs are in jeopardy. Students are graduating into a future that may not have a place for them. Then, somehow, everyone acts surprised when people feel anxious, skeptical, even angry about AI. The New Yorker recently captured the contradiction well: if you keep telling people your AI will upend their lives, take their jobs, and maybe threaten humanity, they’re going to believe you.

Sam Altman recently made that contradiction impossible to miss. In one post, he wrote, “post-AGI, no one is going to work and the economy is going to collapse.” He also said that he was switching to polyphasic sleep because GPT-5.5 in Codex was so good he didn’t want to miss working with it. Put those two ideas together and you don’t get a coherent roadmap for society. You get cognitive and emotional whiplash: apocalypse on one hand, accelerationist adrenaline on the other.

And this is exactly where the narrative breaks. People are reacting to the story being told about AI by some of the people building it. And a growing number of people have had enough.

Gallup’s latest Gen Z research found that while 51% of Gen Z uses generative AI at least weekly, sentiment has moved sharply in the wrong direction. Excitement dropped 14 points to 22%. Hopefulness fell nine points to 18%. Anger rose to 31%. Anxiety held at 42%. Even more telling, 8 in 10 Gen Z respondents said using AI tools is likely to make it harder for them to learn in the future. Among employed Gen Z workers, 48% said the risks of AI in the workforce outweigh the benefits, and 69% said they trust work done without AI more than AI-assisted work. And, 44% of Gen Z workers admit to sabotaging their employers’ AI deployments as a form of rebellion.

The Federal Reserve Bank of New York says recent college graduates entered the end of 2025 with 5.7% unemployment and 42.5% underemployment, the highest underemployment level since 2020. So when young people ask, Where do I fit in this future? that is not fear talking. Unfortunately, it’s reality.

Predications are not Destiny

This is why I think the current AI debate needs to change for the better. We keep arguing capability as if capability alone determines destiny. It doesn’t.

Even inside the AI world, there is no consensus. Anthropic CEO Dario Amodei has argued that AI could disrupt 50% of entry-level white-collar jobs over the next one to five years. Yann LeCun pushed back, saying people should stop listening to AI builders on labor-market effects and start listening to economists instead. And Nobel-winning economist Daron Acemoglu has since argued that these sweeping predictions reflect “motivated reasoning” as much as insight, because labor outcomes depend not just on model capability, but on wages, organization design, job redesign, and whether new forms of work are created at all.

If the people creating the tools cannot agree on what happens next, then maybe we should stop treating every dramatic forecast as inevitable truth and start treating it as what it is: narrative. And right now, that narrative choice is eroding trust.

One reason is that people can see the contradiction in real time. On one side, the public is told not to panic. On the other, executives are openly exploring how AI might absorb more and more managerial and knowledge work.

One report, citing a Wall Street Journal scoop, says Mark Zuckerberg is building a CEO AI agent to help him retrieve information and do parts of his job faster as Meta flattens teams and pushes AI deeper into the organization. The same report says employee reviews are now partly tied to AI usage and describes a rogue internal AI incident that exposed sensitive data for nearly two hours. Whether you view that as innovation or inevitability, it sends a clear message: automation is no longer being aimed only at repetitive tasks. It is climbing the org chart.

So yes, AI has a trust problem. But trust is the downstream effect. The upstream cause feels like cognitive dissonance.

You cannot tell people AI will make them more productive while also telling them it may wipe out the bottom rung of the ladder. You cannot ask students to embrace AI as essential for their future while they watch employers flatten entry-level roles, automate knowledge work, and celebrate being able to do more with fewer people. You cannot effortlessly oscillate between “don’t worry” and “the economy might collapse” and expect the public to interpret that as visionary leadership.

Th Real Opportunity Here is Leadership

At this point, I can’t believe that this is strategy. In fact, I would say that is all starting to accumulate as narrative debt.

And beneath all the noise is a much more important story that leaders are missing.

OpenAI says the typical power user taps about 7x more “thinking power” than the typical user. They call this the “capability overhang:” the widening gap between what AI can do and what most people, companies, and countries are actually doing with it. To me, this is the real divide that matters. The future won’t split neatly between people who have AI and people who don’t. It will split between people who let AI do their thinking and people who use AI to elevate how they think, create, decide, and build.

Here is an invitation for leadership to swoop in…

The real job of leaders right now is to lower the temperature and raise the standard. It is to replace vague hype with a believable path. It is to invest in skills, redesign work, create new on-ramps for early-career talent, and show people how AI can make them more capable, more creative, and more valuable, not more disposable. AI automation is one part, but that’s just a cost center story…and it’s an AI status quo story. But AI augmentation + automation unlocks new value, creating a story of cost takeout and human + AI value creation.

People don’t just want to better understand the future, they want to understand their part in it.

If leaders cannot answer that with clarity, conviction, and humanity, the backlash we’re starting to see now will look basic in hindsight.

The winners in this era will be the ones who make the future feel believable and aspirational. They will build systems where AI expands human potential instead of shrinking human possibility. They will replace fear with fluency, hype with judgment, and inevitability with intention.

If you are a CEO, your job is to explain what AI means for your people, how work will change, how learning will evolve, what new roles will emerge, and why human judgment will matter even more than roles void of human thinking, creativity, and imagination. Show them the roadmap. Show them the reskilling path. Show them how AI can make them more capable, more creative, and more valuable, not more disposable.

That’s leadership.

The future of AI is not something that just happens to us. The future of AI happens because of us.

That is the advantage.

Not automation. Augmentation.

Not doomsday prophecy. Vision and purpose.

Not smarter machines alone. A smarter, more human future because with AI.

The future of AI does not need more prophets. The future needs leaders people can trust.

For the last two years, leaders have talked about AI as productivity and automation stories. What’s emerging now is far more consequential: AI is beginning to shape not only how executives work, but how they decide, whom they trust, and when they override their own instincts.

A March 2026 report highlighted by The Register, citing Confluent’s survey of 200 UK private-sector leaders, found that 62% of leaders use AI to make the majority of their decisions. Not some decisions. The majority. And it gets more unsettling from there: 70% say they second-guess themselves when AI disagrees, 46% say they rely on AI more than colleagues, and 65% say decision-making has become less collaborative since adopting AI.

AI has become the new executive influencer.

AI is no longer simply supporting judgment. In too many cases, it is starting to replace the friction that makes judgment valuable in the first place: debate, collaboration, instinct, experience, context, accountability. And that should set off alarms in every boardroom.

The danger in this next phase of AI is the danger is that leaders will stop noticing when they’ve deferred to it, instead relying on it in the same way they do with earned, trusted advisors.

Pattern Recognition

Why is this happening now?

Speed has become its own form of pressure. Confluent found that 92% of respondents say the speed of decision-making has increased over the last three years, and 82% say they are forced to choose between making a fast decision and making an informed one.

In this case, WTF, doesn’t stand for “what’s the future.”

When decision velocity becomes the operating norm, AI starts to feel like an always-on prophet, but can perform more like a false prophet. It offers the illusion of certainty at machine speed. It gives executives something that looks like objectivity, sounds like confidence, but fast is not the same as wise. Nor is it a definitive pillar of leadership, yet, definitely not in the same way strategic advisors and board members have earned their positions.

Imagine Walt Disney or Steve Jobs leaning on AI to validate their ideas and acquiescing if it disagreed!?

On that end, we’re now seeing the same pattern elsewhere. SAP’s 2025 research found that 44% of U.S. C-suite executives would reverse a decision they were already planning to make based on AI input, 38% would trust AI to make business decisions on their behalf, and 74% place more confidence in AI advice than in family and friends. That last number is more than a WTF reaction. It is telling. What it tells us is that is being operationalized and normalized as a trusted advisor, without earning that position…yet.

At the same time, the performance gap is widening between companies that are merely using AI and those that are actually transforming through it. PwC’s April 2026 AI Performance Study found that 74% of AI’s economic value is being captured by just 20% of companies. In other words, most organizations are running to be part of the AI rush, but only a minority are converting activity into measurable advantage. And even less are converting investment into ROI.

That should reframe the conversation for every CEO and board.

The issue is no longer adoption or fluency.

The issue is whether you are building an AI-forward company or just layering AI over yesterday’s leadership model. If AI is being used to accelerate the same old decision structures, the same old incentives, the same old org chart logic, then you are simply digitizing managerial habit. And habit does not win the future.

As the old saying goes, you’re not measured by your intentions or goals, you’re measured by actions and habits.

The Leadership Mandate

Leadership has to grow up.

McKinsey found that 92% of companies plan to increase AI investment over the next three years, yet only 1% describe themselves as mature in deployment. It’s WTFAOA (the last part means all over again). It tells us that capital and trust are moving faster than operating models, and tools are spreading faster than leadership readiness.

So the mandate now is not to ask, “How do we use AI more?”

It is to ask better questions.

Where should AI inform decisions, and where must humans remain unmistakably accountable?

What decisions can be accelerated, and which ones require tension, interpretation, and lived experience?

How do we redesign collaboration so AI enhances collective intelligence instead of quietly eroding it?

What governance, data discipline, and leadership behaviors must exist before AI is allowed to shape strategic direction at scale?

These are not technical questions. They are proper leadership questions.

The companies pulling ahead are aiming AI at growth, reinvention, and operating redesign, while building the governance and trust required to scale it responsibly. That is exactly what PwC found separates the leaders from the laggards.

If you’ve been to any of my keynotes, you’ll hear me say that AI is a test of leadership.

Leaders need to learn how to think better in an environment where machine confidence can easily masquerade as strategic clarity.

The winners in this next era will not be the organizations that automate decisions the fastest.

They will be the luminaries that know how to scale intelligence without surrendering judgment.

That is the work now. That is the mandate.