Our Friday CMO Coffee Talk sessions can be organized chaos. A live conversation on video while the Zoom chat goes nuts.

A couple weeks ago, the chat was filled with people tired of AI.

These weren’t skeptics or holdouts. They were marketing leaders running AI programs at their own companies, most of them still expanding those programs.

One described the volume of well-worded, impressively formatted documents landing on her desk that turn out to be slop, and the hours (plus the Claude credits) it costs her to deal with them. Another said flatly that his team isn’t allowed to send him AI-generated content, because he’s told them he won’t read it. A third admitted she’s now irritated by the act of reading AI at all.

Two of them apologized for being cranky. But they’re not wrong.

Speed was never the savings.

The AI ROI math is one-sided and incomplete

Just about every AI business case I’ve seen in B2B marketing so far measures a variation of the same thing: how much faster the work gets made. Hours saved on a first draft, assets produced per week, agency line items brought back in-house, headcount avoided. All valuable, and all of it sits on the production side of the ledger.

We forgot to consider the impact of now having to review ALL of that.

This was doable when the output was thinner. It stops being survivable when everyone on the team is producing several times their old volume and every bit of it still lands on someone whose day did not get any longer.

Somebody finally measured it

A recent global survey of more than 2,000 marketing leaders across seven countries found that 76 percent of them spend at least three hours a week editing, fact-checking or correcting AI output, and only 4 percent say AI saves them time at every stage of the process. The survey came from a company that sells AI marketing software, which makes the finding harder to wave off. They called it the revision tax.

In the same study, 54 percent of respondents say leadership underestimates the effort required to get usable output from AI.

Researchers at Stanford and BetterUp Labs recently put a definition on it: content that looks polished and carries no substance, quietly handing the thinking back to whoever receives it. They called it workslop.

In a survey of more than 1,000 desk workers, roughly 40 percent had received “workslop” in the previous month, at a cost of about two hours per instance. They priced it at $186 per employee per month, which runs to something like $9 million a year at a company of 10,000 people.

Why we all missed it

A dizzying example of this showed up in a recent randomized study of experienced software developers working on real issues in their own repositories. With AI tools, they took 19 percent longer to finish. Before they started they expected the tools to make them roughly a quarter faster, and afterward, having actually been slower, they still believed AI had sped them up by about 20 percent.

The gap between what they measured and what they felt is nuts.

We feel the generation but we don’t consider or quantify the cost of reading, because somebody else is doing it. The cost stayed out of the business case for a simple reason: the person creating the work is not the person paying for it.

Aaaand the constraint just got more crowded

Now run this math on your own team. Say eight people each producing four documents a day they’d never have attempted before, all of it flowing toward the one or two people who have to decide whether any of it is right.

Constraints don’t care how productive the stations upstream of them are. Speeding up everything in front of the bottleneck makes the queue longer and we know this from every other operational discipline. Marketing keeps rediscovering it.

Your most senior, most trusted reviewers are the choke point, and the AI program you funded made their job harder.

Volume you can’t defend

Have you heard the term “hollow expert” yet? The person producing a remarkable volume of work who can’t answer a question about any of it. This is how you scale mediocrity.

The fair objection here is the calculator one. Do you trust the colleague who used a calculator less than the one who did the long division? Of course not. But the calculator user still knows what the number means and why they needed it. That part didn’t get skipped, and it’s the part that shows up in the meeting when somebody pushes back.

Writing is thinking. Every document handed to a model is a document nobody thought all the way through, which is fine for a status update and really expensive for a strategy you expect to work.

Start with what doesn’t get sent

The leader who told his team he won’t read AI-generated content is doing quality control, and I’d defend that position in front of any board. His rule puts the cost back where the work was created: if you didn’t read or edit it before you sent it, he isn’t going to read it either. That one stand fixes the accounting problem at its root.

If you want the number that makes this case internally, put both halves on the same page: whatever hours your AI business case claimed to save, and what your reviewers actually spent reading the output. We built a revenue impact calculator that models the first half and has no line for the second, which should tell you how easy this is to miss.

A few other things I’ve watched work:

Build the context into the tools the whole company uses, not just the ones marketing runs. A lot of what marketing is cleaning up right now arrived from sales, product and CS, almost right, which is too often worse than wrong. Brand voice, positioning and operating context loaded everywhere means the first draft lands closer to usable no matter who generated it.

Make the edit count visible. A CMO recently started tracking how many edits she has to make to what lands on her desk and raising it in 1-1s. Not as a gotcha. As a signal for how much people should be reviewing their own work before it leaves their desk.

Ask what sucks before you accelerate anything. Some processes deserve to be stopped rather than sped up. Speeding up a broken one just gets you to the failure faster (with a much better audit trail). One leader recently got her directors in a room, asked them what sucks and went looking for AI to fix those things specifically, in that order. Starting from the pain solves the political problem for you too, because nobody argues with fixing the thing everyone already complains about.

This is orchestration work. Intake, work design, who owns what and where the context lives. Our team did exactly that for a 200-person marketing team that had already tried and failed at it more than once, and what came out was one intake for every request, trackable SLAs and capacity planning the execution team could believe. None of it is sexy. But it’s what separates a team that got faster from a team that got faster at the right things.

Plenty of people can produce at volume now. Far fewer can defend what they sent. I want more of the second kind in my inbox, and if AI helps get us there I’m all for it.

A product launch comes apart because an executive objects to a key element late in the process.

That should just create a bad afternoon.

Instead, it takes the whole launch down because the one person holding the full plan is out of the country. Nobody else can produce the complete sequence, the asset list or who owns which step. The plan exists, everyone has seen it in meetings, but no one actually knows where it’s saved. The only person who seemingly knows is on a beach somewhere.

Variations of this story are incredibly common (and may very well sound familiar as you read this).

We launched a marketing orchestration self-assessment tool a few weeks ago and it’s been fascinating to watch not just CMOs but whole marketing leadership teams complete it.

A common occurrence once their assessment scores are compared to each other: leaders on the same team complete the assessment independently and their scores (on strategic alignment & planning for example) land more than two points apart on a five-point scale.

Same company. Same operation.

The composite comes out somewhere in the middle, which is a perfectly reasonable number that describes almost nobody’s experience of working there

Averages hide what’s really happening

The instinct with any assessment like this is to report the composite and move on. It’s clean, it benchmarks well and it gives leadership something to improve. It also averages away the findings that matter most.

For example, some leaders describe an organization with real planning muscle: a defined intake process, clear ways to handle unplanned requests and established mechanisms for adapting when priorities change.

Others describe an organization where a senior request can override existing priorities in the meeting and everyone scrambles to comply.

Both groups, again still part of the same marketing organization, are reporting accurately about the part of the company they can see.

Sort the responses by composite and there isn’t much of a gradient. They cluster into two groups: one experiencing a relatively mature operating model and another experiencing something far more reactive.

That’s two operating models running side by side inside one organization, with leadership assuming there’s one.

The leader you think is too negative may have the widest view

The leaders describing the widest slice of the operation tend to score it lowest. The ones accountable for smaller, tighter areas score it higher.

The pessimism is highly correlated to scope.

I’d argue this is one of the first things to look for when you run this exercise with your own team. Almost every leadership group has someone with a reputation for seeing problems everywhere. Before you discount that person, check how much of the operation they can see.

Sometimes the person who looks most negative is simply standing higher where the seams show better.

Why a high collaboration score should worry you

Collaboration and communication, ironically, often comes in as the strongest of the four dimensions, with relatively little disagreement among the leaders.

Everybody’s people are good and legitimately trying their best. SMEs show up, teams talk to each other and nobody describes a fundamental communication breakdown between peers.

That’s the finding that concerns me most.

In all too many companies, leaders describe reconstructing campaign status from Slack threads, meetings and memory, then confirming it with the owner before they’d report anything up.

The information existed. It just didn’t live anywhere reliable and took way too long to rebuild (and you know there were still discrepancies in that reconstruction).

But people still go and got it by hand, every time, and they are good enough at doing that that nothing visibly breaks.

That’s where a high collaboration score can hide real problems. Your team is absorbing the cost of infrastructure and process you don’t have, and they’re paying for it in attention. Attention is the one input that doesn’t scale when you add people. Every hire can make that tax bigger, not smaller.

Gartner has found that 84 percent of marketers experience high collaboration drag as cross-functional work gets more complex. When almost everybody is dragging, dragging starts to feel like the job.

Why everyone’s favorite fix is the wrong place to start

Nearly every company we talk to who has tried and failed to overcome collaboration drag surfaces some version of the same original strategy: build a better intake process, so a new request gets weighed against what’s already committed instead of decided live in the room.

It’s the right fix.

It’s also one that fails constantly because teams start in the wrong place.

Intake only works if you can answer, “What would have to move?”

That requires knowing who’s loaded and what’s already in flight. Which requires some consistency in how work is structured and tracked. Which requires clear ownership below the team level.

Many leaders describe accountability sitting primarily at the team level, where everyone can honestly say they did their part while the work stalls in the gaps between them. Most also describe campaigns being built largely from scratch or heavily adapting whatever template exists.

So when intake goes first on the roadmap, it can’t answer the question it exists to answer, and within a quarter it’s already a form nobody fills out.

We’ve seen the same pattern in large marketing organizations that had already tried to fix their workflow more than once.

By that point, the process gap isn’t even the hardest problem. Change fatigue is.

What worked wasn’t starting with another tool or another intake form.

It started with understanding how work actually moved through the organization. Then documenting the planning-to-execution workflow and naming the communication points. Then clarifying ownership and mapping who touched what and when. Only after that did the team configure its project management platform around the workflow.

Centralized intake, the thing everybody wanted first, came much later.

They tested the new approach with a small group and a narrow type of work, met regularly to find what broke, fixed it and only then expanded it.

The impact sounds modest on paper and matters enormously in practice.

Capacity became something leaders could see instead of guess at. Requests started routing more consistently. Service levels became measurable. New teams could adopt the process without rebuilding it. Time to market improved, and so did morale, which is the outcome nobody puts in the business case and everybody feels.

You probably already have the answer

The thing that still gets me most across these assessments: nearly every gap named in the lower-scoring responses is already solved somewhere in the higher-scoring ones.

One group has campaign status available without having to ping a human. Another has a mature project management process with governance behind it. Another has clear expectations at each stage and has defined where AI belongs at the workflow level rather than leaving it to individual habit.

Best practices for almost every gap already exist inside the organization.

But nobody has the mandate to make one team’s good pattern everybody’s best practice.

That’s a very different problem than a capability gap, and usually a cheaper one to solve.

It also explains why it sits there. The work crosses teams, so it belongs to no single leader, and it loses its forcing function the first week something catches fire.

The way it gets unstuck is less clever than people expect: involve the people doing the work early, listen to where the process breaks and build the new approach with them rather than handing it down afterward.

The people doing the work already know where it breaks.

Try three of the questions on your own operation

These assessments work because the questions are scenarios rather than opinions. Each answer option reflects a level of operational maturity, so people pick the one that describes their actual week and the score falls out of that. Nobody has to rate themselves, which is why the answers tend to come back honest.

Three of them, roughly:

You need a campaign’s current status. What do you actually do? The options run from reconstructing it out of Slack threads and memory, to asking the people who would know, to reading it off a shared system in under a minute.

A senior leader asks for something mid-quarter that isn’t on the plan. What happens next? Somewhere between the decision getting made live in the meeting and the request getting scoped against what’s already committed, with the tradeoff named.

Your messaging shifts mid-quarter. How long before in-flight campaigns carry the new version? Days, weeks or never, while campaigns already in market keep running the old framing.

Use the assessment for clarity and consensus

You can access the ungated marketing orchestration self-audit tool here. I recommend having several of your leaders score the operation independently, then look at where they disagree before you look at what they averaged.

The gap between your most and least optimistic leader will tell you more about how your marketing organization really runs than the composite score ever will.

Answer-engine tools, and there are a growing number of them, often share the same three slides:

• Here are the prompts your buyers use to find you.

• Here’s where you show up in the answer.

• Here’s the competitor showing up above you.

It’s compelling stuff. But you should be asking where that data comes from and how reliable it is.

Ask that and things get vague quickly.

Nobody actually has the queries

Some vendors are honest about this. Otterly’s own help documentation says it plainly: “AI search engines like ChatGPT and Perplexity do not publish query data. There is no public keyword tool for AI search.”

Their prompts get built from Google Search Console data, SEO keywords and AI-brainstormed ideas. Ahrefs publishes its methodology too - queries pulled from Google’s People Also Ask and its own keyword database, then expanded into related sub-questions by a semantic system they call Fanout. Both companies deserve credit for showing their work.

Others are less forthcoming. One well-funded platform advertises “real prompts submitted to AI platforms by actual users” without explaining anywhere I could find where those prompts come from. Another builds its suggested prompts from your website and your industry context, then scores them against Google search trends. A proxy for a proxy. Clear as mud, right?

This is not to say that the tools are useless. It means the thing being sold as observation is mostly inference. That’s a whole different level of confidence, and could change how you think about making that investment in the first place.

The three companies who do have the data aren’t sharing

Three companies (guess who??) do have the data you want and none of them have a business reason to hand it over.

Google shipped a new Search Console report for site owners recently covering AI Overviews and AI Mode. Impressions, pages, countries, devices, dates. It leaves out the actual queries and the clicks.

Asked about adding them, Google said it’s “continuing to work with website owners to understand what insights will be most helpful.” OpenAI’s advertising product is just as clear - advertisers “only receive aggregate information about how their ads perform such as number of views or clicks,” with no access to chats, history or memories. Anthropic has said it won’t run ads in Claude at all, so there’s no advertiser pipeline there to ask about.

Google built an entirely new reporting surface for marketers and left out the one thing marketers want most. I’d be careful reading that as a roadmap gap.

Even the answer appears to change regularly

Say you had the perfect prompt list anyway. It appears the answers will still change constantly.

A recent crowdsourced study ran a set of prompts through ChatGPT, Claude and Google’s AI Overview nearly 3,000 times using 600 volunteers. It found less than a one-in-a-hundred chance that two responses to the same prompt return the same list of brands and closer to one in a thousand that they return them in the same order.

A separate study looking at 815,000 prompt-page pairs found that after running the same prompt three times in ChatGPT, only about 2 percent of citations survive. Another study tracking more than 82,000 prompts over 17 weeks found ChatGPT swapping roughly three-quarters of its cited sources week over week.

Rank isn’t a thing here. Visibility measured across hundreds of prompts over months is directional and genuinely useful. A screenshot of where you placed on Tuesday is noise and extremely temporary, and a lot of teams are reporting the screenshot to their board.

What it does read mostly isn’t “from” you

When these systems answer a question about your category, they’re largely not reading your website. A recent analysis of more than 100 million citations across ChatGPT, Google AI Mode and Perplexity found the most-cited domains were Reddit, Wikipedia, Medium, Forbes, LinkedIn and YouTube. No vendor site cracked the top five on any platform.

Other research found that on Gemini, the overlap between the brands named in an answer and the domains actually cited can run as low as 30 percent. You get mentioned (great) and something else supplies the evidence (less great!).

Review sites, in this context, are still incredibly important. G2 looked at more than 80,000 of its own product pages recently and found four out of five get cited by AI systems more often than they get viewed by a human. G2 sells vendor visibility and the tracking came from a company selling AI visibility software, so weigh it accordingly. It still points the same direction as everything else.

So, in summary:

1. Your buyer’s research is being assembled out of sources you don’t own

2. Weighted by how consistently they agree with each other

3. And you can’t see the question that started it

Are we having fun yet?

Which makes this a buyer enablement problem (and opportunity)

This all makes buyer enablement more important, not less.

Recent 6sense research (they sell intent data and have a stake in the answer) found 94 percent of buying groups had ranked a preferred vendor before first contact, and 77 percent went on to buy that preliminary favorite. What’s new is that the ranking is now partly assembled by a machine reading forum threads and review pages on the buyer’s behalf.

Gartner recently found that 69 percent of B2B buyers prefer to validate AI-generated insights with a sales rep. By the time anyone gets on a call the opinion already exists and the call is more about validation than discovery.

A few things I’d do about all of this. Some of it is work we do at Heinz Marketing, so weigh it accordingly.

Build your own question set. You don’t need an outside vendor to tell you what your own data already knows! The questions your buyers actually ask are sitting in your sales calls, your support tickets, your win/loss debriefs and your community threads. That’s an invaluable and competitively-protected source nobody can sell you, because you’re the only one with it.

Optimize for corroboration instead of placement. If consistency across independent sources is what these systems reward, the work is making sure your positioning, your review profiles, your customer stories, your analyst coverage and your own site all say the same true thing. Most B2B companies have four versions of who they serve living in four places. That inconsistency used to cost you a confused visitor and now it costs you the citation.

Arm the champion for a validation conversation. Your champion isn’t discovering you in that first call, they’re defending a choice they already made to a buying group that didn’t make it with them. Different content for the CFO than for the security lead. Proof points procurement can forward. When machines are doing the heavy-lifting early in the buying journey, your champion enablement work is about defending a recommendation and helping to drive consensus and velocity towards a group commitment to change. Different job, different call, different content.

Ungate the evaluation. Most of our clients ungated content years ago once they realized the gate cost more than it captured, and every panelist on a recent buyer enablement roundtable I hosted said some version of the same thing about demos. If a buyer can get a straight answer about your product from a machine in nine seconds, making them wait two weeks and sit through a discovery call before they’re allowed to see it is a disqualifying move. They’ll just go ask the machine again.

What a corroboration audit actually looks like

If that inconsistency sounds familiar it should. It’s the same dissonance you get when sales, marketing and product each carry a different definition of who you’re targeting - chaos and tension inside the building, mixed signals outside it. I’ve argued before that your ICP works better as a living wiki than a document gathering dust in a folder. This is that same discipline pointed outward, and the audience for the inconsistency is a lot bigger now.

The audit itself isn’t complicated:

1. Take ten questions from your own list. The real ones buyers ask, out of the calls and tickets and win/loss debriefs above.

2. Run each one several times across ChatGPT, Gemini and Perplexity. Save what gets cited, not just what gets said. The repetition is the point - one run tells you nothing.

3. Open the cited sources side by side. Your site, your review profiles, the Reddit threads, the press coverage, your team’s LinkedIn profiles.

4. Ask one question of the whole stack. Do these describe the same company, serving the same buyer, solving the same problem? Mark every place they don’t.

5. Fix what you own first. Site, review profiles, LinkedIn, sales collateral. Then work the ones you only influence - customer stories, analyst briefings, press.

The output is a list of contradictions. Most teams are surprised how long it runs, and a good number of them turn out to predate any of this. They were always there. The machine is just the first reader thorough enough to catch all of them at once.

One parallel caution worth watching

These systems are pattern-matchers across sources, which means the buying question and the employment question are drawing from an overlapping pool. A friend described a CEO years ago who was so upset about his Glassdoor reviews that he paid people overseas to write better ones. There was a time when this worked and you could get away with it.

I’m drawing conclusions ahead of the data here, but the logic feels sound. It’s already failing, and from here it reverses. A cluster of glowing reviews that contradicts every other available signal reads as noise to a system built to reward agreement. And it isn’t a big leap from there to an AI summary telling a buyer that a company builds good products and treats its people badly, then leaving them to draw the obvious conclusion about what kind of vendor that makes.

Oh marketers, we so crave control. And I know what you just read means you’re getting even less of it.

What you can control is whether every source that machine reads tells the same true story about who you help and why it works. And in that way it’s a familiar job: building confidence with buyers we’ve always been responsible for, now graded by a machine that never visits the site and never fills out a form.

New rules, new constraints, new opportunities.

Most scoring models in B2B are measuring the wrong thing.

Just because someone clicked on your website a number of times doesn’t make them qualified. Just because they attended your last three webinars doesn’t mean they’re sales ready. It could mean they’re bored.

We equate activity with intent, and that is not always accurate. Somebody filled out a form, attended a webinar, stopped by the booth. Sure, that’s a reason to follow up. It’s also an inaccurate and incomplete way of thinking about what a signal actually tells you.

Everyone has the signals now

We’ve gotten to the point where signals are kind of like contact information. It’s everywhere. It’s no longer a moat to say you have a list of everyone in your industry, and it’s no longer a moat to say you’ve got intent signals, because whoever’s selling them is selling them everywhere. AI can find them all over the place.

I can’t tell you how many times I’ve heard from marketing leaders who say some version of the same thing: I gave these signals to my sales team and they don’t know what to do with them.

That’s not a sales failure. Sales teams are trained to respond to a download, a demo request, an inbound question. If you tell a rep that someone experienced something yesterday, there’s nothing in the standard playbook that tells them what to do with that.

The research shows how wide the gap runs. Gartner found that marketing and sales teams collaborate on just three of fifteen commercial activities, and that 60 percent of the leaders they surveyed report a lack of shared buyer journey insights. In Demand Gen Report’s benchmark survey recently, about one in five respondents said they have an intent data strategy that’s actually being executed and measured. Forrester’s intent data research has found that companies using intent data do get benefits, but fewer than they expected across every category surveyed, with the largest shortfalls in sales use cases.

More companies expected benefits than achieved them.

Gartner also found that 73 percent of B2B buyers actively avoid suppliers who send irrelevant outreach. A signal fired into a form-fill playbook produces exactly that kind of outreach, which means bad activation costs you the account.

What this looks like when it goes wrong

Someone at a target account downloads your compliance guide, comes back for it twice more, then shows up at two webinars. Your model gives them eighty points. They cross the threshold, they become an MQL and a BDR calls to ask about timeline and budget.

Here’s what was actually happening. A regulation changed in their industry, their VP asked them to write an internal recommendation, and they’re three weeks into figuring out what the problem even is. No budget. No project. No mandate to buy anything.

The scoring model saw intent. What existed was a person doing homework on a problem they’d just been handed. The call for that moment was helping them write the recommendation, and instead they got asked when they’re planning to purchase.

Three kinds of signals

Simply using the phrase “intent signal” implies the prospect has intent. Most people hear that as intent to buy. Not necessarily.

Here’s the split that works for the companies I see getting real value out of this.

Intent signals. The prospect has shown specific interest in solutions and is actively exploring alternatives to the status quo. Pricing pages. Comparison content. Competitor evaluation. A demo request.

Need signals. The prospect has expressed or exhibited, directly or indirectly, that there’s a known problem and they’re researching what to do about it. They’ve named the pain. They haven’t started shopping. A job posting that spells out the problem in the requirements. Someone asking peers in a community how they handle it. Category-level research on a review site.

Evidence signals. Signals of a problem that you know are pre-need and pre-intent, but the prospect hasn’t made that translation yet. A funding round that changes what the team is accountable for. A leadership change. A regulatory shift. An acquisition that just doubled the complexity of their stack. A competitor’s security breach.

When the same event seems to fit two tiers, the tier is set by what the buyer has said out loud rather than by what you observed. A job post that names the pain in the requirements is need, because they wrote it down. A hiring spike you inferred the pain from is evidence, because they haven’t. Same company, same week, different play.

That third category is the one almost everybody still wastes.

Think about a cybersecurity buyer. If there’s a breach at a prospect’s competitor, that’s one of those “we should have had earthquake insurance” moments. The signal has nothing to do with your brand. Nobody filled out anything. And it has changed how that company prioritizes a problem more than any webinar you’ll ever run.

Because you know their issues, because you’ve seen this movie before, you can translate evidence into need in a way the prospect can’t yet do for themselves. No signal vendor can sell you that translation.

The playbooks

Evidence signals

What fires it: A change in the account’s world that correlates with a problem they haven’t named. Leadership changes, funding events, tech stack moves, regulation, M&A, a peer or competitor’s public failure.

Who owns it: Marketing. The buyer doesn’t know they have a problem, which means there’s nothing for a seller to sell against yet.

How fast: Weeks. Speed is not the variable at this tier, and treating it like it is will burn the account.

The move: Lead with the pattern. “Nine times out of ten, when we see companies exhibiting this, this and this, problems A, B and C are not far behind. Many companies in your position have gotten ahead of it. Want to see how?” And the playbook you’re offering just happens to be embedded in what you sell.

What not to do: Route it to a BDR as a lead. Drop it into a demo sequence. Ask for a meeting. All three tell the buyer you weren’t paying attention.

How you know it’s working: Reply rates on insight-led outreach, and how many of these accounts show up in the need tier ninety days later. That second number is the real one.

Need signals

What fires it: The prospect has named the problem and is researching what to do about it. Job posts describing the pain in the requirements. Community questions. Category-level research. An RFI about process rather than product.

Who owns it: Shared, and this is where most handoffs break. Marketing supplies the frame and the proof while sales runs the diagnostic conversation.

How fast: Days.

The move: Help them scope and quantify the problem before you position against it. Give them the evaluation criteria they haven’t built yet: what to measure, what to ask vendors, where the hidden costs are, what a good outcome looks like in twelve months.

Buyers form their shortlist early. 6sense, which sells intent data and has a stake in the answer, found that buyers fill about three and a half of four shortlist slots on day one and pick from that day-one shortlist 95 percent of the time. Whoever helps frame the criteria tends to be on it.

What not to do: Pitch. Ask for the meeting before they know what the meeting would be about. Send a case study about a problem they’ve defined differently than you have.

How you know it’s working: Shortlist inclusion rate, and whether your language shows up in their RFP.

Intent signals

What fires it: Active evaluation. Pricing, comparisons, competitor content, demo requests and multiple stakeholders at one account moving at once.

Who owns it: Sales, immediately.

How fast: Hours. This is the only tier where speed is genuinely the variable.

The move: Reduce friction and give up control. Self-service demos, ungated technical content, published pricing, DIY evaluation paths. Giving control to the buyer increases engagement, and it gives your salespeople their time back for the prospects who genuinely want to talk to someone.

What not to do: Treat a demo request as the start of qualification. Make them wait two weeks for a calendar slot. Gate the thing they’re trying to evaluate.

How you know it’s working: Time from signal to useful human response, and win rate on self-served evaluations versus rep-led ones. If the self-served ones win more, you’ve learned something

.

The triage layer

Three things govern all three playbooks.

Look at the account, not just the person. If you see activity surging from four or five members of the same team at one company, something happened. People came out of a meeting or an all hands and suddenly they’re hot to trot on something. Person-level scoring can’t see that.

There’s a cost to getting this backwards. Gartner found that content focused on individual-level relevance can create conflict inside the buying group, with a 59 percent negative impact on consensus, while buying-group relevance made buyers three times more likely to report a high-quality deal. Personalizing hard to one person can actively cost you the group.

Triage, or you’ll just build a new lead-volume problem. This becomes a situation where we flood the sales team with signals, and not all of them are worth working. Know which signals imply the greatest maturity in the prospect’s thinking, and route accordingly. A volume based approach didn’t work for leads and it won’t work for signals either.

Assume everything expires. Our team has found that most B2B intent signals go stale within 30 to 45 days, and that traditional scoring models degrade a couple of percentage points every month nobody maintains them. Some of these signals are static, many are dynamic, which means you can’t just build the list and let it ride.

Workflows you can build without buying anything

The classification and drafting work here is exactly what LLMs are good at, and you can stand these up with whatever you’re already using.

Two rules before you build any of them. Ground the model in your own material (your ICP, your win/loss notes, your actual closed-won patterns) because a model reasoning from general B2B knowledge will give you general B2B answers. And keep a human in the loop on anything that reaches a buyer. I never let AI send outreach without reviewing it.

1. The classifier

Run this against the last twenty signals your team acted on. The output is usually uncomfortable.

You are helping me classify go-to-market signals for [COMPANY], which sells
[WHAT YOU SELL] to [ICP DESCRIPTION].

We classify every signal into one of three tiers:

- INTENT: the account is actively evaluating solutions and comparing
  alternatives to the status quo.
- NEED: the account has named a problem out loud and is researching what to
  do about it, but is not yet evaluating vendors.
- EVIDENCE: something changed in the account's world that we know precedes
  this problem, but the account has not made that connection yet.

Tie-breaker: if the same event could fit two tiers, classify by what the buyer
has stated explicitly, not by what we observed or inferred.

Here is the signal:
[PASTE THE SIGNAL - what happened, who, what account, when, source]

Return:
1. The tier, and your confidence (high/medium/low)
2. The specific reasoning, citing what in the signal drove the call
3. What would have to also be true for this to be one tier higher
4. The single worst thing we could do in response
5. A one-sentence recommended next action, with an owner and a timeframe

If the signal is ambiguous, say so and classify it DOWN a tier rather than up.
Over-classifying is the more expensive error.

That last instruction matters more than anything else in the prompt. Scoring systems default to optimism.

2. The queue ranker

The daily job is deciding which forty of these are worth anyone’s Tuesday.

Here is this week's signal queue for [COMPANY]:
[PASTE: account, contact, role, what happened, when, source, tier if known]

Rank this queue by maturity of the prospect's thinking - how close the account
is to being able to make a decision - not by recency and not by volume of
activity.

For each item return:
1. Rank, tier and a one-line rationale
2. Owner (marketing, BDR, AE) and the window it should be worked in
3. Whether other signals in this same queue are from the SAME account. Group
   those and tell me whether the cluster changes the ranking.

Then give me a "do not work this" pile: everything that looks urgent but
isn't, with the reason it's a false positive.

Finally, name what's missing. What signal would you expect to see for these
accounts that isn't in this list, and what does its absence tell us?

That last question surfaces the gaps in your data rather than the gaps in your follow-up.

3. Evidence tier — the insight builder

Run this in a model with web search turned on, and check every source link before you use anything.

Research [ACCOUNT NAME] and identify EVIDENCE-tier signals: changes in their
business that historically precede the problems we solve, but that they likely
have not connected to those problems yet.

Look for: leadership changes, funding or M&A activity, hiring patterns that
imply a capability gap, technology or vendor changes, regulatory exposure,
public incidents at close competitors, and shifts in their published strategy
or investor communications.

Our context:
- We solve: [PROBLEMS]
- What a bad month of this problem costs a company their size: [YOUR NUMBER]
- The pattern we've seen repeatedly: [DESCRIBE 2-3 REAL EXAMPLES FROM YOUR
  OWN CLOSED-WON DEALS - what was happening at the account 6-12 months before
  they bought]

For each evidence signal you find, give me:
1. What changed, with a source link and date
2. The problem it likely creates in 2-6 months, and why
3. Who in the org feels it first
4. A three-sentence note to that person that shares the pattern and offers
   the playbook, with no ask, no meeting request and no product mention

Flag anything you inferred rather than sourced. If you cannot find a source
for something, leave it out rather than describing it.

4. Need tier — the criteria builder

[ACCOUNT/PERSON] has signaled they have this problem: [DESCRIBE THE NEED
SIGNAL AND WHERE IT CAME FROM].

They have not started evaluating vendors. Help me help them think.

Build a vendor-neutral evaluation framework for this problem:
1. The 5-7 questions they should be able to answer before they talk to
   anyone
2. The costs of this problem companies typically miss when they scope it
3. What "solved" looks like at 3 months, 12 months and 24 months
4. The 3 questions that best separate real solutions from demos that look good
5. Where a company like theirs - [SIZE, INDUSTRY, STAGE] - usually gets this
   wrong

Write it so it would still be useful to them if they never bought from us.
Do not mention our product. If our approach loses on any of these criteria,
tell me which and why.

Leave that last line in. If your solution can’t survive an honest criteria list, you want to know that before the RFP does.

5. Intent tier — the account surge check

Here is the recent signal activity for [ACCOUNT] across our systems:
[PASTE: contacts, roles, actions, dates, sources]

Analyze this as a buying group rather than a set of individuals:

1. Is this a coordinated surge or unrelated individual activity? What's the
   evidence either way?
2. If it's a surge, what internal event most likely triggered it?
3. Map the roles engaging to the buying group we'd expect for a purchase like
   this. Who is missing?
4. Based on what each person has actually done here - not on role stereotypes
   - what does each appear to be optimizing for, and where would those
   priorities conflict? Say "not enough data" where that's true.
5. Give me one message for the group's shared decision, plus what each
   individual needs to hear to be comfortable saying yes.
6. Tell me what would make you wrong about all of this.

Note explicitly where you're inferring versus reading the data.

Ask any model to argue against its own take. The answers get noticeably better, and you find the deals that were never as warm as the dashboard suggested.

Why most teams still don’t do this

This is not easy. It’s also not rocket science. There’s a clear playbook, plenty of companies have already walked it and you can do it in phases.

But it’s worth being honest that adopting this is a culture change rather than a campaign change. You’re asking a sales team to act on something that isn’t a lead, asking marketing to own a customer knowledge base rather than a lead number and asking both to agree on what a signal means before it fires rather than arguing about it after.

Complexity is a law of physics in B2B buying and selling.

Start with the last twenty signals your team acted on and classify them. My guess is that most of what you called intent was evidence, and that you responded to almost all of it the same way.

A marketing leader I know recently spent weeks building a full 12-month marketing plan for a final-round interview. Building blocks, value proposition, messaging, integrated campaigns, budget, quarterly allocations, a dashboard, the sales and marketing alignment process. All of it, free.

The company came back and said they didn’t fully understand what they were reading.

Then the recruiter asked her to leave the presentation behind, because the company had a few clarifying questions.

They couldn’t evaluate the plan and they wanted to keep it anyway. All that work, done for free, and for what?

If you can’t assess the work, the work was never the point.

This has been building for a long time

I’ve been hearing versions of this from B2B marketing leaders for years now, and it hasn’t gotten better.

One candidate was asked for a case study, two brand narratives, partnership recommendations, a content strategy, a PR and thought leadership plan and the metrics behind all of it. Three to four days of research, submitted as a PDF. She was a finalist and she didn’t get the role.

Another was handed an assignment covering the competitive picture, an integrated strategy across demand gen, product marketing and communications, next-year targets, headcount investments, program spend, tooling and the operating cadence to manage it. Forty-five minutes of prepared content, please. She found out later that company used her recommendations verbatim after passing on her.

A third was told the assignment was a formality, that the job was effectively hers. Four days of work later she got a stall, then an offer for a role two levels down. She found out afterward that the identical thing had happened to another candidate at the same company six months earlier.

Another CMO candidate lost the role, then saw pieces of her own work show up in that company’s presentation at an industry event.

When I talk to CMOs and hear these stories directly, I almost always feel a sense of exhaustion and outrage.

We are way past the point of diminishing returns

A 30-60-90 plan outline with some initial observations is a reasonable ask. A full marketing plan or a budget is beyond it.

That line is not complicated and it costs a company nothing to hold. Most opportunities at this level involve some kind of exercise, and candidly, that’s fine. There’s a real difference between asking someone how they’d think about your first quarter and asking them to produce the deliverable you’d otherwise pay a consultant $30-$60K to build.

The assignment doesn’t even work anyway!

Set the fairness argument aside for a minute, because there’s a more practical problem. The homework assignment is a bad evaluation instrument.

It’s built on missing inputs. The candidate is constructing a plan without your data, your customer conversations, your sales team’s real objections or any honest account of what’s already been tried. Whatever they hand you can be picked apart, because it was never possible to get right. As one marketing leader put it, no matter what you present, it’ll get holes poked in it. So you learn very little about their judgement and quite a lot about their willingness to comply.

It also quietly selects for the wrong thing. The candidate who can spend four days on your assignment is the one with four days available. That biases the process toward people who aren’t currently employed and away from parents, caregivers and anyone already running a team.

Your best candidate is very often the one who says no to that much work, and you will read that no as disinterest or a lack of ambition/hunger for the role. That’s simply inaccurate and unfair.

Though there are clearly exceptions, I’d argue many companies doing this aren’t trying to steal anything. They’re just scared.

They’ve been burned by a bad senior hire, they don’t trust their own instincts and asking for more work feels like buying more certainty. It isn’t.

A deliverable built without context tells you almost nothing about how someone will operate with context.

Meanwhile, the other half of the job description went missing

A CMO shared recently that whether he’s talking to a prospective client about a fractional role or to a recruiter about a full-time one, he cannot remember the last time anyone asked about his approach to developing and mentoring young marketing talent. Which, as he put it, is the part of the job he enjoys most. That entire section of the job description has been replaced by a requirement for (poorly understood) AI capabilities.

Two marketing job descriptions came into my inbox in the same 48 hours a couple weeks ago. One says “if you aren’t leveraging AI to do this you are not the right fit.” The other lists “AI-savvy” as a must-have, on its own line.

To be clear, I’m not arguing against the AI requirement. Marketers need to build that muscle before it becomes a requirement, and it is now a requirement.

But somehow (perhaps unintentionally) it’s replaced “how do you build people” as a leadership priority. The leaders who were good at developing people before AI are the same ones who’ll get real value out of AI on a team, because both jobs run on the same underlying skill.

What both moves have in common

The case-study assignment and the AI checkbox look like opposites, one asking for far too much and the other for far too little.

They’re the same failure. Both are proxies for judgement, adopted by companies that have quietly stopped believing they can evaluate judgement directly.

One hands you a deliverable you can hold and the other a capability you can verify on a résumé. Neither measures whether this person can walk into an ambiguous situation, figure out which three things matter, say no to the other ninety-seven and bring a team along with them. That’s the actual job, and it’s the one thing neither proxy touches.

We’ve hired a lot of marketers at Heinz Marketing over the years and gotten it wrong too. Every miss I can remember came from the same place - I evaluated what someone had produced instead of how they decided what to produce. I overindexed on their past vs their capacity and potential.

Back to basics

Someone suggested a while back that marketing needed an interview bill of rights. Nobody ever wrote it. So here’s my attempt at the hiring side, aimed squarely at the CEOs and founders running these searches, as well as the recruiters helping them.

Decide what you’re hiring for before you open the search. If your executive team can’t agree, in one sentence, on what marketing has to fix in the next year, no candidate is going to fix it for you. A whole lot of case-study theater is a company outsourcing its own strategy homework to applicants and calling it diligence. Do that work first, then hire against it.

Use the evidence that already exists. Before you ask anyone to build something new, ask them to walk you through something they already built. Pick a number they owned and go deep: where it started, where it ended, what they personally changed and what they’d do differently. Ask for the artifacts from that work, redacted as needed. This costs the candidate nothing new and it tells you far more than a plan constructed without your data.

If you need to watch them think, do it live and do it together. A 30-60-90 with initial observations, presented in a working session where you push back and they push back and you both change your minds a little. Ninety minutes. You’ll learn how they handle being challenged, whether they ask better questions than you expected and whether they can say “I don’t know yet, here’s how I’d find out.” That’s the take you actually want.

Ask about people, and then check the answer. “Tell me about someone you developed. Where are they now?” It’s a simple question and it’s completely verifiable. Then call that person. References from people who reported to a candidate beat references from people the candidate reported to, nearly every time, and almost nobody does the work to get them.

Test for the negative. “What would you say no to in your first ninety days?” and “What’s the last thing you were badly wrong about, and what changed your mind?” Marketing leaders fail far more often on what they allowed than on what they lacked. Anyone who can’t name something they’d kill hasn’t led a function under real constraints.

Make the AI question about evidence, not fluency. Don’t ask whether they use AI. Ask them to show you something they built with it and walk you through where the output was wrong and how they caught it. One marketing leader I know does exactly this with every hire on her team, and it’s a much better filter than the knockout bullet - it surfaces judgement and tool fluency in the same answer.

Watch what the candidate asks you. The questions a senior marketer asks about your pipeline math, your sales relationship, your last two failed launches and what happened to the person in the seat before them are a better signal than anything they’ll present on a slide. If they don’t ask hard questions, they aren’t going to ask hard questions internally either.

What the board owns

Three things, and none of them happen in the interview room.

Set the runway before the hire and write it down. Marketing leadership tenures can be short, often because expectations were never made explicit and quarter two got uncomfortable. Agree in advance on what year one success looks like, which leading indicators you’ll watch on the way there and what you’ll do when an early number disappoints.

Own the scorecard, and make it a real one. Pipeline contribution, cost per qualified opportunity, sales cycle movement, retention on the marketing team. If the board can’t name the measures before the search, the new leader will spend their first two quarters inventing them and defending them instead of building.

And take a look at the process itself. Boards review sales comp plans and customer churn without blinking, then never once ask how the executive search was run. The process is the first honest look a candidate gets at how your company operates, and it’s the one part of the hire you can inspect before the money is spent.

The candidates you most want are the ones with enough standing to turn down a four-day assignment. Your process is quietly selecting against them, and you’ll never see it happen, because that no looks exactly like a candidate who wasn’t that interested.

Run the process you’d be willing to sit through yourself.

We feel better when things are documented and published. Consistent, tangible, official. But sometimes fluid is better.

Messier? Yep! But when your market and customers are changing this fast, you need to embrace fluidity instead of fighting it.

I’m thinking a lot lately about ideal customer profiles relative to this.

Fluidity is not an excuse for full-on chaos, i.e. not defining your ICP tightly to begin with. But how confident are you that what currently exists as your documented ICP is still right, right now?

The “perfect” ICP that slowly gathers dust (and irrelevancy)

Building an ICP is too often a one-time exercise. You get sales, marketing and maybe product into a room (or more likely a shared document) and you work backward from your best customers. Revenue. Industry. Headcount. Technology stack. Decision-maker titles. You write it down, share it at the next all-hands and put it in a folder.

Fourteen months later, maybe your GTM teams are still creating content and targeting accounts based on it. Maybe.

Nobody updated it after the three biggest accounts in your legacy top-tier segment churned. Nobody updated it when sales started winning deals in a vertical that wasn’t in the profile. Nobody updated it when product shipped a capability that opened a completely new use case with a completely different buyer.

The ICP document that helped you focus initially slowly became a compliance artifact.

The market doesn’t agree that your ICP is finished. Your best customers keep evolving. Your product keeps changing. The companies you sell to keep hiring new people into new roles who make decisions differently than the person you modeled the profile around.

The ICP that closed your best deals a couple years ago is likely missing something that closes your best deals today.

Who SHOULD you be selling to?

The most useful reframe I’ve found (and one we’ve used with clients at Heinz Marketing for years) is changing one word in the question.

Most ICP conversations are built around “who can we sell to?” That leads to lazy targeting. It expands the profile to include anyone who might conceivably buy, and you end up chasing a lot of business that looks right on paper and turns into a messy, sometimes way-too-public divorce.

The better question: who “should” we sell to?

That means the subset of the market that most acutely matches what your product actually delivers: where your go-to-market motion works, where your product team is building the capabilities those customers need and where the expansion motion after close looks as good as the initial deal.

Equally important, and almost never clearly enumerated: who should you stop chasing? There’s lots of bad business out there for all of us. Business that feels good when you ring the new-deal bell and ends badly. The best ICPs include disqualification criteria as clear as the qualification criteria. Most don’t.

Three inputs that should be updating your ICP (and probably aren’t yet)

If the ICP is going to be a living artifact, something has to feed it. Most teams have at least three rich sources of truth they’re almost entirely ignoring.

Lost deals, examined honestly.

Not a CRM disposition code. Not “pricing” or “went with competitor” in a dropdown. A real debrief: What stage did it die? What was the stated reason versus the actual reason? What did the prospect say they were going to do instead? Did that tell you something about whether you were solving the right problem for the right customer?

Almost every loss interview surfaces something. Win-loss research consistently finds that a majority of lost-deal interviews reveal signals that could have changed how the deal was run, or whether it should have been pursued at all. Your pipeline is telling you who’s wrong for you. Most teams aren’t listening systematically.

Churned customers, looked at honestly.

This is the most uncomfortable exercise in B2B. The discipline is to look at the accounts that churned and ask not “why did they leave,” but “should we have taken this business in the first place?”

That question produces a different answer. “Why did they leave” produces blame. “Should we have taken this” produces ICP criteria.

If your top-line churn numbers look fine but certain segments churn at 40% while others churn at 10%, your ICP criteria aren’t discriminating what you think they’re discriminating. You’re capturing accounts that look right at signing and show you something different by month eight.

Surprise wins, unpacked.

The most underused signal in B2B is the deal that shouldn’t have been a great fit, but turned into one of your best customers. Most teams celebrate the win, log it in the CRM and move on.

The better question: what was true about that account that your ICP didn’t predict? Was it a use case you hadn’t mapped? A buying trigger you hadn’t noticed: a leadership change, a competitive displacement, a funding event that created urgency? A team structure that made your motion land differently than expected?

Those anomalies are your ICP’s next version. They’re telling you where the profile needs to grow.

What a living ICP practice actually looks like

This doesn’t require a new tool. It requires a cadence and an owner.

Monthly. After every closed-won and closed-lost deal of any significance: a structured 10-minute debrief captured somewhere the marketing and CS teams can actually read. Not a pipeline note. A flag: did this account fit our ICP criteria on paper? Did it feel right or wrong in practice? What did we learn? These become the raw material for the quarterly conversation.

Quarterly. A 60–90 minute cross-functional review with sales, marketing and CS (and ideally a product voice in the room). Input: the monthly flags, cohort churn data for the quarter and a look at any deals that felt like exceptions. Output: specific, named changes to ICP criteria. This is an editing session, not a philosophical conversation. You’re adding, removing or sharpening the criteria based on what the last 90 days taught you.

Losses tell you more than wins, especially when you’ve built a system that captures the patterns. What did product miss? What did marketing fail to surface? What did sales hear but never elevate? Treated as a learning system rather than a blame audit, this is some of the most valuable time your GTM team can spend each quarter.

Annually. A full rebuild from the top 20% of customers by retention and expansion. Who are they, really? What do they have in common that your current ICP doesn’t capture? This is the reset that prevents incremental drift from compounding over years into a profile that’s fundamentally off from where your best growth is actually happening.

Ownership. Most ICPs have no owner. Marketing thinks sales owns it. Sales thinks marketing does. The fix is a named person. Sometimes a product marketer, sometimes a head of growth, in some cases the CMO directly. That person isn’t writing the ICP alone but is accountable for making sure it gets updated by the people closest to the signal. When nobody owns it, it drifts. Or worse, gets ignored.

AI is making all of this more achievable than it used to be. The pattern-recognition work that once took a RevOps analyst a week now runs faster and with more data: synthesizing win/loss data, flagging cohort churn differences, identifying segments where deal velocity is breaking down. The right AI workflow feeds that conversation with better-analyzed input so the time in the room is spent on decisions, not assembly and argument.

Product capabilities (from you and others in your market) are evolving faster than most sales and marketing motions can absorb them. Marketing is often generating leads against an ICP definition that was maybe right two years ago. Get the teams in the room more frequently, with better data feeding the conversation.

More bad news (and the pragmatic path through it)

Here’s what makes this even more difficult: updating your ICP means accepting that some of what’s in your current pipeline doesn’t belong there. That’s uncomfortable. It means disqualifying accounts sales has been working, acknowledging that a segment leadership endorsed at the last offsite isn’t producing and admitting the last version of the profile maybe needs to change.

This is why I like the “wiki” analogy for ICPs now. A wiki is expected to be updated. A wiki edit means the artifact is working the way it’s supposed to. Nobody treats it as an admission of failure. A document, by contrast, is expected to be right when published. The form itself implies permanence. Changes feel like failure instead of progress.

Changing the mental model from document to wiki also shifts the organizational dynamic. The quarterly update is maintenance. The person who flags that the ICP criteria needs to change isn’t undermining the work that came before. They’re doing the work.

Practically, start with the data you already have. Pull your top 20 closed-won accounts from the last 12 months. Score them against your current ICP criteria. If more than a third of your best customers don’t actually fit your stated ICP, your document is already behind, and you now have the conversation-starter you need to move it forward.

On Tuesday afternoon I’ll close this laptop and head to San Diego (Encinitas more specifically #iykyk) with my family. I won’t touch this laptop again until Monday, July 20th.

I used to dread that. Not the vacation, of course. The day AFTER the vacation.

We do this trip every year now. And if that Monday morning is like the others, I’ll come back to thousands of emails, hundreds of Slack messages and dozens of Slack DMs waiting for me.

Ten years ago that backlog would have eaten my first three days and most of my good mood. Last year, I was down to about 35 emails by noon, Slack was back under control, and I spent the afternoon on a couple priorities I already knew about plus a couple that had come up while I was gone and genuinely needed me.

The difference wasn’t discipline on vacation. I was as checked out as ever, and that’s the point. Our team knows I’m dead serious about PTO — you’ve earned that time and we don’t want to hear from you!

The difference last year, the reason why I didn’t have a pit in my stomach the night before re-entry, was a process I continue to hone.

You deserve your PTO. And everyone should be able to do both halves of it: actually shut it down without guilt, then come back with a plan that gets you your bearings fast instead of drowning you on day one.

Here’s the system. Half of it happens before you leave.

Before you leave

Get to Task Zero, not inbox zero. Your inbox will refill no matter what you do, so chasing it to zero the night before is a waste of an evening you should spend packing. The thing that actually matters is getting your real priorities and dependencies done and off your plate, so they’re off your mind and supporting other people’s work while you’re gone. A clean inbox feels good for about an hour, while finished dependencies earn you two weeks of quiet.

Delegate in advance, and be specific about it. Think through what would normally land on your plate over the next two weeks and make sure someone has it. Not “keep an eye on things.” Name the thing, name the person, make sure they know. Then do this enthusiastically for your teammates when it’s their turn. The whole thing only works if everyone covers for each other without keeping score.

Write an out-of-office message that actually closes the loop. Tell people definitively you won’t reply until you’re back. No “I’ll be checking periodically,” because then you’ve signed up to check periodically, and you’ll resent it by day three of your time off. Redirect anyone with something urgent to the person who can actually help them while you’re out. A good OOO protects your vacation and theirs. Here’s what mine will look like starting Tuesday afternoon.

Block your first day back before you leave. If you can, block the entire first day to get your bearings. Worst case, block the morning, or even just a few hours. Put it on the calendar now, before someone else fills it with a 9am meeting you’ll walk into jet-lagged and unprepared. Bonus points if you also schedule your re-entry briefing now (more on that below).

When you’re back

Delete aggressively. Ninety percent of the backlog either isn’t important, has already been handled while you were out, or will circle back to you on its own if it’s truly important. So treat the backlog as guilty until proven innocent. Scan, delete, move on. The stuff that needs you has a way of finding you again.

Triage the rest into “need now” and “catch up later.” Of the ten percent left, most can wait. Move those into a separate folder you’ll get to over your first week. What’s left after that is a small handful that will feel much more manageable, and that small handful is where you can actually spend time and energy on your actual first day back. Suddenly thousands of messages are down to maybe a couple dozen things that matter and a folder you’ll chip at over coffee.

Get a briefing. Sit down with your team or your manager and get the real download on what you missed. Fifteen minutes of someone telling you what actually happened beats two hours of you reverse-engineering it from email threads. It saves them time too, because they get to point you at what’s urgent instead of watching you rediscover it slowly. Schedule it before you leave so it’s waiting for you.

Give yourself grace. If you’ve been gone a week or two and you truly left work behind, your brain needs time to find its rhythm again. You will be wiped out by mid-afternoon of your first day. That’s not a sign you’re behind. It means you actually rested, which was the whole idea. Let yourself be tired and trust that the rhythm comes back by day two or three.

None of this is complicated. It works because it’s simple and straightforward. It’s also hard because you probably, like me, have to change some mindsets and habits to make it work.

Find a version of this that works for you but preserves your hard-earned time away and respects the people and priorities around you, before and after.

Give yourself that same gift the next time it’s your turn to disappear for a while.

I LOVE asking a room of marketers for their latest or best dumpster fire. One CMO last week didn’t hesitate (and her story is becoming more frequent). Her organic search traffic was sliding, month over month, and nothing she tried was bringing it back.

She wasn’t alone. It’s the quiet panic that isn’t going away - the chart that keeps dropping no matter what you do, and the creeping worry that you’ve lost a channel you spent a decade building.

I don’t have a magic fix, but here’s what’s happening with the data backing it up. Your traffic is down. Your buyers are still out there. The search itself moved.

It’s not your imagination

The decline is real, and irreversible. In a recent SparkToro analysis of clickstream data, more than two-thirds of Google searches - 68% - ended without a single click. Just two years earlier it was 60%. It’s the fastest acceleration of zero-click search in a decade.

The biggest driver is sitting right at the top of the results page. Google’s AI Overviews now appear on more than 20% of searches and cut click-through rates by roughly 60% when they show up. Pew Research put real browsing behavior behind that: when an AI summary appeared, people clicked a traditional result just 8% of the time, versus 15% when there was no summary. They clicked a link inside the summary 1% of the time. And they were far more likely to end the session entirely.

This isn’t a you problem or a bad-SEO problem. SparkToro tracked tens of thousands of domains run by professional marketers actively working to grow, and Google’s traffic share to those sites still fell about 22% in a single year.

The people who are best at this are losing traffic too.

So yes, the dumpster fire is real. But traffic and demand are not the same thing, and that’s where the story gets more useful (and more optimistic) for you and what you can (try to) control.

Where the demand actually went

Your buyers are still researching. They’re just doing it in more places, and fewer of those places hand you a tidy referral (let alone form-fill).

Reddit. After Google signed a $60M-a-year content deal with Reddit, Reddit’s search visibility exploded, and it’s now one of the most-cited sources across AI answer engines, showing up in roughly half of the AI Overviews that cite forums at all. When your buyer wants an honest take, they append “reddit” to the query. Many of you tell me you’re already doing it too.

AI answers. Referral traffic out of tools like ChatGPT and Perplexity is still a sliver of Google’s volume, but it’s growing fast - ChatGPT’s outbound referrals more than tripled over the past year - and it converts better than almost anything else. Similarweb pegs the conversion rate on ChatGPT-referred visits at more than double organic search. Fewer visitors, but they show up further along and more ready to talk.

Everywhere else. YouTube, LinkedIn, niche communities, newsletters, the podcast your ICP listens to on a Tuesday commute, Substacks like this. The “dark funnel” got that name because none of it shows up cleanly in your attribution, which doesn’t make it less real. It makes it harder to see.

Your buyers are doing this on purpose

None of this is buyers being difficult. It’s buyers doing what they’ve always wanted to do, now that the tools finally let them.

Gartner’s research has been pointing here for years: B2B buyers spend only about 17% of the entire buying journey meeting with potential suppliers, and when they’re weighing several vendors, any single sales rep gets a sliver of their time. In the same research, 61% of buyers said they’d prefer to buy with no sales rep involved at all.

Your buyer wants to self-educate, on their terms and timeline, deep into the process before they ever want to talk to you (let alone fill out a form). AI answers and peer communities are simply the best self-education tools they’ve ever had. Of course that’s where the research went.

So what do you actually do?

The CMO with the sliding chart didn’t reach for a technical SEO checklist. She pulled people from sales, product and customer success into a room to figure out, from the outside in, what buyers were actually asking and where.

A few things worth highlighting from her playbook and our own best practices:

Stop treating traffic as the goal. Traffic was always a proxy for demand, and it’s a worse proxy every quarter. Traffic can fall while revenue rises. Build a view that connects marketing activity to pipeline and bookings, and let the visit count be one input instead of the headline.

Optimize to be cited, not just clicked. If most searches end on the results page, the win is being the source the AI summarizes and the community recommends. That still rewards genuinely useful content, clear answers and a real point of view. It just pays off in influence now rather than a session.

Go where the research actually happens. Be useful in the subreddits your buyers trust. Show up on YouTube and LinkedIn. Get quoted by the newsletters and podcasts that reach your ICP. You’re building brand and demand in the rooms where the decision is forming, even when no one clicks back to your site.

Talk to your customers and your sales team. They’ll tell you the compelling events, the language and the questions long before any tool will. The outside-in conversation you start is worth more than another dashboard.

Same job, new map

Audiences constantly move. They always have - from print to search, from search to social and now from search to AI answers and the communities they trust. The channels keep changing every few years. What doesn’t change is the job, yours and theirs: find where your buyers are paying attention and show up there with something worth their time.

The organic search you remember is winding down. The demand it used to capture is still very much alive, just showing up in a dozen new places. We’re used to addressing decentralization as marketers, time to do it again.

I’m working on a new presentation on sales and marketing alignment that will debut at an event in Tokyo this August, and the preparation has given me a chance to revisit some best practices and frameworks on GTM alignment, updating plenty of the detail and nuance based on what’s working in 2026. Here’s a summary and preview of that presentation.

Sales & marketing alignment. Sigh.

It continues to be a high priority, and continues to be largely elusive. The cross-functional rhythms that used to work — weekly pipeline reviews, monthly product check-ins, quarterly planning sessions — have quietly fallen apart, become a shell of what they once were, and in some cases are a pure waste of time.

The dashboards are still being updated. The calendar invites still go out. Everyone can still see the numbers. The numbers, of course, aren’t the problem.

This is where “alignment theater” starts at most B2B companies. Quiet disengagement, dressed up in a tone of voice that lets everyone keep pretending things are fine.

I’ve been talking about marketing, sales and product alignment for two decades. Most of us still don’t have it. And I’d argue we never will if we keep approaching it the same way.

Frameworks haven’t fixed this

MEDDIC. BANT. Challenger. Every alignment framework consultants can sell has been bought and implemented at companies whose GTM teams are still misaligned a year later.

The frameworks aren’t bad. The problem is what they’re solving for.

Alignment is being treated as the goal. The teams that have real alignment got it by building shared, tactical accountability and letting alignment follow.

Most alignment failures I see aren’t process failures.

They’re courage failures.

The CMO who can’t say no to a campaign sales is pressuring her to run. The CRO who won’t push back when product ships something the market didn’t ask for. The head of product who treats marketing as the last step in the workflow instead of a partner in the strategy. These are choices. Nothing in MEDDIC fixes that.

The three strategic conversations you should be having internally and more frequently

What actually works is simpler than the frameworks suggest. Three specific conversations every quarter, with marketing, sales and product in the same room.

All three of these happen occasionally at many companies already, but I’ve come to believe they need to be happening at least quarterly. And refinements should probably happen in real-time based on new data available all around us right now.

1. The ICP conversation

Not “who can we sell to” that leads to lazy targeting but rather “who SHOULD we sell to.” Who do you win with, what’s the subset of the subset of the market that most acutely matches need to solution.

Equally important is to enumerate who to stop chasing. There’s lots of bad business out there for all of us that might feel good when you ring the new-deal bell but it likely to end in a messy, sometimes way-too-public divorce.

More frequent review and optimization cycles even on your ICP is critical. Product capabilities (from you and other players in your market) are evolving faster than the sales motion can absorb them. Marketing is generating leads against an ICP definition that was probably right two years ago. Sales is closing deals nobody is sure should have closed.

Get the three teams in the room and ask: who’s actually winning for us? Where is product capability and market reality diverging? What wrong-fit pipeline can we agree to stop chasing?

The more detailed your criteria, the more likely you can identify signals, new players and engagement opportunities in market in real time – let alone evolve your model based on actual win/loss data (more on that later).

2. The buying journey conversation

I don’t mean the funnel. The funnel is a marketing artifact. The buying journey is what the buyer actually experiences, and it almost never lines up with the marketing funnel (let alone the sales process).

Sales sees where buyers get stuck. Product sees what features get used in trial and freemium options. Marketing sees what content moves people from stage to stage. Each team has a third of the picture. Few are proactively and practically putting it together.

If you’re not having this conversation, you’re guessing.

3. The win/loss conversation

Most win/loss reviews I see are sanitized post-mortems. The losses get explained away. The wins get attributed to the wrong things (often based on internal politics). Nobody learns much, and little changes.

A real win/loss conversation is uncomfortable. What did product miss? What did marketing fail to surface? What did sales hear but never elevate? Treated as a learning system instead of a blame audit, this becomes the most valuable hour your GTM team spends each quarter.

Losses tell you more than wins, especially if you use AI to help identify micro-trends that can help you improve and tighten your ICP, account-centric pitch and much more.

Operating layers that make all of this actionable and accountable

Strategic conversations only work if the daily/weekly rhythm supports them.

I’ve watched companies put the three conversations above on the calendar and then wonder why nothing changed. The conversations need an operating layer underneath. Cadence matters more than format. A 30-minute weekly conversation beats a 90-minute monthly one almost every time.

A few operating moves I’ve found actually work with our clients:

The weekly pipeline review with product in the room. Because product needs to feel the pipeline in their bones, not just see the numbers on a dashboard once a quarter.

A monthly “what we’re hearing” loop. Sales surface to marketing what’s coming up in calls. Marketing synthesize to product. Product close the loop the following month. Three steps. No more.

Shared SLAs, not handoff SLAs. Time-to-first-touch, time-to-second-call, time-to-feedback — owned jointly, reviewed together. When only one team owns the SLA, only that team feels the pain when it slips.

The “no surprises” rule. Product changes hit marketing 30 days before launch, never the day of. Marketing campaigns hit sales 14 days before, never the day of. The cost of surprises is paid in trust, and trust takes years to rebuild.

A joint dashboard with three numbers everyone owns. Not 30 numbers each team owns separately.

Using AI to accelerate the pace, frequency and value of all of these loops. The above cadences work but reflect a pre-AI world. Most of us have enough data every day (or at minimum every week) to identify changes worth adopting before your competitors do.

And honestly? Cancel about half of your existing meetings. Alignment doesn’t come from more meetings. It comes from insights and data that makes those meetings far more actionable (and, believe it or not, fun!).

What AI is changing, and what it isn’t

A bit more on AI’s impact on operational GTM alignment, because it’s getting more and more vital.

AI is collapsing the gap between marketing intent and sales execution. Account intelligence that used to take a week of research now lives in the rep’s inbox before the call. Buying-signal surfacing that used to require a dedicated analyst now runs in the background. Product changes that used to take marketing weeks to translate into campaigns can be drafted (or prototypes vibe-coded) in an afternoon.

However, AI without alignment just creates faster theater. The same broken handoffs, executed at higher velocity. The same misread market signals, summarized more elegantly. The same blame game, with prettier dashboards.

What AI can’t replace is the trust between the people in the room. The judgment that says “yes, the signal is there, but no, this isn’t the right deal.” The willingness to admit a forecast was wrong. The honest win/loss conversation. Without those, the tools just speed up the theater.

Trust is foundational and non-negotiable

Trust is the differentiator. It’s what truly, and finally, creates alignment strategically and operationally across GTM teams.

The companies I admire most have that in common. It isn’t the framework they use or meeting cadence(s) they follow. Their marketing, sales and product leaders trust each other enough to disagree in a meeting and still leave aligned on the decision.

That kind of trust survives a CEO change. It survives a missed quarter. It survives a product pivot. It’s the thing that lets a company keep moving forward when the easy thing is to circle the wagons inside each function.

If you’re a GTM leader reading this, three questions worth sitting with before your next leadership offsite:

When was the last time sales, marketing and product disagreed in a meeting and reached a real decision?

Who actually owns the buying journey across functions in your company?

What gets reviewed in your win/loss process that the other two teams never see?

If the answers make you uncomfortable, that’s where the work is.

Running a healthy, vibrant community is an honor, and what you see above the surface is truly the fun stuff.

The conversations, both in live sessions and 24/7 in Slack. The connections someone makes that turns into a job, or a vendor switch, or a friendship. With CMO Coffee Talk in particular, Friday mornings are when nearly four hundred marketing leaders show up to learn, share, cry. That’s on top of almost 4,000 CMOs in a very active Slack channel.

Those channels and interactions are amazing, and it’s why I keep doing it. But that part is maybe ten percent of the actual work.

The other ninety percent is finding the right people to invite, ensuring the right people are in, getting them in the door and making new members feel welcome, showing up in their feed, proactively engaging them with each other, pulling the best ideas out of a chat window before they scroll into oblivion and making sure the person who shared something brilliant on Tuesday gets a little credit by Friday.

In other words, running a community is a lot of little things every day, every week.

None of it is hard or rocket science. Almost all of it is repetitive, tactical and a little tedious. Which is exactly why most community organizers either burn out doing it or quietly stop doing it at all.

It’s also exactly the kind of work AI is good at now. So over the past year I’ve handed most of it off, grouped into the three jobs that foundationally help keep a community alive.

Quick Disclaimer: Behind the scenes Taylor Berg at 6sense does the Lord’s work managing the membership database and complex Zoom set-up required for a group of this size. I consider that “back of house” work, the automations here are focused on “front of house” community management.

Finding and welcoming the right people

A peer community only stays valuable if the right people keep joining. For CMO Coffee Talk that means actual CMOs and senior heads of marketing, and there are more qualifying potential members every week than any one person can track manually.

So a few things run on their own. Every Thursday, a prospecting agent pulls net-new CMOs from my Sales Navigator saved searches, cross-references them against what’s already in Salesforce and my running backlog, and adds only the genuinely qualified new names to the invite list.

Every weekday morning, another agent takes that backlog and builds the day’s outreach file so I’m working a steady, manageable number instead of a scary pile. And every weekday I get a scan of fresh CMO appointment announcements, with anyone already in the community flagged first so I can congratulate them before the news gets stale.

The welcome experience matters just as much as the invite. When a new member registers, an onboarding agent handles the unglamorous chain that used to eat so much time: update the record in Salesforce, drop them into the welcome sequence, add them to the right Sales Navigator list, follow them back. New members don’t need to see any of this. They just (hopefully) get a great first impression for a community that seems to have its act together.

This is all based on a strategic experience we want CMOs (members and prospective community members) to have. But based on a set of rules and workflows that are largely consistent (with improvements over time), it can be automated with occasional oversight.

More gets done, consistently and successfully, with my time focused on authentic, 1:1 interactions.

Staying in front of members already there

Most communities lose members not in a dramatic exit but in a slow fade. Someone joins, nobody ever engages with them, and six months later they couldn’t tell you they were a member. Staying visible to the people who already raised their hand is the least glamorous work there is, and the easiest to let slide.

A few agents make sure I don’t. Every morning I get a list of community members who posted on LinkedIn in the last twenty-four hours, with direct links, so engaging is a two-minute job instead of an hour of scrolling. A separate digest surfaces wins and content worth amplifying from our Slack and LinkedIn community page, so the people doing interesting things get noticed.

And the actual relationship-building steps I commit to — the connection requests, the follow-up messages, the long backlog of “I should really DM that person” — get pulled from my sequences and teed up so they actually happen instead of living permanently on a someday list.

We also now have an automated monthly CMO-to-CMO matching sequence in Slack that helps facilitate 1:1 interactions between members.

I’ve long had these workflows and ideas/features defined, they have just historically been manual and very time-consuming – which means sometimes they get done and too often they don’t.

But they’re critical to a healthy community. Early and regular engagement matters. A member who hears from you in their first month behaves completely differently than one who joined and heard nothing. And that continues to matter when someone notices that the community notices them, what they’re doing and achieving, etc.

Capturing and repurposing what the community already knows

The best and worst thing about a room full of smart marketers is that the smartest stuff often gets said out loud (great!) and then immediately lost in the flow (bad!).

A killer recommendation or insight shared in the Zoom chat at 9:40 on a Friday and by 9:42 it’s already hard to find. Multiply that across a year and you’re literally hiding the most valuable thing the community produces (it’s collective knowledge).

I sometimes describe the CMO Coffee Talk Slack channel (which includes more than six years of archived content, templates, best practices, etc.) as the greatest CMO library in the world but all the books are on the floor.

So we’ve built a series of habits that now run automatically each week to help members get the most value (including on-demand and searchable access) from everything that exists for them and from them.

For example, every Friday, the chat transcript from that morning’s session gets turned into a clean best-practices summary — the actual tools, tactics and recommendations people shared, organized and saved instead of evaporating. And it’s automatically added to a searchable folder with all past session highlights as well.

Every weekend, the week’s Slack activity becomes our community digest, so even members who missed everything can catch up in five minutes.

Once a month, any new resource someone dropped in Slack gets pulled into a running CMO Resource Library so good documents stop disappearing into the scroll. It’s literally an html file that links back to specific assets in Slack, all organized by topic and subject. Board slides, job descriptions, dashboard examples, everything.

And the best moments from the Friday chat get drafted into short posts I can share in Slack and LinkedIn, which sends a little attention back to the people who said the smart thing in the first place.

Capturing the community’s best ideas and reflecting them back is how members know their contribution mattered. It’s also the work I was really bad at doing consistently when I tried to do it by hand.

The part I still (and will always) save for myself

The point was never to do less of the work. It was to stop letting the tedious ninety percent of the work crowd out the ten percent that is the best use of my time.

The team of agents working behind the scenes now every day lets me spend more time engaging members directly. Connecting them with each other, creating new ways to drive value in the community, gathering feedback on new topics, curating great Friday topics and experts, etc.

A community is a trust account, and trust is built in the human moments. Automate and outsource the foundational but tedious background work. Spend as much of your time as possible on time WITH the community, not tasks for it.

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On Monday I asked Claude to summarize a sales call.

I got back what you’d expect. A tidy recap, bullet points I could have probably guessed without sitting through the entire conversation. It looked useful and helped with almost nothing.

Yesterday I prompted Claude about a similar call but with more context: “Compare this conversation against the problems we say we solve and the language we actually use, and show me where the customer’s words lined up with our language and where they didn’t.”

Same LLM. Same kind of call. A completely different result, and this time something that makes me better.

The second request worked because it handed Claude a system for reasoning with defined problems and a clear description of what we do that it could test the conversation against. The first request gave it none of that. Just a volley and a prayer.

That’s the difference between engineering context and rubbing a lamp. Too many of us are still rubbing the lamp

It’s already quietly happening (with or without you)

What many have been calling “prompt engineering” already has a successor. It’s context engineering — the work of deciding what you want the model to know and take into consideration when it answers.

Not just questions, questions with context.

Anthropic describes this as curating the right information for the model to work with. The team at Cognition has gone further and called it the number one job of the engineers building AI agents.

The hardest part of getting good work out of AI, according to the people who build it, has very little to do with the wording of your request. It’s the structure and quality of what you feed it.

In their latest State of Martech report, Scott Brinker and Frans Riemersma describe marketing ops roles moving from system admin to “stack wrangler” to context engineer, and marketing roles moving from campaign manager to agent operator to value engineer. More than four in five data leaders now say prompting alone doesn’t successfully scale. Every one of those roles is being redrawn around the same skill: deciding what the AI knows when it acts.

The part that’s still yours

AI needs structured context to do anything useful. And it’s genuinely bad at building that structure itself. It will happily improvise one for you, confidently, and it will be subtly wrong in ways you might not catch until your boss or customer does.

So the structure stays our job. It’s closer to plumbing than creative work. Unglamorous, structural and the thing everything else now and moving forward quietly depends on.

Our team at Heinz Marketing has been building “marketing orchestration” workflows and taxonomies for clients for years now. It’s not sexy. But it’s necessary, and many failed attempts to implement AI at scale right now are exposing a lack of adequate work design and context discipline.

What it looks like when there’s no system

The cost of skipping this step is showing up everywhere, in greater volume and frequency, once you know what to look for.

A SaaS company (to remain unnamed but one you probably know) known for plain-talk, no-jargon marketing recently leaned on generic AI to scale their content. Within a few months the work came back polished and completely generic, full of the corporate mush the brand had spent years avoiding. A customer finally told them: “You used to tell it like it is. Now you sound like everyone else.”

We call that semantic drift and it happens because language models are built for probability and clarity, not personality. In other words, left to improvise, they reach for the safe, average, broadly acceptable version of every sentence. Hand them no messaging architecture and no real voice, and they’ll quietly regress your brand to the mean.

The fix is relatively simple. Treat your voice and your messaging as shared infrastructure, one source of truth every tool and every agent pulls from, instead of something you re-explain from scratch in every new chat.

The organizational skill that’s (finally) on the rise

The marketers who win are now the ones who can sit down and define the whole system — the messaging, the named problems, the language, the rules — clearly enough that a machine can run on it and stay on the rails.

Practically, that’s a short list. Write down the handful of problems you actually solve, in the words your customers use. Define your messaging components so they’re consistent and reusable. Build one shared context layer your tools pull from. Make your own content structured enough that a machine can find its way through it.

I told you it wouldn’t be glamorous. Nobody puts “context engineer” on their dream-job vision board. It’s the writing-down-what-you-mean work that careful, in-the-details marketers have always been good at and rarely been rewarded for.

Marketing didn’t get automated. It never will. But the work is being reorganized around the people willing to do the structural, contextual work as a foundation and anchor that keeps your message unique, compelling and actionable.

Three years ago, someone told me the BDR would be extinct by now.

Not diminished. Not redefined. Gone — automated out of existence, a quaint artifact of how we built pipeline before the machines learned to write a passable email.

That prediction missed. And not by a little.

6sense just dropped its fifth annual State of the BDR report, and the headline numbers run the opposite direction from the obituary. Headcount reductions are at their lowest point in five years — only 8% of organizations cut BDR teams in 2026, down from roughly 25% in prior years, while 58% are growing them. Quota expectations are up: 53% of organizations are raising BDR quotas, the highest level in five years. And AI adoption among BDRs is now effectively universal at 99%, up from 62% just a year ago.

So the function didn’t die. Organizations are betting on it again.

But — and this is the part the extinction crowd and the “nothing’s changed” crowd both miss — the job itself is not the job it was three years ago. The share of BDRs delivering fully qualified opportunities dropped from about 75% to 61%. Inbound work nearly doubled, from 15% to 31% of what BDRs do. The function is even migrating back toward marketing: BDR teams reporting into sales fell from 80% to 63%, while marketing-aligned teams nearly doubled. Kerry Cunningham at 6sense calls it the “marketing-sales carousel,” and it’s spinning again.

The role is still there but the jobs to be done changed underneath.

There is no single answer, and that’s the actual insight

Most takes on the “future of BDRs” issue want one verdict. BDRs are dead. BDRs are back. AI replaces them. AI can’t touch them.

There is no single answer that applies to everyone, and pretending otherwise is how you end up making an expensive structural decision based on another company’s irrelevant (to you at least) variables and priorities.

The right move for your team starts with a handful of questions few ask (let alone answer) before they reorganize:

How long is your sales cycle? A 30-day transactional motion and an 18-month enterprise pursuit are not the same business, and they should not staff outreach the same way.

How much time passes between a customer’s purchase decisions? If your buyer re-evaluates every few years (or longer) the relationship work between deals matters more than the next dial.

How much of your pipeline is inbound versus outbound? Because — as I’ll get to — that ratio maps almost directly to where AI helps and where it hurts.

And how much of your motion is direct versus partner or channel-driven? A channel-led business has a fundamentally different first-touch problem than a direct one.

Answer those honestly and the “should we keep BDRs” question becomes specific to you. The teams flailing with their BDR strategy right now are usually copying a model built for a sales cycle, a buying cadence and a pipeline mix that isn’t theirs.

The AI BDR is a boon — for the company and the BDR

I’ve been openly skeptical of AI SDRs “taking over” for a while. I’ve called out the vendors selling “AI that closes your leads” when there’s an enormous gap between closing a conference-ticket purchase and closing $300K of enterprise software. I still believe that.

But skepticism about the overpromise is not the same as missing the opportunity.

Start with what the technology is genuinely good at right now. We’re close to AI handling the inbound work well — qualifying, routing, answering the obvious first questions, getting a ready buyer to the right human fast.

What it still can’t do well is outbound at any level you’d be proud of to let happen autonomously to your enterprise prospects. Generic automated outbound — the burner-domain, hundred-thousand-email kind — doesn’t just underperform. It makes things worse for buyers and it makes you look bad in front of them.

So point the tool at the jobs it’s actually good at, and the math immediately changes in your favor.

You can now engage a far larger universe of qualified prospects than a human team could ever reach — not with spam, but with competent, persistent, genuinely helpful first-touch that knows the category well enough to answer real questions.

The AI doesn’t get tired, doesn’t skip the follow-up and doesn’t pretend to be human. Meanwhile you focus your human BDRs on the prospects who are right and ready — the conversations where judgment, credibility and a real back-and-forth actually move a deal.

There’s a third use that gets overlooked, and it might be the most interesting. AI BDRs can do pre-sales work you would never spend human SDR dollars on. Prospect education. Category context. What I’d call “value deposit” work — showing up useful to a buyer long before they’re in-market, building the kind of trust that pays off two budget cycles from now. No CFO was ever going to fund a junior rep to nurture a not-yet-ready account for eighteen months. An AI BDR can begin and nurture that relationship at a cost that makes it worth doing.

None of that is the BDR dying — it’s the function finally getting capacity (with the right economics) for work it couldn’t touch before

How orgs actually prepare for the next 1–2 years

A few things I’d tell a leader sitting on this decision today.

First, stop making the wrong comparison. Most leaders weigh AI against the senior, credible human they wish they could hire — the analyst-grade rep who can have a real conversation with a real buyer. If you can afford that person, hire them; the tech isn’t replacing them and won’t for a while (maybe never for high-trust consideration purchases).

But most companies were never going to hire that person. They were going to hire a junior rep fresh out of school, hand them a script and hope the AE took the meetings. That’s the role AI is competing with. Against that comparison — competent AI persona versus the junior outbound rep you were probably going to underdevelop and replace within a year — the economics are real and getting better. Pick the comparison that’s actually in front of you, not the one that makes your existing org chart easier to defend.

Second, expect the dip. My friend and podcast co-host Brent Adamson calls this the J-curve problem: when you introduce a new process or technology, performance usually gets a little worse before it gets better. Replace your BDR layer with a tool, and there’s a stretch where the tool is still being tuned and the pipeline sags. Everyone panics. “This didn’t work, hire the BDRs back.”

Some of that is a real mistake. A lot of it is just the dip — and the teams that win are the ones who can tell the difference and hold their nerve. The single best strategy, as Brent likes to say, is consistency. Pick a direction on principled grounds and stick with it long enough to know.

Third, invest in support, not just software. The most durable finding in five years of BDR performance data has nothing to do with AI. BDRs who feel genuinely supported — equipped, valued, set up to win — hit quota at nearly 100%. Those who don’t land at 77%. That gap holds every single year.

More tools and more touches don’t move it; outreach volume nearly doubled to 33 touches per contact and showed no reliable relationship to actually hitting quota. Whatever you automate, the humans you keep need context and support more than they need another platform license.

Fourth, match the structure to your variables, not to the conference-stage consensus. Long cycle, complex committee, ABM motion? Keep credible humans on first touch and let AI handle research, enrichment and the inbound that’s flooding in. Short cycle, high inbound, direct motion? That’s exactly the slot to pilot AI BDRs against your last cohort of junior hires — and measure it honestly on meeting-set rate, conversion and the candidate experience you’d otherwise have burned through.

And it’s entirely possible that within your business both scenarios exist, in which case you need to lean into the complexity of running parallel but different motions. Simpler doesn’t always mean more effective. Often it leads to the opposite.

The BDR I was told would be extinct by now is busier than ever, paid to do different work and pointed at better conversations. The companies that figure out which of their conversations belong to a machine and which belong to a person — based on their own cycle, their own buyers, their own motion — are the ones who’ll get the next two years right.

This has happened to you too, likely recently. You land on a startup homepage — one that comes well-recommended, by a colleague you trust — and within three seconds you’re reading something like:

“An integrated intelligence platform for next-generation workflow orchestration.”

Or: “AI-native SaaS for enterprise digital transformation.”

Or: “The operating system for modern revenue teams.”

The more you read, the less you understand. The more the site tries to explain what it does, the less you’re able to translate what problem they actually solve (let alone whether it’s worth your time to read or explore further).

To keep things simple, let’s separate home page copy and messaging into two camps — product-centric and problem-centric. Product-centric of course is mostly focused on what the company has built. It gets technical quickly (probably far too quickly).

Problem-centric messaging on the other hand more naturally focuses on the customer. Problem first, ideal outcome next, THEN a little about the product that will solve that problem.

Most of us inherently understand the difference and which works best. But boy there’s still a lot of product talk out there!

I wanted to better understand the balance between product and problem-centric messages out there, and decided to start somewhere nearby.

I read, analyzed and scored the hero copy for every company on the GeekWire 200 — the top-ranked Pacific Northwest startups by growth and momentum. I categorized each homepage: product-centric (leads with what the company is or does), problem-centric (leads with what the buyer is dealing with), or mixed. Then I cut the data by a couple variables, including founding year.

What 200 homepages told me (and their customers)

Here’s the product-centric share by founding year cohort:

• Companies founded 2012–2016: 43% product-centric

• Companies founded 2017–2020: 53% product-centric

• Companies founded 2021–2024: 60% product-centric

And the problem-centric share across those same cohorts:

• Founded 2012–2016: 32% problem-centric

• Founded 2017–2020: 21% problem-centric

• Founded 2021–2024: 19% problem-centric

A company from the oldest cohort is roughly 70% more likely to lead with the buyer’s problem than one founded in the past five years. That’s a 17-point swing in product-centric messaging from oldest cohort to newest.

Employee count as a variable barely mattered. Maturity did.

The ranking adds another data point worth noting. The GeekWire 200 is ordered by growth and momentum — a rough proxy for business traction. The top 100 companies on that list are significantly less product-centric than the bottom 100: 47% vs 54%. The problem-centric share is similar across both groups (26% vs 24%), but the top 100 has a higher proportion of mixed messaging — suggesting the more successful companies have at least started the journey from pure product-first toward something more buyer-aware.

I want to be honest about what this data does and doesn’t tell us. This is correlation, not causation. There’s no controlled trial here. We can’t prove that problem-centric homepages convert better — we’re extrapolating from a pattern, not measuring an outcome. But the correlations all point the same direction: more successful companies, more time in market, both track toward messaging that leads with the buyer.

Why this happens

Early-stage companies are almost always founder-driven in their messaging. And founders often do something understandable but self-defeating: they assume everyone gets it.

They get it (that’s why they started the company!). Their co-founders get it. The first dozen employees who interviewed for the mission get it. The very early-adopter customers — the ones who sought them out, who follow the category closely, who enjoy the sport of figuring out new technology — they get it too.

So the homepage speaks to that cohort. And it appears to work. For a while.

Early-stage companies also rarely have an established product marketing function. Nobody has done the real ICP work — the persona research, the jobs-to-be-done interviews, the careful enumeration of what’s actually keeping the buyer up at night. Without that work, the default is to describe the product with precision and hope the buyer can reverse-engineer the value.

Some can. Your early adopters absolutely can.

But Geoffrey Moore’s insight from Crossing the Chasm still applies here: early adopters and mainstream buyers are categorically different. Early adopters seek discontinuity — they want the new thing, they enjoy the translation, they’re motivated by getting ahead of the curve.

The mainstream early majority wants to minimize risk and disruption. They’re not going to do the translation for you. They need to see their problem in your words before they’ll engage with your solution.

Product-centric messaging is written for the early adopter. Problem-centric messaging is written for everyone else.

Time and organizational maturity helps (for some)

The older companies in the GeekWire 200 have had more years of marketing leaders pushing back on the product-first default. More A/B tests on hero copy. More customer conversations that surface what buyers actually search for. More sales calls where reps finally said: “They don’t care about the architecture — they care about the thing it fixes.”

That institutional pressure accumulates and the messaging evolves.

But it doesn’t happen automatically, and frankly it doesn’t happen fast enough for most companies. Three out of five companies founded in the last five years still lead with what they are, not what their buyer is trying to do. Many of those companies will stay that way — not because they lack good product, but because they never built the customer-facing function that does the real positioning work.

Stop making your buyers do the work

Product-centric messaging is fundamentally an act of optimism about your buyer’s willingness to work.

It says: we built something compelling, and we trust you to do the cognitive labor of connecting it to your problem.

Problem-centric messaging takes the opposite posture. It says: we’ve already done that work. We know what you’re dealing with. We built this for that specific thing.

Asking your buyer to do that translation is a reasonable bet on your early adopters. On the mainstream majority you need to reach to actually scale, it rarely pays off.

Most buyers don’t have the time or the motivation to reverse-engineer a product description into their own problem. They’re looking for a signal that the company on the other side of the screen has done that work already.

Your hero copy is that signal. You’ve got about three seconds to send it.

What you should do about all of this

If your hero copy still leads with what you are rather than what your buyer is dealing with, the fix starts with a conversation you may not have had yet: what problem does your buyer describe before they’ve ever heard of your product?

Not the problem you solve. The problem they name.

That answer takes customer interviews, sales call analysis, search data and a product marketing function with the authority to rewrite the page. It takes someone willing to tell the founder: the way we talk about this was built for the people who already believe in us — and we need to rebuild it for the people who don’t yet.

But when a buyer lands on your homepage and immediately sees their problem — not your product, their problem — the rest of the page becomes a very different conversation.

Across the last few months I’ve sat in on a lot of conversations between senior B2B marketing leaders — Friday morning sessions with the CMO Coffee Talk community, 1:1s with CMOs in transition, executive briefings, the quieter Slack threads that don’t make it to LinkedIn. What strikes me is how different those conversations sound from the “news” version of the same moment.

The headlines have been the usual mix. AI is replacing marketing teams. Attribution is dead. Brand is back. Brand is dying. Growth is back. Growth is over.

None of that matches what the CMOs I work with are actually doing, seeing and prioritizing.

I pulled what I’ve been hearing into a 20-page report — State of the B2B CMO: What the first hundred days of the year are telling us about the role, the work and the path forward. The full document is available below as a PDF.

Here’s a peek at the three elements I think are most important:

The job is more financial than it has ever been

For most of the last decade, the CMO-CFO relationship in B2B was characterized by mutual eye-rolling. Both pretended the other was the problem.

That arrangement is evolving in a good way.

The CMOs I’ve been listening to are learning finance not because someone made them, but because the alternative is losing the argument with their CFO every quarter. They’re learning what Rule of 40 means and why Rule of 50 or 60 is the new working benchmark at many growth-stage and PE-backed boards. They know their company’s operating margin. They know what percentage of revenue their marketing budget represents, and whether they’re above or below benchmark.

And quietly, in the last six months, a lot of them have stopped bringing line items labeled “brand” into budget conversations. Not because brand stopped mattering. Because the word triggers the wrong conversation. The moment a CFO or CEO sees “brand spend,” the room is scanning for cuts instead of investments.

The CMOs ahead of this are reframing the same work as “market development,” “Big Bet Budget,” “AEO investment,” “customer evidence and original research.” The work is the same. The vocabulary is finance’s, not marketing’s. And the budget is now being approved instead of defended.

The report walks through the full financial literacy section — including the cap table conversation a lot of CMOs are now having for the first time, and the token economy time bomb sitting underneath every AI-enabled workflow built this year.

AI is now the operating environment, not the topic

We’re past the moment when AI was the conversation. AI is now the operating environment of B2B marketing. Everything else — how you measure, how you spend, how you find your buyer, how you partner with finance — happens inside it.

What that looks like in practice is more interesting than the AI hype cycle would have you believe.

CMOs are vibe coding landing pages their web team’s queue won’t ship in time. They’re running competitive research agents that produce a digest every morning. They’re quietly building Heads of AI inside marketing or right next to it. They’re learning that the agents delivering ROI are mostly the boring ones — the ones that augment a person doing real work, not the ones that promise to replace a function.

The “fully autonomous VP of Marketing” pitches are theater. The teams getting value from AI are the ones who kept a human in the loop on the final write or publish step. The agent drafts, the agent surfaces, the agent recommends. The human ships.

That’s the steady state for the next few years for most B2B teams. Not a transitional one. Teams that forego the human loop to look more “AI-forward” tend to find out the hard way why the loop existed.

The buyer is somewhere new, and we mostly aren’t there yet

A surprisingly large number of B2B marketers I know have personal friends who don’t know what ChatGPT is. The latest data still puts something like 65 to 75 percent of B2B SaaS search behavior through Google. Fifteen to thirty percent has shifted to AI search of some flavor. The rest is direct, community, vendor sites.

Meanwhile, the platforms where the AI models are sourcing their answers — Reddit, YouTube, original third-party research — are mostly the platforms B2B marketers either wrote off years ago or have never figured out how to participate in honestly.

The report covers the Reddit and YouTube citation conversation in detail, along with the pay-to-play listicle problem that’s about to get more attention from the LLMs than it has from buyers in years. It also covers the brand visibility composite metric that a growing number of senior marketing leaders are building to replace the attribution conversation that stopped working some time ago.

The role itself is bending

The CMO job description from eighteen months ago no longer fits the job. That’s the part that’s hardest to write about and most important to get right.

The CMOs who are bending with it are the ones investing in commercial range, financial literacy, durable peer relationships and a small set of disciplines that compound.

What you’ll find in the full PDF

Twenty pages, eight sections, plus a working list at the end with two parts:

• Catch up on this in the next 30 days — six things every senior B2B marketing leader should already be conversant in. If any are blank spots, close them before the quarter ends.

• Stay ahead of this through the second half of 2026 — eight disciplines worth building now, while the rest of the field is still arguing about whether they matter.

If you’re a senior B2B marketing leader, this is the document I would have wanted in my own inbox in April. The PDF is below.

State Of The B2B CMO - 1H 2026

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AI is only as good as the problem we put in front of it. And too often, it’s solving countless problems that aren’t big enough.

So I’ve been asking marketing leaders to name their ACTUAL biggest problems. The biggest friction points in their GTM motions. The greatest frustrations keeping them from accelerating revenue, growth and velocity.

And (surprise, surprise) some age-old topics come up.

• Data silos

• Broken (or nonexistent) processes

• Sales productivity

• Sales and marketing alignment

• Declining response rates

• Slower deal cycles

Your board might be screaming about how fast you’re adopting AI. But AI isn’t the problem. THESE are the problems.

Almost none of these are truly technology problems.

AI gets layered on top of them and they don’t go away. You’ve automated around a broken process. Or given a faster tool to a team that still doesn’t have alignment. Or added complexity to a data infrastructure that wasn’t ready for it.

Or, for better or worse, we automate countless workflows that are important but not urgent. It feels like progress, and it is. It makes for a nice progress report to your board.

But we’re just solving for the directive, not the actual, most important problem.

A sequence that actually works to get (and keep) you focused

What I’ve seen generate real traction follows a specific order:

Objectives first. Constraints second. Strategy and process next. Technology last.

• What specific outcome are we trying to move?

• What’s the friction point actually stopping us?

• What would have to be true for that friction to go away?

Only after you’ve worked through any related questions does the technology conversation become contextual and useful.

A proven, workshop-driven approach

I’ve run this session twice recently — two breakout sessions at Chilipalooza and a similar session at the Demand & Expand CMO Summit. It works with a small leadership team, a cross-functional GTM group or a room of fifty peers at a conference.

The opening talk is fifteen to twenty minutes. Not a training session — more of a framing and call to action. It surfaces the gap between what leaders say is their biggest constraint and what they claim is their top priority. Then it makes the case for sequencing: strategy before process, process before technology and AI firmly in act three.

Then the room breaks into small groups. Four discussion questions, worked in order. The goal is to get somewhere practical rather than somewhere polished.

Question one: What is the single biggest constraint in your GTM motion right now? Not a tool gap. The actual friction slowing you down.

Question two: How much of that constraint is a technology problem versus a process, data or leadership problem?

Question three: Is anyone in this group making real progress on a GTM or AI initiative right now — and what did you do first?

Question four: If you had to pick one thing to fix in the next 90 days, one specific friction point, what would it be?

The facilitation notes matter here. Most groups want to jump to tools by question two. The moderator’s job is to hold the line: name the problem before naming the solution.

The last five minutes, each table brings one thread back to the room. The most common constraint they named. The most honest moment. One thing that’s actually working.

That summary, and the commitment to engage and solve those bigger issues, is always more useful than anything in the opening talk.

Workshop materials you can use right now

I’m sharing access to all of the workshop materials below: the session outline from Chilipalooza, the full small-group moderator’s guide from the CMO Summit and the slide deck I presented to frame it all first.

You can run a rough version of this workshop from the article above. The materials are for when the conversation goes somewhere you didn’t expect — and it will.

Most groups jump straight to tools by question two. Someone gives the safe answer. One voice takes over. The group lands on consensus that sounds real but isn’t. The moderator’s guide gives you the specific language for exactly those moments: what to say to pull the room back, when to push, when to let it breathe and how to get the honest answer instead of the polished one.

The slide deck runs the opening framing for you. The session outline keeps the time honest.

2026 05 Cmo Summit Matt's Intro And Small Group Moderator Guide

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Cmo Summit Start With The Problem

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Chilipalooza Session Outline Matt Heinz

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This past week at SaaStr, there was a session called “Vibe Code Your AI VP of Marketing.”

The premise: why hire a marketing leader when you can build one? More models, fewer marketers.

It’s the unfortunate next step of something that’s been happening in B2B for a while. A growing number of companies have marketing reporting to the CRO. The job becomes basically demand gen and pipeline. One P of the traditional four. No brand strategy, no product positioning, no pricing. Just Promotion — narrowly defined, in service of a sales number.

When marketing is one P, replacing it with AI starts to make sense. You’re not replacing a business function. You’re automating a task.

What both of these trends miss is that marketing at its highest impact has never been simply promotion. And a meaningful number of companies — and the CMOs they’re hiring — are quietly building back to what it was always meant to be.

What marketing looks like when it actually works

Start with the word itself. Marketing is a verb. It’s the act of activating a market — identifying the pockets ripe for growth, building and validating product-market fit, tracking how competitive moves change your GTM motions, deciding which channels and which customers and which offers unlock the next stage of growth.

Sales is one of those channels. So is partnerships. So is product-led growth. When marketing is working at its full potential, it doesn’t simply hand off to sales. It designs the commercial motion that sales executes within — and stays accountable to whether that motion is working.

It’s a model that’s been running in consumer packaged goods for decades.

In CPG, the Brand Manager doesn’t just run campaigns. They own the product. They own the pricing. They own the distribution strategy — which means they own the trade relationships, the channel mix, the retail placement. Sales is a go-to-market arm, not a separate function operating on a different agenda. All four Ps — product, price, place, promotion — sit in their lane. They’re running a business unit. The product team, the finance team, the supply chain team all work around the Brand Manager’s ownership of the market outcome.

That’s not a marketing support function. That’s the general manager of a business and product line.

Too many B2B companies (especially those in tech) lost this model somewhere along the way. Marketing got separated from product strategy. Pricing became finance’s problem. Channel and sales became the CRO’s world. What was left was Promotion — campaigns, content, pipeline. A service function with a lead target.

That reduction is what the SaaStr session this past week and the CMO-reports-to-CRO trend reflect. They’re not wrong about what marketing became in a lot of organizations. They’re just describing a diminished version of the function and treating it as if that’s all marketing ever was.

The quiet promotion (pun intended) back to four

This doesn’t have to be a call to action (another pun intended) because we’re not starting at square one. It’s an observation of what we’re slowly and finally seeing happen in the market.

A meaningful number of CMOs are pushing back on the one-P reduction. Not by arguing about it — by operating differently. Taking ownership of pricing conversations. Getting into the product roadmap. Building a real working relationship with the CFO, not just presenting to them. Owning the revenue outcome, not just the pipeline input.

The BDR organization was a major domino in that direction. CMOs taking ownership of business development — prospecting, pipeline-building, the top of the commercial funnel — became common enough in the last few years that it barely registers as news anymore. But this year the scope is expanding further.

More CMOs are quietly taking on full sales organizations. Some are adding customer success. The logic is consistent with what marketing was always supposed to be: if your job is to activate the market, then sales is how you execute that activation and customer success is how you protect and grow what you’ve earned.

The functions were always connected. The org chart is finally catching up. In some cases these leaders are being elevated to Chief Growth Officer titles, formalizing what was already true in practice: they own the commercial motion end to end, not just the top of the funnel.

I see this trend when CMOs talk to each other as well. Tracking twelve months of peer sessions in our CMO Coffee Talk community I host, and excluding AI, topics in the Career & Executive Role bucket — the CMO’s operating relationship with the C-suite, financial literacy, revenue accountability, the CMO hiring market — went from 5% of the non-AI agenda to 50% in the most recent four months.

Half of every non-AI peer session this year has been about the executive scope of the role, not the execution of the function.

That’s not a coincidence, it’s evidence. It’s a community orienting itself around a different version of the job.

The conversations have moved toward things that only matter if you’re operating at full scope. How do you build a real CFO partnership? What does it mean to own a revenue number rather than a pipeline number? What does the first 90 days in an expanded seat actually require? These are four-P questions, not one-P questions.

What companies are actually hiring for

The job description for new CMOs is also being rewritten. Not everywhere, obviously, but in enough places, and consistently enough, that it’s starting to feel like a real pattern rather than a few outliers.

The conversations I’ve heard from executive recruiters that they’re having with CEOs and board members are no longer about finding a great brand builder or a strong demand gen leader. They’re about finding someone who can own the commercial outcome, operate credibly at the executive table, and connect the marketing motion to the financial performance of the business.

What those conversations are asking for comes down to a not-so-new set of fluencies the role now requires:

1. Talking revenue, efficiency and growth in your CEO’s vocabulary — closed-won impact, retention, expansion, not just pipeline generated.

2. Talking capital efficiency in your CFO’s vocabulary — margin, dilution, Rule of 40, token economics if you’re running an AI-forward stack.

3. Translating the AI conversation into operating economics without losing the brand and demand story underneath it.

4. Designing work at the cross-functional layer — not running campaigns, orchestrating the full commercial motion.

5. Showing up actively and consistently in the executive conversations that aren’t technically in your job description (yet).

How to build toward it now — before the title reflects it

The marketing manager who eventually runs a P&L doesn’t wait to be promoted before thinking like a GM. They build the fluency in their current role, show up in conversations that aren’t officially theirs, and make the ownership obvious before the org chart reflects it.

Title changes come after the work is done and validated.

Here’s what that looks like in practice:

Start thinking like someone who owns the market, not just the marketing.

What does your company’s product-market fit actually look like right now? Which segments are ripe for growth and which are plateauing? How do your competitors’ moves change your GTM motion? These are marketing questions — not product questions, not sales questions, not finance questions. They’re yours.

Learn your company’s pricing model — why it’s set where it is, what the margin implications are, how it positions you competitively. Sit in on product roadmap reviews. Get fluent enough in channel strategy to have a real opinion on it. Understand what the BDR team is running into and what it tells you about the market. Ask questions in the rooms where those decisions get made, even when you’re there as a guest.

You don’t have to own those decisions yet. But understanding them is what makes you a strategic partner in those conversations rather than a function head who shows up when marketing is on the agenda.

Build CFO fluency before anyone asks you to.

Ask your CFO to walk you through the metric they’re most focused on right now. Most CFOs will make time for someone who’s genuinely trying to learn the language — and the ones who won’t are telling you something important about the company’s view of marketing.

The fluency compounds. A marketing leader who shows up to the CFO’s office with real questions — not to pitch a budget, but to understand the business — is already operating at a different altitude.

Move your reporting toward revenue outcomes before you’re forced to.

Your formal KPI might still be pipeline. That’s fine. Build the revenue-attached version of your reporting alongside it. Know your closed-won contribution. Know your impact on retention and expansion. When the accountability conversation comes — and it will — you want to be the person who already has that data, not the one who has to go find it.

The people who get to define the new accountability model are the ones who arrived with it already built.

Get into the rooms where the four-P decisions get made.

Revenue strategy. Pricing reviews. Product planning. Channel decisions. These conversations are already happening in your company right now, somewhere. Start finding ways to be useful in them — not to take them over, but to connect the marketing perspective to the business decision.

The marketing execs who have expanded into CGO roles or taken on sales didn’t get handed that scope. They built their way into it by being valuable in conversations beyond the marketing function. That’s how the quiet promotions are already happening.

Marketing at its best

Marketing is a verb. It’s the act of activating a market — understanding it, finding the growth in it, designing how you reach and win and keep it. Sales, customer success, channel, partnerships — these are the motions through which you execute that activation.

The CMOs being elevated — into expanded scopes, into CGO titles, into the boardroom — are the ones who held that definition and built toward it. They didn’t wait for the org chart to reflect it first.

You don’t need permission. You can (and should) start building towards that now.

Does marketing have a marketing problem?

I spoke last week with a CMO whose numbers were great.

Pipeline contribution was up. Influenced revenue had doubled year over year. She’d built an ABM program that sales actually used.

And yet! Her CTO referred to marketing as the “events and decks” department.

This is a real pattern (and problem) I keep seeing. CMOs with real results, real programs, real pipeline impact — still fighting for credibility, still having to justify marketing’s existence to the CFO, still educating everyone from new hires to board members about what marketing actually does.

The communication system is the gap. Not the marketing itself.

And the question is this: How do you make the marketing you’re already doing land internally — with your CEO, your board, your sales team, your head of finance and beyond?

After talking numerous marketing leaders through this issue, I’ve turned our recommendations and best practices into a toolkit, available now to subscribers.

What’s in the toolkit

Marketing the Marketing Best Practices — A PDF summary of seven core practices, with real examples and context. I recommend starting here.

Pipeline Coverage Dashboard — An Excel tracker that connects marketing activity to pipeline multiple. If you’re still reporting on MQLs and impressions to a CEO who cares about revenue, this is a tool that fixes that.

“Yes, If...” Request Framework — A Word template for replacing reflexive “no” with a phrase that protects your bandwidth and keeps you in the conversation. Comes with a scenario table covering the most common internal requests marketing teams face.

Marketing 101: Internal Course Outline — A five-module framework for educating cross-functional colleagues on what marketing does and how it supports revenue. Built to run as a lunch-and-learn series or a standalone session with your sales leadership team.

Revenue Alignment Charter — A formal document for establishing shared revenue goals between marketing, sales, and CS. Seven sections, ready to adapt. When everyone’s signed it, the “whose number is it?” question largely goes away.

Marketing Transparency Report Template — A quarterly reporting template built around a counterintuitive principle: sharing misses builds more trust than hiding them. Includes a required “misses” section and guidance on writing honest metrics commentary.

The CMOs who are keeping their budgets — and growing them — have made marketing’s impact impossible to ignore. Not through better marketing alone. Through a better communication system around the marketing.

This is a severely underleveraged skill in B2B marketing leadership.

Most CMOs are good at making the case during annual planning. Fewer have a steady-state rhythm that keeps marketing’s value visible the other eleven months of the year.

And frankly, if you’re not filling that gap, someone else is filling it for you — usually with a narrative that you won’t like.

Where to start

Open the highlights PDF first. Be honest about which ones you’re actually doing versus which ones you’ve been meaning to get to.

Then pick one practice, one template to start with. Don’t try to overhaul everything at once. The CMOs I’ve worked with who’ve built strong internal credibility didn’t get there by redesigning their reporting overnight. They started with one consistent behavior — usually the communication cadence — and let the trust compound from there.

The tools are below. I look forward to your feedback and “marketing the marketing” success stories!

The six files are attached below. All are formatted for immediate use — adapt the language to fit your company and team (and email me if you need the Word version of any of these).

Pipeline Coverage Dashboard

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Marketing The Marketing Best Practices

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Yes If Request Framework

60.1KB ∙ PDF file

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Marketing 101 Lms Course Outline

110KB ∙ PDF file

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Marketing Transparency Report Template

133KB ∙ PDF file

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Revenue Alignment Charter

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You’d think a category this hotly and frequently debated would be one B2B marketing leaders actively prioritize and fund. It isn’t.

We’ve been watching attribution conversations, debates and frustrations as high-frequency conversations on LinkedIn, in MOps Slack groups and in CMO peer circles for years. Our CMO Coffee Talk community even has a “Time to Attribution” tracker for Friday morning meetings since it (and AI duh) seem to come up every week no matter what the intended topic.

Attribution as a discrete product line in B2B martech is (finally) winding down, and the vendors who saw the curve early are getting out before the runway gets even uglier.

The biggest names in B2B attribution have either pivoted, disappeared or been acquired in recent months as they all reached the same conclusion their customers had already reached. Nobody is writing serious checks for this category.

This piece is the post-mortem on why that happened. Surprising to some, inevitable to others.

A category propped up by conversation, not commerce

Walking through any marketing operations forum, any CMO peer group, any LinkedIn comment section about complex pipeline generation and conversion you’d think attribution was the most pressing problem in B2B marketing. Threads about first-touch versus last-touch. Debates about W-shaped versus U-shaped. Screenshots of dashboards that try to assign credit across nine touchpoints and a phase of the moon.

While conversation volume has been and frankly still is enormous, the wallet share has been quietly collapsing for years.

I’ve watched marketing leaders I trust spend years asking about attribution platforms, sitting through demos, comparing pricing — and then quietly funding something else when the budget cycle hits. More tactical (vs wholesale) pipeline analytics. Marketing mix modeling (back from the dead!). Better CRM hygiene. A new account-based platform.

Why is such a hotly debated and discussed topic not being funded? Some say it was always an impossible dream. Others say it never passed the confidence test of the c-suite and board room. They’re probably both right.

The product was always a fiction

Multi-touch attribution was sold as a measurement category. What it actually delivered was confidence — and even that turned out to be borrowed.

The promise was simple enough. Wire up enough sources, weight the right touchpoints, let the algorithm run, and you’d know which of your marketing efforts deserved credit. The CFO would get a defensible answer. Marketing would get a defensible budget. Sales would stop arguing about which leads were “real.”

None of that happened. Not at any company I know. Not even the ones that paid the most for it.

We all know this but rarely say it in public: nobody believed the numbers.

The dashboards were defensive theater — useful for board decks, useless for real decisions. CMOs who deployed attribution platforms still made budget calls based on instinct, sales feedback and quarterly pipeline. The platform sat there confirming whatever the team already wanted to believe.

The deeper problem was that the underlying premise of multi-touch attribution doesn’t survive contact with how B2B buying actually happens.

Real B2B deals close because a buying committee of six to ten (plus) people did their own research over three to nine months — and somebody on that committee remembered a podcast, a peer recommendation, a competitor comparison nobody tracked or a webinar from eighteen months ago. The salesperson built trust over five conversations, two of which weren’t logged.

You can’t pixel-track most of that. You can’t weight it. You can’t stitch it together with UTM parameters and a CRM API. The data the attribution model needed was never available, and the data it had wasn’t load-bearing. Mostly because it didn’t exist and was impossible to capture at scale.

In other words, the attribution category sold marketers a solution that depended on visibility we don’t have and (candidly) never will.

Why CFOs finally noticed

These issues have been there since the beginning. So why did it take so long for the category to collapse?

For a long enough time, “we have attribution” was good enough cover. The dashboards looked impressive. The detail felt sound. CFOs believed what marketing was telling them....until they didn’t.

Then a few things changed at once.

Cookie deprecation kept biting. iOS privacy updates cut off mobile signals. GDPR and the state-by-state privacy laws (still expanding) made server-side tracking expensive and legally fraught. LinkedIn closed off most of its data. Reddit, TikTok and the dark-social channels where actual B2B buying conversations happen don’t show up in any attribution model worth the name.

So the input signal got worse over time while the marketing math got harder to defend. CFOs started asking what the platform was actually telling them — and the honest answer was “less than we paid for last year, and probably less still next year.”

A category whose accuracy is degrading does not survive a budget review, let alone a “should we buy this or that” trade-off.

Then add the AI impact.

Modern revenue analytics and pipeline tools — Common Room, Default and the new generation of agent-native platforms — bundle measurement into broader workflows the marketer uses every day. The stand-alone attribution platforms now had to compete not against each other but against a tool that does measurement plus something the marketer needs daily. That’s a fight any dedicated category usually can’t win.

What replaced attribution

Marketing mix modeling — old-school regression on aggregate data — quietly came back into fashion. It doesn’t tell you which lead came from which campaign. It tells you, at the portfolio level, which channels are pulling weight. CMOs who got past their pixel-tracking guilt found out MMM is more useful for the decisions they actually make on a day to day basis to increase campaign effectiveness velocity.

Then a different form of pipeline analytics took over the dashboard real estate. What CMOs really wanted was confidence in the funnel — coverage ratios, stage conversion, velocity by segment, win rates by source. Several tools now answer those questions without pretending to know which of nine touchpoints “caused” the deal.

Qualitative measurement made a comeback. The marketing leaders who have found an equilibrium on this are now running structured win/loss interviews and asking buyers directly: “How did you find us? Who else did you consider? What changed your mind?” That data is messier and slower than a dashboard. It’s also (finally) honest and both rational and believable to your CFO.

And the AI piece also changes the category math entirely. Agents and copilots can synthesize messy signals across a pipeline review faster than any single attribution tool. Many marketing teams will probably stop buying a measurement product altogether and start asking their copilot to look at the data they already have. The job-to-be-done that attribution platforms were hired for is being absorbed into the broader stack — and into the workflow itself.

The conversation will keep outpacing the spend

Attribution will remain a high-traffic topic on LinkedIn for at least a couple more years. It’s a familiar argument with familiar talking points and reliably good engagement. The “first-touch versus last-touch” thread is the B2B marketing equivalent of “tabs versus spaces” — comfort food for an audience that wants to feel technical without committing to a position.

But the spend (and the category) are already gone.

Nobody is launching a new dedicated attribution platform in 2026 with a serious chance of building a sustainable business. Investors aren’t writing those checks, and buyers aren’t requesting demos either. The vendors who saw the curve early — HockeyStack, Full Circle Insights, Mperativ, NavigateIQ — have already moved, pivoted or exited.

If you’re a marketing leader still trying to solve attribution as a discrete problem, the work and conversation have moved on.

What’s worth your real money now: pipeline analytics that show coverage and velocity, mix modeling that shows portfolio-level lift, customer interviews and AI-fueled insights that show real buyer behavior and the tooling that ties them together.

AI acceptance, experimentation and adoption was last year’s focus for leading marketing teams. As those teams widen and deepen their use of AI in 2026, the “token economy” is making things far trickier.

AI broke marketing’s cost model

For the last decade, marketing operated on a fixed-cost SaaS foundation. You paid per seat, per month, per platform. Budgets were predictable because spend was predictable. You could forecast a year out.

Generative AI looked like more of the same at first. Twenty bucks a month for ChatGPT Plus. Maybe a hundred for the team plan. Cheaper than a single ABM platform. The “AI line item” looked like a rounding error.

That math was never meant to scale.

Bigger AI bills are just the start of the story. Recent benchmarks suggest the median mid-market marketing team’s monthly AI spend grew from roughly $1,200/mo in Q1 2025 to $3,400/mo in Q1 2026 — nearly tripling in a year. Enterprise marketing organizations are now budgeting $24,000 to $48,000 per month on AI-specific line items, and 63% of enterprise CMOs report a dedicated line for “agent infrastructure” that didn’t exist twelve months ago.

The dollars are getting not just bigger but more variable and harder to predict.

Tokens are not a fixed cost. They’re a variable cost that scales with usage, and usage scales faster than pricing falls. Claude even promotes cheaper token use during non-peak hours (remember free nights and weekend cell service anyone?).

Every model release pushes capability up; every team that adopts agentic workflows pushes consumption up; every sophisticated user discovers ways to burn ten times what they did a month ago.

The industry’s pricing has actually been improving — Anthropic eliminated long-context surcharges in March, and Opus 4.6’s headline price held at $5/$25 per million input/output tokens. But pricing per token going down 20% while usage goes up 200% is not a savings story. It’s a forecasting nightmare.

Token usage will likely accelerate faster than token pricing falls. AI can replace some fixed cost, but the variable cost that replaces it is harder to predict and harder to manage.

This is what happens when a technology moves from novelty to table stakes and infrastructure.

What it actually looks like inside a marketing team

One of the more honest takes on the new cost reality came from a CMO I work with closely. His finance team is rebuilding their budget models for AI. The easy math would be: AI makes someone 2x more productive, cut a role, spend that on tokens. That’s not what they’re seeing. They’re getting more productivity out of the same people, but it’s costing more money and it’s hard to predict even near-future expenses.

The “conventional” AI ROI story — replace a role with a license — is not what the operators are reporting. The harder, uglier, real story is that the same humans are doing more, but the marginal cost of each unit of work is now genuinely variable and the team’s monthly spend is genuinely lumpy.

Back to my CMO friend. His engineers run on Claude Teams Premium ($100/user/mo) plus extra usage caps. His own account has unlimited extra usage — and he still spends an extra $400-$600 per month beyond the base license. When his power users hit limits mid-day, he gets pings asking for more credits before a 2pm call.

This is not a “$20/mo per seat” story anymore. That was and still is a loss leader for active users with the expectation that they’ll upgrade quickly.

My own experience mirrors this. I went from a Claude Pro account to a 20x Max account at $200/month in one weekend.

There is no “one model”, at least not yet.

One CMO has built every internal tool he uses for less than $250 in total compute spend to date — “I cost a lot more than that.” Another hit sticker shock when an early misconfigured agent burned through API credit faster than expected. A third committed to a $100/month plan specifically because the basic tier was running her out of tokens by mid-morning.

The variances are real and unlikely to go away, and those who don’t model it carefully (and soon) based on their unique GTM motions and needs are the ones who get surprised in Q3 and beyond.

Why your CFO relationship is even more important now

Your CFO wants predictability and zero surprises. Variable token costs are the opposite of that.

Industry survey data suggests only about 20% of CMO-CFO relationships are “truly collaborative.” The other 80% live in a state of managed tension.

Tokens force the issue. You cannot defend a token forecast to a finance team using last year’s marketing-budget vocabulary. You have to learn theirs.

That’s why a recent CMO Coffee Talk community discussion on “CFO Perspectives & Financial Literacy for CMOs” drew the biggest chat thread I’ve seen on a non-AI topic in two years. The agenda items marketing leaders showed up to discuss:

• The emerging Rule of 60 as the new SaaS performance benchmark.

• Marketing budget benchmarks by stage of company maturity.

• Equity and dilution mechanics — why marketing investments compete with hiring, M&A and capital structure decisions.

• AI token cost management as a CFO-side line item.

• How to build a stronger CMO-CFO operating partnership.

Five years ago, that’s a finance bootcamp. In 2026, it’s a CMO career development outline.

The Rule of 60 deserves its own paragraph relative to AI. The old SaaS benchmark — Rule of 40, where revenue growth + free cash flow margin should sum to 40% — has been quietly replaced by the Rule of 60 in the AI-enabled era. Public markets are now rewarding companies that hit 60% combined growth and FCF margin with 2-3x valuation premiums. The reason: AI has moved the efficiency frontier. Companies that deploy AI well are expected to grow and generate cash at rates that weren’t structurally possible before.

For CMOs, that means two things. One, your CEO and CFO are running a different playbook than last year, with a tighter efficiency mandate. Two, every dollar of marketing spend now gets weighed against a higher bar — the spend has to drive growth and show up favorably in operational efficiency.

This is another reason why Forrester is now recommending CMOs reallocate at least 15% of content and digital spend to AI search visibility in 2026 budget plans, and why the Gartner CMO Spend Survey shows median B2B marketing budgets at 9.1% of revenue with software companies at 11.4%. Budgets may be rising again but the scrutiny on every line is moving up faster.

New risks that come with the new model

Variable cost is the macro shift. But the operational risks underneath are arguably more dangerous because they’re less visible until they bite back.

Risk #1: Executive AI on dirty data. This is the one that’s costing CMOs jobs as I write this. Recently I heard a story from a marketing leader whose CEO had “discovered” Claude — a couple months in, just enthusiastic enough to be dangerous. He ran a report against the company’s CRM, walked into the QBR convinced he understood why Q1 had struggled, and brought receipts.

The CMO went through his sixteen “findings” item by item. On every one, Claude had pulled the wrong CRM object, missed stage-conversion signals, or skipped the meeting notes attached to the opportunity. The underlying CRM was a mess, and AI doesn’t fix that — it amplifies it.

Her win was a single message back from the CEO: “You were right, Claude was wrong.”

RevOps hygiene, data lineage and the ability to defend the numbers against an executive AI tool are now CMO-level concerns.

Risk #2: Compute supply chain. A friend raised this bluntly recently: are marketing leaders running advanced agentic workflows actually planning for compute shortages that could lead to rate-limiting or throttling? One CMO’s answer: his team is prepared to move to Amazon Bedrock if necessary. Others are stress-testing local LLMs (DeepSeek-R1, Llama 4, Mistral, Gemma) as fallbacks. This is infrastructure resilience planning at the marketing-team level, which would have sounded absurd two years ago.

Risk #3: Agent governance. Agent-assisted, human posts? Fine. Agent-written, agent-posts? Problematic. The fact that we’re debating this at all in 2026 means marketing leaders have to make policy decisions about disclosure, autonomy and authenticity at the speed the platforms are evolving.

Risk #4: The cost of under-investing is now real. This is the inverse of the spend-discipline argument but worth calling out. The teams that hesitated on AI in 2025 are now structurally behind in 2026 — not because of the tools, but because their workflows are still designed for humans-only execution. Closing that gap takes time, not money. Wait another six months and the gap widens.

Six recommendations for marketing leaders right now

If your 2026 plan is still anchored on a fixed-cost AI line item and a “do more with less” narrative, you have work to do. Here’s what I’d prioritize, in order:

1. Build an AI cost model — separate from your SaaS line. Stop bundling AI spend with martech subscriptions. Break out token spend by team, by use case, by model. Forecast monthly using a rolling 90-day average and an explicit growth assumption. Recent Forrester planning data suggests budgeting $13,500 to $50,000 per month for an enterprise marketing team is now defensible; mid-market teams should plan for $3,000-$5,000 minimum and trending up. Whatever number you pick, show your work.

2. Open the CFO conversation early — before tokens become a surprise. The worst version of this conversation is the one your CFO starts in September because she just spotted a $40K month and didn’t know it was coming. The best version is the one you start in May, with a model, an assumption set and a recommended governance approach. Use the language she uses: variable cost, unit economics, operational efficiency, Rule of 60.

3. Establish observability and per-user governance now. You cannot defend variable spend you cannot see. Implement extra-usage caps by user role (a pattern I’ve seen working: $50/mo for engineers, custom for executives, locked for everyone else). Use enterprise tooling to track per-user, per-tool consumption. Treat token observability the way RevOps treats pipeline observability — you can’t fix what you can’t see.

4. Plan infrastructure resilience. At minimum, know what your Bedrock or Azure-hosted fallback looks like for your top three AI workflows if your primary provider rate-limits you for a week. Local LLMs are a real consideration for sensitive data and high-volume workflows, even if they’re not your default. This is no longer paranoid — it’s planning.

5. Defend your data lineage like your career depends on it. Because it might. If your CEO has API access to your CRM through Claude or ChatGPT — and many do — you need to know exactly what they’ll see, what they’ll misinterpret and what guardrails you’ve put in place. Build a “Claude-readable” version of your monthly business review with clean source data and explicit definitions. Get ahead of the executive AI question before it becomes the executive AI accusation.

6. Reframe productivity metrics around output, not headcount. The old metric — “marketing FTEs per $1M revenue” — is now a bit misleading. The new metrics are output-per-token, output-per-workflow and revenue-per-AI-dollar. None of these are perfectly defined yet. Pick one, instrument it this quarter and report on it next quarter. The first CMO in your category to publish a credible AI-productivity number will own the narrative.

The bigger picture

In 2025, we were asking what AI could do. In 2026, we’re asking what AI is doing to our companies, our jobs, our budgets and our relationships with the rest of the C-suite.

The marketing leaders still treating this as a tool conversation will spend 2026 evaluating products. Make it a cost-model conversation, a work-design conversation and a CFO-partnership conversation — that’s how you redefine what marketing leadership looks like for the next decade.

This past week I was in Phoenix for Forrester B2B Summit.

Tuesday afternoon between Sales Pipeline Radio episode recordings I traded notes with a friend: what we’re seeing, what’s working, what’s broken in her stack — and she stops mid-sentence and says: “You know what’s actually different this year? It’s not the keynotes. It’s the rooms.”

She’s right. And it’s not just this event.

The big main-stage moments were still relevant. The headline theme (”GTM singularity”) leaned hard into the AI conversation everyone is having.

But the energy this year wasn’t so much on the marketplace floor as it was in the workshops and hallways. A new immersive simulation Forrester built for the first time, where you walk into a mocked-up B2B buying environment and watch the dynamics play out in real time (seriously it was pretty cool). The conversations between sessions that ran long because nobody wanted to leave them.

Rooms set up with rounds instead of rows. Sessions that were less lecture and more group therapy/mastermind.

A friend put it best later that night at dinner: “I don’t fly for the content anymore. The content I can get on YouTube next week. I fly for the room.”

It’s always been about the room, not the stage

For most of my career, B2B conferences have been broadcast media with a hotel attached. You fly to a city, sit in rows facing a stage, listen to a vendor parade for three days and write the debrief on the plane home. The learning happened in the speaker notes you could’ve downloaded from anywhere.

Especially post-COVID, that model no longer works.

Look at the format choices smart events are making right now:

Pavilion’s CRO Summit at the Denver Art Museum bills itself as “no stages, no scripts.” Curated execs only. Peer dinners. The center of gravity is the small group, not the main stage.

B2BMX runs 50+ breakouts and a “Marketplace” hub built specifically for peer engagement, not just vendor demos. The pitch is the community, with the curated collective of CMOs, VPs and Directors as the actual product.

Forrester itself just added that immersive buying-environment simulation in 2026 and expanded its workshop and roundtable footprint.

GTM2026 structured the entire three days around hands-on workshops and peer networking. The marketing line is “no-pitch.”

The pattern is consistent across the category: the unit of value has moved from the stage to the table, from keynotes to conversations, from massive ballroom presentations to intimate small-group discoveries.

There’s research behind why this works too. Strategic seating arrangements can lift attendee interaction by 30%. Round-table layouts produce dramatically more cross-talk than rows facing forward.

Schools have been onto this for years. One classroom study found that simply moving students from the front of a row-style room to the back dropped their participation by more than 60%. When you put eight people around a table with a real question to chew on, you get a fundamentally different output than when 800 people stare at a stage.

The data also says we’re hungry for this and not getting enough of it.

The networking gap that won’t close on its own

77% of B2B event attendees say in-person conferences are still the best format for networking. 86% of organizations report positive ROI from events. The case for showing up is strong and the data is on its side.

Now flip the page. Only 15% of event organizers rate their own networking opportunities as “very effective,” down sharply from 2025. The other 60% rate them as merely “somewhat effective.”

We all know what that gap means. Attendees are showing up for the connection, and too many events still aren’t engineered to deliver it. The agenda still treats networking as the thing that happens between the sessions. The “real” content is on the stage. But the hallway conversation is what people actually fly across the country for, and somehow it’s too often still treated as a logistical afterthought.

The best events are now correcting this on purpose. The gap between the ones that do and the ones that don’t will be obvious by the end of 2026.

What in-person can do that Zoom, YouTube and Slack still can’t

I’m in a lot of digital communities, host a weekly virtual gathering of marketing leaders, keep up to date on numerous Slack groups with people I trust. The volume of asynchronous and synchronous-but-virtual touchpoints in a typical week is astronomical compared to ten years ago.

And yet I still got on a plane to Phoenix this week.

The reason was not the content. The content is not better in person — candidly it’s often worse in person, because the production value of a polished YouTube keynote beats most live keynotes I’ve sat through in the last five years. If your goal is “absorb information,” your laptop is more efficient than another conference center and hotel room.

The reason I get on the plane is that an in-person event creates something a digital channel still can’t replicate: a temporary, focused and highly engaged in-person community.

A few hundred or a few thousand people, in a city they don’t live in, with no other agenda for three days and the same problem set on their minds.

You sit next to a stranger at lunch, ask what they’re working on and discover they solved last quarter the thing you’re stuck on this quarter. You bump into someone in the elevator who’s been quietly testing the exact tool you’re evaluating. You end up at a dinner where the most useful sentence of your year gets said over the second glass of wine (or who are we kidding, whiskey).

That happens because of physical proximity and shared time, neither of which Zoom can manufacture. It’s the same dynamic as a great offsite. The same dynamic as a great dinner.

The best events are leaning into this on purpose: cutting broadcast time, doubling structured small-group time, treating the hallway as primary instead of residual.

What this means for how you plan and attend

If you’re planning an event in 2026 — your own customer summit, an executive dinner, a partner roundtable or a regional advisory board — the format choices are doing more work than your speaker lineup. A few practical takeaways:

1. Default to rounds, not rows. If the room can accommodate it, do it. Even for sessions you’d normally call “presentations.” Worth it even for the small talk before or after the presentation. The participation data is overwhelming and your attendees can feel the difference within five minutes of walking in.

2. Cut the broadcast portion of the agenda by at least a third. Replace it with structured small-group time. Workshops. Roundtables. Curated dinners. Topic-tagged hallway sessions. The data says attendees value this more, and your own organizers are telling you the existing networking design is not working.

3. Treat the hallway as content. Stop apologizing for the time between sessions. Lengthen it. Stage it. Give people a reason to stay in the room, a question to bring to the next person they meet, a way to find each other on purpose. The hallway is where the actual ROI gets generated.

4. Curate ruthlessly. “Curated, executive-only” is not elitism — it’s the format that delivers the value people are showing up for. Big general-admission conferences can still serve a purpose, but they are not winning the future of this category and the smaller, sharper-curated events already know it.

If you’re attending an event in 2026, the inverse applies. Measure the trip by the conversations, not the keynote. Pick events where the agenda is engineered for the room rather than the stage. Show up to the workshops. Skip the keynote you can watch on YouTube next week.

A question for event sponsors

The marketers I respect most are getting more selective about which events they show up to and which they sponsor. To the event sponsors reading this: what kind of community is your event actually creating, and is your audience inside it?

“How many leads will we get” is the old question for a model that’s losing. A broadcast event with the seats filled by your ICP is competing with YouTube for those leads, and YouTube is going to win. A temporary in-person community for 72 hours where your customers are inside — engaging with each other, where contact with each other IS the content — is worth a six-figure check and probably more, because there isn’t a digital substitute that does the same job.

I’ll hopefully remember key points from a full-stage keynote, but ask me for the week’s highlights and I’ll more likely cite three or four conversations, two introductions and a dinner Tuesday night where someone said the thing I needed to hear about a problem I’ve been stuck on for months.

That’s the bar your events (those you produce as well as those you sponsor and attend) have to clear now. I’ll see you out there.