B2B Marketing in the Machine Age | Part 2 of 4

This is the second post in B2B Marketing in the Machine Age, a four-part series on the state and future of B2B marketing in the agentic AI era. Post 1, “How B2B Marketing Lost Its Way,” set the historical stage: the decisions, incentives, and technology bets that brought the function to where it stands today. This post, “B2B Marketing Is Broken. Brand Is Why. And Brand Is the Fix,” examines why brand and positioning have become the most underdeveloped and most consequential capabilities in B2B marketing, and why agentic AI makes that gap more urgent, not less. Post 3 covers The Org & Tech. Post 4 covers The Career. Each post stands alone. Together, they make a case the industry needs to hear, and act on.

I’ve spent more than three decades running B2B companies. CEO, publisher, operator, board member. I’ve sat in thousands of marketing reviews. I’ve approved — and questioned — tens of millions of dollars in marketing investments. I’ve watched the function evolve through the rise of digital, the explosion of martech, the era of content marketing, the ABM wave, and now the agentic AI inflection.

Here is what I’ve concluded:

B2B marketing has a brand problem. It has always had a brand problem. And almost nobody wants to say it out loud.

Instead, we talk about tech stacks, attribution models, MQL targets, and AI agents. We fund demand generation and starve brand building. We optimize what’s measurable and neglect what matters. We buy tools that promise differentiation and produce sameness at scale.

The result is a function in crisis, not despite fifteen years of investment, but partly because of it.

And the timing couldn’t be worse. Because the rise of agentic AI is about to make a strong brand the most important competitive asset a B2B company can have. While most of the industry is running the wrong direction.

Let me explain what I mean, and what to do about it.

The Biggest Problem in B2B Marketing Has Nothing to Do with Technology

The Renegade Marketing State of B2B Marketing study: one of the most rigorous surveys of senior marketing leaders published in the past year, asked CMOs across industries and company sizes to name their biggest challenge.

The answer wasn’t AI adoption. It wasn’t martech complexity. It wasn’t budget pressure, though all of those surfaced.

The number one challenge, by a significant margin, was differentiation.

CMOs reported struggling to position their companies in a way that breaks through. Some were dealing with crowded categories where everyone sounds the same. Others were post-acquisition, trying to reestablish clarity. Several acknowledged bluntly that their product wasn’t differentiated, and their challenge was to create clarity where there wasn’t any. A few admitted they weren’t sure what made their company genuinely better, different, or worth choosing.

Less than 10% of B2B companies report fully consistent branding. Read that again.

Positioning is the strategic work of defining exactly what you do, who you do it for, why it matters, and why you specifically. It is the most misunderstood, underinvested area in B2B marketing. And yet it is the foundation of everything. Without a compelling position, nothing scales. Not demand generation. Not content. Not AI. Not pipeline.

This isn’t a new problem. It’s a structural one. And it’s getting worse.

How B2B Marketing Lost Its Brand

The performance marketing era didn’t just change how B2B companies spent money. It changed what the function believed was valuable, and therefore what it built, hired, and measured.

When digital marketing made it possible to track clicks, conversions, and attribution with precision, something seductive happened: what could be measured became what mattered. CPL. MQL volume. Pipeline attribution. Conversion rates. Cost per acquisition.

These metrics aren’t wrong. But they created a gravitational pull toward the measurable and away from the durable. Brand: which operates on longer time horizons, through harder-to-attribute channels, and via mechanisms that resist precise measurement, got systematically defunded.

The rationalization was straightforward: “We can’t prove brand ROI, so we’ll invest in what we can prove.” Reasonable enough, individually. Catastrophic in aggregate. B2B companies spent fifteen years optimizing what was measurable and starving what wasn’t. Brand is the main casualty.

Then came the martech explosion. Fifteen thousand tools — and counting — promised to make up the difference. Better targeting, better personalization, better segmentation. The logic: if we can reach exactly the right person at exactly the right moment with exactly the right message, we don’t need a strong brand. We just need a good enough product and a capable enough tool.

Except that’s not how buyers work.

The martech stack can’t fix an unclear position. All better targeting does is deliver the wrong message more precisely. All better personalization does is tailor the confusion to the individual. The tools amplified the dysfunction rather than resolving it.

The result: B2B marketing became technically sophisticated and strategically hollow. The sector produces more content, more campaigns, more touches, and more sameness, than at any point in its history.

Why This Is Now an Existential Issue

I want to be direct about the stakes, because I think they’re being underestimated.

The agentic AI transition is not a productivity story for B2B marketing. It is a structural disruption that will permanently separate companies that have strong brand and positioning from those that don’t. Here is why.

94% of B2B buyers now use large language models during their purchase journey.

That’s not a trend. That’s a behavioral norm. When your potential buyer has a challenge to solve or a category to evaluate, they are increasingly beginning that research with ChatGPT, Claude, Perplexity, or Gemini, not with a Google search, not with a sales call, and certainly not with your outbound sequence.

When that buyer asks an AI system which vendors to evaluate in your category, your brand either appears or it doesn’t. There is no middle ground. There is no “page two.” There is no sponsored slot. You’re in the answer or you’re not in the deal.

And the brands that appear in those answers are the ones with established credibility, consistent presence in the content ecosystem, and genuine category authority. They are, overwhelmingly, the brands that invested in brand.

Gartner calls this the end of channel-based marketing as we know it. I’d call it the moment when fifteen years of deferred brand investment comes due, all at once.

This isn’t hypothetical. The buying journey is already changing shape in ways most B2B marketing organizations haven’t adjusted for. Outbound reply rates have dropped from 6.8% to 5.8% in two years, and they’re not going back up. AI-generated content is flooding every channel, compressing the signal-to-noise ratio toward zero. The buyer is further along in the journey, and harder to reach through traditional means, by the time they want human contact.

In that environment, the brand that comes to mind before the buyer opens a browser window is the one that wins. That’s what brand building has always been for. The AI era makes it more true, not less.

The Branding Crisis Has a Villain. But It’s Not Who You Think

It’s tempting to blame the CMO. The tenure data suggests the market is doing exactly that: average CMO tenure at Fortune 500 companies fell to 3.9 years, more than 20% of Fortune 500 companies changed marketing leadership in the past year, and the CMO title is disappearing from B2B companies faster than it’s being created.

But the brand problem in B2B isn’t primarily a CMO failure. It’s a CEO failure of a specific kind: confusing marketing execution with marketing strategy.

When the CEO’s primary conversation with the CMO is about MQL volume, pipeline attribution, and campaign performance — not about positioning, differentiation, and market presence — the function self-selects toward tactical execution. It hires for campaign management. It measures what it’s asked to measure. It builds what it’s rewarded for building.

The CEO who hasn’t required a clear, differentiated positioning statement from their marketing leader hasn’t done their job. Because positioning is a business decision before it’s a marketing decision. It requires choices about what you will and won’t be. It requires saying no to market segments that would muddy your position. It requires conviction about what winning looks like, and the discipline to say so clearly.

Most B2B CEOs haven’t had that conversation. Most B2B CMOs haven’t forced it. The result is that the foundational strategic work, the work everything else depends on, never gets done. And everyone wonders why the campaigns aren’t breaking through.

What Strong Brand and Clear Positioning Actually Delivers

I want to make this practical, because “invest in brand” without specifics is too easy to dismiss.

Here is what clear, differentiated positioning actually does for a B2B company:

* It makes every other marketing investment more efficient. When your position is clear, content has a point of view. Campaigns have a claim to make. Sales has something to say that isn’t just a feature list. The tools in your stack have something coherent to automate and personalize. Positioning is the input that makes everything else compound.

* It is the prerequisite for AI discoverability. LLMs surface brands that have a clear identity in the content ecosystem. Consistent point of view, recognizable voice, specific category authority. These are the signals AI systems learn from. A brand with a muddy position produces muddy content, which produces muddy LLM citations, which produces invisibility in AI-generated buyer research.

* It is the filter for your technology decisions. If you know exactly who you’re for, what problem you solve, and why you solve it differently, your martech choices become dramatically simpler. You buy for your actual go-to-market motion, not for the full feature catalog of the category. This is one reason why companies with clearer strategies tend to run leaner, more effective stacks.

* It is the human signal in an AI-generated world. As AI content floods every channel, authentic point of view becomes the primary differentiator. Buyers don’t just evaluate products. They evaluate companies. They want to know who you are, what you believe, and whether they’d want to work with you. Brand — real brand, not simply logo and color palette — is how you answer that question before the first meeting.

* It is the foundation of trust in a buyer journey that is increasingly pre-human. By the time your buyer contacts your sales team, they’ve already formed an impression. That impression came from what they read, what they heard from peers, what showed up when they asked an AI. If your brand has been consistently present, credibly positioned, and genuinely differentiated throughout that pre-contact journey, you start the conversation with an enormous advantage. If it hasn’t, you’re starting from scratch in a conversation the other company has already been having.

In a World Flooded with AI Content, Human Credibility Is the Scarcest Signal

I’ve written before about the emerging importance of credible operator voices in B2B. Practitioners with genuine field experience who can speak to how products and strategies perform in the real world. I believe this is one of the most underappreciated dynamics in B2B marketing right now, and the agentic AI era is going to accelerate it.

Here’s why: in a world where AI can generate unlimited amounts of competent, technically correct, on-brand content, the scarcest signal is human credibility. The peer who says “we implemented this and here’s what actually happened” is worth more than any campaign the vendor runs.

Buyers know this. Gartner’s research found that 78% of consumers say explicit labeling of AI-generated content is a critical factor in maintaining trust. That number will only grow as AI-generated content becomes more prevalent and buyers become more sophisticated about identifying it.

B2B buying is inherently a trust exercise. Large purchases, long sales cycles, significant switching costs, career risk for the buyer. In that environment, peer validation and operator credibility are the trust infrastructure. LLMs are already trained to cite practitioner-authored content; experience-based, specific, verifiably credentialed, over generic vendor content.

What this means in practice: the B2B companies that build systematic programs around credible operator voices, customers, recognized practitioners, industry operators, are building brand assets that AI will cite, buyers will trust, and competitors cannot easily replicate. This is not an influencer marketing program. It is a brand credibility infrastructure program. And almost nobody in B2B is doing it with appropriate strategic intent.

One More Dynamic, And It Involves the Agencies

No honest account of the B2B brand problem is complete without acknowledging the role of ad agencies. Not as villains. The people in those agencies are often talented, experienced, and genuinely committed to their clients. But the agency model itself is under severe structural pressure, and that pressure is showing up in ways that have not helped the brand problem.

I’ve been on both sides of this. The opening of this post describes my experience as a buyer of marketing programs. What it doesn't mention is the sell side. As a media and marketing services CEO, I've produced hundreds of thousands of marketing programs: content, events, digital, demand generation. Sold to B2B marketers across virtually every major industry. I know exactly how those programs get packaged, priced, and pitched. I know what actually performs and what gets dressed up to look like it does. Both perspectives inform what follows.

The structural reality is stark. The Basis 2026 Advertising Agency Report found that 87.3% of agency professionals believe the traditional agency model is either broken today or will be within three to five years. Among VP-level and above, that figure climbs to 91.5%. These are agency insiders reaching that verdict about their own industry. Fee compression, the shift to project-based work, the rise of in-house teams, and now the AI displacement of production work have all combined to put the traditional agency business model under sustained pressure. Forrester’s 2025 B2B Brand and Communications Survey documents the result: a 15-point gap between what B2B marketing leaders value from their agency partners and what those agencies actually deliver, widest in the specialized strategic skills category that should be the agency’s core advantage.

Over my career I’ve worked with genuinely talented, high-impact agencies, creative and brand visionaries who put their clients on the map, built category-defining brands, and delivered extraordinary business value. That work is real and it deserves recognition. Which is why what has happened to the agency model over the past fifteen years is worth naming honestly, and with some empathy for the position the industry finds itself in.

The same dynamics outlined in Post 1 of this series: the shift to performance marketing, the explosion of martech, the compression of brand investment into measurable digital execution, have put B2B agencies in a genuinely difficult position. The business shifted underneath them. Clients stopped asking for brand and creative vision and started asking for digital marketing services, demand generation programs, and performance metrics. Agencies followed the money, as any rational business would. The result: the industry has drifted from creative and brand visionaries toward digital marketing services companies. The capabilities that made the best agencies extraordinary — strategic positioning, creative courage, brand architecture — became less billable, less valued, and less central to what agencies were being hired to do.

When a business model is under that kind of pressure, the response is rarely to double down on the hard, slow, often undervalued work of brand strategy. It is to emphasize what is visible, billable, and easy to point to in a pitch. Which explains what the B2B agency world has become: an industry that has confused winning awards with winning clients.

Open LinkedIn on any given day and you will find a press release dressed as a post. A B2B agency has won an award. The campaign was transformative. The results were industry-defining. The trophy, bestowed by an awards body that virtually no client has ever heard of, is prominently displayed. Scroll further and another agency has won Agency of the Year. Then another. Then another. There are enough “Agency of the Year” designations distributed across enough award programs — the ANA B2 Awards, the B2B Marketing Awards, the Elevation Awards, the Ignite Awards, the Global ACE Awards, and a dozen more — that winning one has become less a mark of distinction than a standard line item in the agency marketing budget.

One agency website I reviewed catalogued 33 award wins in a single year. Nine of them were Agency of the Year. Nine. At some point the designation stops meaning anything at all, and the industry’s willingness to keep issuing and celebrating it says everything about how hard genuine differentiation has become.

In 35 years in this industry — as a buyer of agency services, as a seller of marketing programs, as a CEO who has hired and evaluated dozens of agency relationships — I have never once hired an agency because it was named Agency of the Year. Not once.

This is a symptom of structural disruption, not a character flaw. When margins compress and differentiation is genuinely difficult, peer validation fills the gap. But the result is an industry spending significant energy on recognition its clients don’t value, while the strategic work clients most need: positioning, brand architecture, genuine differentiation, gets systematically underinvested.

Here is the genuine irony. The agentic AI era is precisely the moment when a great agency partner could be most valuable to a B2B company. As AI automates production and execution at scale, the premium on strategic thinking goes up sharply. The positioning work, brand architecture, and creative courage that require deep category expertise and genuine client partnership, is exactly what AI cannot replicate. The agencies that recognize this shift and restructure around it have a compelling future. The ones that continue to compete on production scale and award recognition are running a model that AI is actively dismantling.

This dynamic deserves its own full exploration, including what the agency model needs to become in the agentic AI era and what B2B companies should be demanding from their partners right now. That post is coming. For now the point that belongs here is this: the brand problem in B2B has multiple structural causes, and the disruption of the agency model is one of them. The solution, ironically, runs straight through the kind of strategic agency partnership the industry has largely been moving away from.

The questions worth asking of any agency relationship right now: Where do you think our positioning is weak, and what would it take to fix it? What does our brand stand for that no competitor could credibly claim? And when did you last push back on a client brief because it was the wrong strategic move?

If the answers are compelling, invest in that relationship. The market needs it. If they aren’t, the trophy case is already telling you what you need to know.

What Marketing Leaders Should Do Now

The strategic case is one thing. The operational question is what to do about it. Here are the moves that matter, in order.

* First, before everything else: define your position. This is not a marketing project. It is a business leadership project. Get your CEO, your marketing leader, and your product or commercial leadership in a room with a single deliverable: one sentence that says who you are, who you’re for, what you solve, and why you’re different. Not a tagline. Not a mission statement. A market position. If your leadership team can’t agree on that sentence, stop all other marketing investment until they can. Everything else is built on sand.

* Second, audit what your brand actually says today. Prompt ChatGPT, Claude, and Perplexity with the questions your best buyers ask when researching your category. Read what comes back. Look for where you appear and where you don’t. Read your own website with fresh eyes. Listen to a few sales calls. Ask a customer how they’d describe your company to a peer. The gap between what you believe your brand says and what the market actually receives is almost always larger than leadership expects. Know the gap before you try to close it.

* Third, restore the brand investment ratio. Les Binet and Peter Field's landmark research, the most rigorous analysis of marketing effectiveness ever conducted, established that brands dramatically outperform over time when they balance brand building with sales activation rather than defaulting to performance-only investment. Their subsequent B2B-specific research with the LinkedIn B2B Institute found the optimal B2B ratio is 46% brand, 54% activation, not because brand matters less in B2B, but because longer sales cycles and larger buying committees require more activation support. The number is less important than the direction. Most B2B companies aren't running anywhere near 46% brand investment. They're running 10%. Or 5%. Or zero. That gap is where the brand problem lives, and where the pipeline problem follows.

* Fourth, build your operator voice program. Identify ten credible practitioners; customers, recognized voices, domain operators, who genuinely understand the problem you solve. Build a structured, compensated program around their voices. The goal is content that AI will cite, buyers will trust, and your sales team will use. Not testimonials. Not sponsored posts. Genuine practitioner perspective on real problems your category exists to solve.

* Fifth, designate someone to own your AI discoverability. Not SEO. Not content production. Specifically: how does your brand appear in AI-generated answers? Who is responsible for that question? What’s the strategy for improving it? This role doesn’t need to be a new hire. It does need to be explicitly owned. In 2027 it will be one of the most important marketing functions in your organization.

The Uncomfortable Conclusion

B2B marketing has a brand problem that predates the AI era and will outlast it, unless leadership decides to treat it as the strategic priority it actually is.

The function has spent fifteen years building execution capability on top of a positioning foundation that was never poured correctly. The tools work. The campaigns run. The metrics get reported. And yet differentiation remains the number one challenge, category after category, company after company, year after year.

Agentic AI is not going to solve that problem. It is going to expose it, at scale, at speed, in buying journeys that happen before your sales team knows the buyer exists.

The companies that will win the next phase of B2B marketing are not the ones with the most sophisticated tech stack, the largest content library, or the most advanced AI deployment. They will be the ones that know exactly what they stand for, can say so clearly, and have built the credibility infrastructure to make buyers believe it.

That work starts with positioning. It has always started with positioning. In the agentic AI era, the cost of not doing it is higher than it has ever been.

Next in the series: B2B Marketing in the Machine Age, Post 3 : "The Stack That Broke Marketing (And the Architecture That Can Fix It)." The martech explosion promised efficiency and delivered complexity. What B2B companies need to build — and tear down — to compete in an agentic AI world.

The views expressed in Uphoff on Media are entirely my own. They don’t represent the opinions of any company I’ve led, any board I’ve sat on, or any investor who’s had the pleasure of debating strategy with me over the years. If something I write here sounds brilliant, I’ll take full credit. If it turns out to be wrong, I was clearly misquoted by myself.

B2B Marketing in the Machine Age | Series Opener

This is the first post in B2B Marketing in the Machine Age, a four-part series on the state and future of B2B marketing in the agentic AI era. This post sets the stage: the decisions, incentives, and technology bets that brought the function to where it stands today. Post 2 examines The Work: specifically why brand and positioning have become the most underdeveloped and most consequential capabilities in B2B marketing, and why agentic AI makes that gap more urgent, not less. Post 3 covers The Career. Post 4 covers The Org & Tech. Each post stands alone. Together, they make a case the industry needs to hear, and act on.

There’s a meeting that happens in most every B2B company each month. You know the one.

The CMO walks in with a deck. Forty, sometimes fifty slides. Multi-touch attribution models. MQL-to-SQL conversion rates. Pipeline influence by channel. Cost per attributed lead. First-touch vs. last-touch comparison. A waterfall chart that took someone three days to build. Another slide to explain the methodology behind the waterfall chart.

The CEO asks: “Is the marketing working?”

The CMO begins on slide four.

Forty-five minutes later, nobody can answer the question. The slides didn't answer it. They replaced it.

Here’s the thing: that meeting — and the weeks of work that produced it — is B2B marketing now. Not the customer insight. Not the brand positioning. Not the creative brief or the campaign strategy or the message architecture. The reporting. The attribution model. The dashboard. The deck.

B2B marketing didn’t just get distracted by measurement. It became measurement. And the cost of that substitution is enormous, and about to get dramatically worse.

How Accountability Became a Substitute for Thinking

The original promise of digital marketing was genuine and valuable: for the first time in the history of advertising, you could actually measure what worked. Print ads in trade publications, direct mail, trade shows, these were acts of faith, educated guesses, and brand conviction all bundled together. Digital changed the equation. Clicks. Opens. Form fills. Trackable conversions. The measurement revolution was real.

But somewhere between the promise and the practice, accountability as a capability became armor.

I've had a front-row seat to this shift across twenty-five years: as CEO of UBM TechWeb, President and CEO of ThomasNet, and CEO of Pipeline360. In every one of those seats, I watched the same pattern play out: as marketing budgets came under pressure, CMOs reached for data as a shield. And the martech industry, never one to miss a revenue opportunity, responded accordingly. Every vendor built attribution features. The category of “marketing analytics” became a multi-billion dollar industry built almost entirely on answering one question: Can we prove the marketing worked?

The measurement infrastructure grew faster than the marketing it was supposed to serve.

By the time companies had full-stack attribution — first touch, last touch, multi-touch, algorithmic, time-decay — most marketing teams were spending more time configuring dashboards and preparing readouts than using the insights those dashboards generated to actually manage their marketing.

This is not a technology failure. It’s a behavioral one. And it has three distinct, compounding traps.

Trap One: Activity Displacement

The attribution process has become the work product.

Building dashboards, pulling reports, modeling multi-touch attribution, preparing presentations, reconciling data discrepancies between platforms, explaining why Salesforce numbers don’t match HubSpot numbers, this is now a significant and growing portion of what B2B marketing teams actually do each week.

It crowds out the upstream thinking. Customer research. Positioning development. Message architecture. Creative strategy. The work that precedes any campaign worth measuring. You end up with teams that are extraordinarily sophisticated at reporting on marketing programs and increasingly thin on the judgment and instinct required to build them.

I’ve seen this dynamic in companies of every size. The marketing operations function — which was supposed to be a support function — gradually becomes the center of gravity for the entire department. The metrics review becomes the primary artifact the team produces. And the actual marketing: the campaign, the message, the creative, the positioning, becomes almost incidental. Something that has to exist in order to have something to measure.

Trap Two: The Defensive Posture

Attribution-first marketing is structurally backward-looking.

The question it always asks is: did we justify the spend? That's not a marketing question. It's a survival question. And it shapes everything downstream.

Creative decisions get made by what’s attributable, not what’s right for the brand. Channel strategy gets optimized for trackable clicks, not for where the buyer actually is. Positioning becomes whatever produced the best CPL last quarter. The standard for a good campaign shifts from “does this change how our best prospects think about us?” to “did this generate enough attributed pipeline to survive the next budget conversation?”

The result is B2B marketing that is reactive, incremental, and profoundly unambitious. And, not coincidentally, B2B marketing that buyers experience as interruptive, formulaic, and irrelevant, because it was built around what’s measurable, not what’s true.

The best marketing I’ve been part of started with a question about the customer: what does this buyer actually need to know, believe, or feel in order to make a decision in our favor? That question is genuinely hard to answer. It requires research, judgment, and creative conviction. It doesn’t produce a dashboard. It produces a brief. And from the brief comes the positioning, the creative, the channel strategy, and — eventually, after the work has been done — the measurement framework.

Attribution-first marketing inverts that sequence. It starts with what’s measurable and works backward. The brief, if it exists at all, is a formality.

Trap Three: Attribution as Job Protection

What people are thinking that doesn’t get said.

Here's what nobody says out loud: the more complicated the attribution machinery gets, the harder it is for the CFO or CEO to challenge it. Multi-touch models. Platform-versus-platform data fights. Footnotes about attribution windows. At some point the complexity itself becomes the point

If the marketing leader can produce forty slides of attribution data that are genuinely difficult to interpret, that require specialized knowledge to interrogate, and that no one in the room can fully refute, the budget is probably safe. Not because the marketing worked, but because the machinery of measurement is impressive enough to shift the burden of proof.

This is not cynicism. It’s a rational response to a broken incentive structure. CMO tenure averages somewhere between eighteen and twenty-four months, shorter than virtually any other C-suite role. That kind of insecurity produces defensive behavior. Attribution complexity is the ultimate defensive tool. You can’t be fired for what can’t be measured, and you can’t easily be challenged on what’s too complex to fully understand.

But the cost of this defense mechanism is that the incentive to simplify: to return to the customer insight, the bold positioning, the campaign that doesn’t require forty slides to explain, is almost entirely absent. Simplicity is vulnerable. Complexity protects. So complexity wins.

What This Has Actually Cost

The damage is visible everywhere you look. B2B creative has gotten safer and more forgettable, because every decision runs through an attribution filter that rewards the familiar over the bold. Positioning has gotten blander, because brand differentiation requires conviction that attribution models don’t measure. The CMO has become a data steward in too many companies rather than a market maker.

The role now rewards students of the dashboard over students of the customer. And the buyers noticed: the B2B marketing experience has grown more formulaic and less relevant in direct proportion to how sophisticated the measurement infrastructure became. That is not a coincidence. When you build marketing programs around what’s trackable rather than what’s true, the buyer feels it.

Agentic AI Doesn’t Fix This. It Exposes It.

Here’s where this stops being a management problem you can solve with better habits or a more enlightened CMO.

Agentic AI is about to fundamentally break the attribution model B2B marketing has built its entire operating logic around, and the industry is largely unprepared for what comes next.

AI agents conducting procurement research don’t fill out forms. They don’t click display ads. They don’t register for webinars, respond to nurture sequences, or generate the trackable behavioral signals that multi-touch attribution models were built to capture. When an AI agent is tasked with evaluating a category of vendors, it conducts its research through channels that are largely invisible to the tools B2B marketers have spent a decade building attribution infrastructure around.

There is no UTM parameter on an AI agent’s recommendation.

The buyer journey AI agents conduct is not a modified version of the human buyer journey. It’s a structurally different process that operates on different signals. Brand reputation. Category authority. Quality of content indexed by AI systems. Customer outcomes data. Third-party validation. These are the inputs an AI agent weights when evaluating vendors. They are not well-captured by MQL counts or pipeline influence percentages.

The predictable response — already visible in corners of the industry — will be to build AI-powered attribution tools designed to track AI-mediated buyer journeys. This is the wrong lesson. It is more measurement infrastructure chasing a buyer who has left the building and gone somewhere the measuring tools can’t follow.

What survives in an agentic world is everything attribution-first marketing has been underinvesting in: genuine brand authority, distinctive positioning, authentic thought leadership, customer success stories that hold up to scrutiny. The very things that can’t be easily tracked are the things that will matter most.

The painful irony: at precisely the moment when B2B marketing needs to pivot back to brand-building, customer insight, and creative conviction, most marketing teams are least equipped to do it, because they’ve spent years optimizing for measurement and letting the upstream marketing muscles atrophy.

Where the Puck Is Going

Three predictions, with conviction:

* Attribution models will get more sophisticated and less useful at the same time. The martech industry will respond to AI disruption the way it always responds to disruption: by building new tools. AI-powered attribution platforms will emerge. They will be genuinely impressive. They will also be measuring a buyer journey that increasingly doesn’t exist in the form those models are designed to capture. The investment in this infrastructure will be substantial. The return will diminish.

* The CMO role will bifurcate. One path leads further into the data and operations direction: essentially a marketing operations function with a C-suite title, focused on stack management, attribution governance, and pipeline reporting. The other path leads back to the original CMO mandate: market intelligence, customer insight, brand positioning, and the creative conviction to build something distinctive. Both will exist. The second path will be harder to fill and significantly more valuable. Companies that understand the difference will hire accordingly.

* Brand will reassert itself as the primary B2B marketing asset Not because brand marketers won a long-running argument, but because it will be the only signal that holds up in an agentic buyer environment. The companies that have been quietly building real brand while everyone else was building dashboards will have a structural advantage that will be very difficult to close quickly. Brand takes time to build and time to move. The time to build it is not after the buyer journey has already shifted.

The Entrepreneur and Investor Opportunity

The dysfunction described in this post is not just a management problem. It is a market opportunity, and some of the most interesting entrepreneurs I’ve been talking to lately are starting to see it clearly.

The premise is straightforward: if a significant portion of B2B marketing’s bandwidth is consumed by attribution data collection, dashboard maintenance, platform reconciliation, and reporting preparation, work that is repetitive, rule-based, and largely mechanical, that work is exactly what agentic AI is built to automate. The opportunity is to take the attribution reporting function off the marketing team’s plate entirely and deliver it as a service, purpose-built for B2B marketing organizations.

Call it Attribution as a Service. The model is not complicated in concept, though it is genuinely hard to execute well: an AI-powered system that pulls data from across the martech stack, reconciles it, applies agreed-upon attribution models, and delivers clean, current reporting on a cadence the marketing team sets, without requiring a marketing operations analyst to spend three days preparing the monthly deck. The CMO gets the information. The team gets their time back.

The investor thesis is real. B2B companies are not going to stop wanting attribution data; the CFO isn’t disappearing, and the budget conversation isn’t going away. But the current model, where skilled marketing professionals spend a disproportionate share of their working hours producing that data, is inefficient in a way that agentic AI can directly address. The companies that build this well will sell into a buyer who is simultaneously cost-pressured, understaffed, and increasingly aware that the way attribution currently works is not working.

A few dimensions of the opportunity worth watching:

The integration layer is the moat. The hard problem in this space is not the reporting interface. It is the data. B2B martech stacks are fragmented, inconsistent, and frequently incoherent. CRM, MAP, ad platforms, intent data, web analytics, ABM platforms: these systems do not talk to each other cleanly, and the reconciliation work is where most of the human hours currently go. The entrepreneur who solves the integration and normalization problem owns the defensible position. The dashboard is a commodity. The clean, unified data layer is not.

The model must return time to judgment, not just reduce cost. This is the critical distinction. An Attribution as a Service model that simply makes the existing reporting machinery faster and cheaper accelerates the problem this post describes. It just produces the forty-slide deck in ten minutes instead of three days. The entrepreneurs building something more valuable understand that the real product is not efficient reporting. It is reclaimed marketing capacity. Intelligently automate the mechanical work, return that capacity to the core levers of marketing: positioning, brand, customer insight, creative strategy, and you don’t just have a more efficient marketing team. You have a more impactful one. In B2B, where the gap between average marketing and excellent marketing is measurable in pipeline and market position, that delta is worth a great deal.

Agentic AI changes the addressable market. As AI agents become a more significant part of the B2B buyer journey, the attribution problem gets structurally harder for in-house teams to solve. The signals change. The trackable touchpoints diminish. The models that worked for a human buyer journey need to be rebuilt for an AI-mediated one. A specialized service provider with the technical depth to stay ahead of that evolution will have a durable advantage over an in-house marketing ops team trying to keep up while also doing everything else.

The venture and PE communities have not fully priced this opportunity yet, in part because attribution has been treated as a feature of the broader martech stack rather than as a standalone category. That is changing. The market size is real, the buyer pain is acute, and the technical moment: the convergence of agentic AI capability with martech stack fragmentation, is exactly right. For B2B media and marketing technology investors, this is a space worth watching closely.

The Work That Needs to Happen

There’s a test I’ve applied to marketing programs for most of my career, going back to my time at InformationWeek and forward through the ThomasNet rebuild: where we did more than three hundred customer interviews with Strategyn to understand what industrial buyers actually needed, as opposed to what we assumed they needed. The test is simple: Does this start with the customer or does it start with what's measurable?

Attribution-first marketing almost always starts with what’s measurable. It starts with: what did we spend, what did it generate, and how do we defend it? Customer-first marketing starts with: what does this buyer believe, what do they need to believe, and what would it take to get them there?

The second question is harder. It can’t be answered with a dashboard. It requires research, judgment, time spent with actual customers, and the willingness to act on insight before you have the data to prove it worked. It produces briefs and positioning statements and creative work that might fail before it succeeds.

It is also, not coincidentally, the only kind of marketing that works.

Attribution is a tool. It was always only ever supposed to be a tool. A way of checking the work after the thinking had been done. The industry turned it into a substitute for the thinking itself. Getting back to the actual upstream work is not a retreat from accountability. It is the precondition for having anything worth measuring.

The measurement can follow. First, do the marketing.

This is the series opener for B2B Marketing in the Machine Age, a four-part series on the state and future of B2B marketing in the agentic AI era, published in Uphoff on Media

Next in the series: "B2B Marketing Is Broken. Brand Is Why. And Brand Is the Fix." Post 2 of B2B Marketing in the Machine Age, publishing 5/26/26.

The views expressed in Uphoff on Media are entirely my own. They don't represent the opinions of any company I've led, any board I've sat on, or any investor who's had the pleasure of debating strategy with me over the years. If something I write here sounds brilliant, I'll take full credit. If it turns out to be wrong, I was clearly misquoted — by myself.

Uphoff on Media | Field-Tested Frameworks

When I became President and CEO of ThomasNet.com, I inherited a research project already underway with a firm called Strategyn. They were conducting over 300 individual interviews with Engineers, Procurement professionals, and MROs, all focused on the jobs to be done around custom manufacturing sourcing.

The core value proposition of ThomasNet was supplier discovery and custom manufacturing sourcing. The goal of the research was simple: give us user insights to inform our product roadmap, and surface intelligence we could use when marketing to our customers.

We thought the number of distinct jobs would be relatively small. Maybe a dozen. Perhaps two dozen if we stretched it.

We were stunned.

Strategyn’s research surfaced over 200 primary jobs to be done, and another 150 secondary ones. They ranged from the mission-critical: an Engineer searching for a certified manufacturer to produce a key component for a medical device, where the wrong supplier choice could cost lives. To the granular and human: a Procurement professional simply trying to “get my global team all on the same page” about a potential new supplier.

Two hundred primary jobs. One platform. One team that had been building features based on what we thought mattered.

That research didn’t just inform our roadmap. It demolished it. We went back to first principles and redesigned and reengineered the entire ThomasNet platform around the actual jobs of our audience. It was clarifying in the way that only being genuinely wrong can be.

And here’s what happened next: active registrations grew. Platform usage grew. Revenue followed. The JTBD research became the north star of every product decision we made, not a one-time exercise but a living operating framework. It ultimately led to an unsolicited offer to buy the company at an eye-popping multiple.

I’m not telling you this to brag about an exit. I’m telling you because the causal chain matters. Understanding what your customers are actually trying to accomplish — with rigor, specificity, and humility about how wrong you probably are — is not a soft strategic exercise. It is the work. And the market eventually prices it that way.

The Framework Behind the Revelation

Jobs to Be Done is a theory of innovation developed and popularized by Clayton Christensen, the late Harvard Business School professor and author of The Innovator’s Dilemma and Competing Against Luck. Christensen’s core argument: customers don’t simply buy products. They “hire” them to accomplish a specific job.

The analogy that crystallized it for me came from HBS marketing professor Theodore Levitt:

"People don't want to buy a quarter-inch drill. They want a quarter-inch hole."

I had spent decades selling drills. So had nearly every executive I knew.

Strategyn, the firm we’d engaged at ThomasNet, had built their entire research and consulting methodology around this framework, operationalizing it into a rigorous process for uncovering and prioritizing the full landscape of customer needs. Not what customers say they want. Not what your product team believes they need. What they are actually trying to get done, in the specific circumstances they face, with the social and emotional dimensions that feature checklists never capture.

The Milkshake Story: JTBD in Action

Christensen’s most famous illustration involves something far more ordinary than industrial sourcing, a fast-food milkshake. A major quick-service chain was puzzled: the bulk of its milkshake sales occurred before 8:30 in the morning, purchased by solo customers who ordered nothing else and drove away alone. Traditional market research offered no useful explanation.

Christensen’s team asked a different question. Not “what do you think of the milkshake?” but “what job did you hire it to do?”

The answer was immediate: these customers faced a long, tedious commute. They needed something to keep them occupied and stave off hunger until mid-morning. Something consumed one-handed while driving, lasting long enough to make the drive bearable, without the guilt of a donut. They’d tried bananas (gone too fast), bagels (messy), and coffee (too hot, over too quickly). The milkshake was nearly perfect. It took almost thirty minutes to finish, fit the cupholder, and kept them full.

The chain had been asking how to make a better milkshake. The real question was: what is a 7am commuter actually trying to get done?

Two entirely different questions. Two entirely different answers. And only one of them leads to a decision that actually moves the business.

What the Research Did to My Thinking

After the ThomasNet experience, I took Christensen’s Harvard online course on innovation. The aha moment wasn’t just intellectual, it was visceral. I realized that I, like most business leaders, had been building and positioning products based on my understanding of features and benefits. Not my customers’ experience of them. Two entirely different things.

Christensen observed that firms have never known more about their customers, yet their innovation processes remain hit-or-miss. The reason: product developers focus too much on building customer profiles and looking for correlations in data rather than on the job the customer is trying to get done.

The evidence is brutal at scale: somewhere between 75 and 85 percent of all new products launched don’t succeed financially. Not because of poor execution. Because of a fundamental misread of what the customer actually needed.

Jobs are multifaceted. They’re never simply about function. They have powerful social and emotional dimensions. And the circumstances in which customers try to do them are more critical than any buyer characteristic.

This is why demographic segmentation so often leads companies astray. You can know everything about who your customer is and still have no idea what they’re actually trying to accomplish.

Here’s the Christensen point I think is most underappreciated: if you frame your business in terms of products you’re trying to sell, you get supplanted as products and technologies change. But if you’re organized around delivering a job that’s genuinely done well, you can absorb new technologies as they emerge rather than being displaced by them.

Jobs are stable. Products are not.Print that out. Put it on the wall of every product team you run.

The Agentic AI Accelerant

Here’s why all of this has become urgent in a new way.

We are entering the agentic AI era: where autonomous AI systems don’t just assist human buyers, they act on their behalf. They search, evaluate, compare, negotiate, and in some cases complete purchases without a human ever visiting your platform, reading your copy, or clicking through your feature list.

Analysts project that 90% of B2B buying will be AI agent-intermediated by 2028, driving over $15 trillion of B2B spend through AI agent exchanges.

When I first encountered that number, I sat with it for a while. I've watched several major platform transitions up close: print to digital, trade directories to industrial marketplaces, on-premise software to SaaS. Each one scrambled the competitive map for companies that didn't see it coming. This one is different in speed and scope. And it has a specific implication for JTBD that I don't think enough leaders are tracking.

When a human buyer visits your platform, you have the opportunity to guide, persuade, and educate. You can tell your story. A skilled sales team can build relationships and reframe a conversation.

When an AI agent evaluates your platform on behalf of a buyer, none of that happens. It doesn’t read your positioning statement. It doesn’t respond to your brand narrative. It evaluates your product against the specific job the buyer has tasked it to complete. Either your product does that job — demonstrably, specifically — or the agent moves on.

The AI agent is a pure JTBD machine.

These agents are goal-oriented, context-aware, and ruthlessly efficient at matching products to jobs. Feature lists won’t matter. Brand stories won’t matter. What will matter is whether your product demonstrably accomplishes the job the buyer agent has been assigned.

This is why JTBD isn’t just a useful framework anymore. In the agentic era, it becomes the foundation on which competitive survival is built.

If you don’t know the precise jobs your customers are trying to get done — down to granular specificity — AI-mediated buying will route around you. Not maliciously. Automatically.

What This Means If You’re Running a Business

The implications run across the entire organization.

Product: Most roadmaps are answers to questions nobody asked, built on internal assumptions dressed up as strategy. In an agentic buying environment, the gap between what you built and what the buyer needs becomes fatal. Not because a competitor beat you. Because an AI agent simply moved on.

Marketing: Messaging built around your product’s attributes will increasingly fail to reach AI agents making autonomous decisions. The language of outcomes, specific, measurable jobs your product enables, is the only language agentic systems will evaluate. If your content strategy doesn’t speak to jobs, it’s speaking to no one.

Sales: The human selling motion isn’t going away, but it’s narrowing to where relationships and judgment genuinely differentiate. Everywhere else, AI agents will mediate. Sales teams need to understand the jobs their customers are executing at a depth most have never been asked to develop.

Strategy: The businesses that will thrive in the agentic era are those that can map their capabilities directly to the jobs of their market, with specificity and evidence. Not “we help engineers source suppliers.” But: “we help an engineer with aerospace certifications find a titanium CNC machining partner in North America within a 48-hour sourcing window, with documented quality controls for FAA compliance.” That’s a job. That’s a product. That’s a business.

Do the Research

When our team at ThomasNet discovered 350+ distinct jobs to be done in what we thought was a well-understood market, the response could have been paralysis. Instead, it became a forcing function for discipline. Not every job could be served equally. We had to prioritize, sequence, and build a roadmap anchored in actual user needs, not internal assumptions.

That’s hard work. It requires real investment in understanding your market at a level of depth most companies never attempt. Christensen observed that for most companies, innovation remains a flawed business process — yielding failure rates consistently over 80% — because companies fail to define their customers’ needs with the rigor, precision, and discipline required to discover, prioritize, and capitalize on growth opportunities.

We are not in an era where that failure rate is acceptable. The competitive margin is too thin. The pace of AI adoption is too fast. And the buyers, increasingly assisted or replaced by AI agents that will simply go find a better match, are too empowered to wait for you to figure it out.

The quarter-inch drill metaphor has never been more relevant. And the urgency to understand the hole has never been more acute.

We rebuilt ThomasNet around that understanding. The market noticed. So did the acquirers.

The question is whether you move before the agents do.

The views expressed in Uphoff on Media are entirely my own. They don’t represent the opinions of any company I’ve led, any board I’ve sat on, or any investor who’s had the pleasure of debating strategy with me over the years. If something I write here sounds brilliant, I’ll take full credit. If it turns out to be wrong, I was clearly misquoted by myself.

There is a room I keep walking into. The faces change. The industry changes. The agenda changes. But the missing person is always the same.

The customer isn’t there.

I recently sat with a group of senior Procurement executives. Smart, experienced operators redesigning their workflows for the agentic AI era. The conversation was serious, these were executives working through real transformation. What processes should be automated. What decisions should remain human. How supplier engagement models need to evolve.

But something kept nagging at me. The SaaS platform vendors their organizations depend on, the systems of record for procurement, sourcing, supplier management, kept appearing in the conversation as obstacles, not enablers. The Procurement teams were clearly articulating what they needed these platforms to do. New agentic AI features. Workflow modifications. Integration changes. And the vendors kept saying no. Not with technical explanations. Not with roadmap visibility. With the soft, faintly condescending resistance of organizations that don’t quite understand what their customers are asking for, or why.

The vendors weren’t blocking transformation out of bad faith. They were blocking it because they don’t deeply understand how Procurement actually works.

That’s a different and more troubling problem. You can fix bad faith. You can’t easily fix institutional ignorance dressed up as process.

Then I offered a reframe that landed hard. Throughout the entire discussion — the workflow redesign, the AI feature debates, the vendor friction — not once had anyone mentioned the people this function actually exists to serve. The internal stakeholders. The suppliers. The customers of Procurement.

The room didn’t push back. They recognized it immediately. These were experienced operators, they knew exactly what outside-in thinking looked like. They just hadn’t applied it to their own transformation. The problem had pulled them inward, and they’d followed it.

That’s how it always happens. Not negligence. Gravity.

The Same Disease, Different Organs

I’ve been having a parallel conversation with B2B marketing, media, and technology leaders about brand trust. Same room. Same missing person.

The data on this is worth sitting with. Studies consistently show that roughly 90% of executives believe their companies are highly trusted by their customers. Customers, when asked, put that number closer to 30%. A 60-point gap. And that gap isn’t a perception problem, a communications problem, or a positioning problem. It is a structural orientation problem. B2B leaders have been building brands, systems, and now AI workflows for themselves. Not for the people they serve.

I don’t think this is a coincidence. I think it’s the same failure presenting in two different organs.

We built a generation of B2B marketers who can optimize a CAC ratio but have never sat with a customer and asked what they actually need.

Look at the last twenty years of B2B marketing. What stands out? Not branding. Not the creation of companies that buyers genuinely trust, advocate for, or miss when they’re gone. What stands out is the extraordinary rise of digital marketing, martech, and adtech. The relentless optimization of measurable things. Click rates. Conversion rates. Pipeline velocity. Cost per lead.

These tools aren’t the problem. The problem is what happened when they became the entire discipline. When the metric became the mission. When a generation of B2B marketers learned to optimize data but never learned to understand buyers. When the incentive system rewarded what could be measured and quietly penalized what couldn’t: like trust, like brand equity, like the kind of relationship where a customer stays through a bad quarter because they genuinely believe in the company.

Brand requires something martech can't deliver: genuine outside-in orientation. Knowing your customer as a human being making high-stakes decisions with real accountability. That got hollowed out. Completely.

Faith Popcorn Named It

Faith Popcorn wrote something recently that I can't stop thinking about. Faith is one of the sharpest brand thinkers I've ever encountered, and she named something I've been trying to articulate: The Heartbeat Brand.

The concept is this: consumers trust brands that measure themselves in life outcomes, not transactions. Brands that show up inside the vulnerable, real moments of a customer’s life, not just at the point of purchase. Brands that ask “what does this person actually need” rather than “how do we move more product”.

She writes about Costco launching an IVF program, a warehouse retailer deciding that helping its members have babies is also its business. About the USPS carrier who already stops at every door in America and could become a genuine safety net if someone had the vision to see it. About the 60-point gap between executive confidence in brand trust and what customers actually report, and how that gap is not a perception problem but a strategic blind spot of historic proportions.

Her audience is primarily B2C. But the principle lands harder in B2B, where the stakes are higher, the buying decisions more consequential, and the customer has been more systematically ignored.

A B2B “Heartbeat Brand” is the vendor that shows up in your worst quarter, not just your best. The platform that redesigns its workflow around how you actually operate, not how the product team imagined it.

That’s the standard. And by that standard, B2B as an industry has been failing for a long time.

The SaaS Problem Is the Brand Problem

Back to the Procurement room.

The platform vendors gatekeeping transformation they don’t understand are exhibiting the same failure mode as the B2B marketers who built twenty years of campaigns without deeply knowing their buyers. They sold into Procurement. They deployed into Procurement. They never learned Procurement.

That’s not a product problem. It’s a customer orientation problem. And it shows up as friction precisely when it’s most costly; at the moment a customer is trying to evolve, trying to adopt new technology, trying to do the hard work of organizational transformation.

If these vendors had developed genuine, operational-level understanding of how Procurement functions, the conversation would be different. How sourcing decisions get made. How supplier relationships are managed. How internal stakeholders experience the process. How the job of a Procurement leader has changed as the function has become more strategic. The features would exist. The roadmap would reflect what customers need. The “no” would come with a real reason or not at all.

Instead, the customer is in the room and the vendor isn’t listening. The mirror image of the Procurement team that designs its future workflow without the supplier or stakeholder in the room.

It’s inside-out, all the way down.

This has always been a risk in enterprise SaaS. But the penalty has historically been manageable: churn is painful, switching costs are real, and inertia protects incumbents longer than it should. Agentic AI changes that calculus entirely.

When AI-native tools can replicate and integrate the workflow steps that previously required dedicated point solutions, the switching cost equation inverts. The moat that protected functionally narrow SaaS vendors — “we own this step” — becomes a vulnerability. Vendors who don’t understand the function deeply enough to evolve with it won’t just face difficult renewal conversations. They’ll be architecturally bypassed.

Churn in the agentic AI era won't look like churn always has: a lost renewal, a migration project, a transition cost borne by both sides. It will look like a Procurement team that rebuilt its workflow around an AI-native platform and quietly stopped needing four of the tools it was paying for. No dramatic exit. No contentious offboarding. Just a function that evolved, and a vendor that didn't.

The vendors who are gatekeeping today are revealing something important: they are not positioned to guide their customers through the agentic AI transition. And their customers are starting to notice.

Agentic AI Doesn’t Fix This. It Amplifies It.

Here is where urgency enters the conversation.

Agentic AI is restructuring workflows at the infrastructure level. Right now, organizations across B2B are redesigning the interest-to-invoice journey. They are making decisions about what gets automated, what stays human, how suppliers are engaged, how buyers are supported, how the entire operating system of B2B commerce gets rebuilt for a world where AI agents execute tasks, not just assist with them.

These decisions will be very hard to reverse. You can pull a bad ad campaign. You can reposition a brand. You can’t easily rearchitect a workflow that’s been embedded into your systems of record, your supplier relationships, and your organizational muscle memory.

If the customer isn't in the room during that redesign, we don't just build bad workflows. Not when B2B marketers build agentic demand generation without deeply understanding the buyer journey. Not when Procurement leaders design agentic workflows without engaging the suppliers and stakeholders those workflows serve. Not when SaaS vendors build agentic features without operational-level understanding of how their customers actually work.

We automate the inside-out orientation. We encode the blind spot at scale.

The companies that will compound trust in the agentic AI era are the ones that design outside-in, starting with the customer. The rest will just fail faster.

Three Questions Worth Sitting With

Faith Popcorn ends her piece with a set of questions she recommends sitting with over a good bourbon. I’ll offer a B2B version.

* If your platform, product, or service disappeared tomorrow, would your customers miss it, or just migrate?

Not the switching cost. The actual loss. There’s a difference between a product customers are locked into and one they genuinely value. Most B2B leaders don’t know which one they have.

* Does your process start with what the customer needs or with what the business has already decided to build?

The SaaS vendors blocking Procurement transformation aren’t doing it maliciously. They simply haven’t spent enough time understanding how Procurement actually operates. That’s a choice, even if it doesn’t feel like one.

* Who is designing your agentic AI workflows, and who isn’t in the room?

This is the most urgent question in B2B right now. The workflows being designed today will shape how buyers experience your brand, how suppliers experience your Procurement function, and how customers experience everything in between for the next decade. If the customer isn’t in that design process, you’re not building the future. You’re automating the past.

The gap between what B2B companies think their customers feel about them and what customers actually feel has never been wider. The technology to close that gap has never been more powerful. Neither has the technology to permanently encode it.

The question for every B2B leader isn't whether agentic AI changes your business. It does. The question is whether you'll finally put the customer in the room. Or whether you'll do what B2B has done for twenty years, and just do it faster.

Put the customer in the room.

The views expressed in Uphoff on Media are entirely my own. They don’t represent the opinions of any company I’ve led, any board I’ve sat on, or any investor who’s had the pleasure of debating strategy with me over the years. If something I write here sounds brilliant, I’ll take full credit. If it turns out to be wrong, I was clearly misquoted — by myself.

When the CEOs of Walmart and Coca-Cola announced their retirements, both cited AI disruption as a contributing factor. Let that land for a moment. Two of the most experienced executives in global business, leaders of companies with combined revenues approaching a trillion dollars, named AI as a reason for stepping back.

This isn’t a technology story. It’s a leadership story. It has direct implications for every Procurement leader reading this.

Three Forces Are Converging: Right Now

I was recently invited to speak at Art of Procurement’s Catalyst event in San Francisco, a gathering of senior Procurement leaders from across the enterprise world. The session: a fireside chat with AoP Founder Phil Ideson titled “What Now? Leadership Choices When AI Accelerates”, was one of the more candid conversations I’ve had with a room full of senior practitioners in some time.

I also facilitated a Mastermind breakout with senior Procurement leaders from across the enterprise technology sector. A smaller, candid conversation that surfaced some of the most honest operator thinking I've encountered on these themes. What came out of those sessions sharpened my thinking considerably. Not just about where the function is headed, but about the specific friction points preventing leaders from getting there.

Three forces are reshaping Procurement simultaneously. Their combination creates both the highest risk and the biggest opportunity the function has seen in a generation.

Force 1: Agentic AI Makes Judgment More Valuable, Not Less

The narrative around AI and Procurement tends toward anxiety: automation is coming for sourcing, contracting, spend analysis, supplier evaluation. That narrative is partially right, and almost entirely wrong in its conclusion.

Yes, agentic AI systems will absorb more of the transactional and analytical work in Procurement. But here’s what that actually means: the premium on human judgment goes up, not down. When an AI agent can process a thousand supplier bids in the time it used to take to evaluate ten, the competitive advantage shifts to the person who knows which variables matter, and why. Risk assessment. Relationship intelligence. Strategic trade-offs that don’t fit a model. That’s not the work AI replaces. That’s the work AI reveals.

The teams that lean into this shift will separate from those that resist it. The ones that treat agentic AI as a threat will spend the next three years playing defense. The ones that treat it as an amplifier will spend those years building something that can’t be replicated.

One senior procurement leader who spoke at the event put the human stakes more bluntly than I expected:

“I’m trying to figure out whether we should invest in upskilling around AI or just spend the money on severance.”

That’s not cynicism. That’s a genuine leadership dilemma in real time, and it deserves a real answer.

The answer is neither. The right investment is in the judgment layer above the automation. The capability that makes both the upskilled humans and the AI systems more valuable than either could be alone.

Force 2: Service as Software Changes What Procurement Is Evaluating

Enterprise software is undergoing a structural shift, from SaaS to what’s being called Service as Software. AI doesn’t just assist the work anymore. In a growing number of applications, AI is the service. It delivers outcomes, not tools.

This changes the procurement conversation fundamentally. The old evaluation criteria — features, integrations, security, uptime — don’t disappear, but they’re no longer sufficient. Now you’re evaluating performance accountability when the “vendor” is an agent. Data rights when the service learns from your proprietary information. Vendor dependency when the capability is embedded in AI infrastructure you don’t own or fully understand.

These are no longer purely IT questions. They are Procurement questions.

What emerged clearly from the Mastermind in San Francisco: vendors themselves are part of the problem. Multiple leaders raised a pattern I hadn’t fully framed before: vendors acting as gatekeepers, rolling out AI features on their own timeline and effectively controlling the pace of adoption within their customers’ organizations. Procurement is being throttled by the very ecosystem it manages.

That’s a structural problem, not a planning problem. And it means Procurement leaders need to move the vendor governance conversation up, from contract administration to strategic posture. Which vendors are genuinely enabling your AI capability? Which ones are holding it hostage to their own roadmap? Those are not equivalent relationships and shouldn’t be managed as if they are.

The function that doesn’t get fluent in this new model will watch those decisions get made without them.

Force 3: The Strategic Seat Is There for the Taking

Here’s what I argued in San Francisco, and what I believe most strongly:

Procurement is one of the few functions in any enterprise that has a full playing field view of the business: across systems, vendor relationships, budget allocations, and business unit operations simultaneously. That vantage point is structurally unique. The CFO sees the numbers. The CTO sees the infrastructure. The CMO sees the market. Procurement sees the connective tissue that holds all of it together.

In an agentic AI environment, that vantage point becomes a genuine competitive advantage for the leaders who claim it. Because what agentic AI requires: clean data, trusted vendor relationships, clear decision rights, structured governance, is exactly what Procurement is positioned to provide. No other function has that stack.

COVID tested every function’s ability to respond to a supply chain crisis. The best Procurement teams used that moment to demonstrate strategic value they’d never been given credit for. This moment is different — broader, deeper, more structurally permanent — but the dynamic is the same. Disruption creates openings. The functions that move get the seat. The ones that wait get managed.

But a real obstacle surfaced in San Francisco that the optimistic version of this argument tends to skip past: the attribution gap.

One senior Procurement leader described it this way:

“When we work with a business unit and create a more streamlined tech stack, set of vendor agreements, or workflow, the savings is something that business unit leader can reinvest, so it doesn’t show up as something Procurement did.”

That’s not a small problem. It’s a structural visibility failure. The value is real. The credit never arrives. And without the credit, the seat doesn’t come.

The implication: Procurement doesn’t just need to do the strategic work. It needs to get fundamentally better at narrating it. Internally. At the CFO and CEO level. In the language of business outcomes, not functional metrics.

One finding from the Mastermind worth internalizing: time savings can outrank cost savings as the most valued internal contribution Procurement makes. Not marginally. As a clear priority shift. Procurement leaders still leading with cost reduction are speaking a language the organization has partially moved past. The CFO and CEO care about speed of decision, speed of deployment, reduction in organizational friction. If Procurement can show up in that conversation — and it can — the seat is available. If it keeps leading only with savings, it keeps getting managed like a cost center.

What to Do Now

Most frameworks name the forces and stop. So let me be direct about what I think Procurement leaders should do in the next 90 days.

This Week: Get Situationally Aware

* Audit every AI-enabled tool in your vendor stack. Flag which ones are moving toward agentic capability, autonomous action, not just AI-assisted recommendation. Then flag which vendors are enabling that shift and which ones are gatekeeping it.

* Map where automation is already touching sourcing, contracting, or supplier management in your organization. If you don’t know, that’s the finding.

* Pull your top 10 SaaS contracts. Identify which vendors are pivoting to outcome-based or AI-delivered service models. Those renewals just got more complex, and Procurement needs to lead them.

This Month: Build Your Point of View

* Write a one-page AI risk brief for your CFO or CPO. Cover three things: vendor dependency, data rights in AI-delivered contracts, and performance accountability when the “service” is an agent. If you can’t write it, you’re not ready for the conversation.

* Rewrite your function’s value narrative. Stop leading with cost savings. Lead with time savings, risk intelligence, and business outcomes. The language you use internally shapes how leadership sees you. And the language is shifting.

* Define your attribution story. Where has Procurement created real business value in the last 12 months that isn’t showing up in anyone’s performance metrics? Find it. Name it. Quantify it. Present it.

This Quarter: Move the Needle

* Identify two or three high-visibility business decisions in the next 90 days where Procurement can drive or materially shape the outcome. Don’t wait to be invited. Show up with a POV.

* Claim a seat at one C-suite or cross-functional initiative where Procurement hasn’t historically had a voice. One is enough to start. Make it count.

* Deepen three strategic supplier relationships, the kind built on trust, shared intelligence, and long-term alignment. Specifically: have a direct conversation with each about their AI roadmap, what they’re enabling, what they’re controlling, and where your interests align. No agentic system replicates that relationship intelligence. It’s your moat.

The Moment Is Now

I’ve spent 35 years watching disruption hit industries and functions. The pattern is consistent. The leaders who move early, who treat disruption as a forcing function rather than a threat, end up in a fundamentally different position than those who wait for clarity.

One CPO pulled me aside after the session and told me she wished she'd had this conversation ten years ago. I appreciated that. But the more important point is that having it now, in this moment, is the whole game. The window in which Procurement can move from managed function to strategic asset is open. It won’t stay open forever.

Agentic AI is not going to make Procurement obsolete. It is going to make the difference between strategic Procurement leaders and order-taking Procurement functions impossible to ignore.

The full playing field view is yours. The attribution gap is fixable. The seat is available.

The choice is whether to take it.

One more thing worth mentioning: if you're a senior Procurement leader and haven't encountered Art of Procurement's Catalyst event, put it on your radar. Phil Ideson and the AOP team have built something rare, a room where senior practitioners talk honestly about what's actually happening, not what's supposed to be happening. Keep an eye out for it coming to a city near you. Worth the trip.

The views expressed in Uphoff on Media are entirely my own. They don’t represent the opinions of any company I’ve led, any board I’ve sat on, or any investor who’s had the pleasure of debating strategy with me over the years. If something I write here sounds brilliant, I’ll take full credit. If it turns out to be wrong, I was clearly misquoted by myself.

There’s a lyric from the Oasis song “Wonderwall” that’s been living rent-free in my head every time I read another paywall story: “I said maybe, you’re gonna be the one that saves me.” That’s the emotion publishers brought to paywalls when the model went mainstream in the early 2010s. Not strategy. Emotion. Hope masquerading as a business plan.

I don’t say that to be cruel. I say it because I was in the room. I watched publishers, smart operators with real businesses, latch onto the paywall narrative the way drowning people grab floating debris. The economics of digital media were already brutal. Print was collapsing. CPMs were a fraction of what the industry needed. And here, finally, was something that felt like agency. We’ll charge for content. We’ll own our audience. We’ll escape the platform trap.

Fifteen years later, we have enough data to render a verdict. And the verdict is complicated, more complicated than the paywall evangelists or skeptics will admit. A handful of publishers built real businesses behind paywalls. Most didn’t. And now, just as the model was finally maturing into something defensible, agentic AI has arrived to stress-test the entire premise.

This is the story of what actually happened, why it happened, and what publishers who are still standing need to understand about what comes next.

Act I: The Promise (2010–2018)

Salvation Narrative

The Wall Street Journal had been running a hard paywall since 1996, a fact that sounds prescient in retrospect but was largely irrelevant to the broader industry at the time. The WSJ served a professional audience whose employers often footed the bill. It was a B2B subscription product wearing a B2C costume. Everyone acknowledged it worked. Everyone assumed it wouldn’t work for general-interest news.

Then the print cliff accelerated faster than anyone anticipated. Between 2008 and 2012, newspaper print ad revenue dropped by roughly half. Digital advertising was growing, but it was growing for Google and Facebook, not publishers. The CPM math was unforgiving: a publisher might earn $25–$40 per thousand impressions in print and $1–$3 online. No volume of page views could close that gap.

I watched this from the inside. When I was publisher of InformationWeek, we had 400,000 controlled-circulation subscribers. These readers received the magazine free because they had the right title, the right company size, the right buying authority. That verified, qualified readership built a rate base: the audited circulation number that determined what we could charge advertisers. The currency from the readers was their time, engagement, and loyalty, which was the entire benefit to advertisers. At its peak, InformationWeek generated over $175 million in annual advertising revenue on that model. The paywall era inverted that logic entirely. It assumed the reader's currency should be cash. For most of publishing history, their attention was worth far more.

The New York Times launched its metered paywall in March 2011, and the industry held its breath. The conventional wisdom was that readers would simply navigate around it, that free content was too available online, that the paywall was a noble but doomed gesture. Instead, the Times started signing up digital subscribers. Not immediately transformative numbers, but real ones. Proof of concept.

Between 2017 and 2020, the number of news outlets implementing paywalls nearly doubled each year. The trade press treated it as a rescue mission.

By the mid-2010s, the paywall had become the dominant narrative of publisher survival. Industry conferences devoted entire tracks to paywall strategy. Consulting firms built practices around it. Publishers who hadn’t implemented one felt pressure to explain why. The enthusiasm was genuine, and in hindsight, genuinely disproportionate to the results most publishers would actually achieve.

Act II: The Scorecard (2018–2023)

Who Won, Who Lost, and Why

Let me give you the honest operator’s scorecard, because the industry press tends to celebrate the wins and bury the losses.

The New York Times won. Decisively. By February 2022, three years ahead of schedule, it hit 10 million digital subscribers and reset its target to 15 million by 2027. But the Times didn’t win because it built a better paywall. It won because it built a better product. Wordle. The Athletic. NYT Cooking. NYT Games. The paywall became a bundle gateway, not a content gate. Subscribers were paying for a suite of daily habits, many of which had nothing to do with news.

The Wall Street Journal won. For the same structural reason I described earlier; it had always been a professional subscription product, and first-party subscriber data made its advertising inventory genuinely more valuable to marketers. WSJ reportedly sells 90% of its inventory direct, and advertisers using its first-party data renew at significantly higher rates. The paywall isn’t a barrier; it’s a data-collection mechanism that funds a premium ad business.

The Financial Times won. Same playbook: a professional audience with employer-funded subscriptions and irreplaceable market-specific content.

Everyone else? Mixed at best. The economics became clear pretty quickly: paywalls work when they protect something a specific reader cannot get anywhere else. General-interest news is available everywhere. Local news is undervalued by local readers until it disappears. Niche B2B content; if it’s genuinely proprietary, or delivered with enough utility that it functions as a workflow tool rather than a reading experience, can command real subscription prices. Bloomberg didn’t build a $6 billion terminal business by having better journalism. It built it by embedding that journalism into a platform traders couldn’t do their jobs without. Some B2B publishers followed a version of that playbook: combining content with data, analytics, or workflow integration until the product became operationally indispensable to its audience. When a reader can’t do their job without you, price sensitivity largely disappears. But that bar is high, and most publisher content — if we’re being honest — never cleared it.

Most households will pay for one or two news subscriptions. That budget overwhelmingly goes to the same three or four scaled national brands. Everyone else is fighting over the remainder.

The Sun tried a hard paywall and abandoned it. The Toronto Star did the same. Countless regional newspapers experimented, retreated, and experimented again. Local news operators found that the readers most willing to pay were often those most engaged. The very people who would have supported the paper anyway. The marginal subscriber who needed to be convinced often just didn’t show up.

Meanwhile, a more structural problem was hardening. Publishers had signed up most of the readers who were ever going to subscribe. Growth was slowing not because the paywall model was wrong, but because the addressable market was smaller than assumed. As one Reuters Institute report bluntly noted, publishers had already captured many of those prepared to pay, and in a tight economic climate it had been hard to persuade others to do the same. Subscription growth was plateauing industry-wide by 2022–2023.

Act III: The Innovation Layer (2022–2025)

Smarter Walls, Shrinking Returns

To their credit, publishers didn’t give up. They got more sophisticated. The blunt instrument of the early paywall — a fixed meter, same for every reader — gave way to dynamic, AI-driven systems that personalize the conversion moment. Instead of offering every reader the same number of free articles, smart meters identify which readers are close to converting and apply pressure accordingly; they identify casual browsers and leave them in the open funnel to generate ad impressions.

The results have been real. Business Insider reported a 75% increase in subscriptions after implementing a dynamic paywall system. The Philadelphia Inquirer achieved a 35% lift in subscriber growth using a similar approach. By late 2024, 38% of news publishers were already using or planning to transition to dynamic paywall models. The internal debate between the subscriber revenue team (lock it down) and the advertising team (keep it open) had found a technology-mediated resolution: optimize for both simultaneously using behavioral data.

This was genuinely encouraging progress. Publishers were finally applying the kind of data discipline to subscription conversion that e-commerce had been using for years. It felt like the industry was growing up.

And then the floor started to move.

Act IV: The Existential Threat (2025 and Now)

The Wall That AI Walks Right Through

The paywall was designed to stop humans. It was never designed to stop agents.

In October 2025, researchers at the Columbia Journalism Review documented something that should have sent shockwaves through every publisher’s boardroom: AI browsers like OpenAI’s Atlas and Perplexity’s Comet were able to retrieve and summarize a 9,000-word subscriber-exclusive article from MIT Technology Review without triggering any of the barriers designed to block automated access. These systems aren’t scrapers in the traditional sense. They behave like humans. They load the full page, render JavaScript, pass bot-detection systems, because to the website’s infrastructure, they are indistinguishable from a person using Chrome.

The scale is staggering. AI scraping attacks on streaming and media properties jumped 56% year over year. From Q2 through Q4 2025, the rate of AI content scraping grew at an average of 24.4% per quarter. In March 2025 alone, 26 million scraping attempts ignored standard robots.txt directives. The traditional defenses weren’t built for this.

The paywall was designed to stop humans. It was never designed to stop agents. And the agents have arrived.

But the scraping problem, as serious as it is, may actually be the second-order threat. The first-order threat is more subtle and more devastating: if AI systems can synthesize answers from publisher content without sending users to publisher pages, readers never hit the paywall at all. They never become subscribers. The loss is invisible.

Zero-click search rates: queries that get answered without any click to a publisher’s page, rose from 56% in May 2024 to 69% by May 2025. DMG Media reported an 89% drop in click-through rates in September 2025, attributing it directly to AI Overviews in Google search results. The 500 most-visited publishers saw an average traffic decline of 27% year-over-year. Industry analysis estimates AI-powered search summaries reduce publisher traffic by 20% to 60% on average, with niche publications experiencing losses approaching 90%. In an industry where reach equals revenue, this is devastating.

The paywall assumes a reader arrives at your door. Increasingly, they don’t. They get what they needed from the AI and never make the trip.

The Framework: What Actually Works Now

Post-Paywall Revenue Architecture

The lesson isn’t that paywalls failed. It’s that paywalls were always a distribution fix applied to a value problem. Publishers put a gate on content that, in many cases, hadn’t earned the gate. The ones who built sustainable subscription businesses did so because they built something genuinely irreplaceable, then used the paywall to capture value from it.

That logic doesn’t change in the AI era. It intensifies. Here’s what the evidence suggests actually works:

* Proprietary intelligence over commodity content. If an AI can synthesize a reasonable version of your article from publicly available information, your content has a commodity problem, not a paywall problem. The publishers who will survive the AI transition are those with original reporting, exclusive access, proprietary data, or a unique analytical perspective that cannot be reconstructed from digital breadcrumbs. This is what the WSJ has. It’s what the FT has. It’s what most publishers don’t.

* Memberships over subscriptions. The semantics matter. A subscription is a transaction: pay for access. A membership is a relationship: belong to something. The publishers building durable reader revenue are increasingly framing their asks around community, identity, and shared purpose. This is not AI-proof, but it is significantly more human. People don’t cancel memberships the way they cancel subscriptions.

* Experiences over content. Live events, conferences, workshops, dinners — anything that requires human presence cannot be scraped, summarized, or synthesized by an AI agent. This is where B2B media has always had an advantage: the event, the roundtable, the peer-to-peer connection, is genuinely irreplaceable. Publishers who haven’t built an events business need to start building one.

* First-party data as a B2B product. The WSJ model: using subscriber data to make advertising dramatically more valuable, is replicable in principle by any publisher with a real subscriber base and the discipline to develop it. And for B2B publishers who took the utility path, embedding content into workflows, combining editorial with data and analytics, the first-party data advantage is even greater. A reader who uses your product to do their job generates behavioral signals that dwarf anything a casual visitor produces. The challenge is that most publishers let this data sit underutilized, treated as a byproduct rather than a product. In an environment where third-party cookies are dead and AI is eroding traffic-based ad models, the publisher who owns rich, consented, first-party audience data — especially data generated through active, functional use — has a genuine and defensible asset.

* AI licensing deals. The New York Times is suing OpenAI; a case that has escalated into one of the most consequential AI copyright battles in history, now centered on evidence that AI models memorize and reproduce copyrighted content verbatim. The FT signed a licensing deal with OpenAI. Both strategies may prove correct for their respective situations. But the larger point stands: the question every publisher should be asking is not whether to resist AI, but how to monetize their content's role in the AI ecosystem.

The Song Ends the Same Way

The Oasis lyric has one more line worth sitting with: “After all, you’re my wonderwall.” There’s something heartbreaking in it. The conviction that this thing, whatever it is, is the answer. Publishers felt that about paywalls. The conviction was sincere. The timing was real. The economics were genuinely dire enough to justify almost any hopeful narrative.

But a paywall is not a content strategy. It is not a value proposition. It is not a relationship with a reader. It is a gate. And a gate only has value if what’s behind it is worth the price of entry, and if readers can’t get what’s behind it somewhere else for free.

For fifteen years, the publishing industry argued about the gate. The conversation we needed to have was always about what was behind it.

The AI era is forcing that conversation at last. Not because publishers wanted to have it, but because the old evasions have run out. The scrapers are inside the walls. The readers never arrive. The CPMs are gone.

What’s left is the only thing that was ever going to matter: content so good, so specific, so irreplaceable, that a reader would genuinely feel its absence.

Build that. Then put a wall around it.

The views expressed in Uphoff on Media are entirely my own. They don’t represent the opinions of any company I’ve led, any board I’ve sat on, or any investor who’s had the pleasure of debating strategy with me over the years. If something I write here sounds brilliant, I’ll take full credit. If it turns out to be wrong, I was clearly misquoted by myself.

The most important thing Sequoia Capital published in the last year wasn’t a market map or a fund announcement. It was two sentences:

“The cloud transition was software-as-a-service. The AI transition is service-as-software.”

Ten words. The entire business model of enterprise software: two decades of per-seat licensing, recurring revenue multiples, and stack complexity, reframed in a single breath.

I’ve been running B2B technology and media businesses for 35 years. I’ve lived through the transition from print to digital. From on-premise software to SaaS. From search to social to intent data. Each shift looked, at the time, like a feature upgrade. Each one turned out to be a structural reorganization.

This one is structural. And it’s moving faster than any transition I’ve seen.

The $1/$6 Ratio That Explains Everything

Here’s the number that reframes Sequoia’s thesis from interesting to urgent.

For every $1 a company spends on software, it spends $6 on services.

That ratio — buried in Sequoia’s “Services: The New Software” analysis — is the entire argument. The SaaS era attacked the software market. A $350 billion opportunity. Significant. Transformative. And now largely captured by the incumbents: Salesforce, Microsoft, Oracle, and their horizontal peers.

Agentic AI is attacking the services market. Measured in trillions. And it’s doing it by turning software into a workforce. One that does the work, not just enables it.

This is not an incremental shift. It is a category expansion that dwarfs the cloud transition by an order of magnitude.

What “Service as Software” Actually Means

Let’s be precise, because this concept gets muddy fast.

Software-as-a-Service was about delivery. Software moved from a box you bought to a cloud subscription you rented. The product was still a tool. You still needed people to use it.

Service as Software is about execution. The software doesn’t give your team a better tool. It becomes the team. It does the job. You pay for the outcome, not the access.

Sequoia’s example is Sierra, an AI-based customer support platform. Companies don’t buy Sierra seats. They pay per resolved customer issue. No resolution, no charge. The job-to-be-done gets done, or it doesn’t, and nobody pays. There is no seat. There is no license. There is a result.

That model — pay for work delivered, not software deployed — is the commercial architecture of the next era of enterprise technology.

And it’s already breaking the current one.

The SaaSpocalypse Is Not a Prediction. It Already Happened.

In early 2026, the iShares Expanded Tech-Software ETF fell more than 21% year-to-date. An estimated $2 trillion in market capitalization was erased from B2B software companies in a matter of weeks. Atlassian dropped 36% in a single month. Analysts gave it a name: the SaaSpocalypse.

The market was repricing every software company built on per-seat licensing, because AI agents don’t pay per seat — and neither do the human employees whose jobs they’re replacing. Data emerging from enterprise CIOs showed that for every autonomous AI agent deployed, companies were reducing human software seat requirements at roughly a 1:5 ratio.

The incumbents didn’t argue. They adapted. Salesforce and ServiceNow pivoted to outcome-based pricing, charging for tasks completed rather than seats occupied. That pivot was an acknowledgment, not a strategy. It was recognition that the per-seat model had reached its structural limit.

BCG puts it plainly: 40% of buyers now cite seat reduction as their primary lever to decrease software spending. That’s not a trend. That’s a buyer revolt.

The CMO Who Signed Up to Do Marketing

Let me bring this down to ground level. Because the abstract inevitably becomes personal.

The average B2B enterprise now runs more than 300 SaaS applications. According to Chief Martec's 2025 landscape report, the martech landscape contains over 15,000 tools. And 32% of organizations report not fully using the capabilities of the stack they’ve already built, up from 28% the year before.

The average CMO did not sign up to be a shadow CTO.

They signed up to do marketing. To build brand. To drive pipeline. To win markets. Instead, they’re spending a material portion of their time managing vendor relationships, integration failures, data reconciliation, and a technology stack that requires its own internal expertise to operate.

This is the hidden tax of horizontal SaaS. The software works. Technically. But operating it at scale, tuned for your specific market and workflow, requires vertical expertise that the horizontal platform was never designed to provide. So you hire it, build it, or hire a partner to build it. And you pay for it twice: once in the license, again in the operating overhead.

Service as Software is the exit ramp from that trap.

When the unit of value is an outcome — leads generated, pipeline influenced, content produced at scale, demand signals identified — the complexity of the underlying technology becomes the vendor’s problem, not yours. The CMO’s job returns to marketing. The vendor’s job becomes delivering the result they promised.

This is not a feature upgrade. It is a reallocation of responsibility.

What This Means for Buyers and Buying Groups

Here’s where it gets structurally interesting, and where most analysis stops short.

The shift to outcome-based procurement doesn’t simplify the buying process. It complicates it in new ways, with new stakeholders.

Today’s B2B buying group already involves an average of 10 people, spanning IT, operations, finance, and end users. 79% of significant purchases now require CFO approval. Procurement professionals — historically a late-stage validator — are now identified as primary decision-makers in 53% of buying cycles, involved from the earliest stages.

When you shift from buying software to buying an outcome, procurement doesn’t get less important. It gets more important. Because now they’re not evaluating features. They’re evaluating a performance contract.

The questions change. “Does it have the functionality we need?” becomes “Can you prove the outcome you’re promising?” “What’s the per-seat cost?” becomes “What are the SLAs and what happens when you miss them?” “Does it integrate with our stack?” becomes “How do you measure success and how do we audit it?”

This raises the analytical bar for every vendor in the room. And it raises it specifically for B2B marketing technology vendors, because marketing outcomes have historically been the hardest to attribute and the easiest to obscure.

Service as Software forces accountability. That’s good for buyers. It’s clarifying for vendors who can deliver. It’s existential for vendors who can’t.

The Buying System Is Now Machine + Human

There’s a dimension to this shift that almost no one in B2B is taking seriously yet, and it changes the implications of everything above.

The buyer isn’t becoming a machine. The buying system is becoming machine-human integrated.

Gartner projects that AI agents will be involved in the vast majority of B2B purchases within three years, channeling more than $15 trillion in spending through automated evaluation. Forrester found that 94% of B2B buyers already use AI in the purchasing process, and that twice as many buyers now name generative AI as a more meaningful information source than vendor websites or sales teams.

But here’s the critical nuance: buyers aren’t handing off to agents and waiting for a shortlist. They’re working with agents iteratively; prompting, refining, validating, overriding. The agent surfaces structured signals. The human interprets, challenges, and decides. The agent adjusts. The cycle repeats.

By the time your Service as Software offering reaches a human conversation, it has already been filtered, scored, and contextualized by a machine that the human trusts enough to be working with in the first place.

You are not marketing to a human audience anymore. You are marketing to a system. And that system has two components that must be satisfied simultaneously, not sequentially.

The implications for how you go to market are significant.

The machine component of the buying system doesn’t respond to corporate narrative. It evaluates structured, verifiable, consistent proof. “A case study stating ‘improved productivity’ is invisible to agents,” Forrester noted in its 2026 State of Business Buying report. “Reduced processing time from 14 days to 3 days is specific enough to extract and compare.”

The human component, working alongside an increasingly capable agent, arrives at vendor conversations better prepared and less tolerant of information they’ve already sourced. They don’t need you to explain the category. They need you to demonstrate judgment and point of view that the agent couldn’t surface on its own.

If your pricing is opaque, your outcomes aren’t expressed in quantifiable terms, or your proof infrastructure is thin, you may lose the deal before anyone picked up the phone. And you’ll never know it happened.

What This Does to Partner Marketing

Most channel marketing strategies were built for a licensing economy.

The reseller partner who moved seats earned margin on the transaction. The systems integrator who deployed the platform earned margin on the implementation. The referral partner who brought the deal earned a percentage of ACV. Every incentive in the traditional partner model pointed at the software transaction.

Service as Software decouples value from the transaction. If the vendor is now responsible for the outcome, the traditional partner’s role collapses. Moving a license to a customer who then fails to get results isn’t a win. It’s a liability.

The partner that wins in a Service as Software economy is a different entity entirely: one that configures, manages, and guarantees the outcome on behalf of the buyer. A delivery partner, not a distribution partner. Accountable for outcomes, not for moving licenses. The ISV relationship becomes a managed service overlay on top of agentic capability, not a distribution arrangement.

For B2B marketing leaders managing partner programs: this is not an edge case. It is the direction of travel. Your partner tiering, your co-sell incentives, your MDF programs, all were designed for a software transaction model. Most will need to be rebuilt for an outcome delivery model.

The Transition Is Messier Than the Thesis

I want to be clear about something, because this is an operator’s publication and operators live in reality.

The SaaSpocalypse was the market pricing the transition’s messiness in real time. Two trillion dollars of repricing in a matter of weeks is the capital markets saying: we don’t know which SaaS companies survive this shift, so we’re discounting all of them until they prove it. That’s not irrational. It’s pattern recognition from investors who’ve seen this movie before.

But the actual operating transition is slower and more hybrid than the market event implies.

According to a 2025 SaaS Pricing Benchmark Study, only 9% of companies have fully implemented outcome-based pricing. 47% are actively exploring or piloting it. The remaining 44% are still running the old model, and will be for some time.

BCG expects that most mature vendors will combine models: some subscription baseline, some agent-based pricing, some outcome-based performance fees. Pricing structures will be more complex before they’re simpler. Sales motions will be more consultative before they’re more automated. Buyers will need new RFP frameworks, new measurement practices, and new governance structures before they can fully operationalize outcome purchasing at scale.

And Gartner has flagged that at least 30% of GenAI projects will be abandoned due to unclear business value. The hype cycle is real. Some of what’s being sold as “Service as Software” today is simply repackaged SaaS with a new name on it.

The structural direction is clear. The pace is not. Know which one you’re operating in.

What Operators Need to Do Now

For B2B marketing leaders:

Buy outcomes, not access. Start by auditing your existing stack against a single question: is this vendor making my team faster, or making my team unnecessary? Those are different things, and only one of them is the future. The answer tells you which vendors survive your next renewal cycle, and which conversations you should already be having.

Develop RFP frameworks that demand outcome commitments, not feature lists. Build the internal capability to measure vendor performance against those commitments. If you can’t measure it, you can’t hold anyone accountable for it.

And structure your proof assets for the Machine + Human buying system, not just for a human audience. Quantifiable, verifiable, structured outcomes aren’t just good storytelling. They’re the minimum table stakes for being found and evaluated by the system that precedes the human conversation.

For B2B technology vendors:

The seat count is no longer a growth metric. Outcomes delivered, outcomes measurable, outcomes attributable, those are your new unit economics. If you cannot articulate what success looks like in quantifiable terms and stand behind it contractually, you are not positioned for this transition.

If your go-to-market motion is still feature-benefit selling, you are selling the wrong thing to a buyer whose purchasing system has already moved on.

For B2B media and marketing service providers:

This is your moment. If you can execute. The demand for outcome-based marketing services has never been higher. Content syndication, demand generation, account-based programs: all become far more valuable when they’re measured against pipeline outcomes and delivered at scale with agentic efficiency.

The providers who can price to outcomes and prove them will displace the vendors still selling impressions, leads, and seats.

The Bigger Picture

The SaaS era gave buyers better tools. The Service as Software era gives buyers better results, or it doesn’t get paid.

That accountability shift is a reorganization of incentives across the entire B2B technology ecosystem. When vendors succeed only when customers succeed, the dynamics of product development, customer success, go-to-market, and partner strategy all change. The consultative relationship that enterprise vendors aspired to becomes a contractual requirement.

For the CMO who has spent five years managing a 25-platform stack, wondering when they stopped doing marketing and started running a technology operation, that reckoning is coming. And it’s going to feel like relief.

The next generation of B2B marketing leaders won’t manage a martech stack. They’ll manage a portfolio of outcomes. And they’ll have a lot more time to actually do marketing.

The transition is underway. The direction is clear. The only question is how fast you adapt, and whether you’re buying the real thing or the repackaged version of the old model with a new name on it.

The views expressed in Uphoff on Media are entirely my own. They don’t represent the opinions of any company I’ve led, any board I’ve sat on, or any investor who’s had the pleasure of debating strategy with me over the years. If something I write here sounds brilliant, I’ll take full credit. If it turns out to be wrong, I was clearly misquoted by myself.

Gartner’s latest research: buyers spend just 17% of their total purchasing time in direct contact with vendors. 83% define their requirements before talking to sales. 81% already have a preferred vendor when they finally engage a rep. The buying journey isn’t a funnel anymore. It’s a verdict with an appeals process attached.

The conventional worry about all of this is disintermediation. If buyers reach shortlist without talking to sales, what exactly is sales for?

That’s the wrong question.

The right question is this: when a buyer arrives at 70 or 80 percent of the journey, who is carrying the risk?

It’s not the rep.

The Asymmetry Nobody Talks About

Consider what a CFO, CTO, or Chief Procurement Officer has personally staked by the time they engage a vendor. They’ve built an internal case. They’ve put their credibility on the line with their board, their CEO, their peers. They’ve made a professional judgment about which vendors belong on the shortlist and why. 86% of B2B purchases stall during the buying process, and when they do, someone inside the buying organization owns that failure. If the decision goes wrong — if the implementation struggles, the promised outcomes don’t materialize, the vendor relationship deteriorates — they are exposed. Careers have ended over enterprise software decisions gone bad.

The sales rep carries none of that exposure. If the deal closes and the customer struggles, the rep has moved on to the next quarter. The asymmetry is structural. And it is rarely acknowledged.

This isn’t an indictment of enterprise sales. It’s a design flaw that the industry has been slow to recognize. In the old model, the one enterprise sales was built for, the human relationship formed early, developed over time, and quietly absorbed much of this risk. The buyer didn’t just trust the vendor. They trusted a person. “I know this rep. They’ll go to bat for us. They know what we need.” That relationship was a rational risk transfer mechanism. The rep’s career incentive, their account ownership, their reputation, all of it created an informal backstop for the buyer’s judgment.

Strip away that early relationship, and the risk doesn’t disappear. It stays with the buyer. And now they’re carrying it alone, built on a thesis assembled from digital signals, with no human ally who helped them form it.

When the Story Doesn’t Match

Here’s where it gets more complicated, and where marketing enters the picture in a way most companies haven’t fully reckoned with.

During that 70 to 80 percent journey, the buying committee isn’t operating in an information vacuum. 70% of buyers say they prefer to learn about a product or service through content — blogs, videos, case studies — rather than traditional sales outreach. They are consuming executive interviews, keynote videos, thought leadership posts, product narratives, analyst briefings. 47% of buyers view three to five pieces of a company’s content before talking to a sales representative.

By the time the rep gets the meeting, the buying committee has already formed a relationship. Not with a person, but with a story.

What happens when the rep’s narrative doesn’t match that story?

Not a jarring, obvious contradiction. Something subtler. A different emphasis. A different vocabulary. The marketing narrative positioned the platform around operational transformation; the rep leads with cost reduction. The exec videos talked about partnership and long-term outcomes; the rep is focused on closing the quarter. The content established one set of expectations about how this company engages; the rep’s behavior signals something different.

The buying committee doesn’t diagnose this as a sales and marketing alignment problem. They experience it as unease. Something feels off. In a low-stakes purchase, that feeling gets rationalized away. In a high-stakes enterprise decision — where careers are on the line and 79% of enterprise deals require final CFO approval — it doesn’t. It becomes a quiet reason to slow the process, reopen the evaluation, or move toward a competitor who feels more coherent.

The deal doesn’t die loudly. It just stops moving.

Ground Truth

The most sophisticated enterprise marketing leaders are starting to name this problem explicitly. One framework, shared with me by a senior marketing executive at a major enterprise software company, adds a third dimension to the traditional brand and market truth model. Brand truth is what the company believes about itself. Market truth is what analysts, press, and the broader market reflect back. Ground truth is what buyers actually experience and conclude through their own research journey and customer experience.

The insight is that misalignment between these three isn’t just a messaging problem. It’s a trust problem. And in a digital-first buying journey, trust failures are harder to detect and harder to recover from, because there’s no human relationship in place to spot them and absorb the friction.

This is the same dynamic explored in “B2B Branding Has a New Audience” but where that post examines it from the brand and marketing side, the sales consequences are what make it existential. When agentic AI exposes messaging drift across every digital surface, the rep walking into that 80 percent meeting doesn't just carry narrative inconsistency into the room. They’re closing a gap that an algorithm has already scored against them.

For enterprise marketing, the implication is structural. Every content asset, every executive communication, every campaign narrative needs to be built with the rep’s eventual conversation in mind. Not as a constraint on creativity, but as a discipline of coherence. The rep is the last mile of the content journey. 82% of B2B buyers say thought leadership from individuals, not just corporate messaging, influences their purchasing decisions. That makes every exec video, every LinkedIn post, every keynote a load-bearing element of the sales motion. If the last mile contradicts the miles that came before it, the buyer notices. Even if they can’t articulate exactly what they noticed.

And that framework is about to face a stress test nobody designed it for.

Agentic AI Will Make This Worse Before Anyone Notices

Here is the part of this story that isn’t being talked about yet.

Agentic AI is entering the enterprise buying journey, and it is amplifying every tension described above. 90% of procurement executives say they have considered or are actively planning to use AI agents to optimize their procurement operations. Gartner projects that the majority of enterprise software applications will include agentic AI by 2028. Several major procurement platforms; SAP, Coupa, and others, have begun embedding these agents natively. The planning phase is collapsing into deployment. This isn’t a distant future. It’s the next procurement cycle.

What does an agentic AI procurement system actually do? It doesn’t wait for a human to run a search. It autonomously conducts vendor research, synthesizes reviews and analyst reports, scores vendors against predetermined criteria, and surfaces a ranked shortlist. Often before a human buyer has had a single conscious thought about the category. This is the B2A shift — Business to Algorithm — and it’s not emerging. It’s here. Companies that lack clean, structured, algorithm-ready data risk being invisible in digital buying journeys before a human ever enters the picture. Salesforce’s Agentforce and a growing category of purpose-built procurement AI systems are moving this from thought experiment to Q3 budget conversation.

This changes the risk calculus in a way that nobody in enterprise sales has fully processed.

When a human buyer conducts self-directed research, they are forming impressions, making judgment calls, weighing intangibles. There is still a human in the loop who can be reached, influenced, and reassured. When an AI agent conducts the initial research and builds the shortlist, the process is faster, more systematic, and far less transparent. The buying committee receives a ranked list of vendors, and critically, 83% of buyers modify their initial shortlist after conducting further research, with more than a quarter making significant or complete changes. But if the initial list was built by an agent operating on criteria the rep never knew existed, the vendor who scores poorly may never understand why.

The “something feels off” problem gets structurally worse. When a human buyer felt unease, a skilled rep could sometimes read the faint signals, ask the right question, and surface the concern. When an agent scores your company’s narrative consistency across fifty digital touchpoints and weights it against competitors, the friction is invisible. The deal just doesn’t progress. No signal. No conversation. No recoverable moment.

This is why the Ground Truth framework matters more now than it did even two years ago.

A human buyer might feel it. An AI agent will score it. And it will do so at a stage of the journey so early that the enterprise sales rep will never know the conversation was happening.

How Enterprise Sales Actually Needs to Evolve

The rep entering a 70 to 80 percent buyer journey is no longer a pitcher. They are a validator…and more precisely, a risk translator.

The buyer has built a thesis, often over months, increasingly with AI assistance. The rep’s first job is to understand it, confirm what’s right, and carefully address what isn’t. Not to restart the narrative from the company’s preferred talking points. The buyer has invested heavily in their own conclusions. A rep who ignores that investment creates resistance. A rep who honors it earns trust.

Beyond validation, the rep needs to understand what the buyer has personally at stake. Risk qualification matters as much as lead qualification. What has this buyer committed to internally? What would a poor outcome mean for them specifically? That isn’t soft skills territory. It’s the core intelligence that determines how the rep should engage, what assurances matter most, and where the deal is actually fragile.

The rep’s evolved role is risk translation: helping the buyer convert the thesis they’ve built alone into a defensible internal commitment, with a vendor partner who will stand behind it. That’s the trust transfer the old model delivered through relationship. Now it has to be earned in a fraction of the time, at the back end of a journey the rep didn’t take with them.

At the organizational level, the companies that win in a digital-first world will treat sales and marketing alignment not as a coordination exercise but as a buyer experience imperative. The story the company tells; across every format, every channel, every exec communication, needs to be the same story the rep walks in with. Not identical in script, but coherent in thesis, consistent in values, aligned in vocabulary. Because that story is now being processed not just by human buyers making judgment calls, but by AI systems making scores.

The Quiet Deals You’re Losing

The buyer seemed engaged. The evaluation went well. Then it stalled. Then it went quiet.

Often, something felt off. The buyer couldn’t name it. The rep never knew. And increasingly, the agent that built the initial shortlist made a call no human on either side of the table ever saw coming.

In a high-stakes purchase made by humans who are personally exposed, coherence isn’t a nice-to-have. It’s a closing condition. The companies that figure this out (that the rep is the last mile of a journey the buyer took largely alone, that the buyer arrived carrying real personal risk, and that AI agents are now entering that journey at its earliest and most consequential stages) will win deals their competitors don't even know they lost.

Enterprise buyers have changed. The companies that change with them will define the next era of enterprise sales.

The views expressed in Uphoff on Media are entirely my own. They don’t represent the opinions of any company I’ve led, any board I’ve sat on, or any investor who’s had the pleasure of debating strategy with me over the years. If something I write here sounds brilliant, I’ll take full credit. If it turns out to be wrong, I was clearly misquoted by myself.

B2B brand building is about to be restructured from the ground up.

Not incrementally refined. Not optimized. Structurally inverted.

Here’s the reality most B2B CMOs haven’t fully reckoned with yet: in the Agentic AI era, your brand is no longer being evaluated by a human audience with machine tools. It’s being evaluated by a Machine + Human system. Agents and buyers working together, simultaneously and interdependently, throughout the entire buying journey.

The enterprise buyer isn’t handing off to an AI agent and waiting for a shortlist. They’re working with the agent iteratively, prompting, refining, validating, overriding. The agent surfaces structured signals. The human interprets, challenges, and decides. The agent adjusts. The cycle repeats. By the time your brand reaches a human conversation, it has already been filtered, scored, and contextualized by a machine that the human trusts enough to be working with in the first place.

You are no longer positioning to a human audience. You are positioning to a system, and that system has two components that must be satisfied simultaneously, not sequentially.

Everything about how B2B brands are built, measured, and differentiated follows from that premise.

The Fuel Mix Is Changing

For decades, B2B brand building ran on a relatively stable fuel mix: analyst relationships, trade media coverage, event presence, demand generation programs, and thought leadership content. Each of these was designed for human consumption; a buyer reads, watches, attends, engages, and forms impressions over time.

That mix isn’t going to zero. But the weighting is shifting dramatically, because the audience has changed.

The machine component of the buying system doesn’t respond to corporate narrative. It evaluates structured, verifiable, consistent proof. That means work most marketing organizations have never treated as brand work: schema markup, review platform completeness, certification maintenance, consistent metadata across every surface where an agent might evaluate you, is now core brand-building activity. Not IT work. Not legal work. Brand work.

The human component of the buying system is simultaneously getting more sophisticated and more selective. Buyers working with AI agents arrive at human conversations better prepared, with higher expectations, and less tolerance for information they’ve already sourced themselves. They don’t need you to explain the category. They need you to demonstrate judgment, credibility, and point of view that the agent couldn’t surface on its own.

Building for both components, at once, coherently, is the new mandate.

Trust Becomes the Dominant Variable — But It Now Operates as a System

In every era of market disruption, trust becomes more valuable. When uncertainty is high and the pace of change is relentless, buyers look for the trust signal that most reliably predicts competence and consistency.

This moment is no different. But the agentic era changes how trust is built and evaluated, because trust now has to work across both components of the buying system simultaneously.

Machine trust is built on verifiability. Quantitative ratings, independent certifications, contractual transparency, uptime records, security documentation. Agents weight these signals heavily because they’re legible, consistent, and difficult to fake at scale. You either have them or you don’t. No amount of compelling brand narrative compensates for weak machine trust signals, the agent simply doesn’t register them.

Human trust is built on authority, credibility, and resonance. Does this vendor think clearly? Do they understand my problem better than my own team does? Do I trust their judgment in a world that’s moving too fast to validate everything myself? This is the trust that gets built through thought leadership, practitioner voice, executive presence, and community standing. No amount of machine-legible proof compensates for weak human trust. The buyer ratifies the agent’s shortlist, not the other way around.

There’s a reason B2B brand advertising has always underperformed. Most marketers treat enterprise purchasing as a rational process: feature sets, integration specs, pricing tiers, ROI models. They optimize for the quantitative case and underinvest in the emotional one. But B2B purchase decisions have never been purely rational. Risk is emotional. Trust is emotional. The unspoken question in every enterprise evaluation — if this goes wrong, can I defend this choice? — is entirely emotional. Nobody ever got fired for buying IBM wasn’t a product claim. It was an insight into how fear shapes procurement. In the agentic era, this dynamic doesn’t diminish. It concentrates. The machine handles the rational layer: structured proof, verified signals, consistent metadata. What remains for the human is judgment, confidence, and the emotional calculus of professional risk.

Brand has always lived there. Now it’s the only place left that machines can’t reach.

But here’s the critical point the sequential framing misses: these two forms of trust don’t operate in separate phases. They interact. A human buyer working with an agent is weighing both simultaneously, asking the agent to pull structured data while applying their own judgment about which vendors feel credible.

A message that works perfectly for an agent but lands wrong with the human using that agent is still a failure. The signals have to be coherent within the system, not just adequate on each dimension independently.

The brands that win are the ones where machine trust and human trust are strong, aligned, and mutually reinforcing. That alignment is the new core challenge for B2B marketers.

Agentic AI Will Expose Every Inconsistency You’ve Been Living With

Here’s the near-term operational reality most B2B marketing leaders haven’t confronted yet: your brand is probably already inconsistent across the surfaces that matter. Not because of bad strategy. Because of organizational entropy.

Your website says one thing. Your G2 profile says something slightly different. Your LinkedIn company page hasn’t been updated since the last rebrand. Your partner listings describe a product you pivoted away from eighteen months ago. Your pricing page is deliberately vague while your sales deck quotes specific numbers. Your analyst profile reflects a positioning you outgrew two years ago.

In the human-only era, buyers navigated this inconsistency intuitively. They triangulated, asked questions, and gave you the benefit of the doubt. Inconsistency was sloppy but survivable.

In the Machine + Human era, it’s a trust penalty. A compounding one.

When the machine component of the buying system evaluates your brand across multiple surfaces — and it will — conflicting signals don’t average out. They create doubt. An agent trained to identify reliable vendors treats inconsistency as a risk signal, not a nuance to be interpreted generously. Misaligned messaging, contradictory value propositions, opaque commercial terms, these don’t just confuse the machine. They move you down the shortlist, or off it entirely. And a human buyer who sees those inconsistencies flagged, or encounters them directly, loses confidence in ways that are very hard to recover from mid-journey.

The immediate tactical implication: the CMO who conducts a rigorous brand consistency audit across every machine-legible surface right now; website, review platforms, partner listings, analyst profiles, press coverage, social presence, commercial documentation, has a near-term competitive advantage that costs almost nothing to capture. This isn’t glamorous brand strategy work. It’s the operational prerequisite for everything else in this post.

Do it before your competitors do.

Storytelling Goes Up in Value, Down in Volume

Here’s the counterintuitive move. In a world where the machine component of the buying system handles discovery and structured evaluation, narrative and storytelling don’t become less important. They become more important, and more precisely targeted.

When AI commoditizes the discovery and evaluation layer, what remains as true differentiation in the human conversation? Point of view. Intellectual honesty. The ability to frame a problem so precisely that a buyer feels understood in ways no structured data source could.

Narrative becomes the currency of the final mile. The element of your brand that the machine can surface evidence of, but can’t replicate or replace.

The catch: the human component of the buying system is smaller and more consequential than the broad human audience of the previous era. Fewer people are in the room. So storytelling’s value per impression goes up while its distribution volume goes down. The implication for content strategy is significant: invest in fewer, deeper, more operator-credible pieces rather than volume content designed to feed an automated pipeline. In a Machine + Human buying system, depth and credibility outperform volume every time.

Where to Invest, Where to Pull Back: The Channel Mix Is Being Reordered

Some of this is already in motion. The agentic era accelerates and amplifies each of these shifts, because every medium now has to be evaluated through the lens of what it contributes to the Machine + Human buying system, not just to human awareness.

↑Experiential events: Up. When the machine component handles discovery and initial evaluation, live human presence becomes the scarcest and highest-fidelity signal in the market. In-person connection is where machine-shortlisted vendors become trusted partners. Where the human component of the buying system forms the judgments that structured data can’t. The trade show model continues to struggle. The curated dinner, the practitioner roundtable, the executive cohort — these go up. Not because they’re nostalgic, but because they operate precisely in the space the machine can’t reach.

↔Demand generation: Restructured, not eliminated: The form fill, the gated asset, the email drip sequence, these specific mechanics are structurally misaligned with a Machine + Human buying journey. But the underlying function of demand generation doesn’t go away. What changes is the model.

B2B marketers have hit a genuine tipping point with technology complexity. The martech stack — built layer by layer over the past fifteen years — was designed for a world of human buyers moving through discrete, trackable stages. That world is ending. What replaces it isn’t more technology. It’s a fundamentally different service model.

The demand generation providers who survive and lead in the agentic era will deliver something closer to Demand-as-a-Service: multi-channel engagement, pipeline impact, and analytics integrated into a single coordinated offering rather than a collection of separate tools and tactics stitched together by an overtaxed marketing team. The separation of “content agency” from “media buyer” from “analytics platform” from “SDR team” is a legacy of a simpler era. Agentic AI collapses those distinctions, or more precisely, it makes the cost of maintaining them prohibitive.

Successful demand generation in the agentic era requires coordinated engagement across both the machine and human components of the buying journey, sequenced intelligently rather than executed as parallel, disconnected programs. An agent shortlists you. A human validates. A practitioner-influencer confirms. A curated event closes. That’s a coordinated buyer journey, and it requires a coordinated provider, not five vendors trying to hand off to each other.

↑Thought leadership: Up, but restructured: The white paper with three industry endorsements and a PDF download is finished. What replaces it: structured POV content that’s simultaneously machine-indexable and human-resonant. Short, declarative, operator-credible, and tied to a genuine point of view about the market. The executive voice matters more now, not less, because it’s one of the few signals that works meaningfully for both components of the buying system at once. But it has to be real, not ghostwritten corporate prose.

↑B2B influencers: Significant rise: Not celebrity influencers. Practitioners-as-influencers. People who have actually done the job, carry functional credibility with your buyer, and speak in a register that earns trust precisely because it doesn’t sound like marketing. This signal works across both components of the buying system: machine-indexable as third-party validation, and human-resonant as peer credibility. That dual utility makes it disproportionately valuable. Expect it to become a meaningful budget line.

Academia Is Waning, And That Trend Accelerates

For decades, academic credibility was a core pillar in B2B tech markets. Research papers, university partnerships, PhD-certified methodologies, these were proxies for rigor and third-party validation in a world where buyers had limited tools for independent verification.

That proxy is breaking down, and the Machine + Human buying system accelerates the breakdown from both directions.

The machine component can now source, cross-reference, and evaluate far more diverse evidence than any buyer could manually. Academic affiliation is just one signal among many, and not a particularly fast-moving one. The cycle time problem alone is disqualifying: academic research takes years; B2B markets now move in quarters. The human component, working alongside increasingly capable agents, values current, contextual, operator-grounded analysis over credentialed but dated research.

What fills the gap: analyst-practitioners, operator thought leaders, and community-validated research. The rise of independent expert voices — people with track records, real stakes, and current market engagement — isn’t a trend. It’s a structural shift in how both components of the buying system evaluate what to believe and trust. The machine surfaces them. The human recognizes them. Together, they move the needle in ways institutional research no longer can.

New Tools Are Coming, And the Category Doesn’t Exist Yet

If brand trust now has to operate coherently across a Machine + Human buying system, the diagnostic tools have to change too.

The current marketing analytics stack was built to measure human behavior. Impressions, clicks, downloads, MQLs, pipeline attribution. Those tools don’t answer the questions that matter now:

* When an AI agent queries for solutions in my category, does my brand appear? With what frequency, in what context, with what sentiment?

* How complete, consistent, and machine-readable is my proof infrastructure across every surface the buying system evaluates?

* Does my brand tell the same story across website, G2, LinkedIn, partner descriptions, analyst profiles, and press coverage, or are there inconsistencies creating trust penalties in the machine layer?

* What percentage of credible practitioner content in my category validates or references my brand, and is that content machine-indexable as well as human-credible?

* And eventually, the killer metric: what percentage of agentic buying journeys in my category end with my brand on the shortlist?

Nobody has built this cleanly yet.

Early signals are emerging, companies like Profound are taking initial runs at AI visibility measurement, and some SEO intelligence firms are beginning to pivot from search visibility to agent visibility. The tool that measures machine trust, human trust, and the gap between them doesn't exist yet.

That's not a problem. That's a market opportunity.

The firm that builds a credible Agentic Brand Trust Score; something a CMO can track quarterly, benchmark against competitors, and act on across both dimensions, will define a significant new category of B2B marketing infrastructure. It’s the next essential instrument in the CMO’s toolkit, and it will emerge from AI-native analytics startups rather than from the established marketing technology vendors who built their platforms for a different world.

New Firms Will Win This Market. Here's What They Look Like.

I’ve written about where traditional ad agencies are headed. The creative-driven, brand-narrative firm built for human media consumption is structurally misaligned with what B2B marketers now need, because it was built for one component of a system that now has two.

Five capabilities the market will pay for that barely exist today:

Structured narrative architects build brand signals that work simultaneously for machine legibility and human resonance. This sits at the intersection of content strategy, data architecture, and brand thinking, and it doesn’t really exist inside any current agency or consulting model.

Agentic content strategists understand how AI agents discover, evaluate, and weight vendors, and design content specifically to perform well within that process. This isn’t SEO. It’s something more fundamental: optimizing for how the machine component of the buying system thinks, while maintaining the human credibility that earns the final decision.

Operator thought leadership producers help executive teams develop and distribute credible, practitioner-voice content at scale without sanitizing it into corporate messaging. The voice that works for both components of the buying system is rare. Producing it consistently is a real capability gap.

Trust infrastructure consultants audit and build the proof-point architecture; reviews, certifications, structured data, third-party validations, pricing transparency, that the machine component of the buying system actually evaluates. This work currently sits in no-man’s-land between marketing, IT, and legal. The firm that owns it will be valuable.

Community architects build owned audiences that aren’t dependent on algorithms, platforms, or machine discovery. In a world where automated shortlisting is the default, a trusted community of engaged practitioners is a durable moat, and a source of human trust signals no machine can manufacture.

None of these exist inside a current agency or consulting firm. That's the point.

The Strategic Imperative

The brands that navigate this transition well will understand something most of their competitors won’t.

This isn’t a channel shift. It’s not a new media mix or a technology upgrade. It’s a structural change in the nature of the audience for B2B brand signals: from a human audience assisted by tools, to an integrated Machine + Human system in which both components must be satisfied coherently and simultaneously.

Building for that reality requires rethinking brand strategy, content strategy, measurement infrastructure, and the partner ecosystem at the same time. That’s hard work. Most organizations will do it slowly, partially, and reactively.

The ones that do it deliberately, that treat the Machine + Human buying system as the design constraint for everything they build, will have a compounding competitive advantage that gets harder to close over time.

The question every B2B CMO needs to answer right now isn’t whether this shift is happening. It’s whether they’re building a brand architecture designed for the buying system that exists today, or still optimizing for the one that existed five years ago.

The views expressed in Uphoff on Media are entirely my own. They don’t represent the opinions of any company I’ve led, any board I’ve sat on, or any investor who’s had the pleasure of debating strategy with me over the years. If something I write here sounds brilliant, I’ll take full credit. If it turns out to be wrong, I was clearly misquoted by myself.

When Doug McMillon announced he was stepping down as CEO of Walmart after more than a decade at the helm, he didn’t blame the board. He didn’t cite personal reasons. He said something that should stop every senior executive in their tracks:

“With what’s happening with AI, I could start this next big set of transformations with AI, but I couldn’t finish.”

Read that again. The CEO of the largest company in the world; a leader who guided Walmart through the internet era, through supply chain collapse, through a pandemic, looked at what was coming and made a clear-eyed decision: the transformation horizon ahead didn’t match his own.

Around the same time, Coca-Cola CEO James Quincey said he could lead the company through the “pre-AI” phase, but that the next wave of “AI-driven growth” needed someone with fresh energy. Adobe CEO Shantanu Narayen, after eighteen years, framed his departure around the same idea: the next era of creativity is being “shaped by AI,” and new leadership should shape it.

Three CEOs. Three of the most recognized brands in the world. All saying a version of the same thing on their way out the door.

This isn’t a wave of high-profile executives burning out. It’s something more significant. It’s a cohort of experienced, accomplished leaders making a rational calculation that the technology disruption now underway is categorically different from anything they’ve navigated before — and deciding, honestly, that someone else should be at the wheel for the next leg of the journey.

That level of self-awareness deserves more credit than it’s getting. And it raises an urgent question for every other C-suite leader still in the chair:

What’s your honest answer to that same question?

The Data Behind the Signal

Before we get to frameworks, let’s establish the facts, because this isn’t anecdote. It’s a documented structural shift.

The numbers tell a story that goes well beyond a few high-profile departures.

CEO turnover hit record levels in 2025: a 16% spike in global departures from the prior year, more than 20% above the eight-year average. More than 1,200 U.S. CEOs left their posts in the first half of the year alone. Critically, this isn’t a story about underperformance. In the S&P 500, successions at top-performing companies jumped from 7% to 12%. Boards aren’t reacting to failure. They’re getting ahead of something.

The age data is where the AI signal becomes unmistakable: The average age of departing CEOs jumped from 55 to 63 in a single year. In July 2025, the average age of exiting CEOs hit 70.3, compared to 56.2 in the same month a year earlier. A retirement wave is underway, and AI is accelerating it. Boards aren’t just accepting these departures, in many cases they’re engineering them, actively skipping Gen X and moving directly to Millennial leaders for whom AI fluency is native, not acquired.

Here’s the wrinkle that most commentary is missing: not every experienced leader is heading for the exit. A recent survey found that 58% of Baby Boomers aged 62-plus said they are not currently considering retirement, a dramatic reversal from just a year prior, when only 11% said the same. The most common reason? They want to develop new skills. The best of this generation aren’t running from AI. They’re leaning into it. Which means the real divide isn’t generational. It’s between leaders who are engaging with the disruption honestly, and those who aren’t.

Which brings us to the real question: not whether AI is forcing a leadership reckoning—but how the leaders still in the chair should think about it.

A Disruption Unlike the Others

I’ve lived through several technology disruptions in operating roles; the internet’s first wave, the shift from print to digital, the rise of cloud and SaaS, the mobile platform shift. Each one was significant. Each one produced real winners and real casualties. None of them felt quite like this.

Here’s why this is different.

The internet disrupted business models and distribution. It reshuffled who owned the customer relationship and what information was worth paying for. But the core of what executives did — made decisions, managed people, read markets, allocated capital — remained fundamentally human. Technology changed the conditions. The decisions were still human.

I’ve watched this pattern play out firsthand. In the early days of the internet, I sat across from publishing executives who were genuinely unconcerned. “People will always want their morning newspaper. Magazines have loyal audiences that aren’t going anywhere”. The Internet platforms looked like novelties. They were not.

A few years later, while running The Hollywood Reporter, I was in meetings with studio executives exploring what digital distribution might mean for the filmed entertainment business. The response was remarkably similar. One executive turned my question about the still-nascent streaming model back on me: “Tony, theatrical box office is a $10 billion industry. Take a guess what DVD sales are. A $20 billion industry. We’re not spending much time thinking about streaming these days.” That was 2006. Today, Netflix is worth roughly two and a half times Walt Disney — the most powerful studio on the planet at the time of that conversation. The disruptor is worth more than twice the incumbent. The DVD business is a footnote.

The pattern across every major technology disruption is the same: the incumbents aren’t stupid. They’re rational, defending what’s working, discounting what’s early, and optimizing for the present. The problem is that the present has a shorter shelf life each time.

Agentic AI isn’t acting on the business. It’s acting on the function of decision-making itself. It’s entering the territory that senior executives have always owned: synthesis, judgment, resource allocation, workflow design, organizational structure.

That’s not a disruption to the business model. That’s a disruption to the role of the leader.

Consider what's now within reach:

CFO planning cycles that once required analyst teams for weeks can increasingly be run — at least in significant part — by AI agents.

Enterprise technology stacks that took CTOs and CIOs years to architect are being challenged by AI-native alternatives that bypass the complexity entirely.

Procurement functions that were built on human relationship and institutional knowledge are being tested against end-to-end automation.

Go-to-Market programs built over decades on personal relationships and market intuition are facing autonomous agents that evaluate, shortlist, and in some categories select vendors without a salesperson ever entering the picture.

None of this is fully realized yet. The question isn’t whether these shifts will happen. The capability is already here and moving fast. The question is whether the leaders in these roles are getting ahead of it or waiting for it to arrive. Because the executives who frame this as a threat to their function will lose that argument to the CFO who wants to cut headcount. And the executives who frame it as a transformation of their function’s value — from process management to strategic intelligence — are the ones who will define what these roles look like on the other side.

The EY analysis puts the productivity timeline plainly: the benefits of generative AI will likely arrive within three to five years, compared to multiple decades for the steam engine and roughly ten years for the computers. We are not in the early innings of a slow-moving transformation. We are in the compressed early innings of the fastest-moving general-purpose technology disruption in modern business history.

Morgan Stanley’s economists reviewed five major innovation waves; from the Industrial Revolution through the internet, and found that across every one of them, disruption ultimately complemented employment rather than eliminating it. History suggests we’ll adapt. But the historical record also suggests the transition is where leaders and organizations are made or broken.

The transition is now.

The C-Suite Reckoning: Role by Role

The disruption isn’t uniform across the C-suite. Each function is facing a specific version of this challenge. Let me be direct about each one.

* The CEO. The pressure at the top is the most visible. The McMillon and Quincey moments are proof. But the more common failure mode isn’t the CEO who steps down honestly. It’s the CEO who stays, acknowledges the disruption publicly, and then delegates it to a Chief AI Officer or a transformation task force. Real AI transformation requires the CEO to own the conviction. Not every implementation detail, but the organizational belief that this is an enterprise-wide operating shift. Every time I’ve seen that conviction delegated, the transformation stalled. That’s more than a prediction. That’s a pattern I’ve watched repeat across every major technology shift for thirty-five years.

* The CFO. Traditional financial planning — scenario modeling, FP&A, cost forecasting — is going AI-native. The CFO who built their career on Excel mastery, quarterly reviews, and headcount-based cost structures is watching the foundation move. AI can run financial models in minutes that used to take FP&A teams weeks. The CFO who leads through this will be the one who reframes their role from financial architect to “capital allocation strategist”, deciding where human judgment is irreplaceable and where AI-driven analysis should drive the work.

* The CTO/CIO. Of all the C-suite roles, this one carries the sharpest irony. The executive whose entire mandate is technology leadership now faces a transformation that threatens to outrun the very playbook they built their career on. Enterprise technology stacks assembled over decades — the integrations, the vendor relationships, the architecture decisions — are being challenged by AI-native alternatives that bypass the complexity entirely. The CTO or CIO who leads through this isn't the one who defends the existing infrastructure investment. It's the one who can hold two things simultaneously: managing what the business runs on today while architecting what it needs to run on tomorrow. That's not a technology challenge. It's a leadership one.

* The CHRO. Workforce planning in an era when the CEO of Anthropic has publicly stated that AI could eliminate 50% of white-collar work within five years is genuinely uncharted territory. The CHRO who is still running traditional headcount plans and competency frameworks is already behind. The CHRO who will lead through this is building what I’d call a “dynamic workforce architecture”, a continuous model that maps AI capability against human work, identifies where augmentation creates leverage, and redesigns roles rather than eliminating them wholesale. The future is about Human + Digital Labor.

* The Chief Growth Officer / CMO. B2B marketing as practiced for the last decade is being structurally dismantled. Buyer journeys are being executed by AI agents rather than human researchers. The content, events, and paid media programs that generated pipeline in 2020 are producing diminishing returns. The CMO who leads through this transition will stop optimizing existing channels and start designing for a world where the first buyer in the room may not be human.

* The Chief Procurement Officer. This may be the function most immediately in the crosshairs. Agentic AI is coming for sourcing, vendor evaluation, contract management, and supplier intelligence with a speed and scale that no traditional procurement team can match. The CPO who leads through this isn’t managing a cost center. They’re running an intelligence function, one where the value isn’t in the process they oversee, but in the strategic judgment they bring to decisions that AI can inform but not make.

Three Archetypes for Leading Through This

I’ve spent time thinking about how C-suite leaders are actually responding. Not how they’re describing it in the press release, but how the work is actually unfolding. What I see are three archetypes. Each has a different job to do.

The Transformer

This is the leader who has the runway — in terms of time horizon in the role — and the conviction to lead through the full AI transformation cycle. The imperative for the Transformer is to stop treating AI as a department initiative and start treating it as an operating system for the enterprise.

What that means in practice: every function gets reviewed through the lens of what AI can do that humans were doing, what humans must do that AI cannot yet do, and where the hybrid model creates genuine competitive advantage. That’s not a technology project. It’s a strategic redesign, and it has to be led from the top.

The Transformer also has to be willing to do something most executives avoid: change the incentive structures before the transformation demands it. AI adoption stalls when the people responsible for it are also the people most threatened by it. If the CHRO’s success metrics are still tied to headcount growth, you will not get genuine AI workforce transformation. If the CPO is measured on cost savings from vendor relationships, you won’t get a procurement function redesigned for intelligence-led sourcing.

The Transformer changes the metrics first.

The Sequencer

This is the leader who is honest — privately, at minimum — that they won’t finish the journey. They have enough runway to make real moves, but they can see the horizon. The Sequencer’s imperative is not transformation, it’s architecture.

What does a Sequencer do? They build the capability scaffold that gives the next leader a genuine running start. That means: driving AI literacy into the organization at every level. Building the data infrastructure that AI-driven operations require. Making the succession bet: identifying and developing the leaders who have both the operating experience and the AI fluency to take the baton.

The worst outcome for a Sequencer is the one I’ve seen too often in technology transitions: a leader who neither transforms nor prepares. Who manages inertia, makes incremental moves, protects existing structures, and hands the next CEO a business that is further behind than it looks. That’s not a neutral outcome. It’s an actively harmful one.

The Sequencer who does their job well is underappreciated in real time and overappreciated in hindsight. Do it anyway.

The Advisor

This is the leader who makes the call that McMillon made. Honestly, on their own terms. They step out of the operating role, but they don’t disappear. They move into the work that their experience actually makes them irreplaceable for: board roles, advisory relationships, mentoring the next generation of operators.

I’ll be transparent: this is the archetype I’m living right now. I recently stepped out of a CEO role, one where we spent two-plus years transforming a B2B demand generation business into a digital marketing services company built on a Demand-as-a-Service model. We started the AI transformation. We could see what was coming. And I made the same calculation McMillon described, at a far smaller scale. The next leg of the journey was better led by someone whose horizon matched it. That recognition is what led me to launch Uphoff Advisory and shift into board and advisory work — where thirty-five years of operating experience turns out to be exactly what organizations navigating this transition need most.

The Advisor role isn’t a retirement. It’s a repositioning.

The Honest Question

Let me end where I started.

McMillon said: ”I could start it, but I couldn’t finish it.”

That is a remarkable sentence. It requires both confidence; the conviction that you understand what needs to happen — and humility — the honesty that the journey ahead isn’t yours to complete.

Most leaders in the C-suite right now are not asking themselves that question. They’re managing the present. They’re responding to quarterly pressures, board expectations, and the urgent over the important. The AI disruption is real and they know it, but it hasn’t forced a clear-eyed personal reckoning yet.

It will.

The leaders who will navigate this best, whether as Transformers, Sequencers, or Advisors, are the ones who ask the question now, honestly, and then act on the answer with the same rigor they’d apply to any other strategic decision.

The Transformer commits the enterprise to a full operating redesign and changes the incentives to match.

The Sequencer builds the scaffold, develops the successor, and makes the architecture decisions that matter most.

The Advisor doesn't exit. They redeploy, bringing years of pattern recognition into the boardroom, into advisory relationships, and into the work of guiding operators navigating a disruption they've already lived through once.

None of these are failure modes. The only failure mode is the one that looks like leadership but is actually avoidance: staying in the chair, acknowledging the disruption in every earnings call, and changing nothing that actually matters.

The views expressed in Uphoff on Media are entirely my own. They don’t represent the opinions of any company I’ve led, any board I’ve sat on, or any investor who’s had the pleasure of debating strategy with me over the years. If something I write here sounds brilliant, I’ll take full credit. If it turns out to be wrong, I was clearly misquoted by myself.