B2B Marketing Is Broken. Brand Is Why. And Brand Is the Fix.

When AI Builds Itself: What the Anthropic White Paper Means for B2B Leaders

The most useful thing I can offer on the AI disruption isn’t a prediction. It’s a pattern. One I’ve lived through before, at scale, in this sector. I was in the front seat of the roller coaster. At the time, I was running a fast-growing, $300 million print-centric B2B media business, and by every metric, we were succeeding. Then the Internet arrived. Not gradually, or at least it didn’t feel that way from the operator’s seat. One quarter you’re managing circulation strategies, analyzing market share reports, and launching events. The next, employees are stopping you in the hallway, the board is asking pointed questions, and customers want to know what you’re doing “about the Internet.” I had covered technology disruptions for years. The brands I was running served technology professionals. I understood disruption conceptually. What I had never done was lead a business through one in real time, with real consequences, with people’s livelihoods and a board’s confidence depending on decisions I had to make before I had the answers. No HBR article prepared me for it. No academic framework made it manageable. What got me through it — and what I learned at real cost — was hard, experiential learning. The ability to stay calm when the pressure was extraordinary. The ability to move forward before the path was clear. The ability to hold the organization together while simultaneously rebuilding what the organization did, what it sold, and what it was worth. That moment taught me more about leadership than anything before or since. Let me be clear about something before we go further. If you’re reading this expecting another dramatic proclamation about the impact of artificial intelligence, stop here and go read something else. There is no shortage of that content. AI is saturating every media channel, every business conference and every board agenda. The volume of coverage is inversely proportional to the operational clarity it produces. Most of it is written by people who are analyzing AI from the outside and with minimal practical insight. Very little of it is written by people who have actually led businesses through a technology disruption of comparable scale. I have. And that is the lens for everything that follows. For readers who are digital natives and have built their entire careers in a world where the Internet already existed — which is at least half of you, and probably more — the Internet disruption is history, not lived experience. Some of you were in the early stages of your careers when it happened, not yet in leadership positions where the decisions landed on your desk. I’m not telling you this to establish context. I’m telling you this because the playbook from that disruption is the closest thing to a field guide for this one. The Internet didn’t just change how B2B media companies distributed content. It restructured what content was worth, who would pay for it, and what a media business fundamentally was. The companies that survived and grew were the ones that recognized they were in a different business than the one they thought they were in, and acted on that recognition before circumstances forced it. The companies that didn’t survive treated the Internet as a distribution channel for what they were already doing. They optimized the wrong thing until it was too late to optimize the right thing. We are in that moment again. And it may be more disruptive still. Which brings us to the Anthropic white paper, and why it matters more than the coverage it has received. The Anthropic Institute titled it "When AI Builds Itself." [https://www.anthropic.com/institute/recursive-self-improvement] That title is the thesis. This is not a technology document, and it is not another breathless dispatch from the AI hype cycle. It is a document that describes, in hard data, the operating environment you are already living in, whether your organization has named it yet or not. If you lead a B2B media, marketing, or technology company — or invest in one — the questions it raises are not theoretical. They are arriving in your inbox, your board meetings, and your leadership team meetings right now. It is not AI theater dressed in business language. It is a field guide, written from the operator’s seat, for the decisions in front of you. What the Paper Actually Says: Three Things That Matter Most AI commentary asks you to imagine the future. This paper shows you the present. Anthropic is documenting, in hard operational data: code output per engineer, task completion rates, productivity multiples measured against human performance, what AI is already doing inside one of the most advanced technology organizations in the world. Three findings have direct consequence for every leader in this sector. First, the development productivity compression is not incremental. It is structural. As of May 2026, more than 80% of the code merged into Anthropic’s own codebase was authored by Claude. Before Claude Code launched in early 2025, that number was in the low single digits. The result: in the second quarter of 2026, the typical Anthropic engineer was merging 8x as much code per day as they were in 2024. In a March 2026 internal poll of 130 Anthropic employees, the median respondent estimated they were producing around 4x as much output with AI assistance as they would have produced without it. Read that again. 8x more code per engineer per day. 4x overall output. At the most sophisticated AI development organization in the world, operating at the frontier of what these systems can do. For B2B technology-enabled businesses: companies that use technology to create, develop, and distribute their products and services but are not selling technology directly, this is the most consequential finding in the paper. The engineering backlog that every one of these businesses has been managing as a permanent condition of operating is not a permanent condition. It is a constraint that is beginning to lift. And when that constraint lifts, the question is not how to go faster on the existing roadmap. It is what the roadmap looks like when the binding constraint of the past decade no longer applies. Second, the capability curve is accelerating faster than most executives have internalized. The paper documents that the length of tasks AI can reliably complete on its own has been doubling roughly every four months. In March 2024, Claude could complete software tasks that take a human roughly four minutes. By 2026, that horizon had extended to 12-hour tasks. The paper projects that tasks taking a skilled person multiple days could come into range before the end of this year. Tasks taking weeks could follow in 2027. This is not a projection from an optimistic analyst. It is a measured trend line from observed performance data, documented by the organization building these systems. The pace of change embedded in these numbers is faster than most strategic planning cycles. Which means most organizations are planning against a capability baseline that is already out of date. Third, the paper names three possible futures: and the one most relevant to B2B operators is already underway. Anthropic outlines three scenarios for what comes next: the trend stalls at current capability levels; AI labs see compounding efficiency gains while humans retain direction-setting; or AI achieves full recursive self-improvement. The paper is direct that scenario two — compounding efficiency gains with human oversight — is where the evidence points today. And the operational implication of scenario two is named explicitly: a 100-person company can increasingly do the work of a 1,000-person one, because each employee sits atop a pyramid of AI agents. That sentence is not a marketing claim. It is a structural shift in competitive dynamics. And it is happening now. The All-Too-Human Reality Before the frameworks. Before the action items. First, this. The decisions described in this post are not technology decisions. They are career decisions. Organizational decisions. In some cases, identity decisions, for leaders who have built their professional lives around capabilities that are being restructured around them. The wave of senior leadership departures we are already seeing: Adobe, Walmart, and others whose CEOs have cited AI transformation as a reason for stepping aside, is being reported as a business story. It is also a deeply human story. Experienced, successful leaders concluding that they are not the right person to finish what they started. That takes a form of self-awareness and courage that is genuinely rare, and genuinely hard. I know what it feels like to run a business through a technology disruption where the ground is shifting and the answers are not yet available. The pressure is real. The isolation is real. The temptation to project more confidence than you feel, to buy time with reassuring language while privately figuring it out, is entirely understandable. I have been there. What I learned is that the leaders who navigate disruption well are not the ones who pretended it was simple. They are the ones who stayed honest about the uncertainty while remaining decisive about the next step. Those are compatible. They just take practice. With that said: here is the field guide. For CEOs: The Two Decisions That Cannot Wait Decision One: Are you the right person to finish this? This is the question nobody is asking directly in your organization, which means you have to ask it yourself. It is not a referendum on your career or your competence. The leaders stepping aside at Adobe, Walmart, and elsewhere are not failures. Several of them are arguably demonstrating their greatest leadership act: the clarity to recognize that a different phase of a company’s life requires a different profile of leader, and the integrity to act on that recognition rather than hold the seat. The honest version of this question has three parts. Ask yourself each one directly. Do you have genuine fluency with AI? Not enthusiasm for it. Not familiarity with the vocabulary. Actual working knowledge of how these systems operate and where they create and destroy value. Do you have the appetite for genuine business model transformation? Not the efficiency overlay that is insufficient. The fundamental rethinking of what your business is and what it sells. Do you have the runway to see it through? Personal energy. Board confidence. Team trust. This is an 18-to-36-month transformation, not a 6-month initiative. The Anthropic paper puts this in sharp relief. The organizations that are capturing the gains described in this paper are not doing so by running AI pilots. They are rebuilding how work gets done at a fundamental level. That requires a leader who can hold the organization through sustained uncertainty. Not someone who manages to a quarterly plan and waits for the path to clarify. If the answer to all three questions is yes, your job is to lead with full commitment and full transparency. If the answer to any of the three gives you pause, that pause is worth thinking about before circumstances force the conversation. Decision Two: How do you set expectations that are simultaneously honest and stabilizing? You have four distinct constituencies and they need four different conversations. Conflating them is where leaders make the most consequential mistakes. * Employees need to hear the honest version before they read the trade media version. Your people are not naïve. They can see what is happening around them in the sector. The Anthropic paper documents that AI is already handling more than 80% of code production at one of the world’s leading technology organizations. People doing knowledge work; writing, analysis, research, production, are drawing their own conclusions about what that trajectory means for their roles. Vague reassurance does not calm that anxiety. It amplifies it, because it signals that leadership either doesn’t know or won’t say. What employees need is an accurate picture of what is changing, a genuine account of what it means for their roles, and a believable plan for how the organization is going to navigate it. That plan does not need to be complete. It needs to be real. * Boards need a different frame entirely. Most boards are running on mental models formed in the last disruption cycle. They are simultaneously over-excited about AI as a narrative and under-equipped to evaluate it as a business reality. The paper’s finding that Anthropic engineers are merging 8x more code per day than two years ago is exactly the kind of concrete, operational data point your board needs. Not as a benchmark to match, but as evidence of the pace of change they are governing through. Your job with the board is not to match their enthusiasm or manage their anxiety. It is to be the most credible voice in the room about what AI actually means for your competitive position, your cost structure, and your product roadmap over the next 18 months. Specificity is your currency. Vague AI optimism is not. * Investors, particularly in PE-backed companies with defined fund timelines, need the transformation timeline mapped against the investment thesis clearly and honestly. Efficiency gains from AI adoption can show up in 6 to 12 months. Revenue model transformation: new intelligence products, subscription conversion, data licensing, is an 18-to-36-month story. If those timelines do not align with fund structure, that is a conversation to have now rather than at the next quarterly review. * Customers want proof, not vision. The AI positioning saturating the market right now ranges from genuinely substantive to transparently hollow, and your customers can usually tell the difference. What they need from you is specific capability milestones, honest timelines, and a clear account of how your AI investments translate into value for them. The vendors who earn trust in this period will be the ones who under-promised and over-delivered. For Operators: Three Decisions About How the Work Gets Done Decision One: The development bottleneck is lifting. Are you positioned to act? The Anthropic paper documents something that should fundamentally change how every B2B technology-enabled business thinks about its product development process. When AI systems can write production-quality code at 8x the previous rate, and when that code is passing the review standards of the world’s best engineers, the operating assumption that has governed every technology roadmap for the past decade — that engineering capacity is the binding constraint — is no longer reliable. This is not a small adjustment. For most B2B technology-enabled businesses, the operating rhythm has been: more demand than we can build for, so we prioritize ruthlessly and leave significant product opportunity on the table. The AI-driven productivity shift does not eliminate prioritization. It changes the calculus for what is worth building at all. Workflow improvements that were previously uneconomical become viable. Vertical-specific modules for individual enterprise customers become feasible. The long-deferred infrastructure work that has been on every product roadmap for three years finally gets addressed. The paper notes that Anthropic engineers are now using AI to fix problems that had accumulated for years, including one case where Claude shipped over 800 fixes that reduced a class of API errors by a factor of one thousand, work an engineer estimated would have taken a human four years to complete. The question for operators is not “how do we use AI to go faster on our existing roadmap.” It is “what does the roadmap look like when the constraint we have been managing for ten years is no longer the binding constraint.” Those are different questions and they produce different answers. Decision Two: Is your implementation model a competitive advantage or a liability? The Anthropic paper describes a world where AI agents can work autonomously for hours on complex, open-ended tasks: tasks with no clear specification, where the approach itself has to be figured out along the way. Claude’s success rate on the most open-ended tasks reached 76% in May 2026, up 50 percentage points in six months. For B2B technology vendors, this has a direct implication for professional services revenue. If AI agents can execute configuration, data migration, and workflow setup tasks that currently require billable professional services hours, then implementation revenue, a significant line item in many B2B technology contracts, compresses or disappears. Some operators will experience this as margin erosion and fight it. The sharper operators will get ahead of it by treating faster time-to-value as the selling point it actually is. Shorter implementation cycles mean shorter time to demonstrated ROI, which shortens sales cycles, improves renewal rates, and reduces customer acquisition cost. The math on giving up implementation revenue in exchange for those outcomes is often compelling. The operators who do this analysis now rather than after the revenue line is already under pressure will be in a better position. Decision Three: What not to do, and what to do now The mistakes being made at scale right now in this sector are worth naming plainly, because they are tempting mistakes made by smart people under real pressure. * Premature restructuring announced before the capability exists. Declaring an AI transformation in a board presentation or a customer communication without the underlying product reality to support it. This is not a positioning mistake. It is a trust mistake, and it is very difficult to recover from once customers and employees have seen the gap between the announcement and the reality. * Cutting the talent you will need in 18 months. The current wave of AI-driven workforce decisions contains real efficiency gains and genuine AI-washing in roughly equal measure. The leaders who will regret decisions made in this period are the ones who eliminated domain expertise in the name of efficiency. Editorial judgment. Deep customer relationships. Vertical market knowledge. These are exactly the inputs that make AI-generated intelligence valuable rather than generic. Cut them now and you will spend the next three years trying to rebuild what you gave away. The Anthropic paper is instructive on this point. The human role that remains irreplaceable, even at the most AI-intensive organization in the world, is research taste and judgment. Choosing which problems matter. Knowing which results to trust. Recognizing when an approach is a dead end. That same judgment, applied to your vertical market, is your most defensible asset. Protect it accordingly. * Ignoring the mid-level leadership layer. CEOs get the attention. Investors get the attention. The VP of Product, the Chief Revenue Officer, the Managing Editor, these are the people who will execute the transformation or block it. They are navigating this period with almost no guidance designed for them. The operators who invest in their mid-level leadership now, who bring them into the strategic conversation rather than cascading decisions down to them, will have a significant execution advantage. What to do now: Audit your product roadmap through the lens of a changed development constraint. Identify your implementation revenue exposure and model the unit economics of faster time-to-value as an alternative. Inventory your proprietary data assets, genuinely, not aspirationally. And protect the domain expertise in your organization as the strategic asset it is, because it is what will make your AI-generated intelligence valuable rather than generic. For Investors: The Filter That Matters Now The Three-Characteristic Test The PE-owned B2B media and marketing landscape contains a wide distribution of asset quality, and AI is going to widen that distribution significantly. The Anthropic paper’s scenario two: compounding efficiency gains, with a 100-person company doing the work of a 1,000-person one, is not a uniform benefit across the sector. It is a capability multiplier. And what it multiplies matters enormously. Some companies in this sector are being structurally disintermediated. Media brands whose primary value was information access. Marketing services businesses whose primary value was human execution of repeatable tasks. Research businesses whose primary value was data aggregation rather than actionable intelligence. For these companies, AI does not create a rescue opportunity. It accelerates the pressure. The companies that can use this moment to break out share three characteristics. One: A defensible proprietary data asset. Not licensed data. Not aggregated public data. Behavioral data, transaction data, intent data that comes from a genuine and exclusive relationship with an audience or customer base. Two: A trusted audience or customer relationship. The kind of relationship where the brand is the reason people show up, not just the container for the information. Three: A leadership team willing to pursue genuine business model transformation. Not an efficiency overlay on a declining model. A fundamental rethinking of what the business is and what it sells. The efficiency overlay is the more common response because it is easier to underwrite, easier to model, and easier to defend in a board meeting. It is also insufficient. The B2B media and information companies that navigate this period successfully will be the ones that use AI not to do the same thing cheaper, but to do something fundamentally more valuable: moving from content delivery to intelligence execution, from audience access to workflow integration, from quarterly reports to continuously updated decision support. The Timeline Reality The Anthropic paper’s capability trajectory has a direct implication for investment timelines that most sponsors are not explicitly addressing. If AI systems can complete tasks that take humans days by the end of 2025, and tasks that take humans weeks in 2027, then the window for business model transformation is not a three-to-five year horizon. It is compressed. Companies that begin genuine transformation now have a meaningful advantage over those that begin in 18 months. Not because the technology will be unavailable, but because the organizational learning curve, the product development cycle, and the audience trust that makes intelligence products valuable all take time to build regardless of how capable the underlying AI systems become. The timeline reality for PE sponsors is specific and needs to be addressed. Efficiency gains show up in 6 to 12 months. Revenue model transformation is an 18-to-36-month story. Most fund structures were not built around that gap. The result is pressure to declare transformation victories on an efficiency story that acquirers will correctly identify as incomplete. That misalignment is a risk factor in its own right. Sponsors and portfolio company leadership need to have an explicit conversation about whether the fund structure can actually support genuine transformation. Most are not having it. What AI-Native Leadership Actually Looks Like in Diligence The standard diligence question about AI readiness: “how are you using AI in your business”, produces answers that are almost universally unreliable, because every management team has a version of the answer that sounds good and very few have the substance behind it. Better questions: What proprietary data does this business own that gets more valuable as AI systems get better at synthesizing it? What is the gap between the intelligence this business could produce from its data and the intelligence it is currently producing? Has the leadership team demonstrated the ability to move through ambiguity and make structural decisions rather than incremental ones? And specifically: who on this team has led through a prior technology disruption cycle at the operator level, not the advisory level? That last question matters. The Anthropic paper is clear that the human role that persists, even as AI takes over the execution of complex tasks, is judgment about which problems are worth solving. That judgment, in a business context, comes from having made hard calls under real pressure, with real consequences. It cannot be learned from a course or acquired by reading white papers. The difference between leaders who have run businesses through disruption and leaders who have studied businesses through disruption is significant, and it is not always visible in a management presentation. The Vertical Business Intelligence Window For small and mid-sized B2B media and information companies, the Anthropic paper describes something specific and time-bounded: the infrastructure cost of building a sophisticated intelligence product has collapsed. The paper documents that Anthropic engineers are now using AI to complete work that would have taken humans years. Not through artificial intelligence as a concept, but through AI agents executing complex, multi-step workflows autonomously over hours or days. The same capability that is transforming software development at Anthropic is available to a focused B2B media company building a vertical intelligence product today. Five years ago, building a genuine intelligence product on top of a vertical data asset required a data science team, an engineering team, and capital that most B2B media companies did not have. Today, a focused team of two or three people can build and deploy an agentic pipeline that ingests, synthesizes, and surfaces vertical market intelligence at a level of sophistication that would have required an enterprise analytics operation in 2020. The capability documented in the Anthropic paper is not confined to Anthropic. It is available now, at a cost structure that has never existed before. This matters because the moat in B2B media has always been the proprietary relationship with the audience: the behavioral signals, the transaction data, the intent signals that come from a community that trusts the brand. What was missing was the productization capability. Agentic AI closes that gap. The companies that recognize this and move are positioned to make the long-predicted but never-quite-arrived shift from advertising revenue to audience intelligence revenue. The companies that treat AI as a content production efficiency tool are going to miss the more consequential opportunity. The window is open. The Anthropic paper makes clear it will not remain open indefinitely. The large players are moving, the capability is diffusing, and the organizations that build institutional AI experience now are accumulating a compounding advantage over those that wait. The advantage small and focused B2B media companies have right now is speed, domain specificity, and audience trust. Those advantages are real and durable, but they require action to be realized. What to Do Now If you are a CEO: Have the honest internal conversation before the board forces it. Assess your own AI fluency with the same rigor you would apply to assessing any other capability gap in your organization. Begin the four-constituency communication sequence now, starting with your employees. They need to hear the honest version from you before they read the trade media version. And make the organizational decision about AI leadership explicit: who in your organization owns this transformation, with real authority and real accountability, not as an addition to an existing role but as a primary responsibility. If you are an operator: Audit your product roadmap through the lens of a changed development constraint. The 8x productivity figure from the Anthropic paper is a leading indicator of where this goes, not a ceiling. Identify your implementation revenue exposure and model the unit economics of faster time-to-value as a strategic alternative. Inventory your proprietary data assets, genuinely, not aspirationally, and build toward the intelligence product your vertical market will pay for. Protect the domain expertise in your organization as the strategic asset it is. If you are an investor: Apply the three-characteristic filter to every portfolio company: proprietary data asset, trusted audience relationship, leadership capable of genuine transformation. Have the explicit conversation with each portfolio company about transformation timeline versus fund timeline, and be honest about the misalignment where it exists. Build an AI-native leadership evaluation framework before your next diligence process, because the standard questions are producing unreliable answers. The capability trajectory in the Anthropic paper compresses your planning horizon. Act accordingly. The Path Forward I came out the other side of the Internet disruption a better leader, a sharper operator, and far more clear-eyed about what the job actually requires. What I know now is that the leaders who navigate disruption well share a common characteristic. They do not wait for clarity before they act. They move toward the uncertainty rather than away from it, they stay honest with the people depending on them, and they build the organizational capacity to learn and correct in real time rather than the organizational theater of having all the answers. The Anthropic white paper is telling you, in hard data, that the ground is shifting. The question is not whether this disruption is real. The paper answers that. The question is what kind of leader you are going to be in response to it. That is the most important decision of all. And it is entirely yours to make. The Anthropic Institute white paper “When AI Builds Itself” is available at anthropic.com/institute/recursive-self-improvement. 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. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tonyuphoff.substack.com [https://tonyuphoff.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

How Do We Move?

Last week’s post, “The AI Layoff Myth" [https://tonyuphoff.substack.com/p/the-ai-layoff-myth] generated a ton of discussion: on the blog, on LinkedIn, and in private conversations with operators and executives across the industry. That response pointed directly to the question I'm being asked most in my advisory practice right now. This post is my answer, and the framework I've built around it. “How do we move?” That question comes up in nearly every conversation I have with founders, CEOs, executives and investors in B2B media, marketing and technology right now. Agentic AI is the context. The answers cluster into two camps. The first camp has committed. They are mandating adoption, setting deadlines, measuring usage. The second camp is interested but cautious. They are watching, waiting, gathering information before making a move. Both camps are making the same mistake. The Phase Problem In 1962, a sociologist named Everett Rogers published “Diffusion of Innovations”, a landmark study of how technologies actually move through organizations. Rogers wasn’t interested in which companies adopted first. He was interested in why adoptions fail. His finding: they fail because leaders misidentify which phase they’re in and manage accordingly. Rogers mapped five phases every meaningful technology moves through inside an organization: knowledge, persuasion, decision, implementation and confirmation. Each phase requires different leadership behavior. Skip one, and the implementation collapses, regardless of how good the technology is or how much budget is behind it. The mandate camp is running implementation plays before their organizations have completed persuasion. Compliance isn’t adoption. People follow mandates and wait them out. The wait-and-see camp has convinced themselves that accumulating knowledge is the same as strategy. It isn’t. Knowledge without structured experimentation is just informed inaction. Additive Versus Substitutive Every prior technology wave B2B executives have navigated was additive. Cloud made existing infrastructure cheaper. SaaS made existing workflows faster. Programmatic made existing media buys more efficient. Mobile extended existing experiences to a new screen. Additive technologies reward the fast follower. You can watch others absorb the implementation costs, learn from their mistakes and move when the path is clearer. The workflows you’re augmenting will still be there when you arrive. Agentic AI is substitutive. It doesn’t augment workflows. It replaces them. The research workflow. The content production workflow. The lead qualification workflow. The procurement analysis workflow. These are not getting faster or cheaper. They are being rebuilt from different starting assumptions. In a substitutive cycle, the fast follower strategy breaks down. By the time the wait-and-see executive is ready to move, the workflow they intended to augment no longer exists in its prior form. The organizational muscle memory built around that workflow is a liability, not an asset. This is what’s different. Not the technology itself, executives have managed technology transitions before. The substitutive nature of the shift is what changes the strategic calculus. Trialability: The Argument Against Waiting Rogers identified trialability as one of the strongest predictors of adoption velocity. The easier it is to experiment with a technology on a limited basis, before full commitment, the faster it moves through an organization. Agentic AI has near-zero trial cost right now. That is the fact the wait-and-see camp is underweighting. You do not need a platform decision, a vendor contract or a change management initiative to begin. Here is where to start: * Pick one workflow. Not a strategy. One specific, bounded workflow that your team runs repeatedly: a weekly report, a competitive brief, a content outline, a prospect research summary. Scope matters. Broad experiments produce ambiguous results. * Run it in parallel. Have your team complete the workflow the existing way and with an agentic tool simultaneously for 30 days. You are not replacing anything yet. You are generating comparable data. * Measure what actually changes. Not sentiment. Time to completion, output quality as evaluated by the person who normally does the work, and error rate. These three metrics will tell you whether substitution is real in your specific context. * Expand from evidence. One workflow becomes two. Two becomes a workflow audit. A workflow audit becomes a genuine adoption roadmap: phase-aware, sequenced, grounded in your organization’s actual experience rather than vendor promises or competitor anxiety. This is not a technology project. It is an operational experiment. The cost of running it is minimal. The cost of not running it compounds. The Real Question How do we move? The answer isn’t mandate and it isn’t wait. Rogers spent a career documenting what actually works: structured experimentation, phase-aware leadership and honest measurement. The organizations that navigate this transition well won’t be the ones that moved fastest or most cautiously. They’ll be the ones that moved deliberately, and started sooner than felt comfortable. The workflow you’re waiting to augment is already being rebuilt somewhere else. 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. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tonyuphoff.substack.com [https://tonyuphoff.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

The AI Layoff Myth

Walk into any boardroom or investor meeting right now and you’ll hear the same narrative: AI is eliminating jobs at scale. Executives cite it as justification for workforce reductions. Investors ask about it in every portfolio review. Business media repeats it as fact. It isn’t. The AI layoff narrative is sloppy, opportunistic, and, in most cases, factually wrong. The companies attributing cuts to artificial intelligence are largely using a convenient, forward-looking explanation for decisions that have nothing to do with AI efficiency gains. The evidence is thin. The logic is thinner. And the executives who accept this story at face value are missing what’s actually happening, and what’s actually coming. Here’s what’s really going on. Three Things Being Called “AI Layoffs” That Aren’t 1. The Pandemic Overhire Reckoning Between 2020 and 2022, the technology sector hired at a pace that had no historical precedent. Pandemic-driven demand sent headcount projections into a different orbit. Companies that had spent years managing to lean operating models suddenly found themselves flush with capital and under pressure to grow at all costs. They hired. Then reality returned. According to Challenger, Gray & Christmas, 2025 saw 1.17 million total job cuts in the U.S., the highest level since the pandemic year itself. Of those, approximately 55,000 were explicitly attributed to AI by the companies making the cuts. That’s less than 5% of total layoffs. The rest? Corrections to hiring decisions made when interest rates were near zero, capital was abundant, and growth-at-any-cost was the prevailing operating philosophy. As one workforce economist put it plainly: companies that significantly overhired during the pandemic can now point to AI as justification rather than saying “we miscalculated two or three years ago.” The scapegoating is strategic. It reframes a management error as a technology-driven inevitability. Those are not the same thing. The math does not support the narrative. Layoffs.fyi tracked 152,922 tech job cuts in 2024 and 122,549 in 2025: both significant, but trending down. The largest wave, in 2023, preceded meaningful AI deployment at enterprise scale entirely. If AI efficiency gains were the primary driver, the timeline runs in the wrong direction. 2. Private Equity Margin Engineering Private equity has played workforce rationalization as a value-creation lever for decades. This is not new, and it is not AI. What is new is the framing. PE-backed companies are under sustained pressure. The backlog of unrealized exits hit record levels, with firms sitting on $880 billion in dry powder and aging portfolios that need to show EBITDA improvement before any realistic exit window opens. The standard PE response to that pressure is cost reduction. Workforce is typically the largest controllable cost line. The script used to read: “operational efficiency.” Now it reads: “AI-driven transformation.” The underlying action, reducing headcount to improve margin profile ahead of a sale or recapitalization, is identical. The justification has been updated to match the moment. Research from Revelio Labs confirms the pattern: private equity acquisitions consistently produce elevated turnover and layoffs concentrated in higher-cost roles, regardless of technology context. That dynamic predates AI by decades. When Vista Equity Partners announced in late 2025 that headcount could drop “as much as a third” across its portfolio companies, the financial logic was the same logic Vista has always applied to software company acquisitions. AI was the new language for a very old playbook. Read these announcements clearly: a PE-attributed “AI restructuring” is, in most cases, a margin improvement plan wearing different clothes. 3. CFO Efficiency Theater This is the quietest and most pervasive driver of the AI layoff narrative: planned cost reductions announced with AI language because AI language is currently rewarded by the market. The mechanism is straightforward. Boards and investors reward companies that demonstrate AI commitment. The path of least resistance for a CFO under margin pressure is to combine a planned cost reduction with an AI investment announcement and let the narrative do the rest. The workforce reduction funds, at least in part, the AI spend. The press release positions it as transformation rather than contraction. Workday cut 8.5% of its workforce — roughly 1,750 people — while announcing it was “reallocating resources toward AI investments.” Microsoft laid off approximately 6,000 workers, citing a shift toward an “intelligence engine.” Both are real companies making real operating decisions. But attributing those cuts primarily to AI efficiency gains implies that AI systems are now performing work that humans previously did. In most cases, that is not what’s actually happening. It’s budget reallocation with a narrative wrapper. The tell is in the rehire data. Forrester found that 55% of companies that executed “AI-driven” layoffs subsequently expressed regret. Klarna, one of the most cited examples of AI replacing human workers, replaced 700 employees with AI, watched service quality decline, and began rehiring. If AI had genuinely absorbed the work at scale, the rehire wave wouldn’t exist. The AI layoff narrative is a symptom of executive confusion, not a signal of transformation. The Counter-Intuitive Prediction: Agentic AI Will Fuel a Hiring Surge Here is where the narrative breaks entirely from reality. Not because AI won’t change the workforce, but because the people describing the change have the direction wrong. Winston Churchill famously said “never let a good crisis go to waste.” The crisis here is the confusion itself: the fog of bad narrative, misread data, and fear-driven decision-making that is causing B2B leaders to misallocate attention, cut the wrong people, and miss the actual strategic inflection point in front of them. That inflection point is agentic AI. Not the generative AI tools most teams are experimenting with today, but autonomous systems that plan, execute multi-step workflows, take actions, and operate inside enterprise environments with minimal human intervention. This technology is just beginning to deploy at scale. The efficiency gains executives are citing as the basis for current layoffs have not materialized in any measurable, enterprise-wide way. Most businesses are still in early experimentation. The disruption hasn’t started. What’s coming is not mass displacement. It’s mass reallocation, and with it, a significant wave of new hiring in roles that barely existed two years ago. The operators who see this clearly, and move now, will have a structural advantage that compounds. The evidence is already visible for those paying attention. According to Stanford’s 2026 AI Index, agentic AI job postings grew 280% year-over-year, reaching roughly 90,000 U.S. listings. LinkedIn ranked “AI Engineer” as the number one fastest-growing job title in the U.S. in 2026. A role that didn’t exist three years ago, the forward-deployed engineer, saw postings surge over 800% in 2025 alone. IDC projects forty percent of enterprise job roles will involve direct interaction with AI systems within the year. That's not displacement. That’s integration. And integration requires people who can architect it, manage it, govern it, and translate it into business outcomes. Here are the specific roles where the hiring wave is building and will accelerate: * AI Agent Architects and Engineers. The people who design, build, and maintain autonomous agent systems inside enterprise environments. Not AI researchers: operational builders working at the intersection of engineering, domain knowledge, and business process. This is the fastest-growing category in technical hiring, and demand is outpacing supply by a wide margin. * Forward-Deployed Engineers. Engineers who embed with customers, understand their specific workflow problems, and build custom agent implementations that work in production. The role requires engineering fluency, communication ability, and comfort with ambiguity. Every serious enterprise AI platform company needs them. Most don’t have enough. * AI Process Orchestrators. The operational layer. As agentic systems take on multi-step workflows, someone has to design the process logic, define handoffs between AI and human decision points, monitor for failure modes, and adapt the system as conditions change. This function doesn’t map cleanly onto existing org charts, and every company deploying agentic AI at scale will need it. * AI Governance and Compliance Specialists. The EU AI Act, Colorado’s AI Act, and the SEC’s 2025 model-risk guidance are the leading edge of a regulatory stack that will grow significantly. Translating compliance requirements into product and operational decisions requires people who speak both languages. Senior in-house roles in this specialty are commanding $195,000 to $385,000. The supply of qualified people is minimal. * Human-AI Workflow Designers. The most underestimated role on this list. Every agentic deployment requires someone to map the existing human workflow, identify AI intervention points, design handoff protocols, and train the human side of the system. Part process engineering, part change management, part experience design. It doesn’t exist as a formal function at most companies yet, and it will be table stakes within 24 months. * B2B Revenue Intelligence Operators. This is the role I watch most closely, because it sits at the center of what my core audience manages. As agentic AI reshapes how B2B buyers move through the purchase process, the early and middle stages increasingly happen without direct human contact. Companies need people who can architect the intelligence layer underneath that shift: what signals the system reads, how it sequences outreach, where human judgment re-enters, and how to measure what's actually driving revenue. This is not a job for the current generation of demand generation coordinators. It requires commercial instinct, data literacy, and a fluency with agentic systems that barely exists in the market today. The companies that develop this capability, and the people who fill these roles, will have an asymmetric advantage over those still running playbooks built for a buyer who no longer exists. * AI Infrastructure and Data Engineers. Agentic systems need clean, structured, accessible data to function. Most enterprise data environments are nowhere near that standard. The gap between where enterprise data infrastructure is today and where it needs to be to support autonomous agent deployment represents years of specialized engineering work. The companies that close that gap fastest win. The people who can close it are in very short supply. What This Means for B2B Leaders Don’t buy the head fake. Stop accepting the AI layoff narrative as evidence that agentic AI is delivering efficiency gains at scale. It isn’t. Yet. What you’re watching is pandemic-era correction, PE margin engineering, and CFO narrative management, packaged in AI language because AI language is currently rewarded. The actual disruption is still ahead. And when it arrives, the first-order effect will not be mass layoffs. It will be a capability gap: between the organizations that have built the human infrastructure to deploy and govern agentic systems, and the ones that cut the people who would have done exactly that. The companies making real AI-driven progress today are not the ones eliminating heads in call centers and reissuing press releases. They are the ones quietly identifying which functions are ready for agent deployment, which are not, and hiring specifically for the roles that bridge that gap. Churchill was right. Never waste a good crisis. The confusion in the market right now is the opening. The leaders who cut through the narrative, read the data clearly, and build toward what’s actually coming, not what the headlines say is already here, are the ones who will define what B2B looks like on the other side of this transition. That window is open now. It won’t stay open long. 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. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tonyuphoff.substack.com [https://tonyuphoff.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

Your B2B Marketing Career in the Age of Agentic AI

This is the fourth and final 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. Post 2, “B2B Marketing Is Broken. Brand Is Why. And Brand Is the Fix,” made the case for positioning as the foundation of everything. Post 3, “The Stack That Broke Marketing (And the Architecture That Can Fix It),” examined the technology and organizational transformation underway. This post is personal: what the transition means for your career, what to do about it at every stage, and why the era beginning now may be the best thing that has ever happened to this function. Let me say something directly, and with genuine respect for everyone working in B2B marketing right now. This is one of the most demanding, most underappreciated, and most rapidly changing professional disciplines in business. The people in it are navigating simultaneous disruption across technology, organizational structure, buyer behavior, and the fundamental economics of what marketing work actually is. Campaign managers and content strategists. Demand gen specialists and marketing ops practitioners. CMOs carrying revenue targets that keep moving. All of them, at every level, navigating the same transition at the same time. That deserves acknowledgment before anything else. The transition underway is real. It is accelerating. And it is not the fault of the people it is disrupting. And yet. Acknowledging the difficulty of the moment is not the same as softening what needs to be said about it. The B2B marketing career built for 2020 is being structurally redesigned. Some of that redesign will create genuine opportunity for marketers who understand it and move deliberately. Some of it will be hard for people whose skills and roles are in the direct path of automation. Both things are true simultaneously, and this post is for both groups. But this post is also about something more than career navigation. It is about what comes next for the function itself. Because the argument I want to make: the one this entire series has been building toward, is that the disruption of agentic AI is not the end of great B2B marketing. It is the beginning of it. What the Series Has Already Established The previous three posts covered the structural ground. Post 1 traced how B2B marketing lost its way: the performance marketing trap, the defunding of brand, the substitution of martech complexity for strategic clarity. Post 2 made the case for brand and positioning as the foundation of everything, and why agentic AI makes that foundation more consequential, not less. Post 3 examined the technology and organizational transformation: the coordination layer, the messy middle disappearing, the marketing function being reorganized around the human capabilities that AI cannot replicate. The through-line across all three: Artificial Intelligence is illuminating the value of Actual Intelligence. The more capable AI systems become at executing the mechanical work of marketing, the more visible and valuable the human capabilities become that AI cannot replicate. Judgment. Taste. Curation. Creative conviction. Genuine market insight. These are not soft skills. They are the irreducible human capabilities that the performance marketing era systematically undervalued, and that the agentic AI era is rapidly repricing. This is the context for everything that follows. The career advice in this post is not simply about surviving disruption. It is about positioning yourself for a function that is about to become significantly more interesting, more creative, and more strategically consequential than it has been in fifteen years. The Market Is Splitting. And the Data Is Clear Before getting to career stages, the most important structural reality to understand is this: the market is splitting into candidates with and without genuine AI capability, and the compensation divergence is already significant. Professionals who can master not just AI tools but AI strategy: workflow design, output governance, agent orchestration, are commanding a salary premium of 25% to 35% in advanced markets. The 2026 Salary Guide from Robert Half projects overall marketing salaries rising 1.5% year over year. Read those two numbers together. The aggregate is modest. The redistribution is not. Marketers with genuine AI capability are pulling significantly ahead. Those without it are being left behind by an average that flatters the aggregate while obscuring the divergence underneath it. 95% of B2B marketers use AI at least weekly. 65% use it daily or more. The question is no longer whether AI is in your workflow. It is whether your relationship with AI is making you more valuable, or masking a skills gap that is widening underneath you. The foundational skills like basic prompt engineering are already being eclipsed by what the market is calling narrative orchestration: the strategic human work of deciding what AI will build, governing how it builds it, and exercising the judgment to know when it hasn’t built it well enough. That capability, human discernment applied to AI output, is the career north star for every stage of a B2B marketing career right now. The Early Stage: Building on the Right Foundation If you are in the first five years of a B2B marketing career, you are entering the function at the most structurally complicated moment in its history. You are also entering at a moment of genuine opportunity. If you build deliberately. The conventional early-stage playbook, broad exposure, learn the tools, master campaign execution, build your metrics vocabulary, is not wrong. It is incomplete. The part that is incomplete is the part that matters most right now. Early-career coordinators and associates start at $60K to $75K. Marketing specialists, analysts, and campaign managers reach $70K to $110K. The jump between those bands — and the speed at which you make it — is now determined primarily by one thing: how quickly you can move from execution to strategic ownership. * Develop AI fluency as a genuine capability, not a résumé line. The early-stage marketers who will own the compensation premium are not the ones who list AI tools in their skills section. They are the ones who can architect a multi-step agentic workflow, evaluate its output against a brand standard, identify where it breaks, and fix it. This requires using agents for real work, not just experimenting. Dedicate deliberate time each week to workflow design and output governance, not just content production. * Choose a specialization and go deep, earlier than feels comfortable. Generalism is increasingly a commodity at the early stage. The marketers who advance fastest are those who identify one domain: product marketing, GEO and AI discoverability, brand strategy, community building, and develop genuine depth in it. Specific expertise compounds. Broad exposure without depth is the profile that gets automated fastest. * Learn to speak revenue, not marketing. The single most important communication skill for an early-stage B2B marketer in 2026 is the ability to connect marketing activity to commercial outcomes. Not impressions to pipeline. Not campaigns to MQLs. Actual revenue logic: how does this work create a buyer who is more likely to choose us, sooner, with higher confidence? The marketers who develop that language early advance to leadership roles. Those who don’t stay in execution roles longer than they should. * Build your operator voice now, not later. The credible practitioner who publishes thoughtful, experience-based perspective on how the function is evolving is building professional equity that compounds over time. A LinkedIn content cadence. A Substack. A podcast. Start while your perspective is fresh and your hunger is visible. The best time to build an audience is before you need one. The Mid Stage: The Most Consequential Inflection Point If you are five to fifteen years into a B2B marketing career, you are at the most consequential inflection point in the current transition. Senior enough to have real capability and real credibility. Not yet at the stage where organizational inertia or title protection insulates you from disruption. Three in four marketers say the job market is harder than it used to be. The average placement timeline for currently employed marketers is longer than it has been in a decade. At the mid stage, the marketers who are advancing are those who have made deliberate moves toward strategic ownership. Those who haven’t are finding that execution expertise — however deep — is losing its premium faster than they expected. The mid-stage marketer who cannot demonstrate direct commercial impact: not activity, not programs produced, but measurable influence on pipeline, retention, or revenue, is increasingly difficult to justify at the compensation levels that mid-stage experience commands. * Reframe your career narrative around revenue, not marketing programs. This is not a semantic exercise. It is a fundamental repositioning of what you offer. Every conversation with a current or prospective employer should lead with commercial outcomes: pipeline velocity influenced, revenue retained, buyer journey compressed. The marketing program is the mechanism. The revenue outcome is the value. * Close the AI governance gap before it closes you. The mid-stage marketer who learns to design and govern agent workflows: deciding what AI executes autonomously, what requires human review, and where brand judgment must intervene, is developing a capability that very few people at this stage have yet built. It translates directly into senior leadership accountability and it is in significant organizational demand. * Get proximate to the CEO and CFO conversation. The mid-stage marketers advancing to VP and CMO roles are the ones who have made themselves legible to business leadership, not just marketing leadership. Volunteer for cross-functional initiatives. Develop relationships with sales and finance. Learn to present in the language of commercial risk and return. The career ceiling for marketers who only know how to lead marketing teams is getting lower. The ceiling for those who can lead revenue conversations is getting higher. * Invest in your skills with capital allocation discipline. The half-life of marketing skills has compressed to 18 to 24 months. The expertise that was differentiating in 2024 is becoming a baseline requirement in 2026. Identify the one or two capabilities that would most materially improve your market position in the next 18 months. Not the ones most comfortable to develop, but the ones the data says will be most valued. Treat that investment with the same rigor you would apply to any decision with a clear return and a finite window. The Peak Years: Redefining What Leadership Means If you are fifteen or more years into a B2B marketing career: at the VP, SVP, or CMO level, the disruption is arriving at your door in a specific and personal way. By 2027, a lack of personal AI literacy is predicted to be a top-three reason for CMO replacement. Not team-level AI adoption. Personal literacy. The CMO who has delegated AI strategy entirely to a team member is building a leadership position on a narrowing foundation. One who cannot personally evaluate an agentic workflow. Cannot lead a governance conversation with the CEO. Cannot articulate how AI is changing the buyer journey in specific and operational terms. That profile is increasingly visible to boards and CEOs, and increasingly vulnerable. This concept is worth deeper reflection. The instinct at the peak stage is often to manage AI strategy through the team rather than develop it personally. That instinct made sense when the technology was peripheral. It does not make sense when the technology is becoming the operating infrastructure of the function you lead. * Develop personal AI fluency. Not by delegation, by doing. Use agents in your actual planning and strategy work. Understand how they reason, where they fail, and what they require to operate within your brand’s standards. The CMO who cannot personally demonstrate AI literacy in a board conversation is at increasing risk of being replaced by someone who can — often at a lower cost. * Make the mandate expansion argument before someone makes it for you. The shift from CMO to CGO and CRO is underway in B2B. Marketing leaders are either driving that shift deliberately: expanding their mandate, owning the revenue conversation, redefining what the function is accountable for, or they are having it done to them. There is no middle ground. The leaders who are surviving and advancing are redefining their roles to encompass pipeline quality, sales alignment, customer retention, and commercial outcomes. Present that expanded mandate to your CEO as a strategic proposal, with a revenue logic behind it, before your organization decides it needs a different title to get what it actually wants. * Build and publish your perspective visibly and consistently. The peak-stage marketing leader who is writing, speaking, and publishing, who has a recognized point of view on how the function is evolving, is building external credibility that makes them more valuable inside their organization and more resilient outside it. In a world where CMO tenure averages under four years, external professional equity is career infrastructure, not vanity. * Mentor deliberately. The mid and early-stage marketers around you are navigating a transition without the benefit of having seen a prior structural disruption play out. The peak-stage leader who invests in developing the next generation, sharing the pattern recognition that comes from decades of operating experience, is doing something that AI cannot replicate and that the function genuinely needs right now. The Uncomfortable Numbers Let's be honest about what the data actually shows. Layoffs are up 30%, but unevenly distributed. The execution-layer roles: campaign managers, social media specialists, content producers, SEO coordinators, marketing operations administrators, are bearing the majority of that disruption. The roles that are opening are more senior, more specialized, and more commercially accountable than the roles that are closing. The pressure for constant ROI and visibility is producing burnout at rates the function has not previously experienced. The marketers carrying the most disruption are often also being asked to do more with less, faster, with new tools they were not trained on and organizational support that has not kept pace with the pace of change. This is real. It deserves to be named alongside the data about opportunity and premium compensation. Both things are true. The transition is creating genuine winners and it is genuinely disrupting people who did everything right by the standards that existed five years ago. Empathy for that reality is not weakness. It is accuracy. The Renaissance Is Coming. I want to close this series with a prediction. Not a hope, not a hedge, not the kind of carefully qualified optimism that consultants offer when they want to sound encouraging without being accountable. A genuine forecast, grounded in structural logic and thirty-five years of watching technology disrupt industries I have been part of and led through. The agentic AI era is going to produce a creative, positioning, and branding renaissance in B2B marketing. And the careers built at the center of that renaissance will be among the most interesting and most valuable in the history of the function. Here is the structural case. For fifteen years, the most talented people in B2B marketing have been spending enormous portions of their professional energy on work that was never worthy of them. Managing platform complexity. Running manual reports. Producing content at volume to feed algorithms. Optimizing campaigns by hand across disconnected systems. These were real skills. They mattered. They were also, fundamentally, below the ceiling of what brilliant marketing minds should be doing. Agentic AI is automating that work. Comprehensively and fast. And in doing so, it is creating something the function has not had in a very long time: the organizational space to think, to create, to build brands that genuinely mean something. There is a historical parallel worth considering. When desktop publishing arrived in the late 1980s, the conventional wisdom was that it would commoditize graphic design. Anyone could now produce a decent-looking document. What actually happened was the opposite. Democratizing production elevated the premium on genuine creative vision. When anyone could make something that looked adequate, the designers who could make something brilliant became dramatically more valuable, not less. The technology didn’t replace creative excellence. It made creative excellence the only thing that mattered. The same dynamic is playing out now, at a much larger scale and much faster. When everyone can create content, the competitive advantage shifts entirely to authentic connections: human-centric brand design and genuine creative strategy. As AI-generated content becomes mainstream, brands will stand out through originality and unique perspectives. AI will empower lean marketing teams to do more with less, shifting focus from execution to creativity and brand differentiation. The capabilities that AI cannot replicate: genuine creative vision, earned category expertise, strategic judgment, relational trust, the taste to know the difference between competent and brilliant, are exactly the capabilities that the best B2B marketers have always had. The performance marketing era systematically undervalued them. The agentic AI era is repricing them sharply upward. The future of B2B marketing isn’t about replacing human marketers with AI. It’s about augmenting human creativity, transforming how we understand strategy and judgment, with autonomous systems that handle execution at speeds and scales previously impossible. The marketer who understands that distinction, and builds their career at that precise intersection, is not navigating disruption. They are standing at the center of the most interesting creative and strategic opportunity the function has ever produced. Think about what B2B marketing looks like when the messy middle is gone. When the campaign logistics, the data reconciliation, the reporting cycles, the platform administration, all the infrastructure that consumed the function for fifteen years, are handled by agents running continuously in the background. What remains is the work the best people in this function have always wanted to do. Positioning that is genuinely differentiated. Brands that buyers actually remember and trust. Creative work that moves people rather than simply reaching them. Category strategies built on real market insight rather than keyword research. Buyer understanding that goes deep enough to be genuinely useful rather than just demographically precise. That is the renaissance. It is not a distant possibility. It is the logical consequence of what is happening right now. Visible to anyone who looks at where the technology is going and what it will leave behind when it gets there. The B2B marketing function that masters that balance will produce something the discipline has never seen before. The creative and strategic capabilities it has always had. The technological leverage it has always needed but never properly harnessed. Finally working together. The result will be the most effective, most differentiated, most commercially powerful marketing in the history of the function. That era is beginning now. The career you build toward it starts today. This concludes B2B Marketing in the Machine Age, a four-part series on the state and future of B2B marketing in the agentic AI era. The complete series is available on Uphoff on Media. 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. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tonyuphoff.substack.com [https://tonyuphoff.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

The Stack That Broke Marketing (And the Architecture That Can Fix It)

This is the third 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. Post 2, “B2B Marketing Is Broken. Brand Is Why. And Brand Is the Fix,” made the case for positioning as the foundation of everything. This post examines the technology and organizational architecture of B2B marketing: how it got so complex, why that complexity is now a strategic liability, and what the path forward looks like in an agentic AI world. Post 4 covers The Career. There is a question I have started asking B2B marketing leaders in my advisory work. It is a simple question and the answers are almost always the same. How much of your week is spent on marketing, and how much is spent managing the infrastructure that is supposed to enable marketing? The responses are uncomfortable. CMOs who came up through creative and strategy disciplines describe spending the majority of their time in technology reviews, data governance conversations, vendor negotiations, and reporting cycles. Marketing operations leaders describe teams whose primary work has nothing to do with the market, and everything to do with keeping disconnected systems talking to each other. Demand generation leaders describe building campaigns around what their stack can execute rather than what their buyers actually need. The B2B marketing function built over the past fifteen years is, in large measure, a technology management function with a marketing budget. And no other role in the C-suite has been as quietly, consistently, and thoroughly transformed by technology complexity as the CMO. That is the problem this post addresses. And the arrival of agentic AI is creating a genuine fork in the road: add more technology to what already exists, compounding the problem while calling it progress, or use the disruption as a forcing function to reimagine the architecture entirely. The organizations that choose correctly will emerge with a leaner, more strategic, more commercially powerful marketing function than they have ever had. How We Got Here: The Stack That Marketing Built The numbers that describe the current martech landscape are so large they have become almost abstract. 15,384 commercial martech solutions exist as of 2025, up 9% year over year, and nearly 100 times the number that existed in 2011. The average B2B marketing organization runs 65 to 75 tools. Martech now consumes approximately 22% of total marketing budgets. And despite that investment, martech utilization has dropped to 49%, meaning roughly half of every dollar spent on marketing technology is generating no active output. Only 31% of marketing organizations report that their stack is well integrated. Read those two numbers together. Half the stack is generating no active output. Less than a third of organizations have a stack that actually works as a system. This is not a technology problem in the conventional sense. It is the predictable consequence of fifteen years of accumulation without architecture. The history of how B2B marketing got here is the history of a function buying its way toward capability it never quite achieved. Each wave of tools arrived with a genuine promise. CRM would unify the customer record. Marketing automation would make campaigns scalable. Analytics platforms would make performance visible. ABM tools would make targeting precise. Intent data platforms would make the invisible buyer journey legible. CDPs would unify all the data that everything else had fragmented. And so on, tool by tool, problem by problem, vendor by vendor, each purchase solving the problem it was sold to solve while creating the integration debt that the next purchase would need to address. Data integration is the biggest stack management challenge, cited by 65.7% of organizations. This isn’t a technology problem. It is the consequence of buying tools without a unification strategy. The organizational consequence is the one that gets discussed least openly: the B2B marketing function gradually came to serve the stack rather than the market. Headcount grew around platform management. Titles proliferated: marketing operations manager, martech administrator, data analyst, attribution specialist, CRM administrator, campaign operations coordinator. These are not marketing roles in any meaningful sense of the word. They are infrastructure management roles wearing marketing department badges. The CMO who was hired to build brands, develop market strategy, and drive commercial growth became — in many organizations — the de facto head of a shadow IT department. Responsible for a technology portfolio that required specialized skills in data architecture, systems integration, and platform governance that had nothing to do with marketing, and that consumed resources that should have been building brand, developing positioning, and understanding buyers. This is the stack that broke marketing. Not because the tools were bad. Because the accumulation strategy was never a strategy at all. The Fork in the Road Agentic AI has arrived into this environment and created a genuine choice, the most consequential technology decision B2B marketing organizations will make in the next two years. Path A is the path of least resistance: layer agentic AI tools onto the existing stack. Add AI-powered campaign tools on top of the campaign management platform. Add AI writing tools on top of the content management system. Add AI analytics tools on top of the reporting infrastructure. Call it AI transformation. Announce it in the next board presentation. Watch the complexity compound while the underlying fragmentation remains entirely unaddressed. This path is already being chosen by a significant number of organizations. 68.6% of global enterprises already use generative AI tools within their martech environments. But only 18% have achieved full orchestration maturity. Most organizations are still using AI as standalone point solutions rather than integrated intelligence across their stack. Path A, in other words, is where most organizations currently are. It is the comfortable choice. It is also the wrong one. Path B treats agentic AI as a forcing function for architectural rethinking. It asks: if we were designing this function from scratch today, with the capabilities that AI now provides, what would we build? And it uses the answer to that question as the guide for what to keep, what to consolidate, and what to eliminate from the current stack. Path B is harder. It requires conviction, organizational will, and the willingness to make decisions that will upset some vendors and some internal stakeholders. It also produces a fundamentally different — and fundamentally more powerful — marketing function on the other side. The organizations that will define what B2B marketing looks like in 2030 are on Path B. The ones that stay on Path A will end up with a more expensive version of the problem they have today. The Technology Fix: A Different Application, Not More Application Here is the central insight that Path B is built on: the fix for technology sprawl is not more technology. It is a fundamentally different application of the technology that is now available. Specifically, it is the recognition that agentic AI can serve as the coordination layer that the B2B martech stack has always needed and never had. I have written about the coordination layer concept in previous posts in the context of enterprise software broadly. The principle applies with particular force to B2B marketing. An agentic martech stack is one where AI agents can make decisions, trigger actions, and optimize journeys in real time, while humans define the strategy and boundaries. For this to work, the underlying data structure needs to be unified. The coordination layer is what makes that unification possible without requiring every tool in the stack to be replaced. Think of it this way. The core dysfunction of the current B2B martech stack is not that the individual tools don’t work. Most of them work reasonably well within their own domain. The dysfunction is that they don’t talk to each other in ways that produce coherent, actionable intelligence. The CRM has customer data. The MAP has engagement data. The intent platform has behavioral signals. The ad platforms have performance data. The web analytics system has traffic data. Each system knows something. No system knows everything. And the humans in the middle: the marketing ops teams, the data analysts, the attribution specialists, are the connective tissue holding it all together at enormous cost in time, headcount, and organizational energy. AI agents crave context: the full picture of who a customer is, what’s happening right now, what’s been tried before, and why decisions were made the way they were. They want behavioral signals, campaign performance, financial data, content metadata, approval histories. They want it all, unified, with consistent definitions, accessible without the integration equivalent of a game of Twister. The coordination layer delivers exactly that. Rather than replacing the existing tools, it sits above them, reading from each system, reconciling the data, identifying the patterns, and orchestrating action across the stack. By combining unified data with advanced models, this layer takes insights and translates them into action. Instead of just flagging an account with high churn risk, agentic AI can automatically draft a personalized re-engagement campaign or schedule a follow-up call with the customer success manager. The practical implication: many of the integration projects that have consumed marketing operations budgets for years — the MAP-to-CRM sync, the intent data reconciliation, the cross-platform attribution — become agentic workflow problems rather than custom engineering problems. Faster to implement. Cheaper to maintain. And continuously improving as the agents learn from the data they process. Teams with unified data are 42% more likely to regularly respond to customers in real time and 60% more likely to use AI agents effectively. That gap is not a technology gap. It is a data infrastructure gap. Attribution as a Service One of the most immediate and practical applications of the coordination layer concept is what I have called Attribution as a Service in previous writing, and it deserves specific treatment here because it addresses one of the most resource-consuming dysfunctions in B2B marketing today. The attribution problem in B2B marketing is well understood: long sales cycles, multiple buying committee members, dozens of touchpoints across channels and formats, and a set of measurement systems that were built to track digital clicks rather than complex organizational buying decisions. The result is that B2B marketing organizations spend enormous amounts of human time producing attribution reports that are, at best, directionally useful and, at worst, confidently wrong. The opportunity is to take that function off the marketing team’s plate entirely. An AI-powered system continuously pulls data from across the stack and reconciles it against agreed-upon attribution models. It identifies the patterns that actually correlate with pipeline and revenue. It delivers clean, current reporting on the cadence the organization needs. Without requiring three days of analyst time to prepare the monthly deck. Between 60% and 75% of marketers say their own attribution lacks rigor and trust. Attribution as a Service doesn’t solve the fundamental philosophical challenges of B2B attribution. No system can perfectly model a six-month enterprise buying committee decision. But it removes the mechanical burden from human teams, improves the consistency and currency of the data, and frees the analytical capacity of the marketing function for the interpretive work that actually requires human judgment: understanding what the data means, where the strategy should shift, and how to tell that story to the CFO. Revenue Intelligence: The Bigger Opportunity Attribution as a Service is one piece of a larger architectural possibility that agentic AI is making real: what I have called Revenue Intelligence 2.0. The fundamental problem in B2B marketing has always been the gap between signals and action. Intent data here. Engagement data there. CRM data somewhere else. A human trying to make sense of it all, too slowly, with too little context, and too much noise. Agentic AI can close that gap in real time. A system that continuously ingests intent signals from across the web and correlates them with first-party engagement data. It identifies the accounts and buying committees that are genuinely in-market. It generates personalized outreach sequences and dynamically adjusts channel mix and spend based on what is actually working. No human intervention required between each step. This was the promise that intent data providers made and largely did not deliver. The data existed. The action layer did not. Agentic AI is the action layer that was missing. The organizations that build this architecture — either through platform selection or internal development — will have a demand-to-revenue pipeline that operates at a speed and precision that no human-managed equivalent can match. There is an irony at the center of this transition that is worth naming directly. Artificial Intelligence is illuminating the value of Actual Intelligence. The more capable AI systems become at executing the mechanical work of marketing, the more visible and valuable the human capabilities become that AI cannot replicate. The data processing, the campaign logistics, the content production at volume, AI owns all of it. Judgment, taste, curation, creative conviction, genuine market insight, those belong to humans. The function that spent fifteen years building execution infrastructure is being reminded, at scale and at speed, what it was always supposed to be for. The Organizational Fix: What the Marketing Function Actually Looks Like Technology architecture and organizational architecture are not separate problems. The B2B marketing organization of the future will look radically different from the one that exists today: not simply because the tools will be different, but because the work that requires human beings will be different. The messy middle disappears. This is the most consequential organizational shift in B2B marketing, and it is already beginning. The messy middle is the layer of people, process, and organizational energy that currently exists to manage the complexity of disparate tools, data silos, and reporting requirements. It includes marketing operations teams managing platform integrations, analysts preparing reports that agents can now generate automatically, campaign coordinators executing workflows that agents can now run autonomously, and data specialists reconciling systems that a coordination layer can now connect directly. This work is not going to be automated gradually. It is going to be automated comprehensively, and faster than most marketing organizations are planning for. The teams built around managing complexity will shrink significantly as the complexity itself is addressed at the architectural level. What replaces the messy middle is not a smaller version of the same thing. It is a fundamentally different organizational model. The Marketing Organization of the Future: By Role and Impact What grows significantly in value: * Brand and positioning strategy. The work of defining what the company stands for, who it serves, and why it is different. As discussed at length in Post 2, this is the input that makes everything else compound — and it is precisely the work that cannot be automated. Human judgment, creative conviction, and genuine market insight are the irreducible requirements. This role grows in organizational importance and compensation premium. * Creative direction and brand stewardship. The ability to maintain a coherent, differentiated brand voice across an environment where AI is generating content at enormous volume and speed. This requires taste, judgment, and a clear point of view. The creative director who can brief AI systems with genuine vision, evaluate their output against a brand standard, and push back when the work is competent but not distinctive is a critical organizational role that is currently undervalued and will become significantly more valued. * AI governance and agent workflow design. Somebody has to architect how the agents operate: what they execute autonomously, what requires human review, where brand judgment must intervene, and how compliance and legal guardrails are maintained. Between 60% and 75% of marketers say their own attribution lacks rigor and trust. The governance function ensures that AI-generated outputs meet the standards the organization needs to act on them confidently. This is a new discipline with no established career path and significant organizational demand. * Product marketing and category expertise. The deep understanding of buyer problems, competitive dynamics, and market positioning that makes content and campaigns genuinely useful to sophisticated buyers. This work requires category knowledge and buyer empathy that AI cannot develop from training data alone. As AI floods every channel with competent, generic content, the premium on content that demonstrates genuine expertise grows sharply. * Revenue integration leadership. The organizational capability to align marketing, sales, and customer success around a unified revenue motion. As the boundary between marketing and sales dissolves under agentic AI — when agents are handling prospecting, qualification, and early engagement — someone has to own the integrated architecture of how revenue is generated. This is the emerging CGO function, and it requires marketing leaders who can operate fluently across the entire commercial organization. * Community and relationship stewardship. As AI automates every scalable channel, authentic human community: real relationships with real buyers and real practitioners, becomes the highest-trust, highest-value marketing surface. Building and sustaining those communities requires human presence and genuine engagement that no agent can replicate credibly at the relationship level. What diminishes or disappears: * Marketing technology administration. The role of managing, configuring, and maintaining the martech stack is being automated at the coordination layer. The marketing ops practitioner whose primary function is platform administration is the role most directly in the path of the architectural shift described in this post. * Manual reporting and analytics preparation. Agents generate reports. The human role shifts entirely to interpretation and decision-making, which means the analyst who primarily prepares data rather than interprets it is in a structurally declining position. * Campaign operations and execution coordination. Setting up sequences, managing ad platforms, running campaign logistics, these are the workflows agents are displacing fastest. The execution-layer coordinator is not disappearing from marketing entirely, but the headcount required to staff this function is declining sharply. * Generic content production at volume. Undifferentiated content is now a commodity. The content producer who cannot operate at the strategic or editorial direction level is competing directly with AI systems on their strongest terrain and will not win that competition. What a Day in the Future Marketing Function Actually Looks Like Strip away the org chart and describe the work itself, and the future B2B marketing organization looks something like this: A small, senior team: sets the strategy, owns the positioning, governs the AI systems, and makes the judgment calls that require genuine expertise and organizational accountability. Brand strategists. Creative directors. Product marketers. Revenue integration leaders. They spend their time thinking about markets, buyers, and competitive dynamics. They brief agents. They evaluate outputs. They push back when the work isn't good enough. A set of agentic systems: coordinated by the unified data layer, governed by the human team’s frameworks, executes the operational work: campaign orchestration, content generation and distribution, lead qualification and routing, performance monitoring and optimization, reporting and attribution. These systems run continuously, at a scale no human team could match, and they improve as they process more data. A community and relationship function: maintains the human connections that agents cannot: customer relationships, practitioner networks, partner engagement, and the high-trust peer interactions that generate the credibility signals the brand strategy depends on. The result is a function that is smaller by headcount, significantly more senior in composition, dramatically more strategic in its daily work, and far closer to commercial outcomes than the marketing organizations that exist today. The Recommendations For B2B marketing leaders and the CEOs who lead them, the path forward from the current state of tech complexity and organizational burden to the architecture described above requires deliberate choices. Here are the ones that matter most. * Treat your next technology decision as an architecture decision. Before purchasing the next tool — especially any tool marketed as AI-powered — ask one question: does this add to the coordination layer or to the fragmentation? A tool that integrates cleanly with your data infrastructure and improves the coherence of the system is worth evaluating seriously. A tool that creates another data silo, however impressive its individual capabilities, compounds the problem you already have. * Audit the messy middle before agentic AI does it for you. Map the work your marketing operations, analytics, and campaign coordination teams actually do day to day. Identify what percentage of that work is mechanical, rule-based, and data-processing in nature, the work agents will automate. Then make an honest assessment of what your organizational structure looks like when that work is gone. The organizations that do this audit proactively and restructure ahead of the automation will manage the transition far more effectively than those that wait for the disruption to arrive. * Build the unified data layer first. This is the prerequisite that most organizations skip because it is unglamorous, expensive, and doesn’t produce an impressive demo. But the full potential of agentic AI can only be realized once the foundational data layers of your stack are mostly established. Avoid the mistake of jumping straight to agentic AI deployment before the data foundation is in place. The organizations investing in clean, unified, real-time data infrastructure now are building the foundation on which every subsequent AI capability will compound. The ones deploying AI agents on fragmented data stacks are building on sand. * Consolidate before you automate. The coordination layer works best with a rationalized stack underneath it. If your organization is running 65 tools, the first move is not to add an AI orchestration layer on top of all 65. It is to identify the 15 to 20 tools that are generating the majority of value, build the coordination layer around those, and eliminate the rest. The consolidation case is now an AI readiness case, not just a cost case. Make it that way to your CFO. * Redesign organizational roles around the work that remains. The marketing organization restructuring that agentic AI demands is not a headcount reduction exercise, or at least, it should not be framed as one. It is a redesign of what the function does and what skills it needs to do it. The roles that grow in value — brand strategy, creative direction, AI governance, product marketing, revenue integration — require investment in talent development and compensation that reflects the premium the market is beginning to place on those capabilities. The organizations that treat the transition as purely a cost-reduction opportunity will gut the strategic capability they need precisely when they need it most. The Fork Has a Right Answer The B2B marketing technology landscape of the past fifteen years was built by accumulation. Tool by tool. Problem by problem. Vendor by vendor. Each purchase was rational in isolation. The aggregate result was a function buried under its own infrastructure, managed by teams whose work had less and less to do with marketing, and led by CMOs who had become, in many cases, reluctant CIOs. Agentic AI does not automatically fix that. Deployed carelessly, layered on top of existing complexity without architectural intention, it makes it worse. More tools. More data. More coordination requirements. More organizational energy consumed by infrastructure rather than market. Deployed deliberately, as a coordination layer built on a rationalized, unified data foundation and governed by a smaller and more senior human team, it produces something the B2B marketing function has not had in a very long time. The organizational conditions to do actual marketing. To think clearly about markets. To build brands that mean something. To develop positioning that genuinely differentiates. To understand buyers with a depth and speed that no prior generation of marketing technology made possible. That is the architecture worth building. And the organizations that build it: while their competitors are still debating which AI tools to add to stacks that are already too complex, will define what B2B marketing looks like for the next decade. Next and final in the series: Post 4: “Your B2B Marketing Career in the Age of Agentic AI.” What the technology and organizational transformation described in this post means for your career specifically, and what to do about it at every stage. 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. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tonyuphoff.substack.com [https://tonyuphoff.substack.com?utm_medium=podcast&utm_campaign=CTA_1]