Uphoff on Media Podcast

The CPO Moment

12 min · 11 de may de 2026
Portada del episodio The CPO Moment

Descripción

When the CEOs of Walmart and Coca-Cola announced their retirements, both cited AI disruption as a contributing factor. Let that land for a moment. Two of the most experienced executives in global business, leaders of companies with combined revenues approaching a trillion dollars, named AI as a reason for stepping back. This isn’t a technology story. It’s a leadership story. It has direct implications for every Procurement leader reading this. Three Forces Are Converging: Right Now I was recently invited to speak at Art of Procurement’s Catalyst event in San Francisco, a gathering of senior Procurement leaders from across the enterprise world. The session: a fireside chat with AoP Founder Phil Ideson titled “What Now? Leadership Choices When AI Accelerates”, was one of the more candid conversations I’ve had with a room full of senior practitioners in some time. I also facilitated a Mastermind breakout with senior Procurement leaders from across the enterprise technology sector. A smaller, candid conversation that surfaced some of the most honest operator thinking I've encountered on these themes. What came out of those sessions sharpened my thinking considerably. Not just about where the function is headed, but about the specific friction points preventing leaders from getting there. Three forces are reshaping Procurement simultaneously. Their combination creates both the highest risk and the biggest opportunity the function has seen in a generation. Force 1: Agentic AI Makes Judgment More Valuable, Not Less The narrative around AI and Procurement tends toward anxiety: automation is coming for sourcing, contracting, spend analysis, supplier evaluation. That narrative is partially right, and almost entirely wrong in its conclusion. Yes, agentic AI systems will absorb more of the transactional and analytical work in Procurement. But here’s what that actually means: the premium on human judgment goes up, not down. When an AI agent can process a thousand supplier bids in the time it used to take to evaluate ten, the competitive advantage shifts to the person who knows which variables matter, and why. Risk assessment. Relationship intelligence. Strategic trade-offs that don’t fit a model. That’s not the work AI replaces. That’s the work AI reveals. The teams that lean into this shift will separate from those that resist it. The ones that treat agentic AI as a threat will spend the next three years playing defense. The ones that treat it as an amplifier will spend those years building something that can’t be replicated. One senior procurement leader who spoke at the event put the human stakes more bluntly than I expected: “I’m trying to figure out whether we should invest in upskilling around AI or just spend the money on severance.” That’s not cynicism. That’s a genuine leadership dilemma in real time, and it deserves a real answer. The answer is neither. The right investment is in the judgment layer above the automation. The capability that makes both the upskilled humans and the AI systems more valuable than either could be alone. Force 2: Service as Software Changes What Procurement Is Evaluating Enterprise software is undergoing a structural shift, from SaaS to what’s being called Service as Software. AI doesn’t just assist the work anymore. In a growing number of applications, AI is the service. It delivers outcomes, not tools. This changes the procurement conversation fundamentally. The old evaluation criteria — features, integrations, security, uptime — don’t disappear, but they’re no longer sufficient. Now you’re evaluating performance accountability when the “vendor” is an agent. Data rights when the service learns from your proprietary information. Vendor dependency when the capability is embedded in AI infrastructure you don’t own or fully understand. These are no longer purely IT questions. They are Procurement questions. What emerged clearly from the Mastermind in San Francisco: vendors themselves are part of the problem. Multiple leaders raised a pattern I hadn’t fully framed before: vendors acting as gatekeepers, rolling out AI features on their own timeline and effectively controlling the pace of adoption within their customers’ organizations. Procurement is being throttled by the very ecosystem it manages. That’s a structural problem, not a planning problem. And it means Procurement leaders need to move the vendor governance conversation up, from contract administration to strategic posture. Which vendors are genuinely enabling your AI capability? Which ones are holding it hostage to their own roadmap? Those are not equivalent relationships and shouldn’t be managed as if they are. The function that doesn’t get fluent in this new model will watch those decisions get made without them. Force 3: The Strategic Seat Is There for the Taking Here’s what I argued in San Francisco, and what I believe most strongly: Procurement is one of the few functions in any enterprise that has a full playing field view of the business: across systems, vendor relationships, budget allocations, and business unit operations simultaneously. That vantage point is structurally unique. The CFO sees the numbers. The CTO sees the infrastructure. The CMO sees the market. Procurement sees the connective tissue that holds all of it together. In an agentic AI environment, that vantage point becomes a genuine competitive advantage for the leaders who claim it. Because what agentic AI requires: clean data, trusted vendor relationships, clear decision rights, structured governance, is exactly what Procurement is positioned to provide. No other function has that stack. COVID tested every function’s ability to respond to a supply chain crisis. The best Procurement teams used that moment to demonstrate strategic value they’d never been given credit for. This moment is different — broader, deeper, more structurally permanent — but the dynamic is the same. Disruption creates openings. The functions that move get the seat. The ones that wait get managed. But a real obstacle surfaced in San Francisco that the optimistic version of this argument tends to skip past: the attribution gap. One senior Procurement leader described it this way: “When we work with a business unit and create a more streamlined tech stack, set of vendor agreements, or workflow, the savings is something that business unit leader can reinvest, so it doesn’t show up as something Procurement did.” That’s not a small problem. It’s a structural visibility failure. The value is real. The credit never arrives. And without the credit, the seat doesn’t come. The implication: Procurement doesn’t just need to do the strategic work. It needs to get fundamentally better at narrating it. Internally. At the CFO and CEO level. In the language of business outcomes, not functional metrics. One finding from the Mastermind worth internalizing: time savings can outrank cost savings as the most valued internal contribution Procurement makes. Not marginally. As a clear priority shift. Procurement leaders still leading with cost reduction are speaking a language the organization has partially moved past. The CFO and CEO care about speed of decision, speed of deployment, reduction in organizational friction. If Procurement can show up in that conversation — and it can — the seat is available. If it keeps leading only with savings, it keeps getting managed like a cost center. What to Do Now Most frameworks name the forces and stop. So let me be direct about what I think Procurement leaders should do in the next 90 days. This Week: Get Situationally Aware * Audit every AI-enabled tool in your vendor stack. Flag which ones are moving toward agentic capability, autonomous action, not just AI-assisted recommendation. Then flag which vendors are enabling that shift and which ones are gatekeeping it. * Map where automation is already touching sourcing, contracting, or supplier management in your organization. If you don’t know, that’s the finding. * Pull your top 10 SaaS contracts. Identify which vendors are pivoting to outcome-based or AI-delivered service models. Those renewals just got more complex, and Procurement needs to lead them. This Month: Build Your Point of View * Write a one-page AI risk brief for your CFO or CPO. Cover three things: vendor dependency, data rights in AI-delivered contracts, and performance accountability when the “service” is an agent. If you can’t write it, you’re not ready for the conversation. * Rewrite your function’s value narrative. Stop leading with cost savings. Lead with time savings, risk intelligence, and business outcomes. The language you use internally shapes how leadership sees you. And the language is shifting. * Define your attribution story. Where has Procurement created real business value in the last 12 months that isn’t showing up in anyone’s performance metrics? Find it. Name it. Quantify it. Present it. This Quarter: Move the Needle * Identify two or three high-visibility business decisions in the next 90 days where Procurement can drive or materially shape the outcome. Don’t wait to be invited. Show up with a POV. * Claim a seat at one C-suite or cross-functional initiative where Procurement hasn’t historically had a voice. One is enough to start. Make it count. * Deepen three strategic supplier relationships, the kind built on trust, shared intelligence, and long-term alignment. Specifically: have a direct conversation with each about their AI roadmap, what they’re enabling, what they’re controlling, and where your interests align. No agentic system replicates that relationship intelligence. It’s your moat. The Moment Is Now I’ve spent 35 years watching disruption hit industries and functions. The pattern is consistent. The leaders who move early, who treat disruption as a forcing function rather than a threat, end up in a fundamentally different position than those who wait for clarity. One CPO pulled me aside after the session and told me she wished she'd had this conversation ten years ago. I appreciated that. But the more important point is that having it now, in this moment, is the whole game. The window in which Procurement can move from managed function to strategic asset is open. It won’t stay open forever. Agentic AI is not going to make Procurement obsolete. It is going to make the difference between strategic Procurement leaders and order-taking Procurement functions impossible to ignore. The full playing field view is yours. The attribution gap is fixable. The seat is available. The choice is whether to take it. One more thing worth mentioning: if you're a senior Procurement leader and haven't encountered Art of Procurement's Catalyst event, put it on your radar. Phil Ideson and the AOP team have built something rare, a room where senior practitioners talk honestly about what's actually happening, not what's supposed to be happening. Keep an eye out for it coming to a city near you. Worth the trip. The views expressed in Uphoff on Media are entirely my own. They don’t represent the opinions of any company I’ve led, any board I’ve sat on, or any investor who’s had the pleasure of debating strategy with me over the years. If something I write here sounds brilliant, I’ll take full credit. If it turns out to be wrong, I was clearly misquoted by myself. 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]

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22 episodios

episode The AI Layoff Myth artwork

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]

3 de jun de 202614 min
episode Your B2B Marketing Career in the Age of Agentic AI artwork

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]

1 de jun de 202622 min
episode The Stack That Broke Marketing (And the Architecture That Can Fix It) artwork

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]

28 de may de 202627 min
episode B2B Marketing Is Broken. Brand Is Why. And Brand Is the Fix. artwork

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

B2B Marketing in the Machine Age | Part 2 of 4 This is the second post in B2B Marketing in the Machine Age, a four-part series on the state and future of B2B marketing in the agentic AI era. Post 1, “How B2B Marketing Lost Its Way,” set the historical stage: the decisions, incentives, and technology bets that brought the function to where it stands today. This post, “B2B Marketing Is Broken. Brand Is Why. And Brand Is the Fix,” examines why brand and positioning have become the most underdeveloped and most consequential capabilities in B2B marketing, and why agentic AI makes that gap more urgent, not less. Post 3 covers The Org & Tech. Post 4 covers The Career. Each post stands alone. Together, they make a case the industry needs to hear, and act on. I’ve spent more than three decades running B2B companies. CEO, publisher, operator, board member. I’ve sat in thousands of marketing reviews. I’ve approved — and questioned — tens of millions of dollars in marketing investments. I’ve watched the function evolve through the rise of digital, the explosion of martech, the era of content marketing, the ABM wave, and now the agentic AI inflection. Here is what I’ve concluded: B2B marketing has a brand problem. It has always had a brand problem. And almost nobody wants to say it out loud. Instead, we talk about tech stacks, attribution models, MQL targets, and AI agents. We fund demand generation and starve brand building. We optimize what’s measurable and neglect what matters. We buy tools that promise differentiation and produce sameness at scale. The result is a function in crisis, not despite fifteen years of investment, but partly because of it. And the timing couldn’t be worse. Because the rise of agentic AI is about to make a strong brand the most important competitive asset a B2B company can have. While most of the industry is running the wrong direction. Let me explain what I mean, and what to do about it. The Biggest Problem in B2B Marketing Has Nothing to Do with Technology The Renegade Marketing State of B2B Marketing study: one of the most rigorous surveys of senior marketing leaders published in the past year, asked CMOs across industries and company sizes to name their biggest challenge. The answer wasn’t AI adoption. It wasn’t martech complexity. It wasn’t budget pressure, though all of those surfaced. The number one challenge, by a significant margin, was differentiation. CMOs reported struggling to position their companies in a way that breaks through. Some were dealing with crowded categories where everyone sounds the same. Others were post-acquisition, trying to reestablish clarity. Several acknowledged bluntly that their product wasn’t differentiated, and their challenge was to create clarity where there wasn’t any. A few admitted they weren’t sure what made their company genuinely better, different, or worth choosing. Less than 10% of B2B companies report fully consistent branding. Read that again. Positioning is the strategic work of defining exactly what you do, who you do it for, why it matters, and why you specifically. It is the most misunderstood, underinvested area in B2B marketing. And yet it is the foundation of everything. Without a compelling position, nothing scales. Not demand generation. Not content. Not AI. Not pipeline. This isn’t a new problem. It’s a structural one. And it’s getting worse. How B2B Marketing Lost Its Brand The performance marketing era didn’t just change how B2B companies spent money. It changed what the function believed was valuable, and therefore what it built, hired, and measured. When digital marketing made it possible to track clicks, conversions, and attribution with precision, something seductive happened: what could be measured became what mattered. CPL. MQL volume. Pipeline attribution. Conversion rates. Cost per acquisition. These metrics aren’t wrong. But they created a gravitational pull toward the measurable and away from the durable. Brand: which operates on longer time horizons, through harder-to-attribute channels, and via mechanisms that resist precise measurement, got systematically defunded. The rationalization was straightforward: “We can’t prove brand ROI, so we’ll invest in what we can prove.” Reasonable enough, individually. Catastrophic in aggregate. B2B companies spent fifteen years optimizing what was measurable and starving what wasn’t. Brand is the main casualty. Then came the martech explosion. Fifteen thousand tools — and counting — promised to make up the difference. Better targeting, better personalization, better segmentation. The logic: if we can reach exactly the right person at exactly the right moment with exactly the right message, we don’t need a strong brand. We just need a good enough product and a capable enough tool. Except that’s not how buyers work. The martech stack can’t fix an unclear position. All better targeting does is deliver the wrong message more precisely. All better personalization does is tailor the confusion to the individual. The tools amplified the dysfunction rather than resolving it. The result: B2B marketing became technically sophisticated and strategically hollow. The sector produces more content, more campaigns, more touches, and more sameness, than at any point in its history. Why This Is Now an Existential Issue I want to be direct about the stakes, because I think they’re being underestimated. The agentic AI transition is not a productivity story for B2B marketing. It is a structural disruption that will permanently separate companies that have strong brand and positioning from those that don’t. Here is why. 94% of B2B buyers now use large language models during their purchase journey. That’s not a trend. That’s a behavioral norm. When your potential buyer has a challenge to solve or a category to evaluate, they are increasingly beginning that research with ChatGPT, Claude, Perplexity, or Gemini, not with a Google search, not with a sales call, and certainly not with your outbound sequence. When that buyer asks an AI system which vendors to evaluate in your category, your brand either appears or it doesn’t. There is no middle ground. There is no “page two.” There is no sponsored slot. You’re in the answer or you’re not in the deal. And the brands that appear in those answers are the ones with established credibility, consistent presence in the content ecosystem, and genuine category authority. They are, overwhelmingly, the brands that invested in brand. Gartner calls this the end of channel-based marketing as we know it. I’d call it the moment when fifteen years of deferred brand investment comes due, all at once. This isn’t hypothetical. The buying journey is already changing shape in ways most B2B marketing organizations haven’t adjusted for. Outbound reply rates have dropped from 6.8% to 5.8% in two years, and they’re not going back up. AI-generated content is flooding every channel, compressing the signal-to-noise ratio toward zero. The buyer is further along in the journey, and harder to reach through traditional means, by the time they want human contact. In that environment, the brand that comes to mind before the buyer opens a browser window is the one that wins. That’s what brand building has always been for. The AI era makes it more true, not less. The Branding Crisis Has a Villain. But It’s Not Who You Think It’s tempting to blame the CMO. The tenure data suggests the market is doing exactly that: average CMO tenure at Fortune 500 companies fell to 3.9 years, more than 20% of Fortune 500 companies changed marketing leadership in the past year, and the CMO title is disappearing from B2B companies faster than it’s being created. But the brand problem in B2B isn’t primarily a CMO failure. It’s a CEO failure of a specific kind: confusing marketing execution with marketing strategy. When the CEO’s primary conversation with the CMO is about MQL volume, pipeline attribution, and campaign performance — not about positioning, differentiation, and market presence — the function self-selects toward tactical execution. It hires for campaign management. It measures what it’s asked to measure. It builds what it’s rewarded for building. The CEO who hasn’t required a clear, differentiated positioning statement from their marketing leader hasn’t done their job. Because positioning is a business decision before it’s a marketing decision. It requires choices about what you will and won’t be. It requires saying no to market segments that would muddy your position. It requires conviction about what winning looks like, and the discipline to say so clearly. Most B2B CEOs haven’t had that conversation. Most B2B CMOs haven’t forced it. The result is that the foundational strategic work, the work everything else depends on, never gets done. And everyone wonders why the campaigns aren’t breaking through. What Strong Brand and Clear Positioning Actually Delivers I want to make this practical, because “invest in brand” without specifics is too easy to dismiss. Here is what clear, differentiated positioning actually does for a B2B company: * It makes every other marketing investment more efficient. When your position is clear, content has a point of view. Campaigns have a claim to make. Sales has something to say that isn’t just a feature list. The tools in your stack have something coherent to automate and personalize. Positioning is the input that makes everything else compound. * It is the prerequisite for AI discoverability. LLMs surface brands that have a clear identity in the content ecosystem. Consistent point of view, recognizable voice, specific category authority. These are the signals AI systems learn from. A brand with a muddy position produces muddy content, which produces muddy LLM citations, which produces invisibility in AI-generated buyer research. * It is the filter for your technology decisions. If you know exactly who you’re for, what problem you solve, and why you solve it differently, your martech choices become dramatically simpler. You buy for your actual go-to-market motion, not for the full feature catalog of the category. This is one reason why companies with clearer strategies tend to run leaner, more effective stacks. * It is the human signal in an AI-generated world. As AI content floods every channel, authentic point of view becomes the primary differentiator. Buyers don’t just evaluate products. They evaluate companies. They want to know who you are, what you believe, and whether they’d want to work with you. Brand — real brand, not simply logo and color palette — is how you answer that question before the first meeting. * It is the foundation of trust in a buyer journey that is increasingly pre-human. By the time your buyer contacts your sales team, they’ve already formed an impression. That impression came from what they read, what they heard from peers, what showed up when they asked an AI. If your brand has been consistently present, credibly positioned, and genuinely differentiated throughout that pre-contact journey, you start the conversation with an enormous advantage. If it hasn’t, you’re starting from scratch in a conversation the other company has already been having. In a World Flooded with AI Content, Human Credibility Is the Scarcest Signal I’ve written before about the emerging importance of credible operator voices in B2B. Practitioners with genuine field experience who can speak to how products and strategies perform in the real world. I believe this is one of the most underappreciated dynamics in B2B marketing right now, and the agentic AI era is going to accelerate it. Here’s why: in a world where AI can generate unlimited amounts of competent, technically correct, on-brand content, the scarcest signal is human credibility. The peer who says “we implemented this and here’s what actually happened” is worth more than any campaign the vendor runs. Buyers know this. Gartner’s research found that 78% of consumers say explicit labeling of AI-generated content is a critical factor in maintaining trust. That number will only grow as AI-generated content becomes more prevalent and buyers become more sophisticated about identifying it. B2B buying is inherently a trust exercise. Large purchases, long sales cycles, significant switching costs, career risk for the buyer. In that environment, peer validation and operator credibility are the trust infrastructure. LLMs are already trained to cite practitioner-authored content; experience-based, specific, verifiably credentialed, over generic vendor content. What this means in practice: the B2B companies that build systematic programs around credible operator voices, customers, recognized practitioners, industry operators, are building brand assets that AI will cite, buyers will trust, and competitors cannot easily replicate. This is not an influencer marketing program. It is a brand credibility infrastructure program. And almost nobody in B2B is doing it with appropriate strategic intent. One More Dynamic, And It Involves the Agencies No honest account of the B2B brand problem is complete without acknowledging the role of ad agencies. Not as villains. The people in those agencies are often talented, experienced, and genuinely committed to their clients. But the agency model itself is under severe structural pressure, and that pressure is showing up in ways that have not helped the brand problem. I’ve been on both sides of this. The opening of this post describes my experience as a buyer of marketing programs. What it doesn't mention is the sell side. As a media and marketing services CEO, I've produced hundreds of thousands of marketing programs: content, events, digital, demand generation. Sold to B2B marketers across virtually every major industry. I know exactly how those programs get packaged, priced, and pitched. I know what actually performs and what gets dressed up to look like it does. Both perspectives inform what follows. The structural reality is stark. The Basis 2026 Advertising Agency Report found that 87.3% of agency professionals believe the traditional agency model is either broken today or will be within three to five years. Among VP-level and above, that figure climbs to 91.5%. These are agency insiders reaching that verdict about their own industry. Fee compression, the shift to project-based work, the rise of in-house teams, and now the AI displacement of production work have all combined to put the traditional agency business model under sustained pressure. Forrester’s 2025 B2B Brand and Communications Survey documents the result: a 15-point gap between what B2B marketing leaders value from their agency partners and what those agencies actually deliver, widest in the specialized strategic skills category that should be the agency’s core advantage. Over my career I’ve worked with genuinely talented, high-impact agencies, creative and brand visionaries who put their clients on the map, built category-defining brands, and delivered extraordinary business value. That work is real and it deserves recognition. Which is why what has happened to the agency model over the past fifteen years is worth naming honestly, and with some empathy for the position the industry finds itself in. The same dynamics outlined in Post 1 of this series: the shift to performance marketing, the explosion of martech, the compression of brand investment into measurable digital execution, have put B2B agencies in a genuinely difficult position. The business shifted underneath them. Clients stopped asking for brand and creative vision and started asking for digital marketing services, demand generation programs, and performance metrics. Agencies followed the money, as any rational business would. The result: the industry has drifted from creative and brand visionaries toward digital marketing services companies. The capabilities that made the best agencies extraordinary — strategic positioning, creative courage, brand architecture — became less billable, less valued, and less central to what agencies were being hired to do. When a business model is under that kind of pressure, the response is rarely to double down on the hard, slow, often undervalued work of brand strategy. It is to emphasize what is visible, billable, and easy to point to in a pitch. Which explains what the B2B agency world has become: an industry that has confused winning awards with winning clients. Open LinkedIn on any given day and you will find a press release dressed as a post. A B2B agency has won an award. The campaign was transformative. The results were industry-defining. The trophy, bestowed by an awards body that virtually no client has ever heard of, is prominently displayed. Scroll further and another agency has won Agency of the Year. Then another. Then another. There are enough “Agency of the Year” designations distributed across enough award programs — the ANA B2 Awards, the B2B Marketing Awards, the Elevation Awards, the Ignite Awards, the Global ACE Awards, and a dozen more — that winning one has become less a mark of distinction than a standard line item in the agency marketing budget. One agency website I reviewed catalogued 33 award wins in a single year. Nine of them were Agency of the Year. Nine. At some point the designation stops meaning anything at all, and the industry’s willingness to keep issuing and celebrating it says everything about how hard genuine differentiation has become. In 35 years in this industry — as a buyer of agency services, as a seller of marketing programs, as a CEO who has hired and evaluated dozens of agency relationships — I have never once hired an agency because it was named Agency of the Year. Not once. This is a symptom of structural disruption, not a character flaw. When margins compress and differentiation is genuinely difficult, peer validation fills the gap. But the result is an industry spending significant energy on recognition its clients don’t value, while the strategic work clients most need: positioning, brand architecture, genuine differentiation, gets systematically underinvested. Here is the genuine irony. The agentic AI era is precisely the moment when a great agency partner could be most valuable to a B2B company. As AI automates production and execution at scale, the premium on strategic thinking goes up sharply. The positioning work, brand architecture, and creative courage that require deep category expertise and genuine client partnership, is exactly what AI cannot replicate. The agencies that recognize this shift and restructure around it have a compelling future. The ones that continue to compete on production scale and award recognition are running a model that AI is actively dismantling. This dynamic deserves its own full exploration, including what the agency model needs to become in the agentic AI era and what B2B companies should be demanding from their partners right now. That post is coming. For now the point that belongs here is this: the brand problem in B2B has multiple structural causes, and the disruption of the agency model is one of them. The solution, ironically, runs straight through the kind of strategic agency partnership the industry has largely been moving away from. The questions worth asking of any agency relationship right now: Where do you think our positioning is weak, and what would it take to fix it? What does our brand stand for that no competitor could credibly claim? And when did you last push back on a client brief because it was the wrong strategic move? If the answers are compelling, invest in that relationship. The market needs it. If they aren’t, the trophy case is already telling you what you need to know. What Marketing Leaders Should Do Now The strategic case is one thing. The operational question is what to do about it. Here are the moves that matter, in order. * First, before everything else: define your position. This is not a marketing project. It is a business leadership project. Get your CEO, your marketing leader, and your product or commercial leadership in a room with a single deliverable: one sentence that says who you are, who you’re for, what you solve, and why you’re different. Not a tagline. Not a mission statement. A market position. If your leadership team can’t agree on that sentence, stop all other marketing investment until they can. Everything else is built on sand. * Second, audit what your brand actually says today. Prompt ChatGPT, Claude, and Perplexity with the questions your best buyers ask when researching your category. Read what comes back. Look for where you appear and where you don’t. Read your own website with fresh eyes. Listen to a few sales calls. Ask a customer how they’d describe your company to a peer. The gap between what you believe your brand says and what the market actually receives is almost always larger than leadership expects. Know the gap before you try to close it. * Third, restore the brand investment ratio. Les Binet and Peter Field's landmark research, the most rigorous analysis of marketing effectiveness ever conducted, established that brands dramatically outperform over time when they balance brand building with sales activation rather than defaulting to performance-only investment. Their subsequent B2B-specific research with the LinkedIn B2B Institute found the optimal B2B ratio is 46% brand, 54% activation, not because brand matters less in B2B, but because longer sales cycles and larger buying committees require more activation support. The number is less important than the direction. Most B2B companies aren't running anywhere near 46% brand investment. They're running 10%. Or 5%. Or zero. That gap is where the brand problem lives, and where the pipeline problem follows. * Fourth, build your operator voice program. Identify ten credible practitioners; customers, recognized voices, domain operators, who genuinely understand the problem you solve. Build a structured, compensated program around their voices. The goal is content that AI will cite, buyers will trust, and your sales team will use. Not testimonials. Not sponsored posts. Genuine practitioner perspective on real problems your category exists to solve. * Fifth, designate someone to own your AI discoverability. Not SEO. Not content production. Specifically: how does your brand appear in AI-generated answers? Who is responsible for that question? What’s the strategy for improving it? This role doesn’t need to be a new hire. It does need to be explicitly owned. In 2027 it will be one of the most important marketing functions in your organization. The Uncomfortable Conclusion B2B marketing has a brand problem that predates the AI era and will outlast it, unless leadership decides to treat it as the strategic priority it actually is. The function has spent fifteen years building execution capability on top of a positioning foundation that was never poured correctly. The tools work. The campaigns run. The metrics get reported. And yet differentiation remains the number one challenge, category after category, company after company, year after year. Agentic AI is not going to solve that problem. It is going to expose it, at scale, at speed, in buying journeys that happen before your sales team knows the buyer exists. The companies that will win the next phase of B2B marketing are not the ones with the most sophisticated tech stack, the largest content library, or the most advanced AI deployment. They will be the ones that know exactly what they stand for, can say so clearly, and have built the credibility infrastructure to make buyers believe it. That work starts with positioning. It has always started with positioning. In the agentic AI era, the cost of not doing it is higher than it has ever been. Next in the series: B2B Marketing in the Machine Age, Post 3 : "The Stack That Broke Marketing (And the Architecture That Can Fix It)." The martech explosion promised efficiency and delivered complexity. What B2B companies need to build — and tear down — to compete in an agentic AI world. The views expressed in Uphoff on Media are entirely my own. They don’t represent the opinions of any company I’ve led, any board I’ve sat on, or any investor who’s had the pleasure of debating strategy with me over the years. If something I write here sounds brilliant, I’ll take full credit. If it turns out to be wrong, I was clearly misquoted by myself. 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]

26 de may de 202627 min
episode How B2B Marketing Lost It's Way artwork

How B2B Marketing Lost It's Way

B2B Marketing in the Machine Age | Series Opener This is the first post in B2B Marketing in the Machine Age, a four-part series on the state and future of B2B marketing in the agentic AI era. This post sets the stage: the decisions, incentives, and technology bets that brought the function to where it stands today. Post 2 examines The Work: specifically why brand and positioning have become the most underdeveloped and most consequential capabilities in B2B marketing, and why agentic AI makes that gap more urgent, not less. Post 3 covers The Career. Post 4 covers The Org & Tech. Each post stands alone. Together, they make a case the industry needs to hear, and act on. There’s a meeting that happens in most every B2B company each month. You know the one. The CMO walks in with a deck. Forty, sometimes fifty slides. Multi-touch attribution models. MQL-to-SQL conversion rates. Pipeline influence by channel. Cost per attributed lead. First-touch vs. last-touch comparison. A waterfall chart that took someone three days to build. Another slide to explain the methodology behind the waterfall chart. The CEO asks: “Is the marketing working?” The CMO begins on slide four. Forty-five minutes later, nobody can answer the question. The slides didn't answer it. They replaced it. Here’s the thing: that meeting — and the weeks of work that produced it — is B2B marketing now. Not the customer insight. Not the brand positioning. Not the creative brief or the campaign strategy or the message architecture. The reporting. The attribution model. The dashboard. The deck. B2B marketing didn’t just get distracted by measurement. It became measurement. And the cost of that substitution is enormous, and about to get dramatically worse. How Accountability Became a Substitute for Thinking The original promise of digital marketing was genuine and valuable: for the first time in the history of advertising, you could actually measure what worked. Print ads in trade publications, direct mail, trade shows, these were acts of faith, educated guesses, and brand conviction all bundled together. Digital changed the equation. Clicks. Opens. Form fills. Trackable conversions. The measurement revolution was real. But somewhere between the promise and the practice, accountability as a capability became armor. I've had a front-row seat to this shift across twenty-five years: as CEO of UBM TechWeb, President and CEO of ThomasNet, and CEO of Pipeline360. In every one of those seats, I watched the same pattern play out: as marketing budgets came under pressure, CMOs reached for data as a shield. And the martech industry, never one to miss a revenue opportunity, responded accordingly. Every vendor built attribution features. The category of “marketing analytics” became a multi-billion dollar industry built almost entirely on answering one question: Can we prove the marketing worked? The measurement infrastructure grew faster than the marketing it was supposed to serve. By the time companies had full-stack attribution — first touch, last touch, multi-touch, algorithmic, time-decay — most marketing teams were spending more time configuring dashboards and preparing readouts than using the insights those dashboards generated to actually manage their marketing. This is not a technology failure. It’s a behavioral one. And it has three distinct, compounding traps. Trap One: Activity Displacement The attribution process has become the work product. Building dashboards, pulling reports, modeling multi-touch attribution, preparing presentations, reconciling data discrepancies between platforms, explaining why Salesforce numbers don’t match HubSpot numbers, this is now a significant and growing portion of what B2B marketing teams actually do each week. It crowds out the upstream thinking. Customer research. Positioning development. Message architecture. Creative strategy. The work that precedes any campaign worth measuring. You end up with teams that are extraordinarily sophisticated at reporting on marketing programs and increasingly thin on the judgment and instinct required to build them. I’ve seen this dynamic in companies of every size. The marketing operations function — which was supposed to be a support function — gradually becomes the center of gravity for the entire department. The metrics review becomes the primary artifact the team produces. And the actual marketing: the campaign, the message, the creative, the positioning, becomes almost incidental. Something that has to exist in order to have something to measure. Trap Two: The Defensive Posture Attribution-first marketing is structurally backward-looking. The question it always asks is: did we justify the spend? That's not a marketing question. It's a survival question. And it shapes everything downstream. Creative decisions get made by what’s attributable, not what’s right for the brand. Channel strategy gets optimized for trackable clicks, not for where the buyer actually is. Positioning becomes whatever produced the best CPL last quarter. The standard for a good campaign shifts from “does this change how our best prospects think about us?” to “did this generate enough attributed pipeline to survive the next budget conversation?” The result is B2B marketing that is reactive, incremental, and profoundly unambitious. And, not coincidentally, B2B marketing that buyers experience as interruptive, formulaic, and irrelevant, because it was built around what’s measurable, not what’s true. The best marketing I’ve been part of started with a question about the customer: what does this buyer actually need to know, believe, or feel in order to make a decision in our favor? That question is genuinely hard to answer. It requires research, judgment, and creative conviction. It doesn’t produce a dashboard. It produces a brief. And from the brief comes the positioning, the creative, the channel strategy, and — eventually, after the work has been done — the measurement framework. Attribution-first marketing inverts that sequence. It starts with what’s measurable and works backward. The brief, if it exists at all, is a formality. Trap Three: Attribution as Job Protection What people are thinking that doesn’t get said. Here's what nobody says out loud: the more complicated the attribution machinery gets, the harder it is for the CFO or CEO to challenge it. Multi-touch models. Platform-versus-platform data fights. Footnotes about attribution windows. At some point the complexity itself becomes the point If the marketing leader can produce forty slides of attribution data that are genuinely difficult to interpret, that require specialized knowledge to interrogate, and that no one in the room can fully refute, the budget is probably safe. Not because the marketing worked, but because the machinery of measurement is impressive enough to shift the burden of proof. This is not cynicism. It’s a rational response to a broken incentive structure. CMO tenure averages somewhere between eighteen and twenty-four months, shorter than virtually any other C-suite role. That kind of insecurity produces defensive behavior. Attribution complexity is the ultimate defensive tool. You can’t be fired for what can’t be measured, and you can’t easily be challenged on what’s too complex to fully understand. But the cost of this defense mechanism is that the incentive to simplify: to return to the customer insight, the bold positioning, the campaign that doesn’t require forty slides to explain, is almost entirely absent. Simplicity is vulnerable. Complexity protects. So complexity wins. What This Has Actually Cost The damage is visible everywhere you look. B2B creative has gotten safer and more forgettable, because every decision runs through an attribution filter that rewards the familiar over the bold. Positioning has gotten blander, because brand differentiation requires conviction that attribution models don’t measure. The CMO has become a data steward in too many companies rather than a market maker. The role now rewards students of the dashboard over students of the customer. And the buyers noticed: the B2B marketing experience has grown more formulaic and less relevant in direct proportion to how sophisticated the measurement infrastructure became. That is not a coincidence. When you build marketing programs around what’s trackable rather than what’s true, the buyer feels it. Agentic AI Doesn’t Fix This. It Exposes It. Here’s where this stops being a management problem you can solve with better habits or a more enlightened CMO. Agentic AI is about to fundamentally break the attribution model B2B marketing has built its entire operating logic around, and the industry is largely unprepared for what comes next. AI agents conducting procurement research don’t fill out forms. They don’t click display ads. They don’t register for webinars, respond to nurture sequences, or generate the trackable behavioral signals that multi-touch attribution models were built to capture. When an AI agent is tasked with evaluating a category of vendors, it conducts its research through channels that are largely invisible to the tools B2B marketers have spent a decade building attribution infrastructure around. There is no UTM parameter on an AI agent’s recommendation. The buyer journey AI agents conduct is not a modified version of the human buyer journey. It’s a structurally different process that operates on different signals. Brand reputation. Category authority. Quality of content indexed by AI systems. Customer outcomes data. Third-party validation. These are the inputs an AI agent weights when evaluating vendors. They are not well-captured by MQL counts or pipeline influence percentages. The predictable response — already visible in corners of the industry — will be to build AI-powered attribution tools designed to track AI-mediated buyer journeys. This is the wrong lesson. It is more measurement infrastructure chasing a buyer who has left the building and gone somewhere the measuring tools can’t follow. What survives in an agentic world is everything attribution-first marketing has been underinvesting in: genuine brand authority, distinctive positioning, authentic thought leadership, customer success stories that hold up to scrutiny. The very things that can’t be easily tracked are the things that will matter most. The painful irony: at precisely the moment when B2B marketing needs to pivot back to brand-building, customer insight, and creative conviction, most marketing teams are least equipped to do it, because they’ve spent years optimizing for measurement and letting the upstream marketing muscles atrophy. Where the Puck Is Going Three predictions, with conviction: * Attribution models will get more sophisticated and less useful at the same time. The martech industry will respond to AI disruption the way it always responds to disruption: by building new tools. AI-powered attribution platforms will emerge. They will be genuinely impressive. They will also be measuring a buyer journey that increasingly doesn’t exist in the form those models are designed to capture. The investment in this infrastructure will be substantial. The return will diminish. * The CMO role will bifurcate. One path leads further into the data and operations direction: essentially a marketing operations function with a C-suite title, focused on stack management, attribution governance, and pipeline reporting. The other path leads back to the original CMO mandate: market intelligence, customer insight, brand positioning, and the creative conviction to build something distinctive. Both will exist. The second path will be harder to fill and significantly more valuable. Companies that understand the difference will hire accordingly. * Brand will reassert itself as the primary B2B marketing asset Not because brand marketers won a long-running argument, but because it will be the only signal that holds up in an agentic buyer environment. The companies that have been quietly building real brand while everyone else was building dashboards will have a structural advantage that will be very difficult to close quickly. Brand takes time to build and time to move. The time to build it is not after the buyer journey has already shifted. The Entrepreneur and Investor Opportunity The dysfunction described in this post is not just a management problem. It is a market opportunity, and some of the most interesting entrepreneurs I’ve been talking to lately are starting to see it clearly. The premise is straightforward: if a significant portion of B2B marketing’s bandwidth is consumed by attribution data collection, dashboard maintenance, platform reconciliation, and reporting preparation, work that is repetitive, rule-based, and largely mechanical, that work is exactly what agentic AI is built to automate. The opportunity is to take the attribution reporting function off the marketing team’s plate entirely and deliver it as a service, purpose-built for B2B marketing organizations. Call it Attribution as a Service. The model is not complicated in concept, though it is genuinely hard to execute well: an AI-powered system that pulls data from across the martech stack, reconciles it, applies agreed-upon attribution models, and delivers clean, current reporting on a cadence the marketing team sets, without requiring a marketing operations analyst to spend three days preparing the monthly deck. The CMO gets the information. The team gets their time back. The investor thesis is real. B2B companies are not going to stop wanting attribution data; the CFO isn’t disappearing, and the budget conversation isn’t going away. But the current model, where skilled marketing professionals spend a disproportionate share of their working hours producing that data, is inefficient in a way that agentic AI can directly address. The companies that build this well will sell into a buyer who is simultaneously cost-pressured, understaffed, and increasingly aware that the way attribution currently works is not working. A few dimensions of the opportunity worth watching: The integration layer is the moat. The hard problem in this space is not the reporting interface. It is the data. B2B martech stacks are fragmented, inconsistent, and frequently incoherent. CRM, MAP, ad platforms, intent data, web analytics, ABM platforms: these systems do not talk to each other cleanly, and the reconciliation work is where most of the human hours currently go. The entrepreneur who solves the integration and normalization problem owns the defensible position. The dashboard is a commodity. The clean, unified data layer is not. The model must return time to judgment, not just reduce cost. This is the critical distinction. An Attribution as a Service model that simply makes the existing reporting machinery faster and cheaper accelerates the problem this post describes. It just produces the forty-slide deck in ten minutes instead of three days. The entrepreneurs building something more valuable understand that the real product is not efficient reporting. It is reclaimed marketing capacity. Intelligently automate the mechanical work, return that capacity to the core levers of marketing: positioning, brand, customer insight, creative strategy, and you don’t just have a more efficient marketing team. You have a more impactful one. In B2B, where the gap between average marketing and excellent marketing is measurable in pipeline and market position, that delta is worth a great deal. Agentic AI changes the addressable market. As AI agents become a more significant part of the B2B buyer journey, the attribution problem gets structurally harder for in-house teams to solve. The signals change. The trackable touchpoints diminish. The models that worked for a human buyer journey need to be rebuilt for an AI-mediated one. A specialized service provider with the technical depth to stay ahead of that evolution will have a durable advantage over an in-house marketing ops team trying to keep up while also doing everything else. The venture and PE communities have not fully priced this opportunity yet, in part because attribution has been treated as a feature of the broader martech stack rather than as a standalone category. That is changing. The market size is real, the buyer pain is acute, and the technical moment: the convergence of agentic AI capability with martech stack fragmentation, is exactly right. For B2B media and marketing technology investors, this is a space worth watching closely. The Work That Needs to Happen There’s a test I’ve applied to marketing programs for most of my career, going back to my time at InformationWeek and forward through the ThomasNet rebuild: where we did more than three hundred customer interviews with Strategyn to understand what industrial buyers actually needed, as opposed to what we assumed they needed. The test is simple: Does this start with the customer or does it start with what's measurable? Attribution-first marketing almost always starts with what’s measurable. It starts with: what did we spend, what did it generate, and how do we defend it? Customer-first marketing starts with: what does this buyer believe, what do they need to believe, and what would it take to get them there? The second question is harder. It can’t be answered with a dashboard. It requires research, judgment, time spent with actual customers, and the willingness to act on insight before you have the data to prove it worked. It produces briefs and positioning statements and creative work that might fail before it succeeds. It is also, not coincidentally, the only kind of marketing that works. Attribution is a tool. It was always only ever supposed to be a tool. A way of checking the work after the thinking had been done. The industry turned it into a substitute for the thinking itself. Getting back to the actual upstream work is not a retreat from accountability. It is the precondition for having anything worth measuring. The measurement can follow. First, do the marketing. This is the series opener for B2B Marketing in the Machine Age, a four-part series on the state and future of B2B marketing in the agentic AI era, published in Uphoff on Media Next in the series: "B2B Marketing Is Broken. Brand Is Why. And Brand Is the Fix." Post 2 of B2B Marketing in the Machine Age, publishing 5/26/26. The views expressed in Uphoff on Media are entirely my own. They don't represent the opinions of any company I've led, any board I've sat on, or any investor who's had the pleasure of debating strategy with me over the years. If something I write here sounds brilliant, I'll take full credit. If it turns out to be wrong, I was clearly misquoted — by myself. 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]

20 de may de 202619 min