In this solo podcast episode, Tony Uphoff argues that the rise of agentic AI has fundamentally reshaped B2B buying and marketing. He explains that a new, invisible AI member now sits inside buying groups, creates shortlists, and drives early demand, while marketers continue to use AI mainly to generate more content, creating a mismatch between supply and demand. Uphoff revisits three core questions he posed earlier in the year—who owns demand creation, what defines demand quality, and how the industry can shift from tool‑centric to outcome‑centric models—showing how each has been reframed by AI. He backs his claims with recent data (94% of buyers use AI, 89% of marketers use AI for content) and contrasting company examples (ZoomInfo’s goodwill impairment vs. Clay’s soaring valuation; Salesforce’s divergent P&L). He details how ownership now belongs to the buyer’s AI, how quality is defined by what the AI trusts (verified, specific signals), and how trust has moved from marketing tools to demonstrable outcomes, with trials and procurement playing larger verification roles. The episode then presents five bold 2027 predictions that challenge the notion that AI will simplify B2B: AI will make buying more human, outbound volume will become worthless as agents outnumber sellers, excess content will reduce findability, procurement will become the primary value‑setter, and the most critical marketing hire will be a hybrid leader who owns both the machine’s knowledge and the outcome. Uphoff introduces a two‑axis diagnostic framework (paid‑for activity vs. outcomes; own AI vs. buyer’s AI) that maps product lines into four quadrants—rear‑view mirror, margin without moat, right‑road wrong‑meter, and windshield—illustrating where value is created or destroyed. He concludes with practical advice on allocating AI spend, pricing models, and building capabilities that serve the buyer’s AI, and ends with a call to follow the podcast.
Chapters
Hey, it's Tony. No topic generates more discussion and debate than the current and future state of B2B marketing. Buying groups have now added a new member, one that is invisible to vendor marketing and sales, and one that is increasingly in charge of creating the shortlist that the rest of the group uses to make a final decision. Marketers are unprepared for this shift, and many are responding by producing more of what they've been doing, not stopping and adjusting strategies for a new world. Buyers are adjusting faster, adopting Agentic AI as a buying group partner to advance evaluation, selection, and shortlisting. We're also seeing procurement being brought into the buying process far earlier, perhaps reflecting the need to have humans validate the shortlist assumptions the AI agents are making. In today's episode, I revisit 3 questions that I posed for B2B marketing back in January that have become even more critical since. The related post on Uphoff on Media also provides 2 diagnostic charts that B2B marketers can download and use to align their AI initiatives with their buyers' AI use. I also provide 5 predictions for B2B marketing in 2027. Today's episode is titled B2B Marketing is Driving Forward Looking Backward. Okay, let's get to work. The buyer's AI is answering the 3 questions B2B marketing never did. Get them right, your value rises. Get them wrong, and it falls. 94% of B2B buyers now use AI somewhere in the buying process. Forrester and Sixth Sense surveyed buyers separately and landed on the exact same number. 89% of B2B marketers use AI to create content. Same technology, opposite jobs. Buyers use it to decide. It does the research, narrows the field, and builds the shortlist. And 95% of purchases still come from that initial shortlist. Marketers mostly use it to produce more content, more sequences, more campaigns aimed at a buyer whose AI is now filtering most of it out. That's an industry driving forward while looking in the rearview mirror, and the market has started pricing the difference. The questions were about leadership. Now they're really about value. In January, I published 3 questions B2B marketing still hasn't answered. Number 1, ownership. Who actually owns demand creation and is accountable for revenue? Number 2, demand quality. What does quality mean when buyers are anonymous and AI-driven? And number 3, outcomes. Can the industry move from fragmented marketing tools and systems to trusted outcome-based systems. It's still a leadership problem. 9 months later, it's a valuation problem as well. ZoomInfo took a $650.5 million goodwill impairment in its second quarter on revenue growth of 1.2% and cut its full-year outlook. In September, Clay, which sells Signal and Workflow to the same go-to-market teams, raised money at a $7.1 billion valuation. billion valuation, more than double the valuation it carried 13 months earlier after quadrupling revenue in 2025. Same buyers, same category, opposite valuations. It shows up inside single companies too. Salesforce stock was down 26% by early February as software shed roughly $1 trillion of market value. By late August, its agent force and Data 360 recurring revenue had reached nearly $3.9 billion, Up more than 210% in a year. One P&L moving in 2 directions at once. Agentic AI doesn't lift or sink a sector. It splits product lines on 2 variables: what you get paid for and whose AI you're building for. Most of the industry is getting the second one wrong. Question number 1, the buyer's AI now owns the front door of demand creation. The January question asked whether marketing, sales, or ops owns demand creation. The answer came from a direction none of them were watching. The buyer owns it, and the buyer's machine does more of the work every quarter. Here's the counterintuitive part. Buyers are reaching out to sellers earlier than they used to, about 61% of the way through their journey, down from 69% a year before, according to Sixth Sense. They still start that conversation themselves roughly 80% of the time, and 95% of the purchases still come from the shortlist they built before the first call. Earlier contact hasn't reopened the field. Buyers are calling to check a decision they've mostly made. Gartner expects AI agents to intermediate more than $15 trillion of B2B spending by 2028. Even if Gartner is off by a year, the direction isn't in question. So the ownership question has changed shape. The issue now is who owns what the buyer's AI knows, believes, and repeats about you before the buyer ever raises a hand. In most marketing organizations, nobody does. A ScribeWise and Scrunch survey of 602 marketers this spring found 91% claim a documented AI search strategy, while 62% are still measured on traditional SEO metrics. The job moved. The scoreboard didn't. Question number 2, quality is whatever the buyer's AI trusts. In January, I wrote that the industry still sells leads while buyers behave like a buying group. The buying group just picked up a member nobody counts, the agent doing the research. Forrester's 2026 research puts the typical decision at 13 internal stakeholders and 9 external influencers. When the product itself has generative AI features, the group doubles from 7 people to 14. A lead is one person. An agent doesn't fill out a form. For 20 years, the seller defined quality, a scoring model, an MQL threshold, a handoff rule. The buyer's AI defines it now, and it isn't grading on machine logic alone. It rewards what's verified across independent sources, specific enough to answer a real question, and solid enough that someone in the buying group will stand behind it. It discounts volume. Volume is where most seller-side AI went. A tenfold survey of 400 B2B tech marketers found 46% of them producing 3 to 5 times more content than the year before on budgets up 10% or less. The Content Marketing Institute found 87% of marketers using AI for content report productivity gains and 39% report better content performance. Output multiplied, but results didn't. Compare what happens when quality measurement follows the buyer. Palo Alto Networks dropped MQLs for buying groups, and per a Forrester case study, saw a 17% higher win rate with multi-stakeholder opportunities 8 times more likely to advance. The value math follows. Anything sold by a unit, the buyer's AI makes redundant. Think content records, gated content leads, undifferentiated intent, outbound volume is being repriced down. Verified signal that a buyer's agent needs and can't scrape is being repriced up. The market is paying for signal and marking down records. And question 3, trust moved from the tool to the result. The 3rd question asked whether the industry could move from fragmented tools to outcome-based systems. The software market answered for it this year. As I argued in the post, The Stack That Broke Marketing, the fix for sprawl was never more technology. It was a different application of it. Buyers have stopped trusting claims and started testing them. More than 60% now run a trial before they buy, according to Forrester, and only about a third of those end up buying from the vendor whose trial they ran. The trial has become the place where shortlists flip. And buyers trust the machine about as much as they trust you. Gartner found 51% of buyers worry about misleading information from generative AI, and 49% worry about misleading information from sales reps. So they check one against the other. 69% want a sales rep to evaluate— to validate what the AI told them. Trust sits with whoever's accountable for the result and can prove it. Features, attribution models, and dashboards still don't answer the CEO's questions from January. Evidence does. Both org charts are being redrawn. Only one side is doing it on purpose. The buyer side added people to check the machine. Buying groups double when AI is a part of the purchase. Procurement is a decision maker in 53% of buying cycles per Forrester and is increasingly involved from the earliest stages. Sixth Sense found 58% of buyers engage sellers earlier specifically to evaluate the vendor's AI. Buyers built a verification layer, and it's getting larger. The seller side added machines and subtracted titles. Spencer Stewart found that 31% of S&P 500 companies no longer have an enterprise CMO, and the ones who remain average 4.1 years in the job. Postings for go-to-market engineers, the people who wire data, AI, and workflow into the revenue engine, grew 205% year over year to roughly 3,000 open roles in early 2026, according to one job market analysis. And 91% of the companies that have hired one employ exactly one. Gartner expects AI agents to outnumber sellers 10 to 1 by 2028, while fewer than 40% of sellers will say the agents made them more productive. The money follows the same pattern. Gartner's 2026 CMO Spend Survey puts marketing budgets at 7.8% of revenue, With 15.3% of that going to AI. Only 30% of those CMOs say their organizations are ready to use it, and 56% say they lack the budget for their own AI strategy. Both sides are running into the same wall. Gartner's procurement research calls it a productivity paradox. AI makes individuals faster, and the gain shrinks at the team level. The buyer side is answering with more judgment. The seller side is answering with more output. One side is building a windshield. The other is polishing the mirror. 5 predictions. Here is what I expect to happen from here and what I'm watching for. Each one cuts against the consensus that AI makes B2B simpler, cheaper, and less human. Number 1, AI is making B2B buying more human, not less. Groups double when AI is involved. 2/3 of buyers say they prefer a rep-free experience, and more than 2/3 want a rep to check what the AI told them. Both are true at once. The seller's job is moving from persuasion to verification, and the best-paid sellers of 2028 will be the ones buyers call to check the machine. Number 2, 10 agents per seller will make outbound worthless. When every seller has 10 agents writing to buyers whose own agents read the mail, unsolicited volume gets filtered at machine speed. The cost of a message falls towards zero. The cost of a real meeting with a real buying group goes up. Anyone pricing on outbound volume is pricing the wrong side of that trade. Number 3, more content will actually make you harder to find. Teams producing 3 to 5 times more content are not seeing 3 to 5 times the result. Buyers' AI rewards sources that are specific, verified, and consistent. I expect the strongest marketing teams to publish less, prove more, and show up on more shortlists because of it. Number 4, procurement, not marketing, will set the new unit of value. Procurement already holds a decision vote in more than half of buying cycles, and it will be the first function to hand comparison work to agents. Activity pricing, per lead, per impression, per seat is the easiest thing for an agent to line up in a spreadsheet and squeeze. Outcome pricing is the hardest. That asymmetry will reprice whole categories faster than any analyst model expects. And prediction number 5, the most important marketing hire of 2027 won't carry a marketing title. The CMO role is already splitting. One half is becoming the owner of what machines know and say about the company. The other half is becoming the owner of the outcome number. The leaders who hold both will keep the job and the budget. The ones holding neither will be reporting to someone who does. Where your business sits on the windshield test. Uphoff on Media on Substack. Here is a framework you can download and use as a diagnostic tool in your business. It plots each product line rather than the company as a whole on the 2 variables that decide whether agentic AI is adding value or taking In a way. The vertical axis is what you get paid for, from activity— leads, impressions, content, seats, hours— to outcomes— pipeline, revenue, decisions made. Questions 1 and 3 set your height— who is accountable for the result, whether the customer trusts you for a result or for a tool. The horizontal axis is whose AI you're building for, from your own— more output, lower cost— to your buyers, being what their AI finds, trusts, and recommends. Question 2 sets how far right you sit, whether your idea of quality matches what the buyer's machine rewards. Here are the quadrants. The rearview mirror activity, your AI, your AI makes more of what you already sell faster and cheaper. The buyer's AI filters the volume and the unit price follows it down. Most content syndication, contact data, and outbound volume businesses sit here today, value destroyed. Quadrant number 2, margin without a moat. Outcomes, your AI. Your paid home results and AI cuts your cost to deliver them. The margin is real. The risk is that the shortlist is built before you see the buyer and efficiency doesn't get you on it. Value enhanced for now. Quadrant 3, right road but the wrong meter. Activity, your buyer's AI. You, you show up where buyer's agents look. Verified data, cited research, trusted reviews. You still bill by the lead, the impression, or the seat. So someone else captures the value you create. Value created, not captured. In quadrant 4, the windshield, outcomes your buyer's AI— buyer's agents rely on your data, proof, and judgment, and you're paid on the result. Valuation premiums are moving here. Value compounding. Most businesses will find product lines in more than one quadrant. The same company can be creating and destroying value at the same time, and the P&L will blend the two until the market separates them for you. What to do with the next AI dollar. If you're wanting one of these businesses, split your AI budget into 2 columns, money that makes your own team more productive and money that makes you findable, credible, and verifiable to the buyer's AI. If the first column is most of it, you're looking in the mirror. Then put a name next to the second column and a shortlist number next to the name. If you're funding one, ask 2 questions before you look at the AI slide. What unit is on the invoice? Whose AI does the product serve? AI efficiency on an activity unit is margin on a shrinking base. Price it that way. If you're building one, move the meter before you scale the product. The step from activity pricing to outcome pricing gets harder with every customer you sign on the old unit. And if you're starting one, start on the buyer's side of the machine. Proprietary data their agents need and can't scrape, proof they can verify, and a price tied to the result. In January, I said progress starts by answering 3 questions honestly. The buyer's AI isn't waiting for your answer. It's giving its own, one shortlist at a time. Hey, that's it for the episode. If you found this useful, please do one thing. Follow the Uphoff on Media podcast on Apple or Spotify and leave a rating. It's the single best way to help another operator find this. I'm Tony Uphoff, back at it next week.
