The first serious conversation a B2B buyer has with a vendor used to happen over the phone or at a trade show booth. Today, that conversation happens alone: between a prospect and an AI tool, loaded up with the buyer’s specific metrics, their business problems, and their long-term goals. By the time that same prospect books a discovery call, the competitive ground has already moved.

This is the central argument Peter Farago and Steve Haney laid out in the latest episode of Misadventures in Marketing, S3 Ep4: The New B2B Customer Journey. Their conversation cuts through the hype around AI to land on something more concrete and more urgent: the B2B customer journey has changed structurally, and most marketing and sales teams have not caught up.

The Buyer Is Now Better Prepared Than the BDR

For years, marketers have known that buyers do research before reaching out. The question was always how much. What’s happening now is at a different order of magnitude. Prospects are feeding their specific problems, their performance data, and their internal debates directly into AI tools. In return, they are getting back synthesized, multi-perspective analyses that would have taken a consulting engagement to produce a few years ago.

“By the time they do sit down with a BDR,” Steve observes, “they are way better armed. Their brains are just much more supercharged going into the conversation.” The research isn’t just broader. It’s personalized in ways that generic vendor materials and rep talking points simply don’t match. Buyers are constructing private AI-powered decision engines, running scenarios, comparing vendors in real time, and arriving at discovery calls with sharper questions than most reps are trained to handle.

The implication for business development representatives is significant. Qualifying questions and standard objection handling were already table stakes. Now BDRs face buyers who have pre-qualified the vendor themselves and want the rep to prove the relationship is worth continuing. The rep’s job becomes less about delivering information and more about demonstrating something harder to replicate: genuine competence and trust.

Product Marketing Just Became a Frontline Function

If buyers are using AI to build their own decision-making applications, the content that feeds those applications becomes a direct competitive variable. Peter frames this shift with striking clarity: “All the content has to be available there for the chatbot to take advantage of when it builds that app. You’re constantly influencing the concept, the dialogue, the framing of the problem with all the information you’re putting out on the web.”

This is not a metaphor for good SEO. This is a world where a sophisticated B2B buyer constructs an app, potentially running continuously, that ingests public web content about competing vendors and surfaces ranked recommendations. The vendor whose content is richest, most specific, and most aligned with how buyers actually frame their problems will shape the output of that app. The vendor who produces thin, vague, or inconsistent content will get filtered out before a human ever enters the picture.

Product marketing rises in this environment precisely because the gap between positioning and execution becomes visible at machine speed. A messaging framework that has never been operationalized into granular, scenario-specific content is not a framework — it’s a starting point. FAQs, technical specs, objection-handling documentation, value propositions written against particular buyer personas: all of this becomes raw material for the AI tools buyers are already building and running.

The Discovery Call Still Belongs to Humans — For Now

Peter and Steve ponder on what AI cannot do in a buying process, and the answer involves something economists have been getting wrong for decades. Buyers are not rational actors. They are narrative-driven humans who make consequential decisions based on trust, emotion, and personal chemistry, then work backward to justify those choices with data.

Steve offers a pointed example from his own experience running marketing at a mid-size software company. The CEO wanted to replace a major vendor not because the product was failing or the contract was unfavorable. He wanted to replace it because he personally disliked the vendor’s CEO. The switching costs were enormous. The embedded solution was competitive. None of it mattered against the force of a narrative the CEO had already committed to privately.

“It makes absolutely no sense on a reason scale,” Steve says. The question is whether AI, trained to synthesize data and surface logical recommendations, will be equipped to account for those irrational but deeply human forces. His skepticism is grounded: “I’m not sure I yet trust the subtlety of these LLMs to assess context, to understand the different aspects of what could be driving a decision.”

Peter agrees and draws the conclusion: “People are emotional and not rational. There’s not going to be a substitute for human interaction and what we care about.” The discovery call, particularly its interpersonal dimension of qualifying budget, authority, and timing while building the kind of trust that makes a buyer want to proceed, remains a human-to-human exchange.

AI as a Marketing Team Member, Not a Marketing Tool

One of the sharper reframes in the conversation comes from how both hosts describe their own AI use. Neither treats it as a productivity shortcut for writing copy or summarizing reports. Steve describes sitting in on a webinar where a CMO had built a app, using Claude Code and CLI prompts, that surfaced live sales pipeline and lead funnel data across the entire go-to-market team in real time. “Before, it would be weeks before we would have this data,” Steve quotes the CMO as saying.

Peter’s example is equally instructive where he was facing a hiring decision about whether to bring on a go-to-market engineer. He used Claude to analyze roughly a thousand job descriptions against the internal role he was trying to define. The AI surfaced patterns and trade-offs that Peter would have taken weeks to gather manually. The insight it produced was that the role typically requires substantial existing system volume to optimize against, and the team wasn’t yet at that stage.

Both examples point toward the same principle: the marketers gaining an edge right now are not the ones using AI to do existing tasks faster. They are the ones treating AI as a capable team member who can handle research, analysis, data synthesis, and tooling. Thus freeing the human marketers to operate at a strategic level they previously lacked the bandwidth to reach.

Steve draws the analogy to the ATM. When they arrived, the prediction was that bank tellers would disappear. Instead, the number of tellers grew because the machines handled cash transactions and freed up employees. They had more time to sell financial products, build customer relationships, and do work that required judgment and trust. That same pattern is unfolding for marketing teams now.

What Vendors Must Prepare For

The customer journey was never linear, and has now become even more unpredictable although the outcome remains somewhat predictable. A well-prepared buyer will arrive having done thorough AI-assisted research, holding a relatively formed view of the vendor landscape. They will be ready to test whether the person across the table matches the impression that vendor’s content created.

Winning that test requires preparation on two separate fronts. The first is content infrastructure: making sure the digital surface area a vendor controls is rich, specific, and consistent enough. This helps inform the AI tools buyers are already using. The second is human infrastructure. It’s about making sure anyone in a customer-facing role can operate at the credibility level buyers expect on a first call.

Neither front alone is sufficient. A company with excellent content but underprepared reps will generate interest it can’t convert. A company with strong reps but thin content will lose buyers before anyone ever picks up the phone. Taking a prospect to dinner still matters. The dinner just happens later in a journey that started without any human involved at all.

 Tune in now to Season 3, Episode 4: The New B2B Customer Journey.

 Listen to the podcast on Spotify and Apple Podcasts.

 Check out summaries from other episodes:

• Resilience, Discipline, and Reputation: Lessons for Leaders
• How Modern Marketing Teams Choose the Right Channel Mix
• More Than a Mic: Strategic Speaking That Drives Growth

Misadventures in Marketing is a weekly podcast by the AMA San Francisco chapter. Veteran Silicon Valley marketing execs Peter Farago and Steve Haney explore the messy, rewarding, and occasionally absurd world of high-tech marketing — especially in early-stage startups. Each episode covers real-world challenges, trends, and lessons from the front lines.