Last week I attended PMA's Leaders Summit in New York, a gathering of roughly 75 senior product marketers from some of the most recognizable companies in the world. One of the things I enjoy most about events like this is they provide a reality check.
It's easy to spend time online and conclude that every conversation in B2B is about agents, automation, prompt engineering, and whatever AI announcement happened five minutes ago. But when you put experienced practitioners in a room together, the discussion tends to become much more practical. People are less interested in the technology itself and more interested in what it means for their ability to influence buyers, drive growth, and differentiate their companies.
While AI surfaced in nearly every conversation throughout the day, the most interesting discussion wasn't actually about AI. It was about storytelling. More specifically, it was about how difficult storytelling is becoming in a world where anyone can generate an unlimited amount of content, but while pulling from the same dataset.
Everything Is Starting to Sound the Same
Over the past year, I've found myself visiting more B2B SaaS websites that look different but somehow feel identical. The colors, logos, and product categories vary, but the messaging often sounds interchangeable.
Every company is helping teams move faster, unlock “insights”, streamline workflows, or transform the way work gets done. The problem they're solving always has some variation on “than ever”. Things move faster than ever. Doing something is harder than ever, takes longer than ever, or is more difficult than ever.
Individually, none of these claims are necessarily wrong. Collectively, however, they create an environment where differentiation becomes increasingly difficult to recognize.
Several conversations at the summit touched on this. As more companies rely on the same models trained on the same corpus of internet content, the outputs begin to converge. The issue isn't that AI produces poor content. In many cases, the writing is grammatically correct, logically structured, and entirely serviceable.
When every company can produce competent content at scale, competence ceases to be a competitive advantage.
The problem is that competent isn't memorable. Competent doesn't create preference. Competent rarely causes a buyer to stop scrolling, reconsider an assumption, or tell a colleague, "Hey, come check this out."
AI Can Generate Content. It Can't Generate Taste.
One observation kept resurfacing throughout the day: AI is remarkably good at creating content, but it has no real understanding of what makes a company unique.
AI is remarkably good at creating content. What it cannot do is determine which story deserves to be told.
It can generate a homepage, an email sequence, a launch plan, or an ebook in seconds. What it can’t do is identify the details that actually matter.
It doesn't know why a customer selected your solution over a competitor after months of evaluation. It doesn't know which objection consistently appears in late-stage sales conversations. It doesn't know why one prospect championed your product internally while another lost interest. It doesn't know the moment a buyer finally understood your value proposition.
Those details matter because that's where differentiation actually lives. Not in category definitions or feature comparisons, but in the experiences, beliefs, fears, and motivations of real buyers.
Those insights are difficult to acquire precisely because they require direct engagement with customers and buyers. They emerge from interviews, sales calls, win-loss analysis, support conversations, and countless interactions that never make their way into a public dataset.
The challenge facing product marketers isn't creating content. It's uncovering the raw material that makes content worth consuming.
Many Companies Aren't Competing Against Competitors
Another theme surfaced repeatedly throughout the day.
It’s easy to become focused on the belief that competition always exists. In reality, many new or growing companies are actually competing against inertia.
Product marketers spend enormous amounts of time building competitive positioning, battlecards, and feature comparisons. Those things matter. But for a surprising number of deals, buyers aren't choosing between Vendor A and Vendor B.
They're choosing between changing something and changing nothing.
Nobody wakes up hoping to buy your software. They wake up hoping to solve a problem. Perhaps they've been living with that problem for months or years. They've built workarounds. They've adjusted expectations. They've learned to tolerate inefficiency.
The real question isn't always whether your product is better. It's whether the pain of staying the same has become greater than the pain of change.
That's a fundamentally different challenge.
Generic messaging rarely addresses it because generic messaging tends to focus on products. Buyers are focused on consequences. What happens if I wait another year? What happens if this problem gets worse? What happens if this initiative fails? What happens if I spend political capital on this decision and nothing changes?
Those questions require specificity. They require evidence. They require an understanding of how buyers evaluate risk, not just how marketers describe value.
What the Best PMMs Are Doing Differently
The most effective product marketers I spoke with aren't avoiding AI. In fact, many of them are using it extensively.
The difference is where they place it in the process.
Rather than relying on AI to generate strategy, they use it to accelerate execution. They start with customer interviews, call recordings, survey responses, win-loss research, and direct buyer feedback. Once they understand the story, they use AI to help scale the work of communicating it.
That sequencing appears to be increasingly important.
The teams producing the strongest messaging are not asking AI what buyers think. They are discovering what buyers think through research and then using AI to help operationalize those insights.
AI is functioning as a multiplier rather than a substitute for customer understanding.
The teams getting into trouble are often reversing that order. They generate messaging before they understand the buyer. They create content before they identify the story. They use AI to answer questions they haven't actually investigated.
The result is usually more content, but not better content.
The Advantage Isn't AI
If there was one takeaway I left the summit with, it was this:
The future advantage won't come from simply adopting AI.
Every company will adopt AI.
The differentiator will be the quality of the information those systems are built upon.
The organizations that invest in understanding their buyers will have better stories to tell. They'll create messaging that feels specific rather than generic. They'll be able to communicate the consequences of inaction, address real objections, and connect their solutions to problems buyers genuinely care about solving.
The more content the internet produces, the more valuable original insight becomes.
That's the paradox of the AI era.
Creating content is becoming cheap. Understanding customers isn't.
AI can help you tell a story.
It still can't sit across from a buyer and learn it.