A person is stuck mid-task. They must either wait for help or make a call they’re not confident about.
This insight unlocked a product’s positioning. Not architecture. Not model sophistication. Not the fact that this was something genuinely new in a market still figuring out what AI assistants were supposed to do.
Just that moment, the one every user had already felt, described back to them with enough precision to feel seen.
It became the line that opened almost every discovery call.
In B2B companies, product teams’ work is complex, central to company strategy, and visible to leadership. Marketing teams, by contrast, are often seen as the people who create content and collateral.
This is a structural reality worth examining.
Functional writing, emails, briefs, campaign copy, is so integral to modern work that people assume anyone can do it. Nobody questions the difficulty of engineering because the difficulty is self-evident. But writing well? Assumed to be table stakes.
Generative AI has only sharpened that assumption. When anyone can produce a polished paragraph in seconds, what exactly is a writer’s edge?
But, even with access to the same tools, some content reads perceptibly better than others. Why?
The moat nobody talks about
Judgment isn’t just about knowing what to say. It’s also about knowing what not to say for the sake of the story. It’s the ability to look at a feature list and ask: what is the one thing a reader needs to understand?
Paul Graham captures this well: a good writer doesn’t just think and then write down what they thought. They almost always discover new things in the process of writing. There is a kind of thinking that can only be done by writing. This is why outsourcing it entirely to AI, however polished the output, hollows out the very process that produces insight.
This matters especially when translating technical product information into business language. Which, by the way, is one of the hardest things a content or product marketer does.
How the call actually gets made
Go back to that AI assistant — an early product, built before the category had a name. The engineering behind it was novel. And when it came to positioning, the instinct from leadership was predictable: lead with the complexity. The architecture. The technical depth. The fact that this was not a simple rules-based bot.
The marketing team pushed back. Because complexity is for the provider; for the buyer it’s just the benefit that matters.
What customer conversations kept surfacing was far more simple: people got stuck. At which point they didn’t know where to turn, and either waited for a colleague or made a bad decision. The AI needed to be positioned as the thing that helps you navigate past difficult tasks.
So instead of leading with what the product was, the team led with what it replaced.
That’s the judgment call. Neither a formula nor a framework.
The pattern holds in most markets. Workforce management software is a crowded category in B2B. The instinct, when launching a new solution into the market, is to out-feature the competition.
What customer interviews revealed instead was, again, simpler: nobody wanted more tools. They were already juggling three or four platforms and spending real time reconciling them. The job they needed done was consolidation: one place, full visibility, no surprises at the end of a compliance cycle.
That insight came from asking buyers what was broken. The positioning built around the pain of fragmentation rather than the promise of features produced a 34% higher click-through rate against the feature-led alternative in a direct test, and contributed to 40% more qualified leads in the first quarter after launch. All because of the unified platform messaging.
Two different markets, two different products, the same move: resist the pull toward what you found impressive in your product. Instead, find the moment the buyer already knows by heart.
Why simpler isn’t always better
Economists call this thinking on the margins. The first pass at simplification does the heavy lifting. Further attempts to make it even simpler risks stripping out the very differentiation that made the feature worth explaining in the first place.
In the AI assistant example: one simplification further and the copy becomes indistinguishable from a dozen competitors. One step back toward the technical and the business audience disengages. The skilled translator finds the marginal point between precision and accessibility and holds it.
Product teams, working close to the technical detail, naturally pull toward precision. Marketing teams, working close to the customer, naturally pull toward accessibility. The tension between those two instincts, when balanced well, is where the best positioning lives.
What each side gets wrong about the other
The respect gap between product and marketing teams is a structural problem.
Product teams live in a world of constraints and precision: technical debt, system architecture, edge cases that break things in production. So when marketing describes their product in language that feels layman-friendly, the reaction is instinctive: they don’t really understand what we built.
The constraint marketing is working against is rarely obvious. A buyer has twelve seconds and a head full of competing priorities. The language that sounds imprecise to an engineer isn’t sloppiness, it’s translation under pressure. It’s a deliberate bet on which single idea will survive first contact with a distracted human being.
That is a different, not lower, kind of rigor.
In an ideal scenario, product teams hold marketing accountable to technical truth. Marketing holds product teams accountable to the question the customer is actually asking. Neither dismisses the other.
What real immersion looks like
Before any positioning conversation, the most effective marketing teams get hands-on with the product itself: clicking through every workflow, documenting confusion in real time. That confusion is data. It’s where a buyer’s mind will also snag.
They schedule working sessions with engineering and ask a specific question: “What made this hard to build?” That question is a shortcut to genuine differentiation. The answer reveals what’s actually novel, not what’s been positioned as novel.
And they validate everything with customers. Not “what do you like about the product?” but “what problem were you trying to solve when you found us?” That question almost always reframes the positioning in ways no internal brief anticipates.
The team behind that AI assistant could only make the call they made because they’d sat in enough customer conversations to recognise a pattern. The “stuck” insight came from hearing the same friction described differently until the common thread emerged.
The premium is rising
AI has made functional writing easy. But the judgment to find the right level of simplification, the ability to discover genuine differentiation is still far from easy.
As AI floods every channel with grammatically sound, emotionally flat content, the premium on that judgment is rising. When every team has access to the same tools, the competitive edge reverts to the people: to whoever actually understands the product, the customer, and the precise marginal point where both meet.
The AI assistant wasn’t remembered for its architecture. It was remembered for the moment it described: the one every user had already felt.
That’s what doesn’t get templated. And that’s what earns genuine cross-functional trust.
Musings by Madhav 
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