AI Won't Make Product Marketing Less Important.
AI is going to make a lot of software easier to build. The companies that survive will be the ones that understand their customers well enough that it doesn't matter.
If a CPA can use AI to prototype accounting software, they may know the workflow better than the people building the incumbent tool. They know the edge cases. They know which steps are annoying but tolerable, and which ones make people want to throw the whole system out. They know the weird exceptions that never show up in a polished product demo.
The same is true across a lot of categories. Legal ops. Healthcare admin. Procurement. Construction. Creative production. Every domain has people who understand the work more deeply than the software companies selling into it.
For years, those people could complain, submit feature requests, hack together spreadsheets, or switch vendors. Now they can build around weak products faster. That changes the job of a software company.
The job is not simply to build software customers cannot build. That bar is getting lower. The job is to understand the customer so well that the product reflects their reality better than their own hacked-together workaround.
Why now matters
Good product teams have always said customer understanding is a priority. What is changing is the cost of being wrong about it.
When software was hard to build, a weak product could survive on switching costs alone. When distribution was expensive, incumbents had time to catch up after missing a customer signal. Neither of those buffers holds the way they used to.
The window between "customers are unhappy" and "customers have built something better" is getting shorter. Customer understanding stops being a research phase you run before a product cycle and becomes infrastructure you run continuously.
Where product marketing fits
Good PMM is not just messaging after the product is done. It is a listening system. It translates customer pain, buying triggers, objections, and workflow details into choices the company can actually use.
What should we build? Who feels the pain most sharply? Why does the current workaround survive even though everyone hates it? Which buyer has urgency, budget, and political cover? What language does the customer use before we put our own vocabulary on top of it?
In an AI-native software market, those questions move closer to the center of company strategy. Plenty of product marketing is still trapped too late in the process, polishing launch copy after the important decisions have already been made. The companies that treat customer understanding as an operating system will find the gap between that version of PMM and what it can be increasingly hard to ignore.
They will spot workflow pain before it becomes churn. They will build sharper wedges. They will position around actual urgency instead of internal feature logic.
The risk for software companies is not that every customer becomes a perfect builder.
The risk is that customers no longer have to tolerate products that only understand them halfway.
Code is getting cheaper. Customer understanding is getting more expensive.
