AI Made Building Cheap. Now Products Have To Earn Their Place.

AI has made software easier to build without making customers any easier to win. As development speeds up, the competitive advantage shifts to deciding which problems deserve solving and designing products people have a reason to keep using. That puts customer discovery, distribution and retention inside product strategy, long before launch day.

Divyesh Shah is Vice President and Head of MaRT, covering Marketplace, Retail and Demand/Supply Planning platforms, at Flipkart, the Indian e-commerce group behind Shopsy and Flipkart Minutes. He leads technology strategy and product for the platforms that manage the lifecycle and growth of more than one million sellers and brands, after more than two decades of marketplace and consumer technology leadership at companies including Amazon and Target. He argues that go-to-market belongs inside the product, shaping how people discover it and why they keep coming back.

“In the software development life cycle, the build was the hurdle, because only a few people could do it, and that governed the pace at which products came out. Now that the cycle has shrunk and almost everybody can build, the bottleneck has shifted to choosing the right thing to build. Judgment has become extremely important,” Shah says.

Worldwide app releases rose 60% year over year in the first quarter of 2026, adding to the competition for customer attention. That raises the stakes of two decisions: knowing what deserves building and how a product will reach the people who need it.

Adoption is a product requirement

Shah argues that reach and retention can’t be handed to marketing after launch. “People who are building products shouldn’t be thinking about GTM after the product is built. It should be part of the product itself. How you create reach should be part of your product-led conversations and your capabilities,” he says.

His framework starts where product work always has, with a clear picture of the customer and the pain a feature addresses, articulated with enough empathy that the customer recognizes themselves in the pitch. From there it moves to speed. “Time to value has to be in minutes. It should be very easy to try your product and start building trust, versus spending two hours trying to figure it out before seeing any value.”

From there, every session should leave the product knowing a little more about the person using it. “Every time they use the product, it should get better, and their reasons to switch should become more difficult,” Shah says. “In a B2B product, you know their workflow, their quick actions, their biases. Your data has become much richer because they provided it to you.” Over time, that familiarity can make the product more useful to the customer and harder for competitors to replace.

Fieldwork surfaces the problems LLM research misses

All of that design work depends on an earlier decision about which problem deserves solving. Shah argues that relying on LLMs for customer discovery can make that choice harder, because the research tends to surface problems people are already discussing.

“People are using LLMs to do research, which gives an average of the most important problems. You get the average of what 120 people are talking about, so you won’t find a problem statement that’s unsolved or differentiating. You get those problems through field visits and by double-clicking,” he says. The research on AI-assisted ideation points in a similar direction. People who brainstorm with ChatGPT produce ideas that tend to converge, while people working from their own thinking and web searches land on more unique concepts.

Shah’s alternative starts with the earliest customers. “Your initial 10 or 15 customers have to be your design partners while you’re building the product, and they should really feel, ‘OK, I’m heard, and my problems are getting solved.’” Fieldwork also reveals where those customers spend their attention and which channels they trust, which lets teams build distribution into the product itself. “If those are post facto, they’re just marketing or positioning material. I don’t think that can fly,” Shah says.

Faster prototyping makes customer feedback more important. Teams can run more experiments, but they still need the market to show them which ideas solve a real problem. Shah evaluates each experiment on what it teaches as well as whether it hits its target, giving the next round a stronger basis for decisions. “I read a lot, and I talk to my customers a lot. That’s how the judgment gets sharper,” he says.

Shah sees the same dynamic in AI’s own history. The technology existed for decades before its mass adoption, which he attributes to an experience intuitive enough to reach people who had never considered themselves technology users. “AI has gone from a push capability to a pull capability,” he says.