Incumbents Don't Lose to AI Startups. They Lose to Shifting Customer Problems

By Olivia Hart

Incumbent software companies do not lose to AI-native startups because their technology is older, an Entrepreneur contributor argues. They lose when they stop adapting to customers' evolving needs and leave room for competitors to solve emerging problems.

The contributor, writing in an opinion piece for Entrepreneur, pushes back against the dominant narrative in software investing circles: that incumbents are "bolting a chatbot onto a 10-year-old product" while startups build AI-native solutions from day one — and therefore incumbents are losing their edge.

"And yet, many incumbents are thriving in the AI era," the author writes. "While the age of the software stack can certainly drive customer relevancy, I think that's often the wrong paradigm."

What does the author's own track record show?

The writer claims to have watched this dynamic across three technology curves: web to mobile, then to cloud, and now to AI. In each shift, some incumbents thrived and some decayed; some startups redefined categories while others were, in the author's words, "side-swiped by incumbents."

The winners, the author argues, were rarely the companies with the newer stack. They were the ones that stayed close enough to customers — from their first dollar of revenue to $500 million-plus in annual recurring revenue — to notice when the problem they were hired to solve quietly changed shape, and kept innovating around it.

That is a tidy thesis. It rests entirely on the author's personal observation rather than named case studies or data, and readers should weigh it accordingly.

How does erosion actually happen?

The piece offers a worked example: a cloud-based SaaS company selling small-business accounting software. It built its position by giving owners a single place to track transactions, send invoices and close the books accurately. For years, that sufficed.

But as market share grew, the customer's problem changed. The platform made monthly book-closing accurate but not more efficient. Users still spent hours on repetitive tasks instead of strategic financial work.

At that point the incumbent has two paths, the author writes: declare that efficiency "isn't what we do" and let someone else solve it, or treat the shift as its own problem. Companies taking the first path rarely get disrupted in one dramatic moment. A startup builds an automated revenue recognition tool, then an automated forecasting tool, piece by piece. Eventually customers ask why they still pay for the original software when higher-ROI tools solve today's problems.

Which questions should replace the feature roadmap?

The author's sharpest instruction: "Stop asking customers what else we can build for them." Customers feel frustration long before they can describe the tool that would fix it — and if they could, they would build it themselves. The recommended substitutes:

• Why did you adopt us in the first place, and how has that problem changed?
• How has our solution changed the pain points you experience?
• What are we not thinking about?
• What recurring tasks do your employees hate?
• What will your department look like in five years, people and technology, and why?

The tactical layer is concrete. Pull the last 10 renewal or churn calls and listen for "still" and "but now" — as in "we still use the product for X, but now we also need Y." Ask customer success teams which requests never become tickets because it "isn't really what we do." Sit in on customer calls monthly, not to sell but to hear how customers describe problems in their own words. Spend a full day with an advisory customer who fits the ideal customer profile.

The caveat is obvious but worth stating: none of these tactics comes with evidence of results. The author presents them as practice, not as measured outcomes.

What about the technology itself?

Two structural warnings round out the playbook. First, build for adaptability: if extending the product requires tearing up its original core, the company will always move too slowly. Modular incumbents can absorb an adjacent problem — automated revenue recognition, forecasting — without a multi-year rebuild.

Second, founders should never fully delegate product strategy, even after hiring a strong VP of Product or Chief Product Officer. Staying a student of the customer's historical, current and future needs gives the company a check on the product team's assumptions when it matters most.

The piece closes with a point that cuts both ways for today's AI startups. "When does a startup become an incumbent?" the author asks. "In the AI era, almost immediately." Complacency, in other words, is not a incumbents-only disease — and the next cohort of founders will face the same test at accelerating speed.