Staying Ahead in the AI Era Comes Down to 1 Key Priority

I’ve sat through many conversations lately about which incumbent software categories AI-native startups are about to wipe out. The narrative typically goes like this: Incumbents are bolting a chatbot onto a 10-year-old product while new startups have built AI-native solutions from day one; therefore, incumbents are losing their edge.  

And yet, many incumbents are thriving in the AI era. What gives? While the age of the software stack can certainly drive customer relevancy, I think that’s often the wrong paradigm.

At this point in my career, I’ve watched this dynamic play out across three technology curves: from web to mobile, then to cloud, and now to AI. In each, some incumbents thrived, some decayed. Some startups redefined categories, some were side-swiped by incumbents.

In my experience, the winners often aren’t the ones with the newer stack. They’re those who stayed close enough to the customer (from their first dollar of revenue to hitting $500M+ in ARR) to notice when the problem the customer hired them to solve quietly changed shape — and they kept innovating around that problem set.

A relevant example

Consider a cloud-based SaaS company selling small-business accounting software today. It built a great market position by giving a business owner a single place to track transactions, send invoices and, most importantly, close the books accurately. For years, that was enough.

But as the company grew and gained market share, its customers’ problem set quietly changed. The software platform had made closing the monthly books accurate, but not more efficient. People still spent hours on repetitive tasks instead of strategic financial work. 

At that point, this example incumbent has two paths. It can decide efficiency “isn’t what we do” and let someone else solve that problem, or it can treat the shift as its own problem to solve. Companies that take the first path rarely get disrupted in one dramatic moment. It happens piece by piece: A startup builds an automated revenue recognition tool, then an automated forecasting tool, and so on. Eventually, customers start asking why they’re still paying for the original accounting software when other, higher-ROI tools are solving today’s problems.

So how do incumbents stay product leaders through innovation curves?

The playbook is simple to say and harder to run

Pay close attention to the evolution of the problem. This sounds obvious, but I’ve seen countless startups get worse as they succeed. Go-to-market optimizes the funnel, and product optimizes the roadmap, but nobody, not even the product manager, owns the job of asking whether what the customer hired you to do three years ago is still what they need today.

It’s easy to fixate on the next feature instead of asking how well your solution addresses the evolving problem. Though history wasn’t necessarily my strength in undergrad, I’ve come to see the tremendous value in leaders understanding the history of a customer’s problem. Where did it start 10 years ago? How did it evolve before our solution, and how has it evolved since? How will it change as new technology is adopted?

Ask this instead

The one thing I’d encourage you to pin above a product roadmap is this: “Stop asking customers what else we can build for them.” Customers are generally bad at answering that question, and it’s not their fault. People feel frustration long before they can describe the tool that would fix it, and if they could, they’d build the solution themselves. Ask these questions instead:

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

Ask consistently across enough conversations, and patterns will emerge that transition your product strategy to be as forward-thinking as the startups you compete with. Stop treating a feature pipeline as a product strategy. Both are critical to long-term innovation, but they aren’t the same thing.

A few tactical ways to approach this:

• Pull your last 10 renewal or churn calls and listen for “still” and “but now” (“We still use the product for X, but now we also need Y.”). “But now” may contain some gold.
• Ask your customer success team what requests never become a support ticket because it “isn’t really what we do.” Look for patterns.
• Once a month, sit in on customer calls, not to sell but to hear how customers describe the problem in their own words, and help them articulate their frustrations.
• Spend a day with an advisory customer who fits your ideal customer profile. Walk through the history of the problem and the questions above. They’ll appreciate the attention, and you’ll leave with real product insight.

Build your technology for adaptability. If you can extend the product only by tearing up its original core, you’ll always move too slowly. Incumbents that stay relevant build modularly enough to absorb an adjacent problem, like automated revenue recognition or forecasting capabilities, without a multi-year rebuild putting them behind in the race for customer retention.

Never fully let go of product strategy. Great founders remain students of the customer’s evolving problem, even after hiring a strong VP of Product or Chief Product Officer. They’re students of the customers’ historical, current and future needs. That doesn’t undercut their Head of Product. It gives the company a healthy check on the product team’s assumptions when it matters most. 

By following this playbook, your company can stay innovative, relevant and forward-thinking at every stage of scale. Complacency is a competitor’s opportunity. When leaders get comfortable with success and overconfident about their customers’ needs, a startup down the street starts asking the right questions and solving today’s problems. 

After all, when does a startup become an incumbent? In the AI era, almost immediately.

I’ve sat through many conversations lately about which incumbent software categories AI-native startups are about to wipe out. The narrative typically goes like this: Incumbents are bolting a chatbot onto a 10-year-old product while new startups have built AI-native solutions from day one; therefore, incumbents are losing their edge.  

And yet, many incumbents are thriving in the AI era. What gives? While the age of the software stack can certainly drive customer relevancy, I think that’s often the wrong paradigm.

At this point in my career, I’ve watched this dynamic play out across three technology curves: from web to mobile, then to cloud, and now to AI. In each, some incumbents thrived, some decayed. Some startups redefined categories, some were side-swiped by incumbents.