\n<\/p>\n
\n\t\tOpinions expressed by Entrepreneur contributors are their own.\t<\/p>\n
I\u2019ve sat through many conversations lately about which incumbent software categories AI-native startups<\/a> 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.\u00a0\u00a0<\/p>\n 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\u2019s often the wrong paradigm.<\/p>\n At this point in my career, I\u2019ve watched this dynamic play out across three technology curves: from web to mobile, then to cloud<\/a>, and now to AI. In each, some incumbents thrived, some decayed. Some startups redefined categories, some were side-swiped by incumbents. <\/p>\n In my experience, the winners often aren\u2019t the ones with the newer stack. They\u2019re 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 \u2014 and they kept innovating<\/a> around that problem set.<\/p>\n 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.<\/p>\n But as the company grew and gained market share, its customers\u2019 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<\/a> instead of strategic financial work.\u00a0<\/p>\n At that point, this example incumbent has two paths. It can decide efficiency \u201cisn\u2019t what we do\u201d 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\u2019re still paying for the original accounting software when other, higher-ROI tools are solving today\u2019s problems.<\/p>\n So how do incumbents stay product leaders through innovation curves?<\/p>\n Pay close attention to the evolution of the problem.<\/strong> This sounds obvious, but I\u2019ve seen countless startups get worse as they succeed. Go-to-market<\/a> 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. <\/p>\n It\u2019s easy to fixate on the next feature instead of asking how well your solution addresses the evolving problem. Though history wasn\u2019t necessarily my strength in undergrad, I\u2019ve come to see the tremendous value in leaders understanding the history of a customer\u2019s problem<\/a>. 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?<\/p>\n The one thing I\u2019d encourage you to pin above a product roadmap is this: \u201cStop asking customers what else we can build for them.\u201d Customers are generally bad at answering that question, and it\u2019s not their fault. People feel frustration<\/a> long before they can describe the tool that would fix it, and if they could, they\u2019d build the solution themselves. Ask these questions instead:<\/p>\n 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<\/a>. Both are critical to long-term innovation, but they aren\u2019t the same thing.<\/p>\n A few tactical ways to approach this:<\/p>\n Build your technology for adaptability. <\/strong>If you can extend the product only by tearing up its original core, you\u2019ll 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<\/a>.<\/p>\n Never fully let go of product strategy. <\/strong>Great founders remain students of the customer\u2019s evolving problem, even after hiring a strong VP of Product or Chief Product Officer. They\u2019re students of the customers\u2019 historical, current and future needs. That doesn\u2019t undercut their Head of Product. It gives the company a healthy check on the product team\u2019s assumptions when it matters most.\u00a0<\/p>\n By following this playbook, your company can stay innovative, relevant and forward-thinking at every stage of scale. Complacency<\/a> is a competitor\u2019s opportunity. When leaders get comfortable with success and overconfident about their customers\u2019 needs, a startup down the street starts asking the right questions and solving today\u2019s problems.\u00a0<\/p>\n After all, when does a startup become an incumbent? In the AI era, almost immediately.<\/p>\n<\/p><\/div>\n I\u2019ve sat through many conversations lately about which incumbent software categories AI-native startups<\/a> 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.\u00a0\u00a0<\/p>\n 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\u2019s often the wrong paradigm.<\/p>\n At this point in my career, I\u2019ve watched this dynamic play out across three technology curves: from web to mobile, then to cloud<\/a>, and now to AI. In each, some incumbents thrived, some decayed. Some startups redefined categories, some were side-swiped by incumbents. <\/p>\n<\/p><\/div>\nA relevant example<\/h2>\n
The playbook is simple to say and harder to run<\/h2>\n
Ask this instead<\/h2>\n
\n
\n
Key Takeaways<\/h2>\n
\n