AI makes software development easy, but a good idea remains the real differentiator
For a long time, technology was the limiting factor; many great ideas failed because execution was complex or expensive. Now that this limitation is largely disappearing, it turns out that execution was never the true differentiator. The difference still lies in the ability to define a problem sharply.
Much is being said about how AI makes software development accessible, and rightly so, because where you used to be dependent on developers, budgets, and long timelines, you can now build a working application yourself in a short time using tools like Claude Code, Lovable, and other AI platforms. You describe what you want, who it is for, and roughly how it should function, and the AI then generates the code and assists you step-by-step toward an initial version. This has drastically lowered the technical barrier, making the step from idea to prototype smaller than ever.
But precisely because building has become easier, another reality becomes visible: a good idea and a deep understanding of the underlying business problem remain as scarce and as decisive for success as ever. For a long time, technology was the limiting factor; many great ideas failed because execution was complex or expensive. Now that this limitation is largely disappearing, it turns out that execution was never the true differentiator. The difference still lies in the ability to define a problem sharply, to understand where processes stall, where customer frustration lies, and where existing solutions fall short.
AI can write code, but it cannot independently experience how inefficient a logistics process feels in practice, how cumbersome healthcare administration operates, or how a financial system slows down decisions. That insight arises from years of involvement in a sector, through conversations with customers, and through errors and improvements in the field. Without that insight, AI primarily becomes an accelerator for mediocre ideas: many new tools appear, but few solutions that truly make a difference.
Precisely now that almost anyone can build, the importance of direction increases. Creating an application is no longer the challenge; determining what should be built and why is. A good idea is not a random inspiration, but the result of looking closely at a concrete business issue, understanding the interests at play, and knowing where value is created and where money is gained or lost. Those who have this clarity can leverage AI as a powerful executor. Those who do not will primarily build something that works technically but adds little business value.
The real shift is not that technology has become unimportant, but that insight is once again central. AI democratizes execution, but the ability to recognize a relevant problem and formulate a thoughtful solution remains the scarcest capital. And that is exactly why a good idea, rooted in a deep understanding of the business problem, remains the decisive factor.
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// About the author
Job van den Berg
Mede-oprichter, AI Keynote Spreker & Techondernemer
Tech-ondernemer (1989) met een achtergrond als socioloog (Research Master (MSc) in statistiek en sociologie) en een van de meest gevraagde keynote sprekers over AI en data in Nederland. Als mede-oprichter van Ai.nl, The Automation Group en Proxies leidt hij engineers die agentic AI van prototype naar productie brengen binnen enterprises. Op het podium vertaalt Job die hands-on praktijk naar concrete strategieën. Eerder was Job Chief Data bij o.a. DPG Media en Kantar. Hij is co-auteur van 5 boeken over AI waaronder 'AI Agents' en 'Handboek AI Strategie' en een veelgevraagd expert in de landelijke media.
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