Before launch, product leaders must ask if their AI offering is a true product or just a feature. Slapping an AI label on a tool that automates a minor part of a larger workflow is a gimmick. It will fail unless it solves a core, high-friction problem for the customer in its entirety.
Related Insights
Don't feel pressured to label every AI-powered enhancement as an "AI feature." For example, using AI to generate CSS for a new dark mode is simply a better way to build. The focus should be on the user benefit (dark mode), not the underlying technology, making AI an invisible, powerful tool.
Don't evaluate your team's AI readiness as a standalone capability. True AI strategy requires a deep understanding of customer problems and unique value. Without strong core product competencies, AI adoption is merely tactical, not strategic.
Unlike traditional software that optimizes for time-in-app, the most successful AI products will be measured by their ability to save users time. The new benchmark for value will be how much cognitive load or manual work is automated "behind the scenes," fundamentally changing the definition of a successful product.
Don't just sprinkle AI features onto your existing product ('AI at the edge'). Transformative companies rethink workflows and shrink their old codebase, making the LLM a core part of the solution. This is about re-architecting the solution from the ground up, not just enhancing it.
The most effective application of AI isn't a visible chatbot feature. It's an invisible layer that intelligently removes friction from existing user workflows. Instead of creating new work for users (like prompt engineering), AI should simplify experiences, like automatically surfacing a 'pay bill' link without the user ever consciously 'using AI.'
After building numerous AI tools, Craig Hewitt realized many popular applications (e.g., AI avatars, voice cloning) are worthless novelties. He pivoted from creating flashy tech demos to focusing only on building commercially viable products that solve tangible business problems for customers.
Teams that become over-reliant on generative AI as a silver bullet are destined to fail. True success comes from teams that remain "maniacally focused" on user and business value, using AI with intent to serve that purpose, not as the purpose itself.
Companies racing to add AI features while ignoring core product principles—like solving a real problem for a defined market—are creating a wave of failed products, dubbed "AI slop" by product coach Teresa Torres.
Previously, building 'just a feature' was a flawed strategy. Now, an AI feature that replaces a human role (e.g., a receptionist) can command a high enough price to be a viable company wedge, even before it becomes a full product.
