In a recent LinkedIn post, product strategy expert Melissa Perri discusses the pitfalls of rushing to implement AI features without a clear understanding of customer needs, a phenomenon she terms the AI ‘build trap’. Perri highlights how many companies, driven by competitive pressure and a mandate to “ship AI,” ended up developing solutions that failed to address genuine user problems.
Perri traces this pattern back over a decade, noting that while the core issue predates the current AI boom, the urgency around artificial intelligence has amplified its impact. She points to a common scenario where leaders, observing competitors’ AI announcements, directed product teams to integrate AI into their offerings, often without strategic grounding.
“The ‘oh crap’ moment hit a lot of companies two years ago. Leaders watched their competitors announce AI features, and the mandate went out to product teams: ship something, anything, with AI in it. What followed was a wave of launches that looked impressive on stage and solved no real customer problem.”
The Persistent ‘Build Trap’
Perri argues that this tendency to prioritize outputs – like shipping an AI feature – over outcomes, such as solving customer problems, is a recurring trap in product development. She emphasizes that the AI hype has merely made this established pattern more pronounced.
Focusing on AI Solutions Over User Problems
Drawing on conversations with product leaders featured on the latest Product Thinking Podcast, Perri shares insights from companies like Linear, MongoDB, and Stack Overflow. She specifically references a point made by Jody Bailey, who identified a fundamental error in many AI initiatives.
“One of the mistakes I think almost everybody made, which is so fundamental, is we focused on delivering AI solutions. Not solving user problems or customer problems. We were solving for, we have to have AI.”
According to Perri, this misstep arises from treating the directive to implement AI as a strategy in itself, rather than a potential means to achieve a strategic goal. The critical lesson, as Perri outlines, is not that AI is an inherently poor investment, but that the impetus for building AI features must stem from a genuine customer need.
Shifting the Focus to Customer Problems
Perri advocates for a product development approach where teams begin by identifying and understanding customer problems. Only then should they explore potential solutions, with AI being one of many possible tools.
AI as a Tool, Not the Goal
The most successful teams, Perri explains, do not start with the technology but with the problem. They ask whether AI is the most effective way to solve that specific customer pain point. Often, this customer-centric approach leads to different, more effective product decisions than a technology-first mandate.
Melissa Perri encourages reflection on this topic, posing a question to her audience: “What’s one AI feature at your company that got built because you ‘had to’ rather than because a customer actually needed it?” This call to action underscores her belief in the importance of aligning AI development with tangible customer value.
For a more comprehensive discussion, Perri directs readers to the full episode of the Product Thinking Podcast.
📝 About This Content
This article is based on insights shared by Melissa Perri on LinkedIn.
📅 Originally posted on April 15, 2026 | View original post on LinkedIn →