In a recent LinkedIn post, product management expert Melissa Perri urges business leaders to rethink their approach to artificial intelligence roadmaps. Perri highlights a common pitfall she’s observing: companies treating AI roadmaps as mere lists of features rather than as strategic plans to solve specific business problems.
According to Perri, the excitement around AI capabilities is leading many organizations into what she terms the “build trap,” a phenomenon previously seen with other new technologies. This occurs when teams become so enamored with what AI can do that they bypass the crucial step of identifying the core issues AI could address.
“I’m seeing companies create ‘AI roadmaps’ that are really just feature lists: ‘add AI to search, add AI to recommendations, add AI to customer service.’ This is the build trap with new technology.”
The Pitfalls of a Feature-Centric AI Strategy
Melissa Perri argues that this feature-driven approach mirrors the dysfunction that leads to building products that fail to meet customer needs. By focusing on adding AI to existing functions without a clear problem statement, companies risk investing heavily in technology that may not deliver tangible value.
Perri emphasizes the need for a fundamental shift in questioning. Instead of asking “how do we add AI to our customer service?”, she suggests the more effective question is, “what customer service problems are we trying to solve, and would AI be the best solution?” This problem-first mindset, as Perri explains, forces a critical evaluation of AI’s potential impact.
“Problem-first thinking forces you to evaluate whether AI creates real value or just looks impressive in demos.”
Valuating AI’s True Contribution
Perri points out that a problem-first approach ensures that AI is considered only when it is genuinely the most effective solution. The answer to a business problem might not always be AI; it could be simpler, more traditional solutions such as improving internal processes, enhancing communication clarity, or addressing the root cause of an issue.
“The answer might not be AI at all. It could be better processes, clearer communication, or fixing the root cause,” Perri writes.
The principle of escaping the build trap, a concept Perri has long advocated for, remains relevant. She asserts that the discipline involves starting with customer or business problems, rigorously validating whether AI is the appropriate tool for the job, and then proceeding with intentional development.
Problem-Solving Roadmaps vs. AI Roadmaps
In conclusion, Melissa Perri stresses the significant difference in outcomes between building an AI roadmap focused on features and one centered on solving problems. By prioritizing the ‘why’ before the ‘how,’ organizations can ensure their AI initiatives are strategically sound and genuinely add value, rather than simply chasing technological trends.
“Building an AI roadmap versus a problem-solving roadmap makes a significant difference in outcomes.”
Perri’s insights offer a timely reminder for leaders navigating the complexities of AI adoption, advocating for a disciplined, problem-centric strategy over a feature-led one.
📝 About This Content
This article is based on insights shared by Melissa Perri on LinkedIn.
📅 Originally posted on February 2, 2026 | View original post on LinkedIn →