In a recent LinkedIn post, Melissa Perri discusses the critical importance of strategy when implementing Artificial Intelligence tools, cautioning against the temptation to rush into building.
Perri, a prominent voice in product strategy, highlights how new AI technologies like Claude Code can accelerate development, but stresses that speed without direction leads to failure. She shared her team’s experience rebuilding their website, emphasizing that the initial excitement of AI capabilities could easily lead them into what she terms “the Build Trap.” As Perri states:
“We can build it” has never been the same as “we should.” I’ve spent years teaching teams that shipping fast in the wrong direction is still the wrong direction. AI just lets you get there faster.
The article details Perri’s team’s deliberate approach, which mirrored the strategic process taught in her own courses. This involved rigorous steps before any code was written.
Defining Direction: The Crucial First Step
Perri emphasizes that before leveraging AI, a clear understanding of the product’s purpose is paramount. This involves defining the target audience, the site’s core function, and what success looks like in the near future. Without this clarity, even the most advanced AI can only help in building the wrong thing more efficiently.
Honest Assessment: Analyzing the Current State
The second phase, according to Perri, requires an honest assessment of the current situation, supported by real data. Her team utilized tools like Semrush and competitor analysis to understand their ranking, identify competitors, and analyze shifts in search behavior due to AI integration. Perri notes the unflattering but necessary nature of this step:
The picture was not flattering. That was the point.
This unflinching look at the present state is essential for setting realistic and achievable goals.
Setting the Next Goal and the Build vs. Buy Decision
Only after establishing a clear direction and understanding their current position could Perri’s team set their next objective. This led to the critical decision of whether to build a solution in-house or buy an existing one. Perri admits her initial desire to “build” for the sake of the AI experiment, but stresses that wanting something is not a valid reason.
Her team evaluated SaaS platforms against their vision, calculated the true costs beyond just subscription fees (including long-term maintenance and the technical capabilities of their team), and assessed the ease of future updates. Perri argues that the decision to build their website solution was validated by data and analysis, not by the allure of new technology.
A decision to build is worthless if the team can’t execute it.
Furthermore, Perri highlights the often-skipped question: the team’s actual capacity to execute. Her team actively engaged with Claude Code to understand the practicalities of working with the AI, ensuring they could realistically implement their chosen solution.
Perri concludes that rushing into AI development without a solid strategic foundation leads to costly rework and misdirected efforts. She asserts that true speed comes not from rapid building, but from a well-defined strategy that enables the effective use of advanced tools.
📝 About This Content
This article is based on insights shared by Melissa Perri on LinkedIn.
📅 Originally posted on July 14, 2026 | View original post on LinkedIn →