In a recent LinkedIn post, product strategist Melissa Perri discusses the evolving impact of Artificial Intelligence on product development teams, highlighting a critical bottleneck that persists despite significant gains in engineering efficiency. Perri argues that while AI has dramatically accelerated the ‘building’ phase of product development, the ‘deciding what to build’ phase has lagged, presenting a key area for future competitive advantage.
AI’s Impact: Accelerating the Build, Not the Decision
Perri’s analysis, drawn from insights shared in her “State of AI in Product 2026” survey, reveals a clear pattern: AI’s most significant impact is felt in areas that are easily instrumented and closer to the act of creation. Engineering and development, along with design and prototyping, are seeing the most substantial speed-ups.
“AI accelerates the work that is easy to instrument. Writing code, generating designs, producing documentation. The closer a task sits to making the thing, the easier it is to point a model at it and watch it speed up.”
This observation, as Perri points out, is not accidental. The tasks that AI can readily assist with are those that involve tangible outputs and measurable processes. However, the more complex, human-centric work of strategic planning, prioritization, and even quality assurance, while showing some AI benefits, has not experienced the same level of acceleration. Perri notes that these upstream activities involve “messy inputs and human judgment,” making them inherently more challenging for current AI applications to streamline at the same pace as coding or design.
The Bottleneck: Decision Velocity
The core of Perri’s argument centers on the disparity between the speed of execution and the speed of decision-making. “The work of deciding what is worth making lives further upstream, where the inputs are messy and the judgment is human. That part did not move that much,” she writes. This creates a situation where engineering teams, empowered by AI, can build and ship products at an unprecedented rate, but the strategic direction and validation processes struggle to keep pace.
According to Perri, this creates a new imperative for organizations. “You already have speed. What most orgs lack is discovery sharp enough, prioritization tight enough, and review rhythms fast enough to keep pace with how quickly their teams can now build and distribute their products.” She posits that the next wave of competitive advantage will not come from further accelerating the building process, but from optimizing the decision-making frameworks that guide it.
Prioritizing the ‘What’ Over the ‘How’
Perri challenges leaders to identify where these decision-making lags exist within their own organizations. “Where in your process do decisions still move at the old speed, while everything around them sped up?” she asks. Her insights suggest that companies focusing solely on AI-driven efficiency in development without addressing the upstream strategic and prioritization processes risk creating an imbalanced workflow.
In Melissa Perri’s view, the companies poised to lead in the coming year will be those that “get as serious about the quality of their decisions as they got about the speed of their delivery.” This shift in focus requires a re-evaluation of how product strategy is formulated, how opportunities are prioritized, and how feedback loops are integrated to ensure that the rapid pace of AI-enabled development is directed towards the most valuable outcomes.
📝 About This Content
This article is based on insights shared by Melissa Perri on LinkedIn.
📅 Originally posted on June 18, 2026 | View original post on LinkedIn →