Saeed Khan: Product strategy in the context of AI

Saeed Khan’s June 2026 article on Medium examines what AI actually changes about product strategy — and what it does not. His central argument: strategy is a set of aligned choices that enable a team to achieve a specific, time-bound objective. AI does not replace that logic. What it changes is the context in which those choices must be made. Khan identifies five contexts that product teams need to revisit.

The technology context has shifted at the execution layer. Coding agents compress development cycles significantly, but Khan notes that this capability is available to everyone. Speed of execution is no longer a durable differentiator; the quality of the strategic vision directing that speed is what separates outcomes.

The customer context reveals a gap between perceived and actual AI adoption. Khan cites research showing that 95% of AI implementations have failed to achieve their intended results, and notes that 91% of companies face major obstacles in customer discovery. PMs who skip discovery and assume AI features will find an audience because AI is broadly popular are exposed to both numbers.

The product context is changing structurally. Agentic interfaces represent a shift comparable in scale to the transition from client-server software to SaaS. Products built on traditional UI assumptions may need architecture-level rethinking rather than feature additions on top of an existing interface.

The competitive context now includes an unusual dynamic Khan calls “the Buyers are now Builders.” Enterprises are increasingly building internal AI applications that substitute for vendor products. Product teams serving enterprise customers need to factor this in when mapping competitive threats — the competitor is sometimes the customer’s engineering team.

The business context is shifting from subscription to consumption-based pricing. This changes how product teams model retention, expansion, and cohort health. A subscription-era roadmap and financial model will generate wrong predictions when applied to a consumption-based product.

The article is most useful for product managers and product leaders who are being asked to define or refine an AI strategy, or who want a framework for identifying which parts of their existing strategy need to be reconsidered given the current environment. Khan’s framing is practical rather than conceptual — each context section includes specific questions a product team should be able to answer, which makes it actionable in a planning or roadmap context.