Cheaper AI Can’t Rescue Products Built on the Wrong Problem

Reverse the Build Trap

When AI pricing drops, teams often start with a mandate to use AI and then search for a problem to justify it, a pattern Forrester calls the “build trap.” The author argues that product leaders should first validate the root cause, test multiple solutions, and only then define the AI capabilities required. The approach is illustrated with Netflix, which generated several artwork variants per title as hypotheses about viewer relevance before committing to any AI-driven design.

Hypothesis-Driven Solution Generation

Forrester recommends assembling product, design, engineering, data, risk, and frontline teams to generate options and narrow them by customer value, feasibility, commercial impact, and strategic fit. The guidance stresses naming the value mechanism—productivity, conversion, retention, operating cost, or differentiation—up front.

Right-Sizing Capabilities

The report cautions against defaulting to the most sophisticated model. Precision, speed, autonomy, explainability, and human oversight thresholds differ even within a single product. Southern California Permanente Medical Group’s Kaiser Permanente Intelligent Navigator prioritized precision for high-acuity risk and breadth for care navigation, achieving roughly a 54 % booking success rate. The advice is to build the smallest viable capability configuration that delivers the intended outcome and generates evidence.

Our read: The article reframes AI cost reductions as a double-edged sword—cheaper models lower entry barriers but amplify waste when the underlying problem is misidentified. By insisting on hypothesis-driven exploration, explicit value mechanisms, and calibrated capability stacks, Forrester provides a pragmatic checklist that can keep AI projects from becoming costly misfires.