A recent interview with two FDE partners reveals that fewer than half of enterprise AI projects succeed, with failures clustering around unrealistic expectations, misplaced budgeting, and misaligned employee incentives, and the article outlines concrete steps to avoid each trap.
This article walks through why delivering AI features alone doesn’t prove efficiency, introduces a three‑step cost‑hedging ledger and ROI formula, provides ready‑to‑use budget‑request scripts for finance, managers and business units, and highlights common pitfalls when quantifying AI project value.
The article recounts a failed AI demo rollout caused by unrealistic boss expectations, then outlines a practical expectation‑management framework—including a capability radar, a weekly gray‑testing roadmap, and tailored communication scripts—to keep AI projects controllable and aligned with business realities.
This article breaks down the four essential elements of a model project—business, features, data, and model—explains their priority order, and provides practical guidance on aligning technical choices with core business goals to achieve efficient, high‑impact outcomes.
