Opinion: retail leaders risk AI failures by making three strategic mistakes

The piece outlines three common mistakes. First, “corporate antibodies” keep AI budgets locked in cost‑cutting orders, resulting in isolated tools like recommendation engines or visual‑search plugins that rarely integrate across the business. Second, an inflexible investment thesis prevents leaders from committing to long‑term AI transformation; the author cites Amazon’s decade‑long vision and Stitch Fix’s 40 concurrent AI pilots as examples of all‑in approaches. Third, treating AI as “candy for breakfast” – quick, flashy tools without solid data foundations – leads to short‑lived novelty and missed strategic value.

To avoid these pitfalls, the author recommends a three‑tiered AI investment portfolio (quick wins, staged bets, game‑changing bets) and building data‑ready infrastructure, an intelligence layer, and governance before deploying chatbots or other consumer‑facing AI. A disciplined, data‑first strategy can turn AI into a sustainable competitive edge rather than a wasted expense.