Applied AI in Commerce: Where It Pays Off First

Most AI roadmaps fail because they start with the technology instead of a costed problem. We start by asking which repeated decision is expensive, frequent and already well-instrumented.

In commerce, three answers come up again and again. Personalised recommendations lift average order value with data you already collect. Demand forecasting reduces both stockouts and dead inventory. Support automation absorbs the high-volume, low-complexity questions and escalates the rest with context intact.

Each of these ships as a bounded service with a measurable baseline: current AOV, current stockout rate, current cost per ticket. If the model does not beat the baseline, it does not ship.

Governance is part of the build, not an afterthought — evaluation sets, human review on sensitive paths, logged prompts and outputs, and a documented fallback when the model is unavailable.