Artificial intelligence in commerce

Most applications of AI in e-commerce are either theatre or quiet leverage. The difference is almost always whether a person still owns the judgement.

Content production. The gain is not writing copy. It is compressing the distance between a brief and a testable asset, so a team ships twenty variants where it used to ship three. The failure mode is volume without editorial control, which produces output that is cheap to make and worthless to publish. The teams that get this right put more editorial judgement into the pipeline, not less.

Social media. Scheduling was never the constraint. Response quality, community judgement and knowing which conversation is worth entering are the constraints, and those degrade fastest when automated. The useful application is triage and drafting, with a person deciding.

Operations. The largest and least discussed gain. Demand signals, inventory anomalies, listing regressions, pricing drift and margin leakage are pattern recognition problems on data most brands already hold and never read. This is where the return is, and it is where almost nobody looks first.

SVW applies this across its own brands, and advises a small number of companies on doing the same.