AI tools for ecommerce, honestly

Product imagery

What it does: turns flatlays or existing shots into on-model photography and lifestyle imagery, without a studio, models or sample shipping.

Verdict: genuinely useful. This is one of the clearest wins in ecommerce AI, because the cost it removes — shoot logistics — is enormous relative to the quality gap. Strongest for catalogue-scale work where shooting every SKU on a model is not economic. (This is Quinn's original product: AI product photography.)

Video

What it does: generates video ads, animates stills, or rebuilds proven video formats with your products.

Verdict: depends entirely on the approach. Generating a video from a text brief usually produces something that reads as AI and performs poorly. Approaches that start from real footage — rebuilding a video that already worked, or making existing video shoppable — hold up much better, because a human made the thing that earns the attention.

Copywriting

What it does: product descriptions, meta titles, email copy, ad variants.

Verdict: useful for volume, risky for voice. Excellent for the 500th product description and for first drafts of variant testing. Bad for anything defining your brand voice, and dangerous unedited for claims — hallucinated product specs become returns and, in some categories, legal problems.

Customer support

What it does: answers order-status and policy questions automatically.

Verdict: useful with a clear escape hatch. Deflecting repetitive tickets is real value. The failure mode is trapping a frustrated customer in a loop — always make reaching a human obvious and fast.

Ads and creative testing

What it does: generates ad variants, predicts performance, automates bidding.

Verdict: mixed. Variant generation is genuinely useful because creative testing is a volume game. Performance prediction deserves scepticism — the honest version of it is starting from creative that already worked rather than scoring untested ideas.

Analytics and personalisation

What it does: surfaces patterns, recommends products, segments customers.

Verdict: useful at scale, noise below it. These tools need data volume to beat sensible defaults. On a small store, a well-chosen bestseller row usually outperforms an AI recommendation engine.

Where AI is currently the wrong answer

• Anything where being wrong is expensive — sizing advice, ingredient or allergen claims, compliance copy.
• Your brand's actual voice. Distinctiveness is the point; averaging is what these models do.
• Generating content nobody asked for. More product descriptions of the same product is not a growth strategy.

A sensible order to adopt

1. Imagery — biggest cost removed, lowest risk.
2. Video from real footage — including making existing video shoppable.
3. Support deflection — with a human escape hatch.
4. Copy at volume — edited, never shipped raw.
5. Personalisation — once you have the traffic to justify it.

Questions