How Brands Should Use AI In 2026

Two years of experimentation has produced a fairly clear pattern. AI reliably compresses work that is high-volume, low-judgement and easy to verify. It fails at work that requires taste, accountability or claims you'd have to defend. Most disappointing implementations put it in the second category and hoped.

Where it earns its place

• Research at scale: mining reviews, comments, support tickets and call transcripts for the language customers actually use.
• Variant production: hook rewrites, caption sets, localisation, resizing — multiplying a human idea.
• Analysis: summarising campaign data, flagging anomalies, drafting the first version of a weekly read.
• Conversation: answering repetitive pre-purchase questions in chat and DMs, then handing off to a person.
• Qualification: routing inbound by fit and intent so humans spend time on the right conversations.

Where it reliably disappoints

Strategy without context, brand voice without a strong reference, claims about efficacy or compliance, and anything published without review. The output is confident and average, which is the worst combination in a market where average is invisible.

The workflow that works

1. A human defines the job, the constraint and the standard, with real examples.
2. The model produces volume — twenty options, not two.
3. A human selects, edits and takes responsibility for what ships.
4. You log which outputs performed, and feed that back into the next brief.

That fourth step is what separates a system from a novelty. Without it you're generating, not learning.

Guardrails worth writing down

• No customer data in tools without a reviewed data agreement.
• No synthetic people presented as real customers.
• No unreviewed claims about health, finance, safety or outcomes.
• Named human owner for every published asset.