In production, working
Creative variant generation at scale — dozens of headline and description permutations from a single brief, then let the platform sort them. Anomaly detection on pacing and cost per outcome, flagging a broken campaign hours instead of days after it breaks. Automated reporting narratives that turn a data table into a paragraph a client can read.
These share a trait: the model does a bounded task with a human accepting or rejecting the result.
Working with supervision
Bid and budget reallocation across channels, which works well when constrained and badly when given free rein. Audience expansion suggestions. First-pass QA that reads a campaign against a brief and flags mismatches — genuinely useful, not yet trustworthy alone.
The pattern here is that the model proposes and a person disposes.
Still slideware
Fully autonomous campaign management end to end. Strategy generation that survives contact with a real market. Creative that needs no human editing. Cross-platform attribution solved by a model rather than by measurement discipline.
These are demoed constantly and deployed rarely, because they fail in the ways that are most expensive and least visible.
The practical read
AI has compressed the labor in campaign operations substantially — the work that used to justify large teams is increasingly automatable. What it has not done is replace judgment about offers, markets, and money.
For a business buying media, the right question is not whether your provider uses AI. It is which specific tasks they have automated, and who checks the output.