Your audience research is already available

Before you write a single line of creative copy, you probably already have more audience insight than you're using. Product reviews on Amazon, Trustpilot, and Trust Radius. Reddit threads where your category gets discussed unprompted. Your own historical A/B test results, sitting in a dashboard nobody revisits once a test ends.

This is exactly the kind of unstructured, high-volume information an LLM is good at working through. Feed it into a model and ask it to infer user intent from reviews, tests, and audience signals, not just what your team assumes motivates people, and you get something closer to what your customers actually said, at a scale a person reading one review at a time never gets to. As it builds out what actually motivates each audience segment, an agent can track patterns over time and adapt to preferences as more feedback comes in. It can also handle multi step tasks across multiple steps to turn messy research into actionable audience understanding.

Competitor research, and research outside your category, at a scale a person can't do alone

The same logic applies to competitor research. Facebook, Google, and TikTok's ad libraries are public. Every brand's creative, including yours, is sitting there for anyone to study. AI agents can pull that data and analyze it: how each competitor tells their story, which formats they lean on, which of their assets appears to be running longest (a decent proxy for performance).

Here's the part that's easy to miss. Looking only at your own category tends to produce the same handful of ideas everyone in that category already has. Studying a completely different industry, one with a different customer, different constraints, different creative conventions, tends to surface angles your own category's blind spots hide. A person can do this manually for a handful of brands. Across hundreds or thousands of data points, that's not a research task anymore. It's a data problem, and that's exactly where AI has the advantage over a person doing it by hand.

Pre-testing a concept before it costs you media dollars

New creative direction can be judged by instinct, or it can be judged against real audience data before you spend a cent putting it in front of anyone. Take the audience segments you've already built from public and performance data, and ask an LLM: given everything we know about this segment, how is this creative concept, this copy, this angle, likely to land?

That's not a replacement for a real A/B test. It's a filter before the test. It won't tell you exactly how a concept performs, but it will tell you when a direction is clearly off before you've spent money finding that out the hard way. Skip this step and you're still testing blind, just faster.