When you’ve been in digital marketing as long as I have, you think you’ve seen every framework and “next big thing.” But every so often, someone forces you to rethink the fundamentals. That’s what happened in my recent conversation with Avinash Kaushik, of Human Made Machine, about how AI is reshaping modern brand marketing.
We started with a point many marketers still underestimate: how much of brand success comes down to the creative itself. Avinash argues that 60–70% of brand outcomes are driven by creative, not the knobs we love to tweak—audiences, bids, geo, and targeting. That’s why, in his role at Human Made Machine, he’s obsessed with creative pre-testing: winning before you spend, not after.
What really resonated with me, especially coming from search and performance, was his shift from “audience” to “intent” as the key lens. Instead of obsessing over demographic slices, he uses a simple framework: See, Think, Do, Care. “See” is broad brand storytelling and purpose; “Think” is weak commercial intent, where people explore categories; “Do” is strong commercial intent, where we’re in pure selling mode; and “Care” is deepening relationships and lifetime value. AI is making it far easier for platforms to infer intent in real time, so our real job is designing creative that fits each intent cluster.
We also talked about matching storytelling to attention span. Avinash draws a sharp line between low-attention environments like TikTok and high-attention environments like CTV. The core story may be the same, but its expression shouldn’t be.
Influencers and creators are another area where brands are still getting comfortable. Avinash’s view is that brands should own the “See” layer (the deep brand purpose) but hand much of the “Think” layer to creators.
We couldn’t avoid AI-generated creative. Inside the industry, it’s controversial; in the real world, it’s often effective in the right context. Avinash suggests using AI aggressively for “Do” advertising and for ideation, where you can test huge numbers of variants quickly and cheaply, while being more deliberate about using AI for big brand stories. And he reminds us that the CFO will be a major driver here: once leadership realizes that AI can cut creative production costs dramatically while maintaining or improving performance, the pressure to adopt will grow fast.
As a long-time analytics geek, I appreciated that we wrapped with measurement. Avinash argues for moving away from vanity and activity metrics and toward outcome metrics: revenue, profit, LTV, and incrementality. As platforms become more of a black box, those are the measures that keep marketers honest and help us earn trust (and budget) from the CFO.