Most teams assume they know where to focus. Instead, create a separate team of AI-powered generalists whose sole job is to cheaply test new channels and ideas. When they find a promising opportunity ("strike oil"), a specialist-led team is deployed to scale it.
Contrary to the popular narrative of shrinking teams, data suggests marketing headcount remains stable. The budget is being reallocated, with more spend going to people and a new, significant line item for AI tokens and inference costs, rather than being cut.
A team of only AI-powered generalists will produce good but not great work. The optimal structure mirrors a renaissance workshop: "apprentice" generalists for rapid execution and "master" specialists who use AI to go deeper on high-impact, long-term strategic projects.
Marketing leaders should treat unpredictable AI token costs like a headcount expense rather than a fixed software cost. This mental model better reflects its variable nature, where spend is tied to work output, similar to an employee or contractor.
A new role, the "Super IC" or "zero-to-one marketer," will become central. This individual leverages a personal AI stack—comprising a context layer, connected tools, and custom agents—to execute a wide range of marketing tasks effectively, enabling rapid iteration.
The traditional, hierarchical marketing org chart is obsolete because AI solves the context-sharing bottleneck it was designed for. Marketing should adopt the "pod" model from engineering: small, outcome-focused teams of marketers and AI agents working from a shared context layer.