AI-Native Operating Model · Org Design
The AI operating layer — a marketing org where every function has its own agent squad
I designed an operating model that puts a dedicated squad of AI agents inside every marketing function, on a shared foundation of approved facts, so a lean org ships like a much larger one.
The problem
AI tools were being adopted function by function, in scattered, one-off ways. There was no shared model for how a whole marketing org should run with AI, no common source of truth, and no guardrails, which meant duplicated effort, inconsistent brand and claims, and no compounding leverage.
The model
- 01Human Teams.The existing functions (demand gen, SDR, marketing ops, digital and AI search, corporate marketing, brand and creative), unchanged in ownership.
- 02The Agentic Layer.A dedicated squad of named agents inside each function, each with a specific job, for example a campaign orchestrator, an outreach personalizer, a data-taxonomy governor, an AEO content-refresh agent, a technical-SEO auditor, a PR briefing agent, and a brand-QA agent.
- 03The Shared Foundation.Cross-functional agents every squad draws on (an approved-claims librarian, buyer-intent signals, brand and claims QA, live performance intelligence, a workflow router) sitting on one source of truth: brand kit, knowledge bases, and approved claims and messaging.
How it runs
Every agent is a prompt chain with the right model selected per step, pulls from our live data stack (product analytics, attribution, SEO, search, and CRM), and ships approved output to the CMS. A human reviews every output. Product owners approve every fact before it becomes reusable.
What it shows
I don't use AI as a helper, I design the operating system. This is org-level systems thinking: how people, agents, data, and governance fit together so a lean team produces at a scale that normally takes far more headcount.