- Product squads (Outcomes): Own the KPI, prioritize agents by ROI, run experiments
- AI platform (Capability): Tools, memory, orchestration, evaluation harness
- Safety function (Policy): Guardrails, red-team, audit trail, model risk
- CoE / Chief AI Officer (Governance): Portfolio, funding model, cross-BU standards
Three archetype org models
CoE-led, embedded product, platform-first. Each fits a different starting point. The one that fails most often is the fourth — 'let every BU figure it out' — because it produces 40 pilots and zero platforms.
Who owns what
Product owns outcomes. Platform owns tools, memory and evaluation. Safety owns policy, red-teaming and audit. Procurement, legal and risk are consulted early, not late.
Funding the platform without starving the outcomes
Central funding for the platform, BU funding for outcomes, shared FinOps discipline across both. The formula that keeps AI from becoming another shared-services line item nobody trusts.
Talent bench and retention
Roles that matter: AI product manager, ML platform engineer, prompt engineer, evaluation lead, safety analyst. Career paths and retention practices for a market where every senior IC has three open offers.
- Three archetypes work — the 'let every BU figure it out' non-model does not
- Product owns outcomes, platform owns capability, safety owns policy
- Central platform funding + BU outcome funding is the durable model
- Safety is a function, not a document
Questions leaders ask us
- What is an AI operating model?
- An AI operating model is the org design, funding model and governance that turns AI from a series of pilots into a compounding capability — typically a CoE or Chief AI Officer function plus embedded product squads, a platform team and a safety function.
- Do we need a Chief AI Officer?
- Not always a titled role, but you do need a single accountable executive for AI outcomes, platform and safety. In many enterprises this sits with the CIO or COO with a Head of AI Platform reporting in.
- How should AI be funded across BUs and central?
- Central funding for the shared platform (tools, memory, evaluation, safety), BU funding for outcomes, and shared FinOps discipline across both. This keeps AI from becoming another shared-services line item nobody trusts.
- How many people do we need on the AI platform team?
- For a mid-large enterprise, a nucleus of 8–15 covering AI product management, ML platform engineering, evaluation, safety and applied engineering — scaling with the number of live agent workloads, not the number of pilots.