What does a Head of AI actually do?

The mandate: own the gap between access and outcomes

Most organizations have already bought the tools — 88% use AI in at least one function, per McKinsey's 2025 survey. Far fewer have anything to show for it: only 39% report any EBIT impact. The Head of AI exists to close that specific gap. It is not a research role, and it is not a governance committee with a fancier title.

McKinsey's 2025 "rewiring to capture value" research found that redesigning workflows as part of GenAI deployment was among the strongest predictors of bottom-line impact. Workflow redesign doesn't happen by memo — someone has to own which workflows change, in what order, and how anyone will know it worked. That someone is this role.

What the role owns

Five things, concretely:

• The use-case portfolio — scored, sequenced, and pruned. Killing a stalled pilot is as much the job as launching one.
• Enablement — training that changes weekly behavior, champions inside each function, and coaching for the managers who set the tone.
• Governance and guardrails — acceptable-use policy, model and vendor review, and risk triage that enables work instead of blocking it.
• Vendor strategy — buy-versus-build discipline, consolidation of overlapping tools, and exit plans.
• Measurement — baselines before rollouts, weekly usage tracking, and honest attribution of business impact.

Head of AI vs Chief AI Officer vs CTO

A Chief AI Officer sits at the executive table with enterprise-wide scope and a P&L-level mandate; the title signals that AI is a board topic. A Head of AI is the operating version of the same mandate — typically reporting to the COO, CTO, or chief digital officer, and spending most of the week inside functions rather than in steering committees.

The CTO owns platform, infrastructure, and engineering. The Head of AI owns adoption and outcomes on top of that platform. Organizations that give one person both jobs usually find that the infrastructure half eats the calendar and the adoption half quietly starves.

The first 90 days

Weeks 1–3: listen and baseline. A tour of every function's actual workflows, plus a structured proficiency baseline so progress is measured against a starting line instead of a feeling.

Weeks 4–6: pick two or three workflows. Scored on frequency, pain, data readiness, blast radius, and ownership — the same rubric we use for first automations. Each gets a named owner and a written baseline.

Weeks 7–13: ship and publicize. Weekly demos, visible wins in all-hands, and a kill-or-scale call on a pre-agreed date. The publicity is not vanity — social proof is how adoption spreads.

Do you need one?

Some reliable signals that the answer is yes:

• AI spend exists but no single person owns whether it returns anything.
• Pilots are multiplying while none have a written baseline.
• Governance happens in a committee that meets monthly and decides quarterly.
• Three teams are paying for four overlapping tools.

If that list reads like your org chart, you either hire the role or grow it. Growing it is usually faster — that's exactly what the Head of AI Bootcamp trains operating leaders to do.