What the job involves day to day
A head of AI owns the list of AI projects and decides which ones get people and money. Most weeks mix portfolio reviews, hiring, budget calls, vendor meetings and risk sign-offs. You still need enough technical depth to spot a weak evaluation or an unrealistic timeline, but you rarely write production code yourself.
The BLS duties list for computer and information systems managers maps closely onto this job. It covers weighing the costs and benefits of new projects and justifying their funding, working out staffing needs, directing technical staff, and negotiating with and monitoring vendors. For a head of AI, the vendor work usually means model providers, cloud platforms and data suppliers, and comparing what each costs against building the capability in house.
Governance and risk are part of the job
Someone senior has to answer for AI risk, and that person is often the head of AI. The NIST AI Risk Management Framework is a voluntary framework. Its Govern function says executive leadership should take responsibility for decisions about the risks of developing and deploying AI. It also calls for policies covering third-party software, data and supply chain risk. In practice that means an intake process for new use cases, a risk rating for each one, and the authority to stop a system that fails review.
US federal agencies give a concrete example. OMB Memorandum M-25-21 requires agencies to write an AI strategy and appoint a Chief AI Officer. Under its 2025 compliance plan, the Department of Labor's CAIO office reviews every reported AI use case and approves new AI tools. It co-signs impact assessments for high-impact uses and can order a non-compliant use case shut down.
Head of AI, CTO and chief AI officer: how they differ
A CTO owns the whole technology function: infrastructure, engineering and security. A head of AI owns AI strategy and delivery, and often sits under the CTO or reports to the CEO directly. Chief AI officer is usually the C-suite version of the same job. That title is more common where the board or a regulator expects one named executive to be accountable for AI, as in US federal agencies under M-25-21.
Titles vary a lot. At a startup, the head of AI may lead five engineers and still review pull requests. At a large company, a VP or director of AI may manage several teams, sit on a governance council with the CIO, CISO, privacy and legal, and present AI spend and risk to the executive team.
How people get there
There is no certification for this job and no regulator of it. BLS says CTOs, IT directors and other senior IT managers usually need many years of experience before they are considered. For the broader management occupation, BLS says a bachelor's degree in computer science or IT is typical and some employers prefer a graduate degree. Most heads of AI come up as senior machine learning engineers, data science managers or AI architects, then take on a team, then a portfolio. What gets you hired is a record of shipped AI systems that paid off and teams you built.
Pay
USD a year, before tax. US figures from BLS OEWS May 2025 for computer and information systems managers, moved to 2026 with the Employment Cost Index, showing the 25th to 75th percentile (US median $180,600). BLS has no AI series, so this is the nearest match and covers all IT managers, not AI leaders alone. Company size, equity and bonus can move total pay well above these base figures. Refer to the AI & Automation Leadership salary guide for figures from live adverts.
What you need
- Technical depthYou need enough hands-on machine learning or AI engineering background to judge model evaluations, cost estimates and build-versus-buy choices.
- Management recordExpect to show that you have hired, led and grown technical teams. BLS says senior IT managers usually need many years of experience first.
- Budget and business caseYou will justify spending on people, compute and vendors, and show results against that spending.
- Risk governanceYou should know how to run use-case intake, impact assessments and risk ratings. The NIST AI RMF is a common voluntary reference.
- Vendor managementYou negotiate with model, cloud and data providers, then monitor how they perform.
- Executive communicationYou explain AI plans, spending and risk to the CEO and other leaders, and sometimes to the board.
How to get there
- Years 1-5Build hands-on depthShip machine learning or AI systems to production as an engineer, data scientist or architect.
- Years 4-8Lead a teamManage a small AI or data team and own its hiring, roadmap and delivery.
- Years 7-12Own a portfolioRun several teams or a cross-company AI program as a director, with a budget and vendor contracts.
- Years 8-12Take on governanceChair or sit on an AI risk council and set intake and review rules for new use cases.
- Year 10+Head of AI or chief AI officerOwn AI strategy, budget and risk, and report to the CEO, CTO or board.
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Questions people ask
Is a head of AI the same as a chief AI officer?
They are often the same job at different levels. Chief AI officer is the C-suite title and implies direct accountability to the CEO or board. Head of AI can sit one level lower, under a CTO.
Does a head of AI still code?
At small companies, yes, sometimes. At larger ones, the job is mostly strategy, hiring, budget, vendor and risk decisions. Technical judgment still matters for reviewing your team's work.
How much does a head of AI make in the US?
BLS has no AI leadership series. The nearest one, computer and information systems managers, puts the 2026 middle range at about $142,400 to $227,700 and the median at $180,600. That series covers all IT managers, and equity or bonus can add a lot on top.
Do you need a PhD to become a head of AI?
No source we read requires one. For the broader IT management occupation, BLS says a bachelor's degree is typical and some employers prefer a graduate degree. A long track record of shipping AI systems and leading teams carries more weight.
What governance is a head of AI responsible for?
Usually use-case intake, risk ratings, impact assessments, rules for approved tools and third-party risk. The NIST AI RMF says executive leadership should take responsibility for AI risk decisions. Federal Chief AI Officers carry this duty under OMB M-25-21.
