Which Role Should Be Your First AI Hire?

Which AI role should you hire first?

Hire an AI engineering leader first: someone who still builds hands-on and can also manage people. Of the four functions a company can start with (engineering, data, product or strategy), engineering is the one that gets something working. These leaders usually bring data experience with them, so one engineering hire often covers much of the data side too.

In a sample of more than 300 AI Engineering leaders in our network, hands-on technical building is what both VP and C-suite resumes lead with. The senior title did not stop them building. And 98% of them also manage people: 97% at C-suite level and 99% at VP.

A first hire needs both. On day one there is no team to manage, so the person has to ship things themselves. A year later, if the work is going well, there is a team and the same person has to run it.

A pure strategist stalls in the first phase. A builder who has never managed stalls in the second. Nearly every leader in our sample manages people, and their resumes lead with building, so there is no reason to settle for half.

The best of them also tie the technical work to the business. One director of AI engineering at a healthcare organization worked with product and data science teams to tie AI engineering goals to business objectives, which raised cross-functional project success rates by about 20%.

If you are still deciding whether the company needs a dedicated leader at all, start with whether you need a head of AI.

What do employers require in a first AI role?

Employers ask for a degree almost without exception and put heavy weight on explaining AI plainly at every level. Certifications barely register. Write your rubric around communication and delivery, not credentials.

In AI job postings we analyzed, 99% require a degree, and VP postings do so without exception. That makes the degree a floor. It screens almost nobody out, so it tells you very little about who to hire.

What employers do stress, at director, VP and C-suite level alike, is the ability to explain AI plainly and cut through hype. A first AI leader spends much of their time with people who do not speak the language: a CEO deciding budget, a CFO asking about return, a sales team asking what it can promise customers. A leader who cannot translate becomes a bottleneck, however good the models are.

Certifications sit at the other end. Only 7% of postings name one, and the share falls as the role gets more senior: 9% of director postings, 5% of VP postings and 1% of C-suite ones. At the top, employers have almost stopped asking.

So screen for what employers stress. Ask candidates to explain a past project to the least technical person on your interview panel. Ask what they would tell your board about a tool the board is excited about. Those answers will tell you more than any list of credentials.

The one place a credential starts to matter is at the top, which makes the level of the role your next decision.

Should your first AI hire be a director, VP or chief?

Match the level to the work. Hire a director to ship, and hire a chief only if the role must set research direction. Requirements climb sharply with level, and a C-suite first hire carries a research bar most companies do not need.

In AI job postings we analyzed, C-suite postings mention a PhD or doctorate 3.2 times as often as director postings, 32% against 10%. VP postings sit in between, at 20%. That gap matters because of who holds a doctorate.

Among the more than 300 AI Engineering leaders in our sample, 19% hold one, and among VPs the share is lower still, at 15%. Write a doctorate into a chief AI role and you have narrowed the search to the minority of leaders in our sample who hold one.

Sometimes that is the right call. If your product depends on original research, a leader with a doctorate and research credibility may be what you need. Most companies hiring their first AI leader are not in that position.

They need someone to pick sensible tools, build something that works and grow a team around it. A director or VP with a strong delivery record does that, and comes from a much wider pool.

The title also sets expectations inside the company. A chief title on day one promises board-level strategy before there is much to be strategic about. Settle the level before any AI executive search begins, because it decides who you can find.

What background do first AI leaders usually bring?

Most of the AI engineering leaders in our sample have also worked in data science, and far fewer in machine learning engineering. Expect a first hire with deep data experience, and plan to add engineers who deploy.

In the same sample of more than 300 AI Engineering leaders, 63% have also worked in data science, 29% in data engineering and only 7% in ML engineering. The pattern holds across levels. C-suite leaders are a little more likely than VPs to have worked in data science (65% against 61%), and a little less likely to have worked in ML engineering (4% against 9%).

That is good news with a warning attached. Your first hire probably understands data: how to clean it, model it and judge whether a result means anything. That covers much of what a company needs before it has a data team of its own.

The warning is production. Taking a model out of a notebook and running it reliably for real users is a different skill, and it is the experience a first hire is least likely to bring. Getting a model to work once is one problem. Keeping it working, monitored and affordable to run is another.

So test for it directly. Ask how they deployed past work, who ran it afterward and what broke. If the honest answer is that someone else handled deployment, budget for that someone in your first year.

Even a model that runs reliably is only worth something if the business uses it, and that is a separate test of the hire.

Who should you hire to move from AI experimentation to adoption?

Hire someone whose track record is tied to business results, not the number of models built. The leaders in our sample overwhelmingly describe achievements in customer and employee experience and in revenue, and that is the record to screen for.

Adoption is an organizational problem as much as a technical one. Microsoft's 2026 Work Trend Index found that organizational factors such as culture, manager support and talent practices account for twice the reported AI impact of individual effort alone.¹ A first AI hire who builds tools and waits for people to use them will be disappointed.

In our sample of AI Engineering leaders, 78% describe at least one achievement aimed at improving customer or employee experience, and 63% at least one aimed at growing revenue. C-suite leaders lean further toward experience outcomes than VPs do (80% against 74%).

One VP at a large IT services firm built the systems for zero-to-one development of intelligent automation products across five global regions. That VP ran more than 150 proofs of concept a year for three years, producing a sales funnel of more than $250 million. The useful part for a hiring manager is the last one: the experiments are measured by the business they created.

In interviews, ask for the business result behind each project. A candidate who answers with model accuracy alone has shown you how they will report to you once hired.

What comes next when building an AI team?

Your second hire should cover what the first one lacks, which is most often production engineering or product ownership. Few AI engineering leaders in our sample have worked in product, so do not expect the first hire to own the roadmap alone.

In the same sample, only 17% of AI Engineering leaders have worked in product, with C-suite leaders (18%) and VPs (19%) close together. Put that next to how few have ML engineering experience and the gaps in a typical first hire are predictable. They know data and they can build. They are less likely to have run models in production or owned a product roadmap.

A sensible AI team structure pairs the first leader with a product owner and engineers who deploy, rather than stacking more of the same profile. A second data scientist feels like a safe hire because it matches the first one. It doubles a strength you already have and leaves both gaps open.

Expect the team to grow in stages alongside the business. One C-suite technology leader at a small technology company built global machine learning and data engineering teams from zero to 30 people as the company grew to about $50 million in annual recurring revenue and raised funding.

If you need senior AI direction while the team is still small, fractional AI leadership can cover the strategy seat while your first full-time hire builds.

Your first AI hire is the start of a sequence

Hire a hands-on AI engineering leader at the level your work needs. Judge them on how plainly they explain AI and on the business results they have delivered, and keep the doctorate requirement for roles that truly set research direction. Then look hard at what that person lacks, most often production engineering and product ownership, and make that your second hire.