How Do You Assess AI Candidates When You Are Not Technical?

What should you look for in an AI candidate's background?

Look for long, specific time in AI or data. A senior title is not enough. In a sample of more than 300 AI Engineering leaders in our network, the median leader has spent 8 years working in AI or data, and VPs have spent 10. A senior title earned mostly outside AI tells you about management, not about whether this person knows where AI projects go wrong.

The rest of a typical background is table stakes. 71% of leaders in our sample hold a master's degree or doctorate, and 95% have worked directly with the CEO, the board or the executive team. Those numbers barely move by level: 70% of VPs hold a graduate degree, and executive exposure sits at 93% for the C-suite and 96% for VPs.

A trait nearly every candidate shares confirms someone belongs in the pool. It cannot rank them within it. If your shortlist all have graduate degrees and board exposure, you have learned nothing about which of them will deliver. The useful questions about background are narrower: how many of those years were spent building and running AI systems, in what kind of organization, and at what scale?

Once the time in the field checks out, set the background against what employers ask for at the level you are hiring.

What do AI job postings require at each level?

Employers ask for degrees, not certificates. In the AI job postings we analyzed, 99% require a degree. The academic bar climbs with seniority: C-suite postings mention a PhD or doctorate 3.2 times as often as director postings, 32% against 10%, with VP postings in between at 20%. Certifications go the other way, named in 9% of director postings, 5% of VP postings and only 1% of C-suite postings.

Use that as your baseline. For a top AI role, employers weigh academic depth more heavily, so a doctorate is a reasonable plus when it is relevant to the work. A certification is neither a requirement nor proof of skill at the senior end, and C-suite postings almost never ask for one. A candidate who leads with certificates for a VP or C-suite role is pointing you at the wrong evidence.

The opposite mistake is treating credentials as the whole assessment. LinkedIn research found that companies can grow their AI talent pipeline 8.2x globally by focusing on skills over degrees or job titles.¹ A credential tells you someone cleared a bar at some point. It does not tell you whether they can run an AI program in your business today.

So read credentials the way employers write them. A degree is close to universal, a doctorate weighs more as the role rises, and a certificate counts for little at the top. None of them is a shortcut past assessing the person's real work, and what that work should look like depends on the level.

How should you evaluate AI talent differently by level?

Judge a C-suite candidate on architecture and data foundations, and a VP on winning executive backing for delivery. In our sample of more than 300 AI Engineering leaders, 66% of C-suite leaders have also worked in AI architecture, against 53% of VPs. Their resumes point the same way: C-suite leaders put noticeably more weight on getting data ready for AI than VPs do.

The emphasis flips for the other skill. VP resumes give real attention to winning executive backing, the work of getting sponsors, budget and priority for AI delivery, while C-suite resumes give it little. That does not mean C-suite leaders cannot persuade anyone. It reflects where each level spends its time.

One scorecard for both levels will misjudge someone. Score a VP candidate on deep architecture questions and you may reject a strong operator who gets programs funded and shipped. Score a C-suite candidate on stakeholder management alone and you may hire someone who cannot tell you whether your data can support the strategy they are selling.

The C-suite in our sample includes founders and chiefs of small companies, who often build the foundations themselves. One C-suite leader in our network was also the company's only AI engineer, set the AI product road map with the executive team, and shipped several internal AI tools that saved each team more than 1,000 hours a year. A candidate from that world will look very hands-on. If your role sits atop a large organization, ask how they would lead through others rather than build alone.

Whatever the level, the next test is the same: can the candidate prove what they claim?

How do you assess AI candidates on skills you cannot test yourself?

Ask for the measured result and who outside the candidate's team confirmed it. In our sample, 96% describe at least one achievement aimed at enabling a new capability, and 90% describe one aimed at improving quality or accuracy. The rates hardly differ by level. When nearly everyone makes the same claim, the claim is noise and the validated number is the signal.

You do not need to read code to test this. For each headline achievement, ask four things: what the baseline was, what metric moved, over what time frame and who outside the team checked it. A capable leader answers all four without strain, because they tracked them at the time. A weaker one drifts back to the capability ("we launched an AI assistant") and away from the measurement.

A strong answer is specific. One VP at a large financial services firm in our network shipped four major releases of an AI compliance system in 12 months with no production rollbacks, reaching 91% risk scoring accuracy as validated by compliance directors. Every element is checkable: a count of releases, a time frame, a quality record, an accuracy figure and a named group outside the AI team that signed off.

That is a sharper test than any quiz about models. A quiz rewards someone who has prepared for quizzes. A validated result rewards someone who has delivered, and it gives you a reference to call. The same rule covers screening AI candidates when every resume says "AI" before the first interview: look past the claim to the proof. One area needs harder probing than the rest.

How do you spot a candidate who only sounds credible?

Probe risk and governance, where claims in our sample most clearly outrun the work behind them. In our sample, 72% describe a risk or compliance achievement, 77% of the C-suite and 66% of VPs. Yet AI governance, risk, security and compliance takes up little space on these leaders' resumes at either level. Many claim the result; their resumes say little about the work that produces it.

That gap is where follow-up questions do the most work. A capable leader can name the monitoring, evaluation and controls they put in place. A leader who only sounds credible talks about outcomes and stops there, "we reduced risk," "we ensured compliance," with nothing underneath.

Ask three questions. What broke? How did you know? What did you change? Someone who has run AI in production has stories about drift, bad outputs, failed evaluations or an incident that forced a fix, and they tell them in specifics. Someone who has not will generalize, or treat the question as a chance to restate the success.

Listen for the plumbing. One director at a large financial services firm in our network deployed a production conversational AI system that handled about 250,000 conversations and resolved about 58% of inquiries without a person, with monitoring and logging built in. The volume and resolution rate are the outcome; the monitoring and logging make it trustworthy, and a real builder mentions both. For a fuller set of prompts, see these AI interview questions that separate capability from hype.

Does a non-technical hiring manager need a technical advisor?

Yes, for one part of the evaluation. In the AI job postings we analyzed, employers put heavy weight on understanding how models, data and systems fit together, at director, VP and C-suite level alike. That is the piece a non-technical executive cannot verify alone, and it is too central to skip.

Scope the help narrowly. Bring in a trusted technical reviewer, internal or external, for one architecture conversation: how the candidate would design around your data, where they see the hard constraints and what they would build versus buy. Ask the reviewer for a written read on depth and judgment, not a hire or no-hire verdict.

Keep the decision. Everything else in this article is yours to judge, starting with whether the candidate can explain AI plainly. Leaders put that ability front and center on their resumes at both C-suite and VP level. A candidate who cannot make their work clear to the executive who hires them is failing a skill leaders themselves treat as central.

The reviewer and the hiring executive test different things. The reviewer checks that the architecture holds up. You check whether the candidate can take that architecture to a board, a budget meeting or a skeptical business unit and make it land. If you want a partner to run the technical read and the market comparison alongside you, that is a core part of AI executive search.

The proof matters more than the polish

A non-technical executive cannot test an AI leader's code, but can test evidence. Set the credential baseline employers use at each level, then look past it, because most of the pool clears it. Ask for measured results that someone outside the team validated.

Probe risk and governance until you hear about monitoring, evaluation and controls. Borrow a technical reviewer for the architecture conversation, and keep the judgment for yourself. The difference between a capable AI leader and a credible-sounding one is what they can prove and explain.