I've been speaking to a lot of candidates recently who've been through interview processes this year. Smart people. Commercially aware. Most of them have genuine AI skills that companies are actively looking for.

Almost none of them asked the right questions about AI before accepting their offer. And several of them wish they had.

Key Takeaways

• How AI is currently being used day-to-day — and whether it's real or still on the horizon

• What tools you'll have access to, and whether IT actually supports them

• How your performance will be measured, and whether AI factors into that

• What the company's concrete AI adoption goal looks like in 12 months

• Whether AI is coordinated centrally or everyone is figuring it out alone

Companies are screening for AI literacy harder than ever. According to PwC's 2025 Global Workforce Survey, workers with AI skills already command a  56% wage premium over those without. The expectation that you'll use AI to work more efficiently is increasingly baked into mid and senior level roles, not as a bonus, just as a baseline.

What isn't always baked in is the structure, tools, or support to actually do it. That's why knowing the right AI questions to ask in a job interview matters.

1. "How is AI currently being used in day to day work here?"

Start here because it's practical, not theoretical. You're not asking about strategy or vision. You're asking what's actually happening right now.

Companies with genuine AI adoption can answer this specifically. Someone on the panel gives you a concrete example, a tool the team actually uses, a process it's changed, time it's saved. It doesn't have to be sophisticated. It just has to be real.

What candidates have been finding instead is hesitation. References to tools the company is "exploring." Pilots that haven't quite launched. Interviewers looking at each other before answering.

That's not a dealbreaker. But it's information. The expectation that you use AI efficiently may already exist in the role,  the infrastructure to support it may not.

2. "What AI questions to ask in a job interview about tools, what will I have access to, and is there support?"

This separates companies where AI is infrastructure from companies where AI is aspiration.

The ones that have genuinely invested can name the tools. They can tell you what's approved, what's available on day one, and whether there's any training behind it. It doesn't need to be elaborate. Even a basic answer signals that someone has thought it through.

What mid and senior candidates are increasingly encountering is enthusiasm without specifics. A lot of talk about innovation. Very little clarity on what they'll actually be able to use when they arrive or how long it takes IT to approve it.

If you're being hired partly on your ability to work efficiently with AI, and the tools aren't there or aren't accessible, that expectation falls on you without the means to meet it. That's worth surfacing before you sign.

3. "How is my performance measured, and does AI factor into that?"

This is the question most candidates at this level forget to ask and one of the most important.

If a company is expecting you to use AI to increase your output or efficiency, that expectation should show up somewhere in how you're evaluated. If it doesn't, one of two things is true: either AI isn't actually central to the role despite what the job description implied, or the company hasn't thought through the connection between adoption and performance yet.

Neither is necessarily disqualifying. But knowing which one you're dealing with changes how you approach the role and what you negotiate.

Ask how success is defined in the first 12 months. Listen for whether AI features in that answer at all. If the expectation is high and the measurement is vague, that gap will eventually land on your desk.

4. "What does good look like for AI adoption here in 12 months?"

This forces the company to be specific about outcomes rather than intent.

Strong answers,  even imperfect ones, show that someone owns the direction. They might name a process being automated, an efficiency target, a tool being rolled out. The specifics matter less than the fact that they exist.

Vague answers reveal the absence of a plan. "We want to be more AI-enabled as an organisation" tells you nothing. It means the thinking hasn't been done yet, and the expectation that you'll contribute to AI adoption is floating without any anchor.

For mid to senior candidates, this question also tells you something about leadership. A team that can articulate where it wants to be in 12 months on AI,  even approximately, is one that's having the right conversations internally. One that can't is still waiting for someone to start them.

5. "How is AI being coordinated across the business, is there a shared approach or is it more individual?"

Save this one for last. It's the most revealing question on this list, and the answer will either reassure you or reframe everything you've heard before it.

The companies doing this well have focus. A small number of clearly defined initiatives. Cross-functional teams behind each one. Someone who owns the direction and the governance. They're not trying to do everything at once, and they can tell you clearly what they're working toward.

What I'm hearing from candidates is that a significant number of companies look very different under the surface. Multiple projects running in parallel across different teams, with no central coordination. Everyone doing their own thing. No shared toolset, no common governance, no visibility of what's actually working.

For you, that fragmentation has a direct cost. If you're expected to use AI to work more efficiently but nobody owns the approach, nothing is approved centrally, and IT isn't resourced to support it,  you're not joining an AI-enabled business. You're joining a business where everyone is figuring it out alone. Including you, from day one.

What These AI Questions to Ask in a Job Interview Are Really Telling You

The best companies I speak to aren't necessarily the most advanced.

They're the ones that are honest about where they are. They have a clear view of what they're working toward, a small number of focused initiatives, and governance that someone actually owns.

The gap to watch for isn't between companies that use AI and companies that don't. It's between companies where the expectation of AI adoption is matched by real support and those where the expectation exists but the structure doesn't.

That gap is manageable when you know it's there. It's a problem when you discover it three months into a new role.

Ask the questions. The answers will tell you everything.

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