How to Assess AI Skills in an Interview

In our conversations with Talent Acquisition and Early Careers leaders, one question is surfacing more often and with greater urgency: 

How do we know if a candidate actually has AI skills? 

For many teams, the default approach has been to ask candidates in interviews whether they’ve used tools like ChatGPT or other generative AI platform, but that question alone is proving insufficient. AI is no longer a niche capability. It’s becoming embedded in how work gets done across functions, from marketing and operations to finance and Early Career talent. At the same time, it’s redefining the nature of entry-level work itself, often in ways that cut across roles and workflows. 

Veris Insights’ latest research found that student use of GenAI in recruiting has increased 2.6x since 2023, while 62% of students say they feel pressure to use AI tools to stay competitive in the job market. 

The challenge is no longer about identifying who has access to AI, but who can apply it in ways that drive meaningful impact. 

Why Traditional AI Interview Questions Are Falling Short

Knowing AI exists or using it casually is not the same as knowing how to use it effectively. This distinction is becoming more important as organizations reconsider how Early Career talent contributes. If AI is accelerating or automating portions of entry-level work, traditional indicators of potential may no longer be enough on their own. 

The reality is most AI interview questions are still focusing on surface-level signals:

• “What AI tools have you used?”
• “Are you familiar with generative AI?”
• “How do you stay up to date on AI trends?”

 

These questions measure awareness, but not capability. They fail to capture how candidates integrate AI into their workflows, evaluate the quality of AI-generated outputs, or use AI to solve real business problems. 

What Employers Actually Mean by “AI Fluency”

What employers are actually looking for is closer to “AI fluency.” While the term is still gaining traction, organizations are still trying to define what it really means in practice. What is consistent is the emphasis on applied capability over tool familiarity. 

Many employers are still equating light proficiency with mainstream tools like ChatGPT with AI fluency, even though that is not the full picture. Even leaders from big tech companies have asked us, “How do we identify AI fluency skills in Early Talent coming into more generalist roles? We want them to have some basic AI fluency that aligns with our career expectations.” 

AI fluency is not about technical expertise. It is how candidates operate in AI-enabled environments: 

• Knowing when AI can accelerate a task and when it can’t
• Crafting inputs that generate useful outputs 
• Critically evaluating and refining those outputs
• Integrating AI into day-to-day work in a way that improves results

 

As expectations evolve, hiring decisions will need to reflect not just the tools candidates are familiar with, but how they approach their work. 

The AI Skills Employers Should Actually Be Assessing 

To effectively evaluate AI skills in an interview, employers need to move beyond tool familiarity and focus on four core areas: 

1. Prompting and Tool Use
   Can the candidate use AI tools in a way that produces meaningful outputs? Do they iterate when initial results fall short?
2. Critical Thinking and Validation
   Do they question AI-generated content? Can they identify inaccuracies or gaps? 
3. Workflow Integration
   Is AI embedded in how they approach their work, or is it used sporadically? 
4. Communication and Translation
   Can they explain how AI contributed to an outcome? Can they collaborate effectively using AI-assisted work?

 

How to Evaluate AI Fluency in an Interview

Assessing AI fluency requires rethinking how interviews are structured. Across many organizations, assessment is becoming more dynamic and skills-based, placing greater emphasis on how candidates approach real work scenarios.  

Use scenario-based questions

Ask candidates to walk through how they would approach a real task using AI. This reveals how they think, not just what they know. 

Incorporate live problem-solving 

Provide a realistic task and ask how they would use AI to complete it. Focus on their process, not just the outcome. 

Ask retrospective questions 

Have candidates describe how they’ve used AI in past work. Strong candidates will offer specific examples, clear impact, and evidence of iteration. 

• “Tell me about a time AI significantly improved your work. What changed?”
• “How do you validate the accuracy of AI-generated outputs?”
• “When would you choose not to use AI?”
• “What do you do when AI gives you a poor result?”

 

Employer Approaches to AI Fluency Assessment Are Evolving

Organizations are at very different stages in how they assess AI fluency in hiring. 

Some employers are still early in formalizing their approach. In many cases, AI-related evaluation is happening inconsistently across teams or through lightweight interview questions designed to understand how candidates currently use AI in their workflows. 

More advanced organizations are beginning to operationalize AI fluency more formally. Some employers are incorporating scenario-based assessments, evaluating how candidates select AI tools for different use cases, or introducing interactive exercises where candidates must critique AI-generated outputs, identify inaccuracies, and explain how they would refine the result or prompt. 

A small group of organizations are redesigning hiring processes more comprehensively around AI-enabled work itself. Zapier, for example, has publicly stated that all new hires are expected to demonstrate AI fluency and has translated those expectations into role-specific competency frameworks and interview questions. McKinsey has piloted interview formats in which candidates use the firm’s internal AI tool during live case exercises to evaluate not only outputs, but how candidates apply judgment, iterate, and think critically alongside AI. Meta has similarly adjusted technical interviews to assess how candidates manage and verify AI-assisted work within real coding environments. 

At the same time, some organizations are rethinking the structure of entry-level work itself. IBM recently announced plans to expand entry-level hiring while redesigning junior roles around AI-enabled workflows, shifting more Early Career responsibilities toward customer interaction, judgment, and higher-value problem-solving. 

For a deeper look at how organizations are defining and operationalizing AI fluency in hiring, read “AI Fluency Is the New Baseline. Most Hiring Teams Aren’t Ready.”

The Bigger Implications for Early Career Hiring 

AI fluency is increasingly becoming a proxy for something broader: how candidates approach work in environments defined by rapid technological change. 

Candidates who effectively leverage AI often demonstrate capabilities employers already value: 

• Strong problem-solving
• Adaptability
• Critical thinking
• A bias toward efficiency and iteration

 

As a result, the conversation around AI fluency is beginning to influence much more than interview strategy alone. Employers are starting to reevaluate sourcing strategies, school selection, and even the broader profile of what makes Early Career talent successful. These shifts are reinforcing today’s broader Early Career recruiting trends and the future of Early Careers altogether. 

Several Talent Acquisition leaders described growing interest in identifying which universities are embedding AI meaningfully into the curriculum versus simply offering surface-level exposure. One leader at a big tech company noted that their organization is increasingly evaluating where students are developing practical AI capabilities that can translate into day-to-day work environments, not just technical familiarity.  

That challenge is compounded by the uneven pace of university adoption. While some institutions are rapidly expanding AI-related degree programs and integrating AI into broader student curricula, others are still early in the process. AI-related bachelor’s degree offerings grew 119% from 2024 to 2025, reaching nearly 200 programs. But, employers are recognizing that not all AI programs are created equal. 

Several employers also emphasized that they are still operating with limited long-term performance data when it comes to hiring for AI fluency. One leader described current AI hiring strategies as operating on “hypotheses and initial signals” while organizations work toward a more validated understanding of what actually predicts success over time. 

What’s Next for Early Career Hiring

Employers expect AI to become part of everyday work, not a specialized capability reserved for technical roles. The organizations that gain a competitive advantage in hiring will not be the ones that simply add AI questions to interviews. They will be the ones who rethink what effective work looks like in an AI-enabled environment.

Because in an AI-enabled workplace, the differentiator is no longer just what candidates know. It’s how they work.