The dominant discourse around AI in hiring paints a picture of an arms race: employers using AI to screen, candidates using AI to apply, and distrust increasing on both sides. Perhaps in some contexts, the alarm is warranted. But rather than focusing on the tension AI may be creating between candidates and hiring teams, we should be looking at its impact on how we define and evaluate the skills required to succeed on the job—and why competency-driven hiring is more necessary than ever.

AI Makes Weak Hiring Signals Even Weaker

Among the organizations we work with, AI adoption has been gradual, but it’s gaining momentum. A few years ago, AI rarely came up in conversations about roles and candidate backgrounds. Now, for some organizations, the ability to work with AI tools has become an explicit requirement. Others are creating roadmaps for how AI might help them better serve their missions.

This shift toward using AI more substantively is important because AI can expand what individuals and teams are capable of doing altogether, which can be especially valuable for mission-driven organizations operating with limited staff and budget constraints. But that also changes what we need to look for in hiring. When AI expands what individuals are capable of doing, it widens the gap between the experience we can see on a resume and what someone can actually do on the job.

Research going back decades shows that resumes were never reliable indicators of performance to begin with. Education, credentials, experience working at prestigious organizations—these are all just proxies, signals that someone likely has the skills to do the work. The actual skills are harder to assess, so hiring teams have tended to give undue weight to what they can see on a resume.

This is not a new problem, but AI is bringing it more fully to the surface. Two candidates can present equally polished materials even when their ability to work through a complex problem may be very different. And as AI becomes more capable of generating work products, a candidate’s background producing similar work becomes an even weaker signal, because with the right underlying skills and AI fluency, someone may be capable of work far beyond what their resume suggests. The difference between adequate and excellent results depends on the judgment and context the person brings to it, not necessarily what they’ve done before.

That means we need to closely examine assumptions about what a “qualified” candidate looks like in an AI-augmented world.

What Can’t Be Automated Becomes the Differentiator

If AI can execute at a reasonable quality, then that is no longer the most useful thing to evaluate.

You need candidates who can determine how AI execution fits best into their job function, identify, gather, and correctly use the right inputs to guide the AI, critically evaluate AI outputs, iterate, and test assumptions against the context of your team and organization. That requires understanding the ecosystem in which the work happens: the ability to anticipate how an idea will be used, where it might break down, and what the most likely points of failure will be. The discernment to identify weaknesses in a process and build in safeguards before they become problems. Knowing who matters, what drives impact, and what ideas are shared across the organization.

These are the capabilities that will differentiate candidates in an AI-augmented environment. Recent research supports this direction. A study published in Nature Human Behaviour found that skills like critical thinking, judgment, and social perceptiveness are both strong predictors of long-term career value and the least likely to be automated. And a study from MIT Sloan found that employees who improved performance when using AI were those with strong metacognitive skills. As the researcher put it, “Metacognition — thinking about your thinking — is the missing link between simply using AI and using it well.”

The skills that are hardest to automate are also the ones that determine whether AI becomes a genuine asset or simply a way to produce the same work faster.

Granted, this framing may not apply to all roles and functions, but for organizations hiring senior leaders who will be managing teams and making strategic decisions, it’s worth giving real consideration.

Why Competency-Driven Hiring Matters More in the Age of AI

Structured, competency-driven interviewing has been proven to solve many of the problems of traditional hiring. And new ways of working enabled by AI are only making that case stronger. Evaluating candidates for specific, well-defined, job-relevant skills in multiple contexts gives you a fuller picture of their abilities. Inferring skills from resumes has always been a flawed approach, and in an AI-augmented workplace, it tells you even less.

We’ve long recommended a three-step approach to collect data about candidates’ skills: written prompts asking candidates to explain their experience in defined competency areas; structured interviews designed to reveal the specific impact they made, how they made it, and who they worked with; and a culminating work sample test in which candidates are asked to apply those same skills in a scenario that mirrors work they would do on the job.

Each of these steps adds value, but the work sample test reveals the most about a candidate’s skills and abilities as they apply to the specific role. For senior-level leaders, the work sample often involves working through a strategic issue that requires more sophisticated analytical and relational skills. With a carefully designed work sample and a structured discussion, you can see how these capabilities show up in context. How does a candidate refine their thinking when challenged? Where do they push back? How do they incorporate feedback into their thinking constructively? Do they probe for deeper insights, or just answer the questions in front of them?

The structure for more reliable and accurate candidate assessment in the age of AI has already been built.

Where Do We Go From Here?

The skills that enable your teams to perform at a high level are specific to your organization, the function, and the role you’re hiring for. That will be true wherever your organization sits on the AI adoption spectrum.

Start by defining what those capabilities actually are. Where is your team strongest? Where are the gaps? What expertise do you need most? Then design your hiring process to evaluate those competencies directly, through structured interviews and work samples that reveal how candidates think.

As AI becomes more integrated into the workplace, organizations that are already hiring for judgment, discernment, and strategic thinking will be better positioned to expand what their teams can do with these tools. How you define “qualified” may continue to evolve, but strengthening how you evaluate candidates now creates a process that can adapt along with it.

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