Ninety-five percent of U.S. organizations now require AI fluency when hiring. Yet 59% of hiring managers report that, in the last year, they’ve hired someone who demonstrated AI fluency in the interview but ultimately failed to perform on the job. That doesn’t necessarily point to an AI hiring problem. It reflects a challenge hiring managers have always faced: determining whether a candidate can apply a particular skill effectively within the realities of a specific role.
AI fluency should be treated like any other skill in a job description. The level of proficiency required will vary significantly by role, and hiring managers need to determine whether it is something a candidate must bring on day one or can reasonably learn on the job.
For roles responsible for building, implementing, or directly managing AI systems, deep AI expertise is essential from day one. For other roles, a baseline understanding may be enough, particularly as employees across the workforce are still developing these skills. The decision is no different from the one hiring managers make with any other capability: What can be taught, what is the candidate’s aptitude to learn, and how much time does the organization realistically have to develop someone before they need to contribute?
The bottom line is that AI fluency is one part of a much broader hiring decision. Someone may know how to prompt a tool, summarize information, or automate a task, yet still have difficulty applying those skills when instructions are incomplete; an output is wrong, or a decision carries actual consequences. AI fluency can tell a hiring manager whether someone knows how to use the technology. It can’t, on its own, tell them how effectively that person will apply it within the realities of the job.
AI Fluency Is Not the Same as Job Readiness
Skills and experience matter, but they’re only one part of a hiring decision. Even when a candidate checks every qualification listed in a job description, hiring managers still have to determine how well those skills will translate into the realities of the role.
That’s why the broader qualities someone brings to the job matter, too. Depending on the role, AI fluency is likely something an employee can develop over time. Hiring managers still must weigh that against how quickly someone needs to contribute and what the organization realistically has the capacity to teach. Some qualities, such as judgment, adaptability, communication, and comfort operating in ambiguity, can be much harder to develop. Those are often the qualities I’m looking for when I’m trying to determine whether someone will truly be successful, not just whether they look qualified on paper.
AI is also changing how hiring managers evaluate these qualities. Every candidate has access to technology that can produce a polished résumé and near-perfect interview responses. Because everyone has access to those tools, perfection reveals very little about how someone thinks.
That’s why I’m more interested in a candidate who gives an imperfect but thoughtful answer than someone who delivers a flawless response without revealing their thought process. For most roles I’m hiring for, I want to understand how someone will respond when an answer is unclear, technology falls short, or the role changes six months after they’re hired – all very common scenarios in the modern workforce.
None of this diminishes the importance of AI skills. They simply need to be evaluated alongside the other capabilities the role demands. Putting that into practice starts before the interview. First, hiring leaders need to clearly define what success in the role looks like.
Redesign Hiring for an AI-Enabled Workplace
Job descriptions can often be the first source of confusion. AI can be a useful tool for drafting them, but hiring teams still need to do the proper analysis to ensure the description accurately reflects what the role requires. A job description should be focused on specific expected outcomes, not vague responsibilities. That includes being specific about the level of AI proficiency the role actually requires rather than treating “AI fluency” as a universal qualification. Hiring teams should also separate the skills someone truly needs on day one from the ones that are nice to have or can be learned on the job.
From there, candidates should be evaluated against consistent criteria that reflect everything the role requires. If AI expertise is one of those requirements, hiring managers should assess not only a candidate’s technical proficiency, but also how effectively they can apply that knowledge alongside the reasoning, judgment, and adaptability the job demands.
The Human Capabilities That Predict Success
As AI takes on more work, the differentiators that separate high-performers from low-performers become more human. However, knowing how to determine whether a candidate has those qualities requires hiring managers to be more intentional about the questions they ask.
I do this by focusing on behavioral and scenario-based interview questions, such as “Tell me about a time you had to make a decision without all the information you wanted” or “Describe a situation in which a tool or data source gave you an answer you didn’t trust.”
Those answers tell me far more about how someone thinks and what they could bring to the role and the organization than simply asking how they use AI.
What Hiring Looks Like Moving Forward
Given the pace of AI innovation, the tools or AI fluency skills listed on someone’s résumé today likely won’t be the same in a year, or even six months. That makes aptitude and the ability to learn increasingly important considerations. The strongest hires will be the people who can learn what comes next, question what technology gives them, and apply sound judgment that transcends what AI recommends. The people who know how to think in any situation.
As baseline AI fluency becomes more common across the workforce, simply having this skill will become less differentiating on its own. Just as with any other capability, the goal should not be to hire for AI fluency in isolation, but to understand what the role requires and evaluate the whole candidate against those needs. The candidates who stand out will be those who pair that fluency with the judgment, adaptability, and critical thinking needed to turn technology into meaningful results.
With over two decades of HR experience, Katherine Loranger leads Safeguard Global’s people strategy and development as Chief People Officer. Her strategic human resource planning allows Safeguard Global to hire the best talent available, while positioning the company as an employer of choice.