January 16, 2026

Tech-savvy is no longer good enough. Employers want AI-fluent talent, and recruiters are winging it

15min

AI is all the hype right now, and for once, it’s justified. Who would have thought a “machine” could one day draft documents, analyze data, brainstorm ideas, write code, and so much more?

And the most disruptive part is that AI isn’t locked away in government labs, gate-kept by big tech, or only used by engineers and data scientists. Everyone can access it – from large businesses and startups to recruiters using AI for hiring and even freelancers working from home.

AI is reshaping work as we know it, so it’s only natural that, across sectors, companies want employees who are proficient with using AI – a skill now called “AI fluency.” In fact, one survey showed that over a quarter of hiring managers already view AI fluency as a “baseline requirement.”

The problem? Most recruiters don’t actually know what AI fluency means, let alone how to measure it. So, they’re shooting in the dark, or doing “a lot of vibes-based hiring”, as Barry Kunst, VP marketing of Solix Technologies, puts it. One can only imagine the fallout: poor-quality hires, wasted time, and frustrated leaders.

Ivan Vislavskiy, CEO and co-founder of Comrade Digital Marketing Agency, has seen this kind of guesswork go wrong.

“We had a guy join us, said he was using AI to write content faster. And yeah, he was fast[...] But when we looked deeper, there was no depth, no SEO, no structure[...] That’s not AI readiness[...] We had to wipe it all out and start again.”

Honestly, I don’t blame recruiters – AI is moving faster than most people can keep up. But the good news is, you don’t need to be an AI expert to hire well. What you need is to understand what AI fluency means and how to assess it properly. And that’s exactly what I unpack here.

Leveling up: Why AI-fluency is the new baseline

In a large-scale survey, Nexford University found that 4 in 10 hiring managers said they’d trust a university graduate more if they’d been taught how to use AI across all subjects. This is telling. It suggests that AI capabilities have become a baseline capability for modern work, cutting across roles, functions, and even levels.

The question is: Why?

AI tools are already embedded in everyday work

The use of AI across jobs isn’t some sort of distant theory; it’s already happening.

Zapier, a software company, revealed that more than 97% of enterprises are already using AI. The latest Gallup Workforce survey found that 76% of tech and more than half of finance and professional services employees are also now using AI in their daily roles.

AI tools are increasingly being used across jobs - 97 percent of enterprises are already using AI graphic

If you think about it, AI is baked into so many of the tools people already use at work – from Google’s Gemini and Microsoft’s Copilot to applicant tracking systems, customer relationship management software, design platforms, and more. Employers want candidates who are already proficient in using these tools so they can hit the ground running as soon as they join.

External pressures have accelerated the use of AI

A tough economic climate with higher interest rates, tighter funding, and ongoing uncertainty has put cost pressures on most employers. They need to do more with the same headcount, and AI is the lever that makes that possible.

In one study, AI assistance boosted customer support agents’ productivity by 15%, proving that AI can help employees achieve more with the same time constraints and resources.

Additionally, today’s investors want AI-ready companies, putting even more pressure on employers to adopt AI. PwC’s Global Investor Survey 2025 showed that 78% of investors would increase their investment in a company pursuing enterprise-wide AI transformation.

AI can’t run on autopilot

Contrary to all the “AI will replace humans” chatter over the last few years, the reality is that AI is not ready to run things by itself. Algorithms are actually trained on human intelligence, and often replicate the same flaws and biases humans make. And that’s not all. Many AI tools have also been shown to “hallucinate,” providing inaccurate information to users.

In fact, these problems have often had legal ramifications – for instance, one California attorney had to pay a $10,000 fine for filing a state court appeal with fake information generated by ChatGPT. Meanwhile, Workday, an HR software company, was sued for alleged age discrimination related to its AI hiring tool.

These are only a few of many cases. That’s why companies seek employees who can oversee AI tools and catch mistakes before they blow up.

The bitter truth: Hiring for AI fluency is going horribly wrong

It’s clear why companies want AI-fluent talent. Unfortunately, they’re struggling to hire the right folks. Chris Kirksey, founder & CEO at Direction.com, tells TestGorilla: “I've seen many companies hire individuals with the stated skills of using AI, but these employees cannot actually resolve problems nor verify the accuracy of their outputs.”

Here’s what seems to be happening.

Recruiters have little to no guidance from employers or job descriptions

Let’s be clear about one thing: Talent acquisition teams don’t need to be experts to hire for technical skills. Historically, they’ve relied on employers’ guidance and job descriptions as a solid starting point to assess incoming applications.

But when it comes to AI, recruiters are finding themselves alone in the deep end because employers and job descriptions are unclear.

Research by McKinsey suggests “Employers do not know how many AI experts they will need with what type of skills.” And according to another recent report, out of 74% of employers who used the term “AI” in a job posting, only 2% were specific about the actual AI skills needed.

What’s worse, when I Googled “operations manager” job descriptions, I found that even templates from hiring giants like LinkedIn didn’t mention AI, let alone provide specifics.

Enter: Self-reporting, resumes, and other vague signals of AI-readiness

When recruiters are asked to find AI-fluent talent with no information on what that means, it’s no surprise they’re left working with scraps, like self-reported use of AI tools, relevant buzzwords, micro-credentials, and so on.

Ali Yilmaz, co-founder & CEO at Aitherapy, explains, “Most teams rely on weak proxies: job titles, resumes listing AI tools, or candidates self-reporting experience. None of these [shows] whether someone can actually integrate AI into real work. The result is hiring for perceived fluency rather than demonstrated judgment.”

Anastasiya Levantsevich, head of People & Culture at Pynest, also agrees. “If a candidate writes in their resume that they are proficient in a couple of trendy tools[...] that doesn't necessarily mean it's true.”

And research backs them.

A recent report revealed that 92% of professionals feel confident about their AI skills, but 65% of companies have had to abandon AI projects, noting a lack of AI skills in staff members.

Majority employees exaggerate AI knowledge quote graphic

The report suggests that the “majority of executives and employees exaggerate their AI knowledge, and likely overestimate the AI skills they actually have.” Worse, results showed that nearly 8 in 10 IT professionals and executives faked their AI competence.

That means you really can’t – and shouldn’t – assume someone is AI-ready just because they’ve mentioned AI tools in their resumes, or their interview. As Pragya Keshap, technical architect at Charles Schwab, explains:

“In many organizations, AI fluency is treated like a checkbox: if someone has worked with an AI tool or mentions LLMs in an interview, they’re assumed to be ready.”

Hiring teams treat AI fluency like a single, technical skill – completely ignoring soft skills

One of the biggest mistakes hiring teams make is treating AI fluency like a standalone technical skill, similar to programming or coding. If someone knows how to work Gemini or has completed a Gemini certification, you assume they’re AI – or at least Gemini – ready.

This shortcut is seriously flawed. AI fluency is so much more than knowing how to operate a tool. It’s about soft skills, cognitive abilities, and personality traits – how people interact, question, and adapt while using these tools.

Let’s talk about communication, for instance. In one survey, 22% of hiring managers ranked AI fluency equally or more important than communication skills. But isn’t so much of AI about good prompting to produce accurate outcomes? In fact, Kunst actually listed “collaboration” as a crucial aspect of AI fluency.

And a number of other experts we spoke to also rate some of these softer qualities as more important than the tools themselves.

Karina Tymchenko, founder of Brandualist, echoes this, citing “critical thinking skills, taste, and the ability to oversee AI” as the most important AI skills. “AI performs best with humans who will challenge its output,” she explains.

And because AI tools evolve constantly, the willingness to pivot, upskill, and re-skill can’t be ignored. As Sasha Berson, co-founder and chief growth executive at Grow Law, explains: “AI tools change all the time, prompts stop working, platforms get replaced. If you’re curious, you keep up. If you’re adaptable, you adjust without panic.”

Recruiters who ignore these dimensions of AI fluency aren’t even looking for the right qualities to start with.

No one’s thinking about AI stigma

Another hindrance to finding skilled talent is AI stigma. Many still see using AI as cutting corners.

Recent research showed that, despite strong demand for AI skills, 61% of employers view the use of generative AI tools as “lazy,” and 73% of C-suite leaders reported that this stigma is still prevalent in their companies.

These results exposed a strange dynamic – employees are using AI to move faster and do better, but they’re not always admitting it, and in some cases actively hide it.

Now think about how this translates to hiring. If AI-fluent candidates believe listing AI skills in their resumes or applications could hurt their chances, they might leave those signals out entirely. And if they’re not using the right AI keywords, neither you nor your search tools will find them.

This begs the question: How much of this is an AI skills gap issue versus a visibility problem? Perhaps the right talent is out there, but you’re just not discovering them.

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Breaking it down: How to find AI-fluent talent

Once you know how to define, source, and measure AI-fluent talent, hiring decisions will finally be grounded in evidence rather than guesswork. So let’s look at how to do this.

Understand the basics: What does AI fluency mean?

To give our colleagues in recruiting an easy-to-work-with definition of what AI fluency entails, here are the key AI fluency skills you can go back to whenever you’re sourcing or assessing candidates:

Applied AI use and workflows: This skill is a candidate’s technical ability to use AI tools to take a task from end-to-end with speed and without constant rework. But importantly, it’s also about candidates knowing what AI can and can’t do, choosing the right tools, and avoiding misuse.

As Tymchenko says, “A strong candidate will be able to discuss the trade-offs associated with using AI, validate AI outputs, as well as identify times when AI should be corrected.”

Learning speed and digital agility: Can candidates pick up new tools and workflows quickly? Can they adapt and refine how they use AI as technology changes? This is key to ensuring they don’t fall behind as AI continues to evolve.
Systems thinking and problem-solving: This refers to candidates’ ability to break complex problems down into smaller parts and tasks, and consider how AI can be used in each individual task.
Responsible and ethical use: This skill involves candidates’ awareness of the ethical, privacy, and legal risks associated with AI use. Great candidates won’t use AI blindly or in haste. Instead, they’ll document choices, add safeguards, and regularly audit outputs for flaws and biases.

I like how Keshap puts it: “AI fluency isn’t about knowing the latest model. It’s about responsibility, judgment, and the ability to work with systems that are probabilistic by nature.”

Human-AI collaboration and communication: Do candidates use AI as a collaborative tool rather than a crutch? Can they explain the assumptions and outputs transparently, know how to prompt for accuracy, and judge when to verify results? For Yilmaz, “AI fluency isn’t tool familiarity. It’s the ability to think critically alongside AI.”

Make your company’s stance on AI skills crystal clear

Spelling out where AI fits into your company's work and where it doesn’t is crucial, yet often overlooked. It ensures candidates don’t shy away from sharing their AI abilities in their applications, resumes, or interviews, but also tells them where you draw the line in the sand.

For example, a content business might welcome AI for research or idea generation, but not for writing full drafts without review and fact-checking.

When you add this kind of clarity to your company’s career pages, social media, and especially job descriptions, you’re much more likely to attract AI-fluent talent.

Ditch (vague signals) and switch (to real-life skills assessments)

When we asked experts about their secret mantra for assessing AI fluency, all their answers pointed in the same direction. They verify rather than believe every claim they see on a resume.

Vislavskiy shares: “Saying ‘I’m good with AI tools’ doesn’t mean anything until you actually watch it happen. These days, I make sure candidates in marketing or strategy show us how they’d solve a problem we’ve already faced. The difference between someone who can actually use AI and someone faking it becomes obvious right away.”

Berson also takes a similar approach, with a unique technique. “Honestly, I trust screen shares more than anything. I want to actually see how someone uses AI in real time. For example, if you’re a marketer and I say, ‘Use ChatGPT to improve a landing page,’ I’m watching the steps you take. Are you just grabbing the first draft it spits out, or are you pushing it, refining it, asking for tone shifts and different angles? That whole flow tells me everything.”

It’s hard to argue with that. Watching someone work is one of the best ways to see who’s really talented. But there are two problems it doesn’t solve for:

Most recruiters don’t know what “good” AI usage looks like in every role, and they shouldn’t have to.
Screen sharing isn’t scalable. It might work for a handful of candidates, but when application volumes surge, it’ll turn into a huge time suck.

That’s where talent assessments come in. You can put candidates through an expert-designed AI test, as well as tests for communication, problem-solving, and even personality traits, to get a holistic evaluation that covers every angle of AI fluency. The best part? They’re auto-scored, so you don’t need to become a cross-functional AI guru.

That said, passive candidates are a different challenge. If you’re building a pipeline of AI-fluent talent, you can’t ask every one of them to take a test before they’ve even applied for a job.

Luckily, pre-vetted sourcing pools, like TestGorilla Sourcing, can help. It gives you access to more than 2 million skills-tested candidates, and you can filter profiles for the specific skills you need. This solves the visibility problem I mentioned earlier, making it much easier to discover AI-fluent job seekers you may have otherwise missed.

Ask the right interview questions

Finally, interviews are a great way to probe candidates’ AI fluency deeper – great for roles that use AI every day or in complex ways.

In terms of what to ask, Keshap suggests focusing less on tools and more on judgment and adaptability. “When evaluating candidates, I look for how they think under uncertainty. I’ll ask how they’ve handled ambiguity, how they noticed something was ‘off’ with an AI result, or how they adapted when a model didn’t behave as expected.”

Some interviewers push it further. Marshall Scabet of Precision Sales Recruiting asks candidates to work live:

“I assess AI fluency by giving a problem statement and then asking the candidate to generate an AI prompt[...] For example, I told a recruiting candidate for my own company that they needed to write a job description for a new client.”

Scabet said the candidate “crushed it” by walking through each step and showing clear, thoughtful control over the process.

In this way, combining AI skills tests with structured interviews makes it easier to hire candidates who are truly skilled in AI, in the exact way you need them to be.

When hiring for AI fluency, getting the basics right makes all the difference

Most employers today want AI-fluent talent, but few are succeeding in landing them. While some of this could be linked to a skills gap, there’s also a fundamental problem with how recruiters are hiring for AI fluency. They’re guessing what it looks like, relying on vague signals, and missing it when it’s right in front of them.

The solution? Understanding the breadth of AI fluency, signaling expectations openly, using expert-created AI assessments, and asking the right AI interview questions. When you do this, you can hire people with the precise AI skills you need instead of leaning into empty claims and gambling with the results.