Stephen Snyder is CEO of CareCloud, leading healthcare IT innovation via M&A, AI-powered solutions, and transformative tech strategy.
Last year, I made a hire that, on paper, I probably shouldn’t have made.
We were filling an attorney role, and the candidate I chose had less experience than several others in the pool. There were people with stronger résumés, longer track records and more conventional credentials. A year before that, I likely would have hired one of them.
I didn’t.
What changed wasn’t the market or the role. It was one question I asked in the interview: “How are you actually using AI in your day-to-day work?”
Not in theory. Not what the candidate thought it might do. How were they actually using it right now?
The gap in answers was immediate.
The more senior candidates spoke in generalities. They understood the tools, had opinions and maybe had experimented a bit. But their responses were high-level.
The candidate I hired didn’t speak in generalities. They walked me through real workflows—specific prompts, where the output fell short, how they adjusted and what they learned from it. It was clear that AI wasn’t just an idea for them; it was part of how they worked.
A year later, my decision to hire them has held up.
With guidance from more experienced attorneys on the team, this lawyer is now negotiating contract terms, producing thoughtful, well-supported memos and turning work around at a pace I wouldn’t have predicted from their résumé alone.
That hire forced me to rethink how I evaluate talent. Experience still matters. Judgment still matters. But on top of screening for experience and judgment, I also want to know: How does a candidate actually use AI to do their job?
Since I’ve added that question to my hiring process, I’ve observed a widening gap. Take two professionals with similar backgrounds. Put one in a workflow where they’re actively using AI to draft, refine and pressure-test their work, and the other in a more traditional process. They’re arguably not doing the same job anymore. The output is different. The speed is different. Even the baseline quality is different.
I see this as not about intelligence or work ethic, but about leverage.
I see this across functions—legal, finance, operations, sales. In my experience, the people who have integrated AI tools into how they work consistently produce more, and with fewer obvious errors. Not perfectly, but predictably better.
There’s also a second effect of AI usage that’s harder to measure, but also important. When part of the work gets compressed—first drafts, basic research, organizing information—it creates space. Not a huge amount, but enough to step back and ask better questions, such as:
• “What are we missing?”
• “Where are we making assumptions?”
• “Is there a simpler way to approach this?”
That’s where I’ve seen a significant advantage emerge. Initially, I underestimated how much this applies at the senior level. I’ve found that in the corproate world, there can be a tendency among leaders to treat AI as something junior team members should figure out, while they themselves focus on judgment, relationships and strategy. I used to think about it that way too.
In practice, however, I’ve found that it often works the opposite way. The more senior the role, the more these tools can amplify the person’s skills and experience.
In interviews, I test for this in a straightforward way. I ask candidates how they use AI. Then I ask them to walk me through something that actually worked (a real example). I strive to find out:
• What were they trying to do?
• What did they input?
• What came back?
• What did they change?
In my experience, people who use AI tools regularly don’t tend to speak in polished summaries. They generally talk about iterations, missteps and adjustments. However, this isn’t a foolproof rule-of-thumb, as some candidates who do talk about AI fluently haven’t really applied it.
AI is not the be all and end all, of course. And AI fluency is a filter with real limitations.
The first limitation is that from my observations, AI fluency is easy to fake and hard to verify. A candidate who’s read enough about prompting can sound fluent without having produced anything. The signal isn’t how well someone talks about AI—it’s evidence they’ve actually used it. People who have will tell you where the output was wrong and how they caught it.
The second risk is more dangerous. You can end up selecting for confidence with using the tools instead of judgment about using them. The person who leans hardest on AI isn’t always the one you want. In my view, the real skill is knowing when not to trust the output—catching the fabricated citation or the number that’s plausible but wrong. Reward enthusiasm over discernment, and you’ll end up hiring people who move fast and import errors at scale.
So how should you weigh this against conventional qualifications? There are a few things I keep in mind.
First, I recommend treating AI fluency as a multiplier, with judgment and expertise serving as the base of the foundation. AI amplifies whatever’s already there, including bad judgment.
I also advise that you screen for mindset, not just current proficiency. What actually matters is interest and a willingness to work through failure. A candidate with the right qualifications and the right disposition but limited hands-on experience can be trained.
And that reskilling, I’ve found, is usually faster than what leaders might expect. In my experience, if someone has the judgment, closing the AI gap can take a matter of weeks. One best practice is to pair them with an AI-fluent colleague on real work rather than giving them to abstract training. Another best practice is treating the failed prompts and wrong outputs as part of the learning process.
At the core, I’m screening for judgment, and using AI fluency as one window into how someone thinks and adapts. The tools will keep changing. The willingness to get good at something new before you’re forced to—that’s what holds up.
If you’re a leader, I recommend that you make judgment something you actively evaluate in hiring by using AI fluency as the lens. Consider adjusting your interviews to test for it. And before doing that, honestly examine your own usage. AI fluency is a trait I’m emphasizing in my hiring now—at every level. It’s also the one I’m holding myself to.
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