My First Day Using AI to Screen 500 Candidates

If you’re reading this, you might be a business owner, an HR leader, or an executive who’s curious about how AI is changing recruiting—or maybe you’re just tired of sorting through endless CVs. I want to share what it really felt like to use AI to screen 500 candidates in a few minutes , what surprised me, what scared me, and what I learned along the way.

The Night Before: Doubt, Deadlines, and a Little Bit of Fear

I’ll be honest: I didn’t sleep well the night before. I’ve spent years in executive search, reading every CV myself, making notes, and worrying about every shortlist. I’ve led teams and delivered on mandates where a single bad hire could cost millions. But 500 candidates in a few minutes? Even with the best team, it would take a few days.

We built DEX AI and CVFlow to solve this exact problem. Still, I had doubts. Would AI actually work? Would it make my team unnecessary? Would it turn recruiting into a cold, mechanical process? I wasn’t sure. But I was ready to try.

08:00 — The Flood

At 8:00 in the morning, the candidate list hit my inbox. I uploaded everything into our system. CVFlow doesn’t care if you’re hiring for a trade marketing specialist in Chile, a robotics sales director in Italy, or a finance lead for a Chinese company. It just gets to work. The AI started screening, using information from platforms like Lusha, Apollo, ZoomInfo, and public sources. No coffee yet. No frantic emails. Just a dashboard slowly filling up with scores⁠⁠.

09:00 — The First Shock: Speed Is Not the Point

By 9:00, I had a ranked list. The AI had scored every candidate from 0 to 5, flagged clear mismatches, and surfaced profiles I would have missed in a manual review. But speed wasn’t the real surprise. It was the clarity.

Instead of a pile of “maybe” candidates, I saw clear patterns—groups who fit perfectly, and others who were close but not quite right. The system didn’t just filter; it explained, in plain language, why someone was a strong match or where they fell short. For the first time, I wasn’t buried in noise. I could see the signal⁠⁠.

10:30 — The Human Layer: Judgment Still Matters

Here’s where I almost made my first mistake. The top-scoring candidate for a key role looked perfect on paper. But as I looked closer, something felt off—too many short jobs, a pattern of job-hopping. The AI flagged this as “neutral,” but it was up to me to look deeper.

This is where leadership comes in. AI doesn’t replace judgment; it makes it more important. It gave me space to ask better questions, to focus on “why” instead of drowning in “what.” I discussed with a colleague, and we decided to move the candidate to “waitlist.” The machine made us faster, but it didn’t make us lazy⁠⁠.

13:00 — The Efficiency Dividend

By early afternoon, I realized something had changed. Normally, I’d be interrupted by calls from clients, Slack messages from my team, and requests for updates. Instead, everyone could see the dashboard. Progress was transparent. Bottlenecks were obvious. The process was suddenly less political, less about who shouted loudest, more about facts.

I could focus on coaching my team, strategizing with clients, and thinking about the next market we want to enter. The technology didn’t just save time; it gave time back to the work that only humans can do⁠⁠.

15:00 — The Unexpected Discovery: Bias, Surfaced and Challenged

Here’s the part I didn’t expect. As the AI surfaced patterns, it also surfaced my own blind spots. I realized we’d been unconsciously favoring certain universities and career paths. The algorithm didn’t care about pedigree; it cared about evidence. It forced us to confront our biases and rethink what “qualified” really means.

That was uncomfortable. But it was also liberating. We started to see talent we would have missed before. The shortlist became more diverse, more global, and more in tune with the markets we serve⁠⁠.

17:00 — The Human Cost: What Happens to Recruiters?

Let’s be real. If you’re in HR, you might worry: Does this mean I’m out of a job? Here’s my honest answer: it depends on what you want your job to be.

If your value is in repetitive screening, yes, the machine will do it better. But if your value is in judgment, coaching, building relationships, and understanding the complexity of people—AI just cleared your calendar to do more of that.

The best recruiters on my team didn’t resist the change; they leaned into it. They became mentors, advisors, and strategists. They stopped being bottlenecks and started being multipliers⁠⁠.

19:00 — What I’d Do Differently

I made mistakes. I let the algorithm’s scores sway me too much, too early. I forgot to check in with my team as often as I should have—old habits die hard. And I underestimated how much the process would challenge not just my workflow, but my assumptions about talent, leadership, and fairness.

But I learned. And tomorrow, we’ll do it better⁠⁠.

Final Thoughts: Technology, Humanity, and the Future of Leadership

AI in recruiting isn’t about replacing people. It’s about freeing them. It’s about making space for better decisions, deeper relationships, and more creative strategies. The companies that thrive won’t be the ones that automate the most; they’ll be the ones that use technology to become more human, not less.

If you’re leading a business, don’t wait for permission. Experiment. Fail. Learn. The future of recruiting isn’t a machine. It’s a partnership—between data and judgment, speed and wisdom, what can be measured and what can only be felt.

Tomorrow, I’ll screen another 500. But tonight, I’m going home on time⁠⁠.

Key Takeaways (For Busy Readers)

AI screening is not just about speed. The real value is in clarity and transparency.

Judgment still matters. AI helps you focus on the right questions, not just the right data.

The process surfaces hidden biases and can make your shortlist more diverse.

Recruiters who adapt become more valuable, not less.

The real win is getting time back for the work only humans can do.

Claire Jin, Principal, DEX AI & Senior Consultant, LYC Partners