Using AI to Find Candidates: What Works, What Fails, and What No One Tells You
AI is everywhere in recruiting.
Sometimes it feels like a superpower. You find talent faster, you spend less time digging through noise, and you move from “overwhelmed” to “in control.” Other times it does something quietly risky: it removes great candidates before anyone on your team ever sees them.
The difference isn’t AI. It’s how we use it, and what we let it decide.
When AI is used well, it makes recruiters sharper. It takes the messiest part of hiring—the beginning—and brings structure to it. Early in a search, there’s usually too much volume and not enough signal. That’s where AI can genuinely help. It can scan huge pools quickly, surface skills that don’t show up neatly in job titles, and point you toward people who may never apply through traditional channels. In that moment, AI isn’t replacing judgment. It’s clearing the fog so a recruiter can spend time doing what actually matters: evaluating a person, not sorting a pile.
The problems start when AI stops being a helper and becomes the gatekeeper.
Algorithms can’t read ambition. They don’t recognize curiosity, resilience, or the kind of growth you only see when someone gets the right manager and the right environment. They struggle with non-linear careers. Career changers, candidates with gaps, people from unconventional industries, or anyone who didn’t collect the “right” titles at the “right” companies can get filtered out simply because their story doesn’t look familiar to the system. A fully automated process might be efficient, but that doesn’t make it intelligent.
It’s also worth saying the quiet part out loud: AI doesn’t automatically remove bias. In many cases, it can amplify it.
AI learns from historical data. If past hiring decisions favored certain schools, certain companies, or certain “polished” paths, the model will learn that pattern and repeat it at scale. That’s why the question isn’t whether AI is fair. The real question is whether the way we train it, configure it, and rely on it is fair—and whether we have humans involved to catch what the model can’t.
This is also where candidate trust comes into play. People can tell when they’re interacting with a system instead of a company. When the process feels opaque, overly automated, or strangely impersonal, the candidate experience weakens. And that cost is real, even if it doesn’t show up neatly on a dashboard.
So if AI is not the answer to every hiring problem, what is?
Better matching of tool to purpose.
Different platforms tend to excel with different kinds of roles and different stages of the funnel. LinkedIn’s matching tools, for example, work well when the talent you need looks “traditional” on paper—mid-to-senior professionals with recognizable titles and clear career progression. It’s fast and powerful, and it’s excellent for passive talent discovery. But it can also overweight brand names and job titles, which means it’s less reliable when you’re trying to find unconventional candidates or people with potential that doesn’t look obvious yet.
Assessment platforms like HireVue or Pymetrics-style tools can be useful when you’re hiring at scale, especially for early-career or structured programs where consistency matters. They can bring more data into the process than a resume alone. The trade-off is that they can feel cold to candidates, and they require careful design to avoid disadvantaging neurodiverse candidates or people from different cultural backgrounds. Used thoughtfully, they can support fairness. Used lazily, they can turn hiring into a test of who performs best in that particular format.
Talent intelligence platforms like Eightfold, Beamery, or Gloat tend to shine in larger organizations that care about skills, internal mobility, and workforce planning. They’re better at seeing skill adjacencies and surfacing people who could grow into a role rather than only those who have done it in a perfectly matching title. The downside is that they’re heavier lifts. They rely on clean data and thoughtful adoption, and the return is usually long-term rather than immediate.
Then there are resume screeners built into ATS systems or layered on top of them. These can be helpful when you truly need hard filters—licensing requirements, strict eligibility criteria, compliance-heavy rules. But they’re also one of the easiest ways to lose great talent quietly. Many of these tools are still heavily keyword-driven, which means nuance gets missed and strong candidates can be rejected for the wrong reasons without anyone realizing it.
And finally, conversational AI tools and chatbots can make hiring smoother when they’re used to reduce friction—scheduling, FAQs, updates, and basic communication. Candidates like responsiveness. Recruiters like getting time back. The risk is that when these tools become the “voice” of the company too early or too often, the process starts to feel robotic. In recruiting, speed matters—but feeling valued matters too.
This is why the smartest teams aren’t asking, “Should AI replace recruiters?” They’re asking, “Where can AI give recruiters leverage without removing human judgment?”
AI is excellent at surfacing options, reducing noise, and bringing order to complexity. Humans are essential for interpreting potential, understanding context, and making the kind of decisions that build strong teams rather than just filled seats.
The future of hiring isn’t automated. It’s augmented.
And the organizations that get that balance right won’t just hire faster. They’ll hire smarter, keep great people longer, and build trust in a process that’s increasingly easy to turn into a machine.
#AIinRecruiting #HiringWithAI #TalentAcquisition #FutureOfWork
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