Imagine you’re going away for a month and trying to figure out who can watch your house. Not just someone who knows where the spare key is, but someone who’ll water your plants, make sure you didn’t leave any rotting food in the fridge and call if something goes wrong. You’d turn to someone you’ve known for years who you trust, not just the neighbor down the street who claims to have a green thumb.
That’s how the best retained search professionals operate in an era of agentic AI, when the depth of relationships matters more than ever before.
For years, experienced recruiters have weathered one disruption after another—internal talent team dynamics, the onslaught of LinkedIn, allowing company HR teams to pop in keywords and treat talent like a search result and now AI. Each wave prompted the same question: Do recruiters still matter? And each time, the answer has been yes—but the reasoning has evolved. Today, the value of recruiters comes down to something simple but high-stakes: Organizations can’t afford to hire the wrong person.
See also: How AI platforms connect employers with executive job seekers
The resume isn’t the person
AI has greatly improved many aspects of recruiting. Algorithms can scan thousands of profiles, identify adjacent skills and surface qualified candidates at speeds no human can match. These tools help HR accelerate preparation and cull long lists of potential candidates and skillsets. But they can’t predict who’ll actually succeed.
Resumes disproportionately reflect opportunity, storytelling ability and historical context—not future suitability. Our research consistently shows that past performance is a poor predictor of success in larger or more complex roles without also assessing potential and learning agility. These capabilities—operating under pressure, leading through ambiguity, how a person fits within a company’s culture—are harder to see, even by the most skilled internal HR teams. That gap is where mismatched hires happen. I’m fond of saying, “You get hired for what you know and fired for who you are.”
Consider a controller who arrives at a financial firm with stellar credentials, excellent references and a history of transforming the function. Yet the culture, team dynamics and the pace of business at the firm don’t fit how she works. Within a year, she’s gone, and the organization is back at square one.
We see this all the time. Culture fit and potential are the two most important predictors of whether someone sticks around, earns promotions and grows with a business. They’re also the two things AI is least equipped to evaluate. A machine can identify who looks like the person who held the role before, but it can’t tell you who will excel in the role as it’s evolving.
This is where retained search thrives. Recruiters often build and maintain a trusted network cultivated through repeated interactions and candid conversations, allowing them to really understand who will watch your home and who may just say they’ll do it.
Merging science with instinct
The most effective retained search today uses data to validate what experience already suspects, and human judgement to interpret what the data can’t fully explain. Korn Ferry has analyzed more than 100 million assessments to understand what great looks like across four dimensions: competencies, experiences, traits and drivers. Of those dimensions, our data shows that agility captures the traits most relevant to a world in constant flux—tolerance for ambiguity, curiosity, adaptability and focus. Organizations that hire for agility dramatically outperform their competition. But that data, and the ability to gauge a candidate’s agility, still require interpretation by humans. That’s why we’re not surprised that as AI has become more embedded in search, we’ve seen retained search grow instead of shrink.
As agentic AI’s role continues to expand, the best outcomes won’t come from choosing between data and instinct. They’ll come from combining both. AI can accelerate some parts of search. But it takes a talent partner who has spent years building real relationships to know who will actually water the plants. That’s not a role the machines are coming for. It’s one they’re making more valuable.
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