Patricia McKeating

From Prague to Global Scale

It was in Prague that McKeating entered the corporate world, joining ExxonMobil and encountering, for the first time, professional and structured talent acquisition operating at immense scale. The discipline of a global machine taught her what rigor looks like. But the real turning point came later, at a fast-growing healthcare technology company in the UK, where she built the team in Europe and then scaled it across AMEA, then APAC, then LATAM.

That was the assignment that rewired how she thinks about the function. She could trace her work directly into business growth: the company grew, new markets opened, acquisitions happened, and clinicians ended up with better tools to deliver patient care. The line from a hiring decision to a patient outcome was suddenly visible. "I stopped seeing myself from being just someone that was finding people jobs," she says, "but to someone that was helping companies to become what they were trying to be." The passion for matchmaking did not disappear. It grew up.

Lessons From Every Kind of Leader

Ask McKeating about the managers who shaped her and she does not offer a single mentor story. She offers a taxonomy. There were years in which she had more than one leader per calendar year, and she insists she took something meaningful from each of them.

The inspiring ones are the template for her own leadership style. They handed her opportunities she was not quite ready for, roles she could not yet picture herself in, and then watched her grow into them. The absent ones were frustrating in the moment and valuable in hindsight, because they forced her to stand on her own two feet and to look for support and direction beyond her direct manager. That search is where she built her most meaningful professional relationships, the ones that outlasted any org chart.

Then there were the genuinely difficult ones, and she refuses to reduce them to a cliche about how not to lead. What they really gave her, she says, was a mirror. In their behavior she could recognize small tendencies of her own, uncomfortable to see and impossible to learn about in a training course. That self-awareness let her go and fix things. "We all have blind spots," she notes, and in her view the best leaders are the ones who shine a light on them and help you through, making you a better leader almost as a byproduct.

Bringing a Whole Team to AI Fluency

At Oracle, a company she describes as a big leader in AI with AI agents embedded in nearly everything it does, McKeating's instinct was not to master the technology privately and reveal it later. Her first move was collective. "It wasn't just about me going and understanding," she says. Every function has its early adopters and its wait-and-see contingent, and she wanted the entire team at a baseline of fluency rather than a handful of enthusiasts running ahead.

So she put the early adopters to work as teachers. They designed workshops grounded in real scenarios her recruiters actually face: a vague brief, a market constraint. Small groups, safe environment, permission to experiment their way to a solution using AI. Peer-led beat formal training, she found, because people learn by doing and because the benefits landed with them immediately rather than in theory. That was phase one. The team has moved past fluency and is now building things together and seeing results.

Diagnostics Before Tools

McKeating believes talent acquisition is standing at a genuine crossroads. AI will transform not only how the function works but what work actually gets done, and she is candid that not everyone will make it to the other side. The professionals who will are the ones already operating strategically: not service desks, but practitioners bringing market intelligence and data to the table and questioning the assumptions behind a requisition. For them, she argues, the job becomes far more meaningful. AI fluency, in her framing, has graduated from a technical skill into an operating professional mindset.

What she has no patience for is adoption as theater. The most common mistake she sees organizations make is applying AI where the pain is most visible rather than where the root cause sits. Point it at the front of the funnel when the real problem is decision-making and nothing improves. "You're just going to make that wrong thing faster and more expensive," she says. Diagnostics come first. The market is crowded with tools, not all of them fit the need, and prices are climbing, which makes return on investment a question leaders have to answer honestly rather than assume.

Her enthusiasm for what AI can genuinely do is real. It goes well beyond automation, surfacing patterns a human might miss and delivering talent intelligence in real time instead of the two weeks it once took her to return to a hiring manager with workforce planning insight. But the ownership question is settled in her mind: "AI may change who does the work, but it does not change who owns it."

The Candidate on the Other Side

McKeating's sharpest warning concerns the person the industry exists to serve. In the current rush for efficiency, she worries some companies are losing sight of the candidate on the other side of the process, people navigating real market uncertainty who are trusting a recruiter with something that matters enormously to them. And she is certain the evaluation runs both ways. Candidates are assessing how a company applies AI to its talent function just as intently as that AI is assessing them. Over the coming year she expects this to become a major component of employer value proposition, because the way an organization deploys AI in hiring is now a public statement about how it treats people.

Which is why she frames the online debate about human versus technology in talent acquisition as a false choice. Automation is fantastic and she wants every hour it can give back. But empathy and accountability are not line items to be optimized away. They are what candidates need, and they remain the job of the human in the function.