AI in Hiring: Why Human Judgment Still Matters in Talent Acquisition

AI is reshaping HR in many ways. In this conversation, we zoom in on hiring inside Talent Acquisition, where the pace has shifted clearly. Market mapping, sourcing, screening, and early shortlisting that used to take weeks can now happen in days, often with a polished summary and a confident recommendation attached.

Athena Tavoulari is one of our Executive Search Partners at KennedyFitch. She speaks with TA leaders, CHROs, and business stakeholders every week about how hiring is changing as AI becomes part of the workflow, externally and internally. So we asked Athena: Where, in today’s faster hiring workflow, does human stewardship matter most?

What is the most important shift AI is creating in how hiring decisions get made?

The speed is not the interesting part anymore. Everyone sees that. The interesting part is what that speed does to judgment.

Market mapping and shortlisting can move from weeks to days now. Not because one magic tool changed everything, but because it is happening in different layers at once: in how people work day to day, using AI for first drafts, synthesis, and sharper briefs, and in the systems around hiring, where AI is increasingly embedded in ATS and talent platforms that structure, parse, and surface information.

Help me unpack that. What does good judgment look like when AI is shaping what gets surfaced and summarised?

The value sits in what is hardest to automate: being clear on what success in the role actually looks like, and translating that into signals you can test, not just stories you can summarize.

And then there is the operating model around AI. Leaders need to decide what role AI plays in the workflow: is it a tool that supports steps, or a collaborator that shapes thinking and judgment? That choice drives everything else: what data sits behind it, which sources are in or out, who can use it and for what, how humans and AI hand off work, and what minimum accuracy you require before an output is allowed to influence a decision.

This is also where strong collaboration with People Analytics function becomes essential. If AI is touching screening, matching, or prioritisation, you need a way to validate what the system is rewarding over time: which signals actually predict success, where outcomes drift, and whether speed is improving quality or just amplifying noise. TA keeps the human accountability for the call. People Analytics helps you keep that call defensible, and keeps the learning loop alive.

Where do you see the process start to feel more certain than it really is?

When the summary becomes the evidence. AI is very good at producing something that sounds complete. And when everyone is busy, a complete-sounding narrative can feel like rigor. The risk however is not that AI lies. The risk is that it makes it easier to stop questioning what is true. So it is important to keep a clear line between what is a signal, what is an assumption, and what is simply a well-written explanation.

We hear a lot about “perfect CVs” and AIpolished applications. What is real, what is exaggerated, and what should teams do differently?

The “perfect CV” effect is real. I hear it often: everything lines up with the job description, the language is polished, the summary reads like it was written for that role. Then interviews start, and sometimes a gap appears between what is written and what someone can actually explain, reason through, or ground in real experience.

At the same time, we should not overreact. A CV is not only wording. Career steps, scope, context, outcomes, and the specifics behind achievements still matter, and you cannot fake a coherent track record at scale without it showing somewhere. Strong TA and research talent have always been able to decode potential beyond a perfect narrative.

Where teams need to adjust is how they use AI, because the right “human plus AI” setup depends on the hiring context. In high volume hiring, AI can be very effective as a tool for first round triage at scale, surfacing skills signals, de-duplicating, and prioritising follow-up. The human value is then in setting the success criteria and validating that the signals predict performance, not just fit on paper.

In executive search, the job is different. AI is less about selecting for you and more about helping you go deeper. At KennedyFitch for example we use it to sharpening the conversation around ambition, drivers, context, and judgment.

The shift is to treat the CV as supporting material, not the main gate, and move differentiation into grounded validation: structured questions tied to real work, consistency checks across steps, and clear criteria for what evidence is required before an AI output is allowed to influence progression. Research even shows LLM evaluators can systematically prefer resumes generated by themselves, that is why we should ask ourselves the practical question about what your process is rewarding.

The advantage shifts to teams who protect decision credibility by keeping a clean line between signal, assumption, and explanation, and by designing the human moments that actually reveal capability.

What do you think TA leaders should always have in place during the hiring process?

For me, it comes down to three: fair, safe, and human.

Fair means being clear on what the process is optimising for, and checking whether outcomes match that intent. Bias is not something you outsource to a tool. It is shaped by inputs, constraints, and the decisions humans make around it.

Safe includes governance, but I also think it includes confidentiality. In sensitive hiring, leakage is not only personally damaging. It can change perceptions, affect internal dynamics, and create real consequences. Treating data governance as professional integrity becomes essential.

Human is about trust and what you can actually learn about someone. Candidates share differently when they feel evaluated by something they cannot understand. Human means clarity, care, and consistency at the moments that shape trust.

In practice, this is less about principles and more about clear rules and ownership: decision rights, guardrails, what happens when something feels off, and who reviews outcomes. The strongest set-ups are cross-functional by design, but that is a whole discussion on its own.

Where does skills-based hiring fit in?

On skills, I see both opportunity and risk. AI can help surface skills signals at scale, but it can also over-weight what is easiest to label and rank. So the work is getting aligned on what “good” looks like in skills terms for this context, then using hiring to test and refine that, not treating skills like a static checklist.

Where do you see the biggest opportunity when internal mobility and external hiring start to run as one system?

I think this is one of the most exciting shifts. When you connect hiring to internal mobility, the question changes from “can we fill the role?” to “can we move capability to where the business needs it, with the least friction?” Every hire, move, and promotion produces learning about what the work actually required, what predicted success, and what did not. If you capture that and feed it back into how you scope roles, assess skills, and build pipelines, the system gets smarter over time.

Looking ahead to 2026, what is the biggest opportunity for TA leaders as AI becomes more common in hiring?

The biggest opportunity is to make decision quality more explicit and more consistent, without making hiring a heavier process.

For me, that starts with keeping humans in the loop where it matters most: getting crystal clear on what success in the role looks like, separating real signal from a polished story, and testing judgment in real conversations.

It also means breaking the silo between TA and data teams. Strong partnering with People Analytics can help TA to predict success profiles, to co-create market maps, validate which signals matter, and keeping a learning loop alive as outcomes come in.

And then there is governance. If AI is shaping screening, matching, or prioritisation, someone needs to set clear rules on what AI can automate, what must remain human, and who owns oversight. My one non-negotiable is accountability: someone sets the guardrails, someone reviews outcomes, and someone can step in when patterns look off. With that in place, speed becomes a strength rather than a risk. And even if none of us can predict exactly where this goes, I am genuinely optimistic. We have a chance to make hiring both smarter and more human: less noise, clearer signals, better conversations, and decisions leaders can stand behind. These are exciting times, and there is real opportunity ahead for teams who choose to shape the shift, not just react to it.