Onrec Explores / AI & Automation

AI agents are entering recruitment. Where should human judgement begin?

The promise is less administration and more time with people. The real test is whether automated hiring workflows improve decisions—not simply accelerate them.

Onrec Explores · 17 September 2026 · Analysis · 10 minute read
Last updated: 30 September 2026

EXCLUSIVE FEATURE SPONSORSupported by GaiaIndependent Onrec editorial. Sponsorship does not determine contributions.

Meet the contributors

Jamie MistinCo-founder & Client DirectorRecruitment RevolutionBarb HymanFounder and CEOSapia.aiBen KeighleyCo Founder/DirectorGaia

A recruiter opens a vacancy, gives an AI tool a brief and receives a suggested shortlist. The same system may also prepare outreach or help organise the next step. The attraction is obvious: fewer hours moving information between screens, more time speaking to people.

But each step raises a different question. Drafting a message is one thing. Deciding who gets to receive it is another. As recruitment tools take on more of a workflow, employers need to decide which actions can happen automatically and which require a person to stop, check and take responsibility.

Why the conversation has moved beyond writing job adverts

LinkedIn’s January 2026 research reported that 93% of recruiters surveyed planned to increase their use of AI during the year, while 66% said finding qualified talent had become harder. The underlying research included 6,554 HR professionals across multiple markets, including the UK; these are international findings, not UK-only figures. Read LinkedIn’s research and methodology.

The same announcement describes AI agents for finding, shortlisting and contacting candidates. That is a useful indication of the direction of recruitment products, although a supplier’s product claims should not be treated as independent evidence that every employer will achieve the same results.

For this discussion, an AI agent means a system that can carry out a sequence of tasks towards a goal, within the access and permissions it has been given. The practical distinction is between asking a tool to suggest something and allowing it to act.

More attention to the conversation

For Jamie Mistin, Co-founder & Client Director at Recruitment Revolution, the clearest benefit of AI so far has been helping recruiters give conversations their full attention.

“We use AI across the business for admin, data analysis, reporting and connecting our systems. But the biggest win, hands down, has been transcribing and analysing client onboarding calls and deep discovery calls with candidates. Our Talent Partners can actually stay focused instead of scribbling notes, and we catch everything! That makes a real difference to how we take briefs, understand the nuances of each role and assess candidates, so we can move the best ones through the process faster.”

“Our Talent Partners can actually stay focused instead of scribbling notes, and we catch everything!”

His example shows how AI can support the information-gathering that informs a hiring decision. The next question is how much authority it should have over the decision itself.

Speed is easier to measure than a missed opportunity

Time spent screening is visible. A qualified applicant who never reaches the shortlist may be much harder to spot. That is why a faster process, by itself, is an incomplete measure of success.

In a May 2026 analysis, ILO senior economist Janine Berg argues that recruitment systems need clear objectives, suitable data and meaningful oversight. Her discussion includes a system that attempted to assess a growth mindset through candidates’ use of particular words—an example of how an apparently sophisticated assessment can measure a poor substitute for the quality an employer actually wants. Read the ILO analysis.

Our view is that employers should start by defining what a good decision looks like for the role. A confident explanation from a tool is not a replacement for evidence that its recommendations are useful.

Barb Hyman, Founder and CEO of Sapia.ai, puts that responsibility at two points in the process:

“People should own two decisions: what good looks like, and who gets hired.”

“The first comes before anyone applies. A hiring manager knows what separates a colleague who thrives from one who leaves within weeks. That judgement should shape the criteria the AI assesses against, and a person should sign it off.”

Give the tool a task—and a boundary

A sensible pilot might let a tool draft interview invitations for a recruiter to approve. A more consequential pilot might generate a shortlist, but require reviewers to compare it with a sample of applications the system did not select.

Those are different levels of responsibility. Treating them as separate decisions makes it easier to choose a useful starting point and to understand what additional checks would be needed before expanding the tool’s role.

Before a pilot, agree who can change the criteria, who reviews errors and what would trigger a pause. Give the reviewer enough time and information to challenge the output. A person clicking “approve” on an unexplained recommendation is a weak checkpoint.

At Recruitment Revolution, that boundary is the shortlist. Mistin says the decision remains with recruiters, even as AI helps them capture and analyse the conversations behind it.

“The shortlist is always a human decision, because a CV can't show motivation, potential or team fit. We've seen AI make some interesting decisions, and we simply don't trust it… yet. And real people have real conversations with candidates, particularly for senior and exec hires, because that's how you get a true read on who they are, whilst keeping them engaged and building the trust that ultimately wins the best talent.”

That distinction puts the responsibility in a specific place: AI supports the work, while a recruiter remains accountable for who progresses and for the relationship with the candidate.

Hyman also places a human checkpoint at the shortlist, with an emphasis on understanding and challenging the evidence: “The second comes at shortlist. AI can interview every applicant and score them consistently. The final call still needs someone who can see the reasoning behind each recommendation and question it.”

She points to Holland & Barrett as an example of changing both the assessment criteria and the way managers review recommendations:

“Holland & Barrett built its process this way. The team stopped treating experience and education as the benchmark. Managers then reviewed shortlists with the evidence behind every recommendation. Early churn among retail colleagues fell from 74% to under 10%.”

For Hyman, oversight depends on what the reviewer can understand and influence: “Oversight becomes meaningful when people set the standard and can interrogate the result. The right AI provides context and is understandable by the humans responsible for signing it off.”

Judge the outcome beyond the application

The same question about who sets the standard applies before a candidate enters the selection process. Ben Keighley, Co Founder/Director at Gaia, describes how that works in recruitment advertising:

“At Gaia, our agents don't follow fixed rules. They're given a goal and decide how to reach it, testing combinations of audience, creative, timing and channel, then moving spend to whatever is working. People stay in charge of the sign-off points: approving ads before they run and agreeing what a good outcome looks like.”

“If applications rise but attendance, pass rates or retention don't, it's optimising for the wrong thing, however good the dashboard looks.”

Keighley points to Gaia’s work with DHU Healthcare as an example of measuring success beyond application numbers:

“That second decision matters most. With DHU Healthcare, the NHS 111 provider for the East Midlands, the brief wasn't more applicants, it was the right ones. Twelve-month retention rose from 36% to 74%, and time to start fell by 39 days.”

His test for whether an agent is pursuing the right goal follows candidates further through the process: “The way to spot an agent going wrong, in a TA platform, is to judge it on what happens after someone applies.”

Five questions to ask before expanding a pilot

Be careful with the bigger jobs narrative

Automation within a hiring team should not be confused with proof that AI is driving the wider recruitment slowdown. In June 2026, LinkedIn said its EU and UK analysis had not found evidence of a broad AI-driven fall in hiring at that point, pointing instead to economic conditions and business uncertainty. That is a finding from its data, not a guarantee about future employment. Read the EU and UK analysis.

For an individual recruitment team, the more useful question is specific: what happens to the time you recover? If it goes into better briefings, more thoughtful candidate conversations and clearer feedback, the benefit is easier to explain than a claim about processing more applications.

The next step should be evidence, not a bigger promise

AI agents deserve a serious trial, but a trial needs a test that the system could fail. Start with a defined task, record the result and make room for recruiters and candidates to explain what the numbers miss.

The strongest case for automation will come from teams that can show both the time saved and the quality protected. That is the conversation Onrec wants to bring together: practical accounts of what worked, what needed changing and what should still be done by a person.

This feature combines linked research, Onrec analysis and contributions from Jamie Mistin of Recruitment Revolution, Barb Hyman of Sapia.ai and Ben Keighley of Gaia, alongside suggested questions for teams evaluating recruitment technology. Gaia is the exclusive sponsor of this feature. Onrec retains editorial control; contributions are selected independently of sponsorship.

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