How to Launch Your First AI Project in Hiring (Without Screwing it Up!)

I feel like I’ve had the same conversation about five different ways in the last few months – on podcasts, at roundtables, over Zoom meetings, even in the back corners of a conference hall where someone whispers the question:

“How do you actually launch your first AI project in hiring?”

Not a CRM replacement. Not a better scheduling tool. Something intelligent. Disruptive. Transformational. And also (let’s be honest) something the business doesn’t entirely trust yet.

For most companies, hiring is the first real AI use case to make it to the big leagues. Which sounds exciting, until you realize there’s actually no roadmap. No real internal precedent. And no appetite for risk-flavoured ambiguity. Simply put, the playbook doesn’t exist.

But after hearing the same themes again and again, I think we can start to sketch one out.

Regulation Isn’t the Problem – It’s the Path

AI triggers all kinds of alarms in an organization, especially in relation to recruiting which has even been classed by the AI Act as a “high risk” activity. But oddly, the places with the most regulation might be the easiest ones to move in.

Why? Because regulation offers a shared language. In a world where everyone’s afraid of stepping on a landmine, a predefined map – even a restrictive one – is incredibly useful. In markets with clear AI laws, like in the EU, you can say: “Here’s what the law says. Let’s use that as our yard stick.”

And people do. Compliance stops theorizing. Legal stops inventing worst-case scenarios. And the business starts to engage. Compare that with a more fragmented setup, where there’s no single rulebook. Suddenly, step one is an internal debate about which rules to follow and you’re not launching a project anymore, but hosting a regulatory TED Talk! And the initiative gets stuck before it ever leaves the whiteboard.

If you’ve got a real framework to anchor to, use it. That clarity might be the most valuable asset you’ve got.

What You Call it Matters More Than You Think

I’ve heard a few versions of this now: leaders walking into legal or risk meetings and dropping the word “AI” only to watch every pair of eyes in the room narrow in unison. The reaction? Panic. Caution. Delay.

But when the same leaders came back and said, “We’re automating part of our process,” the conversation changed entirely.

Same tool. Same outcome. Totally different reception.

This isn’t about being sneaky. You still need transparency, and yes, AI governance is a real thing. But if your initiative looks like an automation project and acts like an automation project, then maybe that’s the smarter entry point, at least to start with anyway, to build your case.

There’s a meaningful difference between disguising risk and de-escalating fear. Framing helps with the latter.

No Big, Painful Problem? No Deal.

Here’s the blunt truth: no one is going to back your AI project because it’s cool. In this current environment, they’ll back it because it saves or makes money. Full stop.

The leaders getting this stuff through aren’t just evangelizing the tech either, they’re anchoring it to clear, urgent, financially painful problems – and quantifying them.

We’ve talked about this in the newsletter before, but if you want a shortcut? Ask finance.

“What does attrition actually cost us? In hard numbers.” Or: “How much are we spending on job ads, agencies, or onboarding delays?”

Let them do the maths. Then build your case on their number. Because if you say, “I think this tool will help us screen faster,” you’ll get a pat on the head. But if you say, “This tool can help reduce a £1.2 million annual leak,” you’ll get a meeting.

Learn more: Redefining Talent Acquisition in 2025

No Budget? Get Creative.

Money and budget are always going to be a compounding issue when it comes to rolling out an AI project. One TA leader I spoke to, however,  didn’t ask for a dime.

They reallocated job board spend to cover the cost of the pilot figuring if the pilot worked, they’d need fewer ads anyway. That kind of thinking gets noticed. Not because it’s frugal, but because it’s committed. It says: I believe in this enough to bet on it.

In a market where everyone’s protecting headcount and freezing spend, the most credible TA leaders aren’t waiting for permission. They’re funding their bets with what they’ve got and inviting the business to buy in after the proof.

Pilot like your Reputation Depends on it (Because it Does)

Launching an AI tool org-wide on day one is a great way to make sure you never get to day two!

One global leader I spoke with learned this the hard way. Big launch. Lots of buzz. But edge cases started piling up fast and with no containment, the tool buckled. And so did trust. Her takeaway?

“Start small. Fail fast. Learn loud.”

Pick one business unit. One geography. One use case. Set some hard success metrics before you begin. Then test, adapt, and grow. That’s what can build some real momentum.

And one more thing: pilots need air cover. If you don’t have an exec sponsor who can pull stakeholders into a room when everyone else is “backed up till Q3,” your pilot dies of calendar starvation!

This Isn’t a Replacement. It’s a Reinvention.

Most AI tools don’t just slot neatly into your existing hiring process. They demand new workflows. They collapse steps. Sometimes they even force you to rethink how you hire altogether. You might start out looking to replace one assessment platform and end up reshaping the entire funnel.

That’s not a problem. That’s the point.

Just be ready for it. Bring ops and IT in early and set expectations because this will require iteration and understanding. Unlike a standard tool, you can’t just plug a smart AI system in and watch it whirl!

Don’t Sell the Side Effects. Lead with the Impact

AI in hiring can drive secondary gains: better candidate experience, improved diversity, stronger quality-of-hire. All of which are hugely important. But don’t lead with those… 

Your pitch needs to start with hard ROI:

• Headcount reduction
• Attrition cost savings
• Ad spend cuts

One of the biggest learnings I’ve seen is that soft savings like “saving recruiter time” don’t cut it anymore. Time is only money if it results in fewer people, less spend, or faster revenue.

If you’re not drawing a straight line to business impact, you’re not ready to pitch a new AI tool.

Final Thought

A LOT of companies spent a LOT of money on recruitment tech in 2021–2022. And, the unfortunate truth is: not all of it delivered. Failure fatigue is absolutely a real thing.

So there’s skepticism in the air. People are wary of being burned again. If you want to win hearts and minds, you can’t bring a solution in search of a problem. You need to show – not tell – that this is different. That it’s targeted. That it’s strategic.

While there’s no perfect playbook, there are patterns emerging. Leaders who succeed at rolling out AI in hiring:

• Anchor to clear, high-cost problems
• Use regulation as a roadmap
• Frame it smartly
• Pilot first, scale second
• Get creative with funding
• Demand hard ROI
• Treat the tool like a teammate, not a toy

There’s no shortcut. But if you get this right, the rewards are massive. The right AI tool won’t just automate. It will amplify. And that, in this hiring market, is the edge we’re all chasing.

Also, a huge thanks to Barb Hyman, Erin Scruggs, Meghan Rhatigan, and Matthew Howe for the insights that helped frame this post.