Glenn Lindley

From Server Racks to Search

Lindley started his career as an IT engineer and infrastructure manager, responsible for servers and IT systems. When he relocated to London, he took the first IT job he could find: IT and server manager for a recruitment company. It was meant to be a technical role and nothing more.

Then the requests started. Help us interview these IT candidates. Help us find them. Help us find clients. He moved from candidate assessment into sourcing, and from sourcing candidates into business development and winning new customers. "Over the course of about six months, I went from being IT manager to IT recruitment," he says. He did not plan the pivot, but he has spent the years since compounding it, and the engineer's habit of proving a system works before trusting it has never left him.

An Engineer's Verdict on AI

Lindley's read on the current AI wave is refreshingly free of both panic and evangelism. His central point is blunt: a great deal of AI is not production ready. There are genuinely good tools, and he has implemented them himself. Scheduling can be automated. A lot can be done with sourcing. Process gets faster, candidate experience improves, costs come down. But in his view, none of the tools he has seen are at a stage where a company can responsibly cut a recruiting team by 50, 60, or 70 percent, which is roughly what a lot of CEOs are now expecting.

What he is watching play out across the industry is a quiet squeeze. Doing more with less has become the default assumption in leadership rooms: if I had five recruiters, surely now I need two, or three, plus AI. Scheduling gets automated, sourcing gets faster, and headcount math races ahead of what the technology can actually carry. The gap lands on the recruiters, who absorb it in more requisitions and more hiring manager relationships than any one person can properly serve.

There is a real irony in that, and Lindley names it precisely. "AI was meant to free us up to focus on the business, and focus on the relationships, and focus on the candidates," he says. What it has often delivered instead is smaller teams that are just as busy, unable to do the very things it was supposed to unlock. His estimate is that the industry is another six to twelve months away from a point where teams can genuinely be smaller without the quality of the work suffering. He believes the tools are improving. He simply refuses to pretend they have already arrived.

Test It, Then Say Only What You Can Prove

His advice to other talent leaders follows directly from that position, and it comes in two parts. The first is technical discipline. Stay ahead of the game. Evaluate tools as they come in, and evaluate them against what you actually need rather than what the label promises. "Don't just assume that because the labeling says this tool will make me no longer need a scheduler," he says. Test thoroughly. Run side by side comparisons. Do the full deep evaluation, then select. He has watched companies implement a platform on the strength of a pitch, declare a function obsolete, and discover the hard way that the capability was not there yet.

The second part is political courage, and it may be the more valuable of the two. When a CEO arrives asking for AI implementation, shorter time to hire, better quality of hire, and lower cost all at once, a recruiting leader has to be fluent enough in the technology to hold the line. That means understanding the tools well enough to say yes to what is real, to articulate the specific cost saving and time saving a platform will deliver, and to push back where the promise outruns the product. "Make sure that you're fully aware of what they're going to be able to do for the company before you over promise," he says.

It is a fitting philosophy from someone who spent a first career keeping systems running and knows exactly how a promise sounds when it fails in production. Lindley is not the leader who tells the board what it wants to hear. He is the one who runs the comparison first, and then tells them what is true.