One thing I’ve been doing inside the PromptMates community is pulling Talent Acquisition leaders aside and asking how they’re approaching AI.
Not the LinkedIn version.
Not the conference version.
The real version.
What are they learning? What are they experimenting with? What is actually pushing them to invest time in this space?
Recently, I spoke with a Talent Acquisition Operations leader at Lightspeed Commerce, and one answer stuck with me.
When I asked what motivated her to start investing time in AI, she said:
“The decision mostly made itself.”
A lot of people assume AI adoption starts with curiosity, pressure from leadership, or fear of getting left behind. In her case, it started with the work sitting directly in front of her.
She was in the middle of evaluating ATS platforms, and as she worked through that process, she realized AI wasn’t a future roadmap discussion anymore. It was already embedded into the products, workflows, and decisioning capabilities she was comparing across the systems she was responsible for choosing.
If you’re accountable for recruiting systems, you don’t really get the luxury of ignoring the technology shaping them.
That’s what stood out most.
She didn’t wake up one day and decide she wanted to become an AI expert. Understanding how AI showed up inside the platforms she was responsible for became part of doing her job well.
I think that’s where a lot of recruiting teams are heading.
Many TA leaders are still waiting for the “perfect” entry point into AI. They’re looking for the right course, the right tool, or the right amount of free time.
But the entry point may already exist.
It might be your next ATS evaluation.
It might be a reporting challenge.
It might be candidate communication.
It might be application volume.
The work itself is increasingly forcing the conversation.
Another thing that stood out was where she sees opportunity. Most AI discussions in recruiting eventually land on sourcing. She took a different view.
The areas she kept coming back to were reporting, interview feedback, scorecards, candidate follow-up, and making data more accessible to recruiters and leaders.
In other words, the operational middle: the work that keeps recruiting moving but rarely gets attention.
She also pointed to something many recruiters are already feeling: volume.
A single role can attract hundreds of applicants. Some are great fits. Some are not. Increasingly, some may be AI-generated, misrepresenting experience, or simply fraudulent bad actors. Her view wasn’t that AI should make hiring decisions. It was that AI can help recruiters cut through the noise faster so they can spend more time on the conversations that matter.
The same thinking showed up when she talked about candidate experience.
One of the biggest complaints candidates have is simple: nobody follows up.
Recruiters get busy. Priorities shift. Things fall through the cracks.
AI may not solve candidate experience on its own, but it can help ensure communication happens consistently and fewer candidates are left wondering what happened.
Toward the end of our conversation, I asked what advice she would give someone who knows they need to upskill but doesn’t know where to start.
Her answer was simple: pick one task you do every week and experiment.
Don’t try to master AI. Don’t try to learn every tool. Take a recurring piece of work and see if AI can help you do it faster, better, or differently.
The feedback you get from that process is often more valuable than another webinar or another article.
That might have been the biggest takeaway from the conversation.
The leaders making progress with AI aren’t necessarily the ones chasing every new tool.
They’re the ones paying attention to the work in front of them, and the systems shaping that work.
And in many cases, the decision to learn AI isn’t separate from the work.
It shows up inside the ATS decisions, workflow design, and platform choices already happening on the ground.
Thanks, Lolwa, for sharing your insights and journey with me!