Spend enough time on LinkedIn and you’ll start to think AI in recruiting only means one thing: sourcing candidates faster.
Every conversation seems to come back to resume screening, candidate matching, outreach automation, or fears that AI is making hiring decisions.
I had a conversation with Dan, a fellow Promptmates member and Talent Operations professional at Zapier, and what struck me most was what he didn’t focus on.
Dan works on the technical side of recruiting operations. Rather than spending his days sourcing candidates, he’s building internal tools, data pipelines, and AI-powered workflows that help recruiters access information and execute processes more efficiently.
When I asked where he’s seeing the most value from AI, sourcing wasn’t at the top of the list.
The Best AI Used Cases Aren’t Always Candidate Facing
One of the themes that came up repeatedly was that AI is often most valuable when it improves the infrastructure behind recruiting rather than the recruiting itself.
Dan described building internal systems that aggregate information from multiple sources into a single place. Instead of recruiters opening multiple platforms, running reports, pulling labor market data, and manually stitching everything together, they can ask questions conversationally and receive the information they need.
So the value isn’t that AI is making decisions. The value is that recruiters can access data faster and spend less time navigating systems.
When many people imagine AI in recruiting, they picture automation replacing judgment.
What Dan described was automation reducing friction.
The Most Interesting Use Case Wasn’t Sourcing
The example that stood out most to me involved something every recruiter understands: role kickoff.
When a new position opens, there is often a surprising amount of work before the first candidate is ever contacted.
Interview plans need to be created.
Question sets need to be drafted and approved.
Communication templates need to be built.
Hiring processes need to be documented.
Historically, much of that work starts from a blank page so Dan’s perspective was refreshingly practical.
If AI can take intake information and generate a first draft of those materials, recruiters aren’t starting from zero anymore. They’re starting 50% to 70% of the way there.
That doesn’t eliminate recruiter involvement but removes repetitive setup work so teams can spend more time on activities that require human judgment.
Interestingly, Dan said he would rather keep these workflow automation capabilities than lose them in favor of sourcing tools. That caught my attention because it runs counter to how many conversations about recruiting AI are framed today, especially the ones I have with engineering job seekers.
What Candidates Should Take Away From This
Another part of our discussion focused on candidate concerns around AI screening and automated rejection.
If you’ve spent any time talking to job seekers recently, you’ve probably heard some version of:
“I’m getting rejected because AI is being used to screen me out.“
Although Dan isn’t on the front-lines, his perspective was different.
His view was that recruiters have always relied on patterns to determine relevance. Before AI, that pattern matching happened manually. Today, technology helps surface information faster, but the fundamentals haven’t changed as much as many people assume.
Candidates still need to communicate a clear story.
What did you do?
What impact did you have?
What products, teams, or initiatives did you support?
Why does your experience align with the role you’re applying for?
Those questions mattered before AI and they matter now.
As Dan pointed out, he personally transitioned from running a CrossFit gym into a technology career. That transition required tailoring his story and helping employers understand how his experience connected to the opportunity in front of him.
Technology may change, but the need to communicate value clearly remains remarkably consistent.
The Skills Conversation Is Shifting
One observation from our discussion that stuck with me was how hiring requirements may continue to evolve.
Historically, companies often focused heavily on specific technologies, frameworks, or programming languages. Today, with AI accelerating learning and increasing access to information, adaptability may become increasingly valuable. The ability to learn quickly, navigate ambiguity, and solve new problems could matter as much as expertise in any individual tool.
That doesn’t mean technical skills are becoming irrelevant but it means the conversation may be shifting from “What language do you know?” toward “How quickly can you learn something new and apply it?”
My Takeaway
Before this conversation, I expected another discussion about sourcing, candidate matching, and resume screening.
Instead, I walked away thinking about operational efficiency.
The most compelling examples Dan shared were about removing friction.
Giving teams faster access to information.
Helping recruiters start 70% complete instead of 0%.
Reducing the time spent on repetitive setup work.
As AI adoption continues across talent acquisition, I suspect we’ll spend less time talking about whether AI can replace recruiters and more time talking about how it can improve the systems recruiters rely on every day.
Grateful to Dan and the Promptmates community for the conversation and the perspective.