Both American job candidates and talent acquisition professionals find themselves between a rock and a hard place as 2026 kicks off. According to new LinkedIn data, more than half of professionals surveyed report being on the hunt for a new job this year, but about 80% say they’re unprepared. At the same time, two-thirds of talent acquisition pros are struggling to find qualified candidates, even as the vast majority say they’re facing greater pressure to do so.

AI could ease friction on both sides of the equation, according to LinkedIn’s report out this week, based on surveys of 19,000 consumers and more than 6,500 HR professionals, conducted late last year.

Unsurprisingly, AI is squarely on the radar of TA professionals. LinkedIn found that, among those already tapping the tech for recruiting and hiring, nearly 60% say it’s helped them find better-fit candidates, and more than 90% anticipate increasing their use of AI this year. However, gaps often exist between intention and operationalization.

“TA—like all functions in every business—is just struggling to absorb the innovation that AI is presenting to us,” talent expert Hung Lee of Recruiting Brainfood said in a recent conversation with Erin Scruggs, vice president and head of global talent acquisition at LinkedIn. “That’s one of the biggest challenges that we’ve got in 2026: How do we not get drowned in all of the new stuff? How do we identify the bits that we think are going to make the biggest impact—quickest—in amongst all the innovation?”

Focus on the problem, not the solution

TA is often under pressure from leadership to lean into the technologies that will drive efficiencies. However, Scruggs says it’s more effective to prioritize tools with a demonstrable impact on the quality of the hiring experience, as efficiencies will follow.

When it comes to where to look for those opportunities for AI integration, Scruggs recommends thinking small. At LinkedIn, TA teams are in “heavy pilot on 100 different things.”

“I have in past lives gone very big on technology implementations” with multi-year plans, she says, only to find gaps between the tech’s promise and the outcomes.

Now, at LinkedIn, she advocates for “small, controlled pilots.”

“Instead of going big on one initiative or one AI promise, we are really dissecting every step of our process and figuring out where AI can have the most impact,” she says.

That work needs to be rooted in a deep understanding of the business problems the team is hoping tech can solve. Being “much more in love with the problem than the solution that’s being offered,” she says, has given LinkedIn’s TA function more confidence in where it should scale.

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