Anna Prorock

From the Therapy Track to the Talent Function

The pivot happened in a university classroom. Prorock was a psychology major, oriented toward clinical work, when she took an HR class and realized how much of what fascinated her about human behavior was already unfolding inside companies. "There was so much psychology happening in business," she says. The realization reframed the whole plan. Rather than working with individuals one at a time, she could partner with organizations and help the people inside them.

She tested the theory the practical way, through an HR and recruiting internship, and fell in love with the work. That origin still shapes her practice. Recruiting, in her framing, is not a transactional exercise in filling requisitions. It is applied psychology: understanding what motivates a candidate, what a hiring team actually needs, and how to align the two before either side has fully articulated it. The discipline she once imagined using in a therapist's office turned out to be just as useful across a hiring table.

A Behavioral Read on the AI Moment

On artificial intelligence, Prorock is neither evangelist nor skeptic. She is, characteristically, an observer of behavior. The first shift she names is not technological at all: everyone is talking about it. Every team is asking the same set of questions. How do we add this to our tech stack? How do we automate? How do we make things more efficient? And underneath all of it, the question people ask less comfortably: what does this mean for the recruiter in the future?

Her answer is a matter of placement rather than replacement. "Where can AI help us most to allow our talent acquisition teams to be most strategic?" she asks. She is explicit about not discounting the value recruiters bring or the human component that hiring still requires. The outcome she keeps returning to is getting the right talent into the right companies and the right seats as quickly as possible, with technology serving that end rather than obscuring it.

She has watched implementations succeed and fail, and the pattern is consistent. The organizations getting it right are the ones taking time to evaluate properly, partnering with their teams, and bringing people along on the journey so everyone understands why AI is being applied to, say, an application review process, and what that will mean for their work. Then they measure. Prorock is direct that nobody gets it right at the very beginning, and she argues that honest feedback to AI partners is precisely how the tools improve for everyone in the space. Rigor about ROI is not a brake on innovation. It is the mechanism that makes innovation stick.

Slow Down to Move Faster

Prorock's counsel to other talent leaders navigating this period compresses into one counterintuitive line: take a step back, move a little slower, and eventually move faster. In a market saturated with buzz and tools, she urges leaders to do the research and trial several options before committing. There is no one size fits all answer, she notes, because every company has different needs at different points in the recruiting process.

The failure mode she has observed is speed without foundation. Move too quickly, leave the people behind, skip the technical infrastructure, and the deployment will not work. Worse, the organization may then conclude that AI cannot deliver what was promised, when the real problem was the rollout. "But I think if you do it the right way, it can be really powerful," she says, and that conditional is doing deliberate work. The power is real. It is simply earned rather than purchased.