Donnie Dewey

Nine Years in the Building

Dewey grew up inside Kforce, and he means that almost literally. He spent nine years in the building before he ever got a chance to go out to the field and work, which gave him an unusually wide view of how a staffing company actually functions: the systems, the operations, the people who never appear on a client call but determine whether the client call goes well.

It also gave him a reference point for hype cycles. Job boards arrived in 2000, and the rumor mill declared that staffing was on its way out. "It's 26 years later and here we are," he says. That memory shapes how he reads the current moment. He has watched an industry survive its own predicted extinction once already, and he is skeptical of anyone who thinks a new tool alone will decide the outcome.

Old Thinking Applied to New Tools

Dewey recently wrote a ten-page white paper on artificial intelligence in talent, and his central complaint is not that the technology is overrated. It is that the conversation around it is too small. Most of the discussion he hears is about tasks: what AI can do for a recruiter's inbox, a screening step, a follow-up sequence. "I've seen a lot of kind of old thinking applied to AI," he says, "versus rethinking or reframing what this really looks like on a global scale."

His own team is building toward something different: a global talent ecosystem with the capacity to provide services and talent in effectively any part of the world, and an always-on architecture in which there is no productivity debt because AI is managing specific functions of candidate inflow around the clock. The value he chases is not automation but signal. Recruiting has been so KPI oriented for so long, he argues, that the genuinely useful indicators have been hidden inside the process: engagement, enrichment, how often a person returns to the ecosystem. Read those correctly and you get talent that is ready to deploy on demand.

The organization made a serious bet on this two years ago, purchasing an AI-based platform, and Dewey is candid about how much his own understanding has moved in the 24 months since. When they first bought the company, he did not see self-service the way he sees it now. Roughly 60 demos of different AI tools later, plus the reps and iterations of actually operating the thing, the picture sharpened. He now believes the winners will be the ones who solve the orchestration layer early and correctly. "They're going to jump way out in front," he says. For everyone else, bolting tools on and wiring them back to an ATS and a CRM, "it's going to be a long road."

Reversing the Experience

Dewey explains what he is building with an analogy about two people in a Starbucks. He casts himself as the older one, updating a resume and firing applications at everything that looks interesting. The second person, younger, has the entire process in their hand, on their phone, receiving well matched roles as they surface. "If they want to engage on that opportunity or hear more, all they have to do is click a button."

The point is inversion. Today a candidate can open a posting, see thousands of applicants and hundreds more from the past day, and watch the status sit at actively reviewing. "That is not going to lead to experience where people want to come back," he says. Speed, velocity and quality still matter to him, but the word he keeps returning to is precision: efficiency for the people managing deployment inside his organization, and genuine self-service power for the people looking for work.

Which is why he resists framing the future as a hiring problem at all. "It's not about filling jobs," he says. Those focusing on jobs first will not be positioned to win. "I think it's about building systems that make jobs and talent finding each other a lot easier than it used to be." He offers this with characteristic understatement, unsure he is qualified to give advice yet, though he says he is trying. For a leader 23 years into the industry, still doing 60 demos and still reframing his own assumptions, the humility reads less like modesty than method.