The Human Element

The Human Element, presented by Wisq, is a podcast hosted by Barb Bidan where CHROs and senior HR leaders share candid stories and practical perspectives on how AI and innovation are shaping the future of HR. In this episode, Barb sits down with Fred Skinner, CHRO at Eversana, to talk about what it takes to implement AI thoughtfully in a highly regulated environment, where to start, and how to measure whether it is actually working. Subscribe today.

Fred Skinner is the first to tell you that Eversana is not the most advanced AI shop in the room. "Spoiler alert to anybody listening, we're not as far advanced as probably some are. I think we're still a novice in the space." What makes that admission worth paying attention to is what comes right after it: his team saved over 1,000 hours in five months in talent acquisition alone. In a highly regulated life sciences commercialization company hiring two to three thousand people a year, that is a meaningful number, achieved without sacrificing quality of hire.

Skinner leads people strategy at Eversana, a company that helps life sciences organizations bring therapies to market and scale their commercial operations. The regulated environment that might seem like a constraint has turned out to be an advantage.

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Clear the Clutter: Why Project Windex Comes Before AI

Before any AI tool could deliver value, Skinner's team had to confront something unglamorous: their data was a mess. Years of acquisitions from across the globe had left Eversana with inherited tech debt, fragmented processes, and inconsistent data. "We've been very acquisitive in our history and from all over the globe. So we've inherited a lot of tech debt and just data messes and processes."

Their solution was an internal initiative they called Project Windex, a roughly two-year effort focused on adding clarity and cleaning to their own data and processes before layering any AI on top. "All of that work really is fundamental. It's just table stakes to what you need to do before you get into some of the deep AI things."

For HR leaders in regulated industries, the rigor imposed by external compliance bodies turns out to be an internal asset. The documentation standards and audit trails required for clients become the same foundation that AI needs to function reliably. Getting your own house in order is not a detour from AI adoption. It is the prerequisite.

Start with the Obvious Wins: Internal Processes and Talent Acquisition

With cleaner data in place, Eversana focused its initial AI efforts on two areas: automating internal HR processes and improving talent acquisition. Both were chosen because they were, as Skinner puts it, the easiest places to start, not easy to execute, but clear in their opportunity and measurable in their results.

On the internal process side, the focus was identifying repetitive tasks ripe for automation. Even small things add up. "How do you capture notes and then how do you thematically look at those notes and then have takeaways?" Automating those micro-tasks creates time that compounds across a team.

The bigger impact came in talent acquisition. Eversana implemented an AI-driven system that takes a job description for a specific role in a specific business unit, identifies the key criteria, and generates a tailored introduction to the organization for candidates. AI-powered video interviews then assess candidate strengths before anyone on the hiring team has to spend time reviewing. "It removes bias and does all that stuff. But that alone has created so much scale in our talent acquisition team that it just makes it more effective."

The results in the first five months: more than 1,000 hours saved just in combing through, searching, and validating who advances to the next round. For a team hiring two to three thousand people a year, that is capacity that gets redirected to higher-value work.

How to Measure Success: Productivity and Quality

Skinner's metric framework for judging an AI pilot is deliberately simple. "It's productivity and quality. If I can look at the productivity gain that we get and it's still valid and has the quality that we need, it's a success."

On the quality of hire side, Skinner is honest about where things stand. His read is that it has held steady, though he is candid that he would need to verify the numbers. What he is confident about is the direction. "My prediction would be as we start to get better at using this tool that the quality of hire will continue to increase as well." For now, a massive efficiency gain with quality holding flat is the win, and he is taking it.

This points to something worth internalizing for any HR leader evaluating AI. The value used to be in finding the right insight. Now it is in getting to the insight fast and trusting it enough to act on it. "You still have to pressure test it, but that's where the value is. Because then you can act on it." Efficiency is not a consolation prize while you wait for more sophisticated outcomes. It is what makes the more sophisticated outcomes possible.

Basic Change Management Is Still the Engine of Adoption

Even with a compelling proof point like 1,000 hours saved, adoption does not happen automatically. Skinner is direct about the biggest barrier he encounters: "You hear forums like this and you're like, okay, that sounds great. And then it's like, how do I actually do that?"

Lowering the barrier to entry is everything. Give people a specific tool, a specific set of steps, and a specific prompt to try. "Here's the tool that you can have. Here's, click on this, this, this, and this. Here's the prompt to do, and you'll be blown away." Once someone sees a four-hour task done in 30 seconds, they are sold.

Beyond that initial hook, Eversana runs regular sessions focused on proof points rather than theory. "We did this, and this is how we did it, and here's the result that we found from it." In some parts of the organization, the adoption was organic, with a few early enthusiasts on the talent acquisition team pulling their colleagues along naturally. In others, it required more deliberate work instructions and structured learning. Both paths led to the same place.

The misconception Skinner pushes back on most directly is that AI will take jobs. His version of the reframe is blunter than most: "We'll take your job if you don't start adopting it and figuring it out." His advice for leaders just starting out is equally direct. Everything is a grand experiment. Try something, measure it, and build from there. "I promise you, you'll just start to gain momentum, and it'll become easier and easier and easier."

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