Leading at the Intersection of People and Technology: Turning Growth Ambition into Operational Reality

In this conversation, Jackson shares how people centered leadership is reshaping operations, why mindset now matters more than technical pedigree, and how organizations can move beyond early AI experimentation to achieve real, sustainable impact—by designing systems that work with people, not around them.

Post-Covid, the role has changed so much. Previously the scope was pretty squarely around the technical leadership of HR policies, benefits, hiring—the classic HR areas—and now so much of this role has become about leadership, development, culture and systems change. The change management component of it is much heavier than it used to be. From a leadership perspective the role has really broadened to require enterprise-wide thinking and understanding.

Secondly, the rise of agentic AI is driving a meaningful connection between IT teams and HR‑centered roles. Responsibilities are broadening across functions, which is such a shift from what the role was even five years ago.

I frequently reference the example of Moderna, which was one of the very first organizations to combine engineering with the digital experience, and make it overseen in one role, the chief people and digital officer. We based our approach on their model, but we decided to broaden it further by including both engineering and IT.

I believe HR leadership is critical, regardless of whether technology formally sits under the CHRO. I took on AI because I was genuinely interested in it, and since I already oversaw IT, the alignment made sense. I’m not a technologist by training—and that’s actually an advantage. It allows me to stay focused on culture, strategy, and translating AI enablement into real value for our teams. That said, this model only works with a strong technology partner. Our Head of Technology excels at bridging deep technical expertise with business acumen. Without that ability to translate technology into practical application, having tech under this role wouldn’t be effective—we’d struggle to turn capability into impact.

At ButcherBox, we started by forming a task force to decide whether to develop technology internally or purchase it externally, and we chose to build it ourselves. This taskforce (which is made up of our Head of Data Science, a member of our Engineering team, a project manager and me) has played a crucial role in peer coaching and training. By consulting with business units about their biggest frustrations, the taskforce worked to address those specific problems. Sparking curiosity among employees was our primary priority, and as soon as we started to demonstrate impact and stress the positioning—to make our employees lives easier – we got traction.

We also collaborate with expert partners and draw inspiration from what other companies are doing. As examples: we partner closely with BetterUp and their lab to further enhance our strategies; and the inspiration for our AI strategy came from Alex Dogliotti, an exceptional COO and CHRO at Mindvalley. He enabled every employee to build their own agent, including those who didn’t have a technical background to accelerate understanding and comfort with the capabilities and how it can help reduce friction points. I loved the practicality of this.

I am a big believer in getting outside perspectives. So much of making progress is about finding like-minded people who can stay optimistic while solving problems and learning from them. I try to spend time collecting interesting ideas, and I am a big believer that you don’t always need to have an original idea—you just need to be able to think critically, explore existing concepts and apply what is appropriate to your own context. This approach has inspired several of our business pivots.

AI adoption often plateaus because it dramatically reshapes how work gets done, and many organizations aren’t fully ready to acknowledge that it requires a completely different way of working. While there’s plenty of experimentation and piloting underway, far fewer teams are redesigning systems end-to-end to truly capture the efficiencies AI can unlock.

Take Microsoft as an example. If you learn the full suite of tools, your workflow looks very different. But when people adopt just one tool in isolation, it can feel like little more than an extra step layered onto existing processes. The real value comes from rethinking the workflow itself, not simply adding AI on top of it.

That’s why learning environment design matters so much. Employees need space to step back, adapt, and build new habits, but creating that space is hard in fast-paced organizations where capacity is already stretched.

We’re starting to address this by experimenting with monthly, nomeeting learning sessions. The first half is dedicated to instruction, and the second half to hands-on application. We also block 30 minutes before and after the session to make attendance truly feasible. Creating distance from the daily grind seems to unlock better conditions for experimentation, learning, and adoption. Changing habits, not just deploying tools, is such an important part of making AI stick.

For me, it always comes back to the same question: How do you build the right operational processes in service of what’s best for people?

Not because you want to add another layer of red tape. Not because you want a manager to take 19 rigid steps to do the thing. But how do you design systems that actually make employees’ lives easier?

A lot of that mindset comes from my earlier work in school operations, and many people on my team came from similar experiences. They really understand what it means to work in service of others and the end user. That perspective changes how you design everything.  

Mindset is 100% where we are focused. We screen for it in hiring and continue to cultivate and coach it once people join us. You can have an extremely technical resume, but during the interview process, if we don’t feel the mindset is right, we won’t move forward. That can be controversial, but it’s a line we hold. 

Our core values have been the same since we started—they’re not just on the wall, they are built into everything we do, from training to performance management and feedback. We use rubrics and real examples, talking about it as “baked in, not sprinkled on.” People give feedback directly tied to those values; that’s what we live and breathe. That’s a core component of how we assess mindset. 

On top of that, a growth mindset is critical. We look for curiosity and optimism: How resilient are you when things get hard? Can you keep going, reframe, and tackle something tough rather than shutting down? 

The last element, which we have not yet nailed, is the ability to lead through constant change. So much of what we see in the world is that every day feels different than the one that came before. Whether you’re using a VUCA framework or BANI, the question is how to hire people capable of effectively leading through this ongoing change—especially change they haven’t seen before, and when there isn’t someone internally who can say, “Oh, I can help you do this.” Leaders today have to figure it out themselves. We’ve started experimenting with a framework for hiring to capture this new essential mindset. 

The role is changing in really important ways. There’s so much more emphasis now on mindset, adaptability, and how leaders show up for people during uncertainty. That work, helping leaders navigate change while staying grounded in values and humanity, is exactly where HR and people leadership can have the greatest impact.