The Future of Hiring: Where AI Ends and Leadership Begins

In this episode of Building People, Companies and Careers, presented by AccruePartners, host Ben Brandon sits down with Nathan Sloan, Principal at Deloitte, for a candid conversation about AI, talent strategy, and how organizations across Charlotte and the Carolinas can prepare for the future of work.

Nathan leads within Deloitte's human capital consulting practice and helps the firm think about talent management horizontally — matching people's skills to the work, rather than to where they happen to sit. Below, he unpacks why so many AI efforts stall, how workforce planning has to evolve, and what separates companies that are genuinely skills-based from those that have only changed the labels.

Key Takeaways

• Beware "pilot-itis." The biggest AI mistake leaders make is running scattered pilots without asking how the work itself should be redesigned.
• Plan for capabilities, not just headcount — and start thinking about how to workforce-plan for AI agents, not only people.
• Build a "stop doing" list. When AI creates capacity, the instinct is to pile more on. The higher-value move is deciding what to pause or retire.
• Talent athleticism means train, perform, recover — adaptability shouldn't mean permanent exhaustion.
• Skills-based is a system, not a label. It only counts when skills drive hiring, learning, performance, and workforce planning decisions.
• Keep humans accountable for hiring decisions. AI should assist and augment, not own the call.
• Don't stop hiring junior talent. If you cut entry-level roles, you have to solve for the experience gap you're creating.

Ben: We always like to start with a classic interview question. Tell me about yourself.

Nathan: First of all, happy to be here — we have a tremendous partnership, so I appreciate the invitation back. As you mentioned, I'm a Principal at Deloitte, which means we wear a lot of different hats. Technically I sit in our human capital consulting practice as one of the leaders there, serving clients in that space. But internally I also have a role helping us figure out how we look at talent management horizontally within the practice. We have P&L owners who are vertical within the practice, and when we deploy professionals to different projects, we look at the skills they have versus where they sit. Part of my role is helping us continually test and pilot that approach inside the firm.

Ben: Everyone's talking about AI transforming the workplace. From what you see, what are leaders getting wrong, and what should they pay attention to instead?

Nathan: Honestly, the biggest challenge companies have is that they get stuck in a kind of "pilot-itis." They run a bunch of different pilots without a lot of control. You have to strike a balance between driving innovation and reining it in — but the real opportunity is understanding how AI actually changes the way work is designed.

There's real pressure from shareholders and external stakeholders saying, "Show me how AI improves the bottom line. How many people can we cut based on how AI is disrupting the work?" What we've found is far more powerful is understanding how AI plus the work can help people get better — how it's incremental to what they already do. If you just sit there and demand more from people, you create more stress in the system, you risk mistrust with your employees, and you head toward burnout and disengagement.

So we need to balance support for innovation with real intentionality — and HR has a big role to play there. Think about how AI companies deploy "forward-deployed engineers" who land at a client site to help solve a problem with the technology. HR has an opportunity to do something similar with what you might call work-design engineers — people who understand how the work itself will shift in the face of AI. That's a big opportunity.

Ben: That's super interesting. We hear it from our clients too — everyone's interested in deploying AI and pointing to the ROI, and that ROI has been a little slower to materialize early on. But even at a smaller company like AccruePartners, seeing how far our own AI usage has come from when we started, it's moving so fast that you have to know what's happening out there and make the investment.

Nathan: One thing I'd add is that we've seen more uptake when you encourage employees to figure out how they can bring AI into their daily work — just experimenting a little with tasks they'd normally do manually. That early-adopter mindset matters, and part of the job is helping leaders see where the opportunities are and encouraging their teams to explore them.

Ben: When roles and skills are changing faster than most planning cycles, how does workforce planning need to change at the executive level?

Nathan: We were just having this conversation internally yesterday. Historically, a lot of companies still treat workforce planning as current-state, backward-looking headcount planning. Strategic workforce planning is about more than headcount — it's about capabilities and the work itself. And there's even an opportunity to plan for agents. How do you workforce-plan for AI agents? Most people aren't thinking about that today, so there's a huge opportunity there.

The other piece is that a lot of organizations focus only on the data they already have internally. The world is moving so fast — skills and capabilities are changing so quickly — that there's a real need to ingest external data too. There are platforms that help you sense what's hot in the market and which skills companies are hiring for, and factoring that into your planning is key. Some of the partners that have traditionally focused on org design and org visualization have moved into workforce-planning capabilities, so tying those pieces together matters, instead of just staring at org charts.

Ben: AI is supposed to make us more productive, but it often just raises expectations. How can leaders actually help teams prioritize instead of piling more on?

Nathan: There's such an initial expectation of, "Well, I'll just demand more, because you have AI tools now and you should be able to do more with less." A lot of the pinch point ends up being with managers. So we've seen companies focus on building manager capability — making sure leaders understand their role in setting realistic expectations while also integrating AI into their own work. And a lot of this comes down to how you organize the work, putting people on stretch assignments that give them room to develop.

There's also a natural inclination to just pile more on and ask what people can do more of. An important counter-move is creating a list of things you can stop — things you don't need to do anymore. Managers and employees often gloss over that. They say, "Here's what else I can take on," instead of, "Here are the things I need to pause and stop so I can do better going forward."

Ben: There's a learning curve to adopting AI day-to-day, and then the question of how your role evolves. If you're used to hitting a transactional number, there may not be a bigger number to hit — but the role can evolve toward more analysis of what you're processing or building.

Ben: Something I've heard recently is about talent, athleticism, and adaptability. What does that really look like day to day, and how do companies build it without burning people out?

Nathan: If you think about the term "athlete," that's a strategy we take at Deloitte — we hire for best athletes, if you will. A lot of that comes down to people who can truly adapt their skills: they know how to frame a problem and solve a problem. It comes back to core human skills and judgment.

It's also about setting realistic guardrails for how people move within the organization. It can even show up in performance management — some of our clients are thinking about how to measure performance based a bit more on someone's ability to be agile and focus on self-development. Instead of being told to go do something, are they taking the initiative to understand how their skills need to adapt as the work changes, and then upskilling themselves, internally or externally?

If you go back to the sports analogy, we're not running a sprint twenty-four/seven. The same applies in organizations: you have to train, you have to perform, and then you need to recover. Being intentional about all three throughout the day and the week gives you sustainability — it drives engagement, and it drives retention.

Ben: That's a great nugget for candidates and clients tuning in. This is going to have to be embedded in the interview process — how do you uncover whether someone can adapt in an environment that's changing, maybe more unstructured than a clearly defined job? AI might change the very job they're interviewing for, and soon. So how do clients form questions to make sure talent aligns, and how do candidates develop stories about adapting to change in systems, technology, or scope?

Nathan: A good summary point: adaptability does not mean you need to be exhausted all the time. There's often an interpretation of, "You're constantly asking me to adapt or learn new skills" — and six months or a year later you're exhausted because you don't know where the expectations are. You need to maintain a good schedule and a clear plan for yourself.

Ben: A lot of companies say they're becoming skills-based. In your experience, what tells you a company is serious versus just changing the labels?

Nathan: A lot of our clients have focused on building a skills inventory. They may invest in the technology and the platform, but it's not actually driving decisions in the organization. In our mind, the skills-based organization is really a system-based approach. If you're truly embedding skills into hiring, learning, performance management, and workforce planning, then you're serious about it — and it drives mobility within the organization too.

Many companies are now looking at skills as hiring or promotion criteria. You may not even need a four-year degree to be good at a specific job, so focusing on the skills required to get the work done is getting embedded into those processes. The real shift is when you think about your talent as a true marketplace — internal or external — rather than just a framework or taxonomy of skills. Start embedding it into learning in the flow of work, and reward people for acquiring skills. Embedding it across the full talent life cycle says a lot.

Ben: It's a fascinating concept. I worked with a large Charlotte company a few years ago going through this same transformation toward a skills-based organization — clearly a heavy lift. Especially for, say, a retailer with a thousand locations, there may be high-performing people who are underemployed where they sit. Building out that inventory helps you retain and promote talent that could thrive at corporate headquarters or in an elevated role. I'd love to see that trickle down to more mid-market and smaller companies.

Ben: AI is playing a bigger role in hiring, from screening to assessments. Where does it genuinely add value, and where do humans still need to be firmly in the loop?

Nathan: AI was really first used in hiring — think about the manual tasks like summarizing notes and candidate interviews for hiring managers. There's a maturity model across the AI-and-hiring landscape: from AI-assisted, to AI-augmented, to truly driving decisions. The real opportunity is for humans to remain the ones accountable for certain decisions, with AI assisting them.

Ben: At our IT Power Breakfast, a CIO made a comment that stuck with me — that AI has become the "sexy term" used to get projects funded. I see some companies adopt AI tools built to source light-industrial or field employees, but internally it's sold as AI for all corporate functions. Then the business isn't getting the candidate flow it expected, and because they've already invested in the tool, talent acquisition and HR are told they have to use it rather than going outside. There's real misalignment in how AI sometimes gets applied to hiring. Any thoughts?

Nathan: That's a fair trap a lot of companies fall into, depending on who's making the decision to procure the technology and where AI is expected to support the process. Breaking down the real process flow — to understand where AI adds value and where it can actually cause harm — is going to be important. In hiring, the candidate experience may not be positive if they're interacting with AI rather than a person, so considering that matters too.

Ben: If you were advising a CEO right now, what's the single most important workforce capability they need to invest in over the next few years to stay competitive?

Nathan: That's the million-dollar question. It goes back to where we started: there's been so much focus on running AI pilots without truly thinking about how the work needs to shift and be redesigned. So working with functional leaders to help them understand their role in redesigning the work would be priority number one. Easier said than done, of course — a lot of organizations don't have that capability internally. This is a big opportunity for HR to step up and say, "We helped you build a job architecture and do job design; now let's take it down to the next level and talk about the work design itself, and how it needs to be redesigned."

Nathan: Another thing we hear is companies trying to figure out which jobs AI will replace — whether they'll need to hire junior staff in the future. Maybe that's the case in some industries and not others. But if you're not hiring as many entry-level or junior roles, how do you fill the experience gap? How do you develop people? Are you going to hire directly into a manager role, and how do you build those leadership capabilities and an understanding of the organization's culture? I'd caution organizations against concluding they simply won't need to hire junior people anymore.

Ben: That resonates. I started my career in public accounting, and I first felt this when so much first-year staff work got outsourced to global delivery centers — a real need given the cost pressures, especially in audit. As more first-year work moved out, I started asking, "How do you become a second-year associate if you never do the first-year work?" The Big Four have incredible training programs, which is part of why they attract great people — but it underscores that we have to train talent for today's jobs as well as the jobs AI will create. I read recently that something like a hundred new jobs will come out of AI, and none of them are roles we recruit for today.

Nathan: Right — and some of them may not even exist yet. That's the real opportunity. Even in our professional services business, it unlocks more ways to help clients. And think about the students in college right now: how do we keep communicating that it's valuable for them to join us?