The Great Skills Reshuffling: Adapting Compensation Models in the AI Era

Introduction

Nolan: Hello, and welcome to the Learning and Development podcast, sponsored by Infopro Learning. As always, I’m your host, Nolan Hout.

Joining me today is Charlie Franklin. Charlie started his career at Mercer, a leading global HR consulting firm, where he advised companies on compensation strategy, including many Fortune 500 companies in technology, finance, life sciences, and energy. After that, he joined Juniper Networks, working with executive compensation and people strategy.

He then moved to a company we all know—Workday—where he led HRM&A, and global mobility. After gaining all that experience, he co-founded a company called Compa, which we’re excited to learn more about. We’ll let him talk a bit more about what the company does.

Throughout this podcast, we’re going to focus not just on the technology, but on compensation—a topic we haven’t explored much in past episodes. I’m excited to dive into it, because as we all know, how people are compensated directly impacts how they show up and perform at work. If you can’t compensate your people correctly, everything else becomes a challenge.

With that, let’s get started. Charlie, welcome to the podcast.

Charlie: Nolan, thank you for having me, and thanks to everyone for listening today.

My name is Charlie Franklin, and I’m the Co-founder and CEO of Compa. A little about what we do—Compa is a compensation intelligence platform that serves some of the largest enterprises in the world. We provide real-time market data to help compensation professionals understand current trends and use that information to shape their compensation strategies.

We support companies in the Fortune 100 and Fortune 500 that require high precision and up-to-date insights to compete effectively for talent in today’s fast-moving market.

Inside Today’s Talent Landscape

Nolan: Thank you for that. I’d like to start off with a question you mentioned earlier—how to compete for talent in today’s marketplace. You’re deeply involved in this space and in constant conversation. What does the talent market look like right now?

Charlie: It’s a complex and fast-moving market. Certain areas, like AI and AI engineering, are extremely active. Other sectors are more volatile as companies reassess their talent needs and adjust to the broader economic uncertainty we’re all witnessing.

Compensation teams are navigating this by balancing three key factors: cost, competitiveness, and fairness. Which of these takes priority can shift depending on the company’s strategy, the specific business unit in focus, and the market they’re hiring in. It’s a highly dynamic environment—one that has been particularly challenging over the past five years and continues to evolve rapidly in 2025.

Impact of AI on Hiring and HR Tech

Nolan: What are your thoughts on this? You mentioned that the hottest talent markets right now are in AI and AI tech. I think cybersecurity is also gaining momentum. There’s a lot of talk about AI eliminating jobs, but I’ve been saying that AI is going to be a leveler for many organizations. That’s why so many companies are rushing to adopt it—because it dramatically shortens time to market.

I was speaking with someone who started an LMS company. He attended a Silicon Valley startup and said he felt bad for the entrepreneurs there. These kids had been working on something for two years, excited to show it off. He turned to me and said, “I could build that in two weeks using AI now.” That’s how much things have changed.

If we believe technology is becoming more accessible through AI, then talent becomes the true competitive advantage. Would you agree?

Charlie: I would absolutely agree—and I’d add that it’s only a leveler for companies that embrace it quickly. We’ve seen a noticeable shift in HR tech over just the past six to twelve months. A year ago, talking about AI in HR tech felt risky. People were unsure about its safety, security, and privacy.

Now, in many cases, it’s practically a mandate. Some companies are explicitly saying, “No new headcount until you’ve fully adopted or experimented with an AI solution.” Teams that are proactively exploring AI—not just to boost productivity, but to fundamentally change how they work—are going to find new ways of operating that achieve business goals in unexpected, nonlinear ways.

Evolving Compensation Strategies

Nolan: In a world where technology is changing rapidly and the demands on our workforce are evolving just as quickly, how are leading companies adapting their compensation and hiring models to attract and retain top talent?

Charlie: I’ll answer that in two ways, as they cover different topics.

First, compensation professionals are adjusting their strategies specifically to compete for AI engineering talent. That’s a unique challenge—figuring out how to deploy a compensation strategy in a difficult, zero-sum talent market to ensure the company can effectively navigate this tech wave. But I think your question is more about how the actual practice of compensation is changing for professionals in the field.

Traditionally, compensation analysts spend a lot of time in spreadsheets, crunching numbers. One of our partners at Roblox, Supriya Bahri, put it well—she said the role is shifting from “compensation analyst” to “compensation scientist.”

To put it simply, if today’s analyst is writing VLOOKUPs in Excel, tomorrow they’ll be managing AI agents to perform that analysis.

And it’s important to understand that compensation professionals are essentially asset managers, responsible for billions of dollars in workforce spending. These are small, highly leveraged teams that have an outsized impact on a company’s largest expense: its people.

These are highly leveraged teams with an outsized impact on the company’s largest line of expense. Finding ways to scale their impact yields significant returns—both in day-to-day operations and in the overall business outcomes.

Nolan: Charlie, if you don’t mind, I’d like to dig a little deeper into the fundamentals of compensation. Many of our listeners are in leadership positions. They manage teams, handle headcount, and often face tough decisions around raises and promotions.

So, how can a company better leverage compensation experts and data? For example, if I’m the Chief Learning Officer at a company like Pepsi, with thousands of employees, how do I begin using compensation data—or even tools like Compa—to better manage one of my biggest expenses: our people?

Charlie: Absolutely. Compensation touches every part of the workforce and the employee experience. It influences decisions around hiring, promotions, and career paths—typically guided by the career architecture developed by compensation teams.

It’s integrated across various functions—whether in learning, talent management, finance, or talent acquisition. For a learning leader, the key is to understand how market conditions are shifting for the most critical skill sets you’re trying to attract or grow internally.

Then, it’s important to examine how performance connects to compensation philosophy and outcomes. For example, if you’re a pay-for-performance organization and use a standard 1-to-5 talent rating system or a nine-box framework, close collaboration with compensation is vital.

You need to determine: What outcomes are we rewarding through performance? Are we recognizing this through base salary or incentives? What behaviors does our compensation model encourage? These are the types of questions that require input not only from the compensation team but also from market data to make informed decisions.

Nolan: Yeah, and I would assume that’s the case, because the landscape around compensation has changed significantly. You’ve likely been at the forefront of some of the most difficult conversations. I can think of my own experiences—like during the shift to remote work—where we all had to navigate complex discussions. For example, John is in San Francisco, so he’s on a higher pay scale. Julie, on the other hand, is in Lincoln, Nebraska. No offense to Lincoln, but the cost of living is likely lower there.

You’ve been right in the middle of all this. What are the trends you see coming this year, next year, and beyond? What are the challenges we should be preparing for—especially related to the workforce—some of which we may not have fully solved yet?

Charlie: Yeah, compensation has always been an interesting field for those of us in it. But over the past five years, the profession has faced multiple, simultaneous disruptions that challenge many long-standing principles. Remote work is a great example. Traditionally, the cost of labor—the wages we pay people—has closely tracked with the cost of living. For instance, how much it costs to live in Lincoln, Nebraska versus San Francisco. But we’re seeing that relationship shift in some places.

We recently published an article showing that many parts of California are now as expensive as San Francisco. Historically, everyone expected to pay more for talent in Silicon Valley.
While the cost of living is lower in other areas of California, the cost of labor—what companies are paying for talent—in places like San Diego, Los Angeles, and Santa Barbara has caught up to the Bay Area. That’s a micro example of the kinds of geographic shifts we’re seeing as the world moved toward remote work and is now pulling back. It’s forcing companies to rethink how they group cities into geo tiers and develop compensation strategies accordingly.

International compensation markets have also been unusual. We’re seeing rising costs in historically lower-cost markets like India. And there are other examples too. Along with that, we’ve seen a rapid expansion of pay transparency laws since 2021. Pay equity became a major focus, though it has pulled back somewhat in the past six months—but it remains institutionalized within most companies. Inflation has changed how people view pay. The talent shortages—remember the Great Resignation? —reshaped our compensation philosophy.

And of course, the unusual economic conditions we’re in now—no one’s officially calling it a recession, but between layoffs and tariffs, it’s become harder to plan effectively. All of these disruptions have thrust compensation into the spotlight. What was once considered a dusty corner of HR is now central to business strategy. We’re rethinking core compensation principles. The companies adapting the fastest are pleasing their CFOs with better cost control, competing more effectively for talent, and offering fairer employee experiences. It’s all about speed now.

Compensation for On-Demand Talent

Nolan: Speaking of speed, Charlie, that’s actually how we got connected. I believe there was a Forbes article I wrote about how skills, talent, and labor are all being reshuffled — and that it’s increasingly about speed and momentum.

One of the recurring challenges I hear from leaders is this: they want to hire top talent — say, three people at $120,000 a year each — but they can’t even get a year-long commitment from their teams. In today’s volatile market, hiring a full-time resource for a year has become a risky proposition.

Are you seeing anything from a compensation model perspective that helps organizations better leverage on-demand talent, flexible staffing, contractors, or vendors?

Charlie: Yes, absolutely. What we’re seeing in practice is that compensation teams within HR are now spending significantly more time working closely with their counterparts in finance.

There has always been collaboration when it comes to workforce planning, but as the reliability of forecasts and strategic plans decreases in this uncertain market, those relationships have become much more essential.

Teams are now working together more frequently to replan and make decisions in shorter cycles. Where compensation used to operate somewhat independently — focused on spreadsheets and internal data — it’s now becoming a strategic partner to talent acquisition, learning and development, and finance.

As a result, compensation professionals are increasingly required to be strong communicators, lead change management efforts, and even manage internal perception and communication challenges.

This shift is fundamentally changing how compensation work is practiced, and I believe it’s a positive evolution — one that’s long overdue and much needed for modern organizations.

AI in Compensation Intelligence

Nolan: And speaking of that space, you talked a lot about how AI is having a macro impact on everything you do. But within the compensation world—the actual day-to-day jobs of these professionals—how is AI transforming your work? For example, how is Compa using AI to support some of the challenges created by AI? It’s starting to feel like Groundhog Day—you’re stuck in a feedback loop.

Charlie: Yes, it’s like the bots are talking to each other. Compa is a compensation intelligence company. Our mission is to connect and analyze all the world’s compensation data to power smarter decisions.

AI gives compensation teams the ability to scale their insights across many more decisions. At a company with, say, 100,000 employees, pay decisions total billions of dollars and are made across hundreds or thousands of individual cases—whether it’s a recruiter making an offer or a manager approving a promotion.

Before AI, compensation was managed with broad guardrails—things like salary ranges with wide spreads. For example, as long as you’re paying between $80,000 and $120,000, you’re within range.

But AI and real-time data now allow us to manage compensation with far greater precision—aligning pay with the market, ensuring performance-based rewards, and adjusting costs for specific, high-value skills.

Nolan: Makes sense.

Charlie: AI removes the administrative complexity that used to prevent this level of detail. Put simply, if compensation teams previously influenced 10 to 15% of pay decisions annually, AI now gives them the opportunity to influence nearly 100%. And that opens the door to an entirely new set of possibilities.

Linking Skills to Compensation

Nolan: That’s fascinating. The reach is almost instantaneous because there’s essentially no added cost. There may be a small one somewhere, but the cost of applying insights across the entire workforce has significantly dropped. One of the topics we discussed earlier is how Learning & Development often talks about skills—specifically, how we’re connecting skills to compensation.

We had an interesting back-and-forth about what it means to pay people for skills. For example, if an accountant takes an SAP training course and learns to be an LMS admin, are you now responsible for paying them more because they’ve acquired a new skill? Should they receive equal compensation for that skill?

I assume many compensation models consider various factors. I’m not 100% sure what all of them are—what determines why Nolan Hout earns a certain amount versus someone else—but are you seeing a trend where companies are beginning to use skills as the benchmark for compensation? Is that becoming a more common conversation?

Charlie: Yes, it’s starting to happen, though it’s complex. Skills, as an area of investment, are inherently cross-functional. There are use cases across the entire enterprise. One of the earliest areas where we’ve seen great potential is in L&D—how can we upskill the workforce?

We’ve also seen it in internal talent marketplaces—finding ways to move talent more efficiently and create more meaningful career paths. As skills start to influence compensation, it’s running into some skepticism. Compensation leaders who set the guardrails are now facing new questions: What defines a job? What skills make up that job? How are those skills changing?

And which ones should we pay for? I break the role of skills in compensation into two areas: policy and market. Policy includes things like salary ranges—statements of what we intend to pay for. Incorporating skills into policy is complex. It requires close collaboration across departments and a clear strategy on what we aim to attract, retain, and reward—and why.

Even the most progressive compensation teams are still in the early stages of maturity when it comes to integrating skills into policy. On the market side, what we’ve seen through a product Compa introduced last year, called skills-based market data, is that you can detect significant pay differences based on the presence of specific skills—especially in high-demand areas.

Take AI engineering as an example. There’s a big pay difference between someone building a chatbot and someone building a self-driving car, even though both are considered AI engineers. How do you distinguish between them? Skills.

For instance, there’s a skill called computer vision associated with self-driving technology. We’ve observed that skills like computer vision, autonomy, and related technologies command substantial premiums—not just over general software engineering or systems engineering, but even over other AI disciplines. Whether companies are embedding those premiums into compensation policy is another question—but the market is clearly differentiating based on skills.

Charlie: The market is clearing at different rates. At a minimum, compensation teams need to understand the differences in market rates based on specific skills. Translating that understanding into policy, however, is a much more complex challenge.

Nolan: If I’m in HR or training—or whatever function owns skills in the organization—I feel you’d be my go-to partner. For instance, if I can use data to show that someone with AI vision skills commands four times the market rate, and I have a person currently building chatbots, embedding that new skill in them could be a game changer.

An investment of $10,000 to upskill might seem steep, but if it offsets the cost of hiring someone at a $40,000 to $50,000 premium—or if we can’t even find that talent externally—then it’s clearly worthwhile. That’s a meaningful conversation to have. It connects compensation and talent development in a way that puts real dollar value on skills.

If we can assign a financial value to skill development, we can start building sound investment strategies—identifying which skills to develop and calculating how quickly we can recoup the return. Of course, this depends on being able to measure and validate skill levels accurately, which is a challenge in itself.

But still, I’d want you as a partner if I were designing training programs. I’d want to know: how much have I improved my workforce, and what’s their increased value?

Charlie: Exactly. The missing piece has always been price—what are the actual market rates for different skills? When you look at compensation through the lens of competitiveness, cost, and fairness, all three intersect with skills.

For example, if we’re trying to compete for computer vision talent, we may need to pay a premium. But then the question becomes—how does that impact our overall cost?
If we’re planning to hire 200 people and those roles end up being 50% more expensive than anticipated, we may have to reconsider—can we afford it, or is it worth the investment? That’s where workforce planning and finance come in. What exactly are we willing to pay for, and is the ROI worth it?

On the fairness front, many companies invest heavily in ensuring equal pay. But if two people are in the same role and earning vastly different salaries, skills could be the missing independent variable that explains the difference.

If we can accurately quantify skills, we can integrate them into our compensation strategy in a meaningful way. The hard part is the assumption we’re both making—that we can reliably identify and assess those skills.

Nolan: Yeah, I think that’s always been the challenge. With AI, I tell people the old way of trying to define a skill—or what someone needs in a skill—was extremely difficult. It sounds simple as an idea, but when HR and L&D teams try to identify the skills a marketer needs, they have to go to the CMO—the Chief Marketing Officer—because that person sets the strategic direction for which skills are valued.

That individual essentially needs to be as embedded in the process as you are because they assign the value to the skill. Reaching agreement used to be easier for a few roles, but once you dig deeper into how one skill connects to another, reaching consensus becomes nearly impossible. At some point, the CMO just has to put their foot down and say, “This is what we’re going with—it’s the best we’ve got.”

That’s what AI is starting to do. It doesn’t have all the answers, but it gives us a solid starting point based on available data. People are more willing to trust that. But the harder question is: can we truly match a person’s skills accurately? And that’s the risk when linking compensation to skills—your ability to measure and match them correctly.

Charlie: Exactly. Everything starts with demand—what the business needs to execute its strategy. As you said, it’s critical for the CMO or the head of engineering to define which skills are necessary to build new technology or enter a new market. After that, it becomes a marketplace—one defined by supply, demand, and price.

We need to break down the concepts of skill demand and supply. For example, if we decide we need Kubernetes expertise in our tech stack, we first need to assess: who on our team already knows Kubernetes? If no one does, we need to decide whether to upskill internally, hire externally, or outsource that capability.

That build-buy-rent decision is grounded in cost. It all comes back to compensation. Compensation is that moment where you don’t just say “I value you”—you show it. It matters deeply, both to individuals and to organizations responsible to shareholders. Attempting to solve for skills without understanding cost is an incomplete exercise.

Closing Thoughts

Nolan: Yeah, 100% agree. And with that, Charlie, what a great place to wrap up. Thank you for spending time with us and giving us insight into your world—one that I haven’t spent much time in, but obviously value because, like everyone else, I have to pay for things. I think everyone cares about compensation in some form. Thank you for educating us on the topic.

Charlie: Thanks for having me, Nolan.