Why we hired Liz Tan Levy as our Chief Product Officer—and what it says about where Degreed is going.
Every company in enterprise HR tech claims to be skills-first right now.
You’ve seen the decks. The category language. The product screenshots with skill tags and proficiency metrics. The press releases announcing that AI will surface the right learning at the right time. The market has converged on a single story, and everyone is telling a version of it.
Here’s the thing about skills intelligence: saying it and building it are very different problems.
Building real skills intelligence—the kind that gives organizations a clear view of the capabilities they have, the gaps that exist, and what to do about them—requires a specific and rare combination of things. Deep expertise in how skills are defined, validated, and mapped across a workforce. The ability to connect learning signals to meaningful outcome data. Intuition about how to turn that complexity into something people can actually act on. And the patience to do it right, because shortcuts in skills infrastructure compound over time.
That combination of things doesn’t come packaged in a product roadmap. It lives in people who have spent their careers at this intersection—and there aren’t many of them.
That’s why we hired Elizabeth Tan Levy as Degreed’s Chief Product Officer.
Why Liz, and why now.
Liz has spent her career at the exact place where skills data, workforce intelligence, and AI product development meet. Not as adjacent experiences that add up to a skills background, but instead, as a consistent, deliberate focus on understanding how organizations can make skills visible, measurable, and useful.
Most recently, she was building an AI-enabled research and data team at The Burning Glass Institute, the independent research organization that is widely regarded as the most rigorous and trusted source of labor market skills intelligence in the world. Before that, she drove product-led growth through multiple stages at Lightcast, the workforce analytics company whose skills taxonomy and labor market data sits underneath some of the most important workforce decisions made by enterprises, governments, and universities. And earlier, she developed and launched personalized learning and content discovery products at Reed Elsevier.
What connects those experiences is the consistency of the problem she’s been working on: How do you take the enormous complexity of skills—how people develop them, how organizations need them, how the market values them—and turn it into something that actually drives decisions?
That problem is exactly what Degreed is built to solve. And the next question—the one we’re building toward now—is how you make that intelligence act, not just inform.
The market is asking harder questions now.
For a long time, the skills conversation in enterprise technology was about ambition. Skills-based organization strategies. Skills passports. Skills taxonomies. The vision of a workforce where everyone’s capability is understood, and development is driven by what people can actually do, rather than where they went to university or how long they’ve been in a role.
That vision is still right. In fact, Fosway’s most recent research shows upskilling has been the top L&D priority for two consecutive years. The market timing has never been better.
But the conversation has shifted. Organizations aren’t asking, “Should we be skills-first?” anymore. They’re asking: “How do we know it’s working?” CFOs are asking for ROI evidence. CIOs are scrutinizing the data infrastructure. L&D leaders who’ve spent years building the business case are being asked to show outcomes. Not strategic narratives, not completion rates, but evidence that skill gaps are closing and the business is moving faster because of it.
That’s a harder question. And it requires a different kind of product.
What we’re building, and where Liz comes in.
Degreed’s vision is to build the world’s leading skills profile: a comprehensive, living picture of what every person in an organization can do, what they need to develop, and how that maps to what the business actually requires. A skills profile that’s not static. Not a snapshot. But an intelligent, continuously updated asset that organizations can act on.
That vision has two components, and they’re inseparable.
The first is skills intelligence depth—the rigor of the data layer, the comprehensiveness of the skills taxonomy, the accuracy of inferred skills across diverse learning signals. This is infrastructure work. It’s not glamorous, but it’s the foundation everything else stands on. Shortcuts here compound over time, and Degreed has always taken this seriously.
The second is AI—the capability to move from insight to action. Degreed’s Maestro is our bet on this future: an AI that doesn’t just surface the right learning at the right time, but actively helps organizations close skill gaps, recommend development paths, and surface workforce readiness in ways that directly connect to business decisions.
Liz is here to advance both. Her background in workforce data and AI-enabled product means she understands the full stack, from the integrity of the skills taxonomy to the product experience that makes intelligence feel actionable. Her job is to take what Degreed has built and push it to where the market is going.
One more thing worth saying.
We know what it means to bring in a new CPO and write a blog post about it. There’s a version of this announcement that’s all signal and no substance, where the impressive background and the confident framing paper over the fact that nothing has actually changed yet.
So let’s be clear about what this is and isn’t.
This is Degreed making a deliberate investment in the people and the product depth required to lead the AI-powered skills intelligence category into its next era. It’s a signal about where we’re going, not a claim that we’ve already arrived.
Liz starts now. The work is already underway. We’ll let the product do the talking from here.
Elizabeth (Liz) Tan Levy is Chief Product Officer at Degreed. She joins from The Burning Glass Institute, where she led AI-enabled data and research capability.
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