When I first sat to interview Sandra Loughlin, chief learning scientist and global head of the talent enablement and transformation practice at EPAM, I knew I would learn something new. What I didn’t expect was to be taken on a 30-year journey — one that began long before “skills-based organization” was a buzzword and continues today as a model of what’s possible when talent data is treated as a strategic asset. 

The result of those conversations is now a full case study available to Forrester clients. Here I share some highlights for non-clients and thank Sandra and her colleagues for sharing so much information that can educate and inspire us. 

A Quiet Revolution In Talent Intelligence 

EPAM didn’t wait for the market to catch up. Back in 1993, the company began building its own skills taxonomy, not because it was edgy, but because EPAM understood what many leaders don’t: The success and growth of your business depends on people’s skills. Over time, investment, and effort, that initial taxonomy evolved into a dynamic, AI-powered talent intelligence system that now spans more than 130 applications and 15,000 skills. Each employee has an average of 30 verified skills, but skills aren’t the only piece of talent intelligence the company uses — its system draws on over 25 attributes to match people to work and opportunities. 

This isn’t just about technology. It’s about vision, persistence, and a deep belief that “people — not code — are the secret to success.”

What Stood Out 

Here are just a few of the highlights that left a lasting impression with me: 

  • Executive sponsorship from day one. EPAM’s CEO was a top advocate for this strategic use of talent data from the very beginning, ensuring it is embedded in business strategy — not siloed in HR. 
  • A fully integrated talent ecosystem. EPAM’s TelescopeAI platform connects all talent and business applications, enabling real-time, data-driven decisions. 
  • AI-powered task and talent intelligence. EPAM’s systems don’t just track skills — they understand the work itself, enabling smarter workforce planning and automation. 
  • Employee empowerment. With tools like Level Up, employees can chart their own career paths, access personalized learning, and receive mentorship — all based on verified skills. 
  • A culture of validation and trust. Skills aren’t self-declared; they’re observed, assessed, and endorsed. This rigor builds credibility and fairness into the system, a flywheel that supports this talent data as critical to running EPAM’s business. 

Results With AI Take Effort 

What struck me from our conversations, what I keep coming back to, is the understanding that to future-proof your organization “from” AI, you must wholly embrace and imbed AI. To gain efficiencies and boost performance from AI, you must understand and track what people do and learn every day, their skills, their aspirations. Managing and making sense of that amount of data is only possible with AI. But it takes time and intentional effort to build your infrastructure and readiness. And EPAM’s journey is a testament to both the power that AI offers and the effort and attention it requires.