Partner of the Year Award winner EPAM teamed up with Albert Heijn to deploy a secure AI assistant, simplifying retail workflows and setting up enterprise-wide transformation.
What does it take to move from AI experimentation to enterprise-wide scale? For EPAM, the answer emerged from a year of foundational work—helping customers fix data estates, aligning stakeholders, and building repeatable engineering practices. As a Microsoft Global Systems Integrator (GSI), EPAM supports customers preparing for scalable adoption, with a focus on agentic AI and production-grade deployments.
The company’s long-standing partnership with Microsoft includes 17 specializations across AI, app innovation, and data, as well as designations across all Microsoft solution areas. And the company is invested in internal enablement, deploying Microsoft 365 Copilot to more than 2,000 employees and reducing external collaboration hours by 20%. Together, their well-established credentials and internal enablement efforts position EPAM to deliver high-value engagements and accelerate innovation for enterprise customers.
This expertise, partnership, and proven ability to deliver value is part of what earned EPAM the 2025 Innovate with Azure AI Platform Partner of the Year Award. Their partnership with Albert Heijn, a leading grocery retailer in the Netherlands, was an example of this award-winning work. That deployment was just one part of a broader posture as the team focuses on helping customers reimagine business processes with AI—moving beyond pilots and into production.
A year of experimentation, not scale
For many organizations, 2025 was a year of experimentation with generative AI—but not yet scale. EPAM saw customers across industries testing new capabilities, often in isolated pilots. “A lot of companies tried,” said Dmitry Tikhomirov, Vice President, Technology Solutions at EPAM. “Some of them failed, and very few scaled generative AI to production.”
EPAM identified two key barriers to broader adoption. The first was data readiness. Many organizations lacked the infrastructure to support enterprise-grade AI. “It’s very hard to scale AI if your data is not ready for that,” Tikhomirov explained. “Building out data platforms and moving your data closer to AI models—modernizing and simplifying data—was a big trend.”
The second challenge was organizational alignment. In many cases, AI initiatives were driven by technical teams without full buy-in from business stakeholders. “If you just patch part of a process with AI, you’re not necessarily accelerating the entire process,” said Tikhomirov. “Your return on investment is less tangible, in such cases.”
EPAM spent the year helping customers address these foundational gaps. That included fixing data estates, navigating compliance, and aligning internal teams around shared goals.
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