The companies winning AI aren't choosing better models. They're building better teams.

The companies winning AI aren't choosing better models. They're building better teams.

As AI evolves at record speed, competitive advantage no longer comes from choosing the best model. It comes from skilled engineers and trusted technology partners who can turn AI into measurable business value.

Written by Balazs Fejes, CEO & President at EPAM Systems, Inc.

Key takeaways 

• The biggest obstacle to enterprise AI is no longer the technology; it's the shortage of engineers who can integrate it.
• EPAM is investing in educating and upskilling AI-certified engineers to help close that gap.
• The future belongs to organizations that combine human expertise with an open ecosystem of AI partners, not a single model.
• Forward-deployed engineers may become the most valuable role in enterprise AI over the next decade.

Two years into the generative AI boom, one thing has become clear: buying access or signing a commitment to the latest model isn't the hard part. Integrating it across a complex enterprise is. That's why EPAM is investing in one of the industry's largest AI engineering initiatives, training 10,000+ Claude-certified architects, 5,000+ OpenAI forward-deployed engineers (FDEs), 5,000+ Gemini-certified specialists and hundreds of additional elite FDEs to help enterprises move from AI experimentation to AI execution.

The competitive advantage in AI is shifting from models to execution, and execution depends on people who can deliver measurable business results. That requires engineers who understand frontier AI, data and modern cloud architectures as well as the business domains, operating models and economics that those technologies must serve. Their role is to integrate AI in ways that improve performance, create value and support a sustainable total cost of ownership.

In my conversations with clients, I’m seeing the same challenge across every industry. Organizations aren’t struggling to experiment with AI; they’re struggling to put it into production at scale. The ones that pull ahead will be those that combine deep human expertise with modern engineering practices, strong data foundations and the right ecosystem of AI and cloud partners who can integrate frontier models into legacy systems, reengineer business processes and design solutions around regulatory requirements and real customer needs.

Investing in Forward-Deployed Engineers  

Success in this new AI wave requires a rare combination of complex technical skills and deep business and domain understanding. This is why EPAM is investing in a new generation of FDEs: T-shaped engineers who combine deep technical expertise with broad business fluency, consultative skills and responsible AI practices to help enterprises move from experimentation to production. Working directly with clients, FDEs translate business problems into technical possibilities, rapidly prototype solutions and continuously refine them based on what works in practice. In doing so, they bridge the gap between powerful AI models and real business transformation by helping organizations reshape how they operate and create new value with agentic capabilities integrated into existing enterprise environments.

EPAM is backing that strategy with significant investment. Through intensive training and transformation programs across the entire frontier AI stack, including Anthropic and OpenAI, we are building one of the industry’s largest communities of enterprise AI practitioners. But training alone is not enough. EPAM is also acting as “client zero,” applying the same AI capabilities, platforms and engineering disciplines inside our own enterprise that we bring to clients. We are rethinking how work gets done across engineering, sales, marketing, HR, legal and finance, starting from the business outcome and determining which activities should be eliminated, automated or fundamentally reengineered with AI at the core.

This internal transformation gives our teams firsthand experience of what it takes to become an AI-native enterprise. That includes modernizing systems and infrastructure, preparing data, redesigning operating processes and addressing governance, security and organizational change. By testing these approaches against the complexity of our own global business, our engineers can bring clients proven lessons that accelerate implementation and help deliver measurable business value rather than theoretical guidance alone. 

By the end of 2026, EPAM will have 10,000+ Claude-certified architects and 250 specialized, FDE Black Belts. This builds on a broader base of AI and cloud expertise. Today, EPAM is home to more than 11,000 Microsoft, 5,000+ AWS, 2,000 Databricks and 3,000 Google Cloud certified professionals. Beyond formal certifications, EPAM regularly scales learning through enterprise-wide technical events. For example, a recent AWS Elevate Day drew more than 5,400 registrations, 2,300 unique participants and nearly 950 concurrent attendees at its peak, demonstrating the breadth of engagement across our engineering community.

Hackathons help put these skills into practice. Teams design, secure and deliver AI-native solutions across the diverse tools and constraints of client environments. Thousands of team members participate from a range of functions spanning engineering and data to security, design and project management. The goal is to have a shared team capability that can be tailored to each client’s needs and continuously improved over time.

"Enterprise AI is won in the last mile — inside the systems people already depend on," said Dimitry Tovpeko, VP, Global Head of AI Engineering, SDLC and Modernization at EPAM. "That's the work we've done for 30 years, and our forward-deployed engineers do it now with agentic capability: build it, get it into production, keep it defensible in front of a client. Partners like Cursor put that capability where the work actually happens: the developer's workspace, running workflows a team already follows."

For our clients, the value isn't access to more AI tools. It's access to engineers who know which tools to use and how to implement them. Through a single trusted engineering partner, clients gain the expertise to evaluate, integrate and operationalize the right AI technologies for each use case without having to manage multiple AI vendor relationships. It's the same value proposition that made the cloud partner model so successful, now applied to the frontier AI ecosystem. 

The Power of an Agnostic AI Ecosystem  

Enterprise AI doesn’t run on a single model or cloud platform. It requires engineers who can navigate an increasingly complex ecosystem of frontier AI models, hyperscalers and enterprise technologies, and know when each is the right choice. As organizations scale AI across their business, flexibility has become a competitive advantage. The ability to combine multiple AI models, cloud platforms and engineering disciplines is what enables organizations to solve increasingly diverse business problems.

EPAM's partnerships across Anthropic, ServiceNow, Cursor, OpenAI, Databricks, AWS, Google Cloud and Microsoft Azure aren't about checking boxes. They ensure our engineers can recommend the right technology for each business challenge, rather than forcing every problem into a single vendor's ecosystem.

“The value of an agnostic AI ecosystem comes from the expertise behind it and infrastructure around it” said Vlad Agres, VP, Global Head of Amazon Business Group at EPAM. “Today, roughly one-third of all Anthropic-certified architects worldwide are EPAM engineers using AWS Bedrock, a reflection of our commitment to building AI expertise at scale. Combined with our rapidly growing collaboration with AWS, that gives clients access to the engineering talent and ecosystem of tools needed to architect and implement secure, scalable AI solutions.”

Multi-cloud agility enables teams to build adaptable systems that can evolve as business needs and technology change. By designing solutions that can move across clouds, services and model layers, our FDEs are building and integrating intelligent solutions more effectively, applying the best technology for each use case. That adaptability is what enables cross-industry innovation, from financial services and healthcare to retail and manufacturing, because the same engineering approach can be tailored to each sector’s unique data, compliance and customer experience needs. This is where EPAM’s strength comes into focus: equipped engineers who can bridge innovation and execution and help shape the next generation of intelligent enterprise.

“Multi-cloud agility starts with engineering agility,” said Alexandros Katsioulis, VP, Global Head of Google Global Business Group at EPAM. “As AI capabilities continue to evolve, our investment in the Gemini Center of Excellence is helping equip EPAM engineers with practical expertise in AI integration, governance and agent development. That continuous learning enables us to guide clients achieving their business goals while helping them to evolve their AI tech stack, as solutions change rapidly.”

One example is our work with FirstService Residential. Rather than starting with technology, we started with users, mapping resident and employee needs before selecting the AI architecture. The result was a cloud-native, SMS-enabled GenAI assistant that streamlined interactions while improving the resident experience. More importantly, the transformation was more than just a technology initiative. It shows how the right mix of strategic vision, technical depth and equipped engineers can turn adaptability into a business reinvention.

“The real measure of enterprise AI isn't how advanced the technology is, it's how naturally it fits into the way people work,” said Dimitry Tikhomirov, VP, Global Head of Microsoft Business Group at EPAM. “By combining Microsoft's AI ecosystem with EPAM's engineering expertise, we're helping clients embed intelligent capabilities into everyday workflows, making AI easier to adopt, easier to trust and ultimately more valuable to the business.”

From AI Access to AI Action

An agnostic AI ecosystem gives organizations the freedom to choose the right models, cloud platforms and technologies for each business challenge. But technology alone doesn't create transformation. The real value comes from operationalizing AI, embedding intelligent capabilities into the workflows employees use every day and the business processes that drive the enterprise.

As AI moves into production, that freedom of choice must also be governed by economics. In this context, tokenomics means understanding how model consumption, orchestration and human oversight combine to affect the cost of delivering a business outcome. The right model is not simply the most powerful or the least expensive per token. It is the model, or combination of models, that delivers the business case at the required level of quality, speed, security, governance and cost.

An agnostic architecture makes this possible. Engineers can compare and route work across different models and platforms, monitor consumption and performance and adapt as capabilities and commercial terms evolve. This creates the flexibility to optimize each use case while reducing dependence on any single provider.

Token consumption, however, is only one component of total cost of ownership. A credible business case must account for development, integration and maintenance, together with the cloud, data, security, governance and operating capabilities required to sustain the solution. Those costs should be measured against tangible returns using established baselines such as process cost, cycle time, quality, revenue impact and customer experience. Rather than minimizing AI consumption in isolation, the goal is to maximize the value delivered by the end-to-end business process.

This is where engineering expertise becomes the differentiator. Turning AI into measurable business outcomes requires more than selecting the right technology; it requires integrating AI into enterprise operations in a way that is scalable, governed and adaptable over time.

 "Most enterprises have settled at augmented — an engineer prompts an agent, reviews what comes back and moves on," said Dmitry Tovpeko, VP, Global Head of AI Engineering, SDLC and Modernization at EPAM. "The space has shifted significantly since then. The teams pulling ahead run end-to-end software factories: a defined delivery pipeline for a specific job, assembled from reusable skills, subagents, rules and commands, integrated into the client's own environment. That's an operating-model shift. Getting there takes a solid vertical AI stack, exactly where Cursor shines, and a new kind of engineering team.

The Last Mile Advantage 

AI models create possibility. Partnerships provide access. But neither delivers transformation – or a sustainable business case – on its own. The hardest part of enterprise AI is engineering the complete system of models, data, cloud platforms, controls, workflows and people so that it can deliver reliable results in production at an acceptable total cost of ownership.

That last point matters. Enterprises aren't blank slates. They're complex, functioning organizations with established systems, accumulated processes and people who have built entire careers around how things currently get done. The challenge isn't finding a powerful model or signing the right partnership. The challenge is threading agentic capability into the fabric of how an enterprise actually operates, without pulling the whole thing apart in the process.

That's why FDEs have become such a critical role. Deep technical expertise matters. So does real-world business experience. But it is the combination of both that defines success in this new wave of AI, and that success depends on engineers who can build production-grade agentic systems and understand the operations, workflows and decisions those systems need to serve.

The market is already moving in this direction. In recent months, leading AI labs and cloud providers have invested heavily in forward-deployed engineering because they’ve recognized the same reality: enterprise AI succeeds only when technology is paired with people who know how to implement it. 

EPAM's position here is not accidental. We have spent decades building the engineering capabilities this moment demands.  Our globally distributed engineering teams work where our clients operate, our people are certified across the leading AI ecosystems and our partnerships are structured so that the technology our partners build flows directly into the transformation our clients need delivered, at scale, in production, where it counts.

But as that footprint scales across enterprise environments, new demands emerge. How organizations meet them will define the next chapter of competitive advantage.

The Next Layer of AI Transformation

As organizations move from AI experimentation to enterprise-scale integration, a new challenge emerges. AI is no longer an isolated technology initiative; it is becoming embedded in the cloud platforms, data environments and identity systems that power the business. As adoption accelerates, so does the need to understand and manage an entirely new set of security risks.

This is why cybersecurity can no longer be treated as a separate workstream. It has to be engineered into every stage of the AI lifecycle, from design and implementation to governance, monitoring and continuous improvement. Organizations that do this well will move into production with greater confidence, resilience and speed. At EPAM, we see engineering, cloud and security as inseparable disciplines. Helping clients operationalize AI responsibly means embedding security into the transformation itself, not layering it on afterward.

 “AI is reshaping enterprise architecture, and that demands a different approach to cybersecurity,” said Miroslav Sklansky, Chief Information Security Officer at EPAM. “Security can no longer function as a standalone discipline; it has to be an architectural principle, embedded throughout the AI lifecycle. We help our clients build secure AI ecosystems where engineering, governance and security work together, so they can innovate faster, with more confidence, at greater scale.”

In our next article , we'll examine how our partnerships with frontier AI providers and platforms like Wiz are helping organizations build secure, resilient AI ecosystems.