Agentic AI and Enterprise Transformation – ‘Rethink the Work before Writing the Code’

Agentic AI and Enterprise Transformation – ‘Rethink the Work before Writing the Code’

A Q&A with Dmitry Tikhomirov, Global Head of Microsoft Business Group, EPAM.

What is agentic AI and how is it reshaping the business landscape?

Generative AI has already proved useful for drafting, summarizing, analysing and creating content. But in most cases, the work still starts with a human prompt and ends when the model gives a response.

Agentic AI changes that pattern. An agent is given a goal, not a script. It can work out the steps, choose the tools it needs, bring in other specialised agents where useful and continue until the task is complete. In an enterprise setting, that has to happen within clear guardrails, including permissions, policies, data controls and human oversight where needed.

That shift matters because it changes how organisations think about work. Instead of using AI only to help people complete individual tasks, businesses can begin to redesign processes so that agents handle more of the coordination, information gathering and decision support across systems.

The result is a move toward more adaptive ways of operating. Work can happen faster, decisions can be made with better context and teams can spend less time moving information from one place to another. That is where agentic AI starts to create a different kind of enterprise value.

Where are organisations getting real value today, and how should success be measured?

The value is showing up in the challenging parts of the enterprise. Underwriting, customer onboarding, risk analysis, software engineering, field operations, store operations and service centers. These are the places where people spend too much time pulling information from different systems, checking documents, chasing approvals and translating between tools that were not necessarily designed to work together.

That is where agents start to earn their keep. A strong agentic workflow removes friction from the process. It helps people get to the right answer quicker, with fewer handoffs and less manual interpretation along the way.

Retail is a good example. Albert Heijn, part of Ahold Delhaize, has used Azure OpenAI as part of an employee-facing assistant within its store app. Teams can use it for questions around restocking, onboarding, product lookup and inventory information. At that scale, across hundreds of stores and tens of thousands of employees, even small improvements in everyday work can start to make a big difference.

The measurement, though, has to be more than “people saved time.” Our AI Value Capture research shows what good measurement looks like. In one software engineering example, agentic workflows reduced documentation time by 80% and code generation time by 45%, which translated into a 30% increase in net sprint velocity. In a contact center example, conversational AI drove 40% call deflection, improved efficiency on remaining calls by 15% and lifted CSAT by nine points.

So, success should be measured at the level of the process. Time saved is useful, especially early on, but the stronger measures are process cost, cycle time, decision quality, revenue impact, customer experience and the amount of work that can move through the system without adding headcount.

The practical lesson is to start where friction is visible and value can be measured. Establish the baseline before the pilot starts. Then track whether the agent improves the economics of the workflow, not just the productivity of an individual task. That is how organisations move from interesting AI activity to measurable business impact.

Why do so many agentic pilots never reach production and how can that be navigated?

This is where many businesses lose momentum. Despite there being plenty of experimentation, there’s still a real gap between promising pilots and production-grade AI.

Our own research points to the same pattern. In EPAM’s 2025 AI report, only 26% of disruptors and advanced companies delivered AI use cases to market, compared to 9% of competent companies and just 3% of beginners. Additionally, EPAM’s AI Value Capture research adds another important signal: 43% of companies struggle to secure AI budgets because they cannot prove ROI, and 60% of successful pilots fail to scale across the enterprise.

The reasons are usually familiar. The pilot is interesting, but it is not tied tightly enough to a business priority. The team proves that the technology works but does not clearly establish the financial case. The workflow depends on data, permissions or systems that were never prepared for autonomous execution. As a result, governance arrives late and change management is delayed even further.

Organisation

The first step is to build the operating model around the ambition. That means clear executive sponsorship, accountable owners, AI-literate teams and a funding path that can move from validation to scaling. EPAM’s AI report found that only 1% of companies say they have a fully effective AI governance model, which tells us that many organisations are still building the management capability needed to scale AI safely.

Process

The work itself has to be redesigned. Agentic AI creates value when it can operate across a real business process, with access to the right data, tools and decisions. A pilot should start with measurable baselines and prove value against operating metrics such as cost reduction, revenue impact, cycle time, quality or customer experience. Without that link to the workflow, even a technically successful pilot will struggle to justify the next round of investment.

Technology

The stack has to be modular, governed and observable from the beginning. Agents need secure access to enterprise data, clear permissions, audit trails, monitoring and lifecycle management. When an agent is expected to plan, decide, call tools and act across systems, the enterprise has to know what it can do, what it has done and when a human should step in.

Organisations that navigate this well treat the pilot as the first stage of production, rather than a simple experiment. They define value before they build, prepare the process and data around the agent and put governance in place early enough to support speed.

Read the full Q&A here.

Discover how EPAM and Microsoft help global enterprises turn complex challenges into scalable, responsible AI solutions that accelerate transformation and unlock real business value: epam.com/about/who-we-are/partners/microsoft