RESULTS
10%
Reduction in engineering costs in one year
15-30%
Productivity gain across delivery
9K
AI-enabled engineers in year one, with a target of 15K
2x
Faster pull requests
1
Global governance standard
across 250 tech stacks
Executive Summary
A financial services institution faced a critical challenge: rapidly upskill a massive engineering workforce to raise baseline productivity and reduce engineering costs while operating in a regulated environment, at scale. In one year, EPAM transformed 9,000 engineers into an AI-enabled workforce — helping the company achieve 15-30% productivity gains and 10% lower engineering costs.
SERVICES
- Talent Enablement
- AI-Native Engineering
STRATEGIC PARTNERS
- Microsoft
- GitHub
INDUSTRY
- Financial Services
The Challenge
A leading financial services institution needed to reinvent how its global engineering organization builds software to improve baseline productivity, speed up time to market and reduce costs. This meant shifting from traditional delivery to AI-native, agentic engineering across the full software development lifecycle (SDLC) to keep up with the market.
To achieve this, the institution was looking at a massive undertaking: transforming its 15,000 traditional engineers into an AI-native workforce through GitHub Copilot and a new agentic SDLC platform. While already a daunting task on its own, several additional factors added to this complexity:
01
No prior AI experience at scale
Engineers had no reference model for working with AI, so upskilling had to cover not just the tools but the ways of working — how they communicate, how they review and how they hand off.
02
Unestablished AI economics
Token consumption, budgeting and cost attribution were new disciplines. Without them, AI usage was an unmanaged cost line rather than a productivity lever.
03
Uneven adoption maturity
A keen early-adopter group moved fast, while others sat further along the curve — non-use, underuse and misuse all appeared. This meant a single global rollout approach could not serve both ends.
04
Strict policy environment
Standard LLMs had no knowledge of the institution's codebase, coding practices or environments, so out-of-the-box output was often wrong or non-compliant — generating cost instead of benefit until context and guardrails were built.
05
No one-size-fits-all
With 250 tech stacks and globally divergent ways of working, the platform and enablement had to be built distributively and collectively with local ownership, rather than pushed as a single central standard.
The Solution
Leveraging EPAM AI/Run™.Transform and the client’s existing Microsoft Azure infrastructure, we worked together to rapidly roll out an AI transformation program and help build a new agentic SDLC platform to turn its 15,000 engineers into an AI-native workforce – with a short-term goal of 5,000 engineers using GitHub Copilot daily within the first four months of the project. EPAM’s team got to work putting together a multi-wave transformation program that could deliver results fast.
WAVE 1
First, EPAM’s team focused on driving AI adoption across the enterprise and implementing new AI-enabled ways of working for the engineering team. We helped train and upskill the engineers by developing educational materials, training courses and coaching programs that enabled them to quickly familiarize themselves with GitHub Copilot and make it an integral part of their workflows. We organized AI-centered events, like hackathons and presentations, that would further deepen their understanding of the technology.
To make AI genuinely effective in a highly regulated environment, we began building a library of skills, agents and workflows that encode the institution's context — its codebases and architectural standards, as well as its security, resilience and compliance requirements. This context engineering is what turned general-purpose AI output into code that met the company’s standards by default — becoming the foundation for an agentic marketplace.
In just one year, we helped transform 9,000 engineers into AI-enabled engineers, integrating GitHub Copilot into their daily workflows. Using AI has already increased productivity, improved quality of work and delivered significant annual savings. With a 15-30% productivity boost across delivery, engineers are merging pull requests (PRs) two times faster than before. These productivity gains have enabled the company to reduce annual spending by 10%.
As we finalize wave one of the project, we continue to scale AI adoption and deliver positive outcomes, improving productivity and code quality across engineering.
WAVE 2
We are now transitioning to the next level of maturity in the AI transformation program where we are scaling AI-native engineering across the entire team and enabling a fully agentic SDLC. This wave of the program is centered around three key pillars that will enable the client to achieve its transformation goals: an agentic SDLC platform for both forward engineering and legacy modernization, an agentic marketplace, and a more comprehensive measurement framework to track value impact and AI adoption at scale.
As we finalize wave one of the project, we continue to scale AI adoption and deliver positive outcomes, improving productivity and code quality across engineering.
The Agentic SDLC Platform
Agentic Hub
AI Adoption at Scale & Value Tracking
Future State
Upon completion of Wave 2, the financial services leader will have:
15,000 AI-native engineers, with AI embedded in core engineering processes and decision-making
A variety of end-to-end workflows to support the agentic delivery of new features and legacy modernization
A distributed, continuously maintained knowledge graph that supplies agents with targeted, domain-specific context — owned and kept current by the teams closest to each domain
Ultimately, the shift to AI-native ways of working should deliver a 50%+ productivity gain and dramatically reduce time-to-market for the client.
PARTNER WITH US
We can help transform your enterprise into an AI-native organization.