AI at Scale: Why Enterprises Need Stronger Data, Engineering and Delivery Models

AI at Scale: Why Enterprises Need Stronger Data, Engineering and Delivery Models

As enterprises race to move beyond AI experimentation, the conversation is shifting from what artificial intelligence can do to how it can be scaled effectively across complex business environments. While pilot projects have become commonplace, achieving enterprise-wide, production-level deployment remains a far more intricate challenge—one that extends far beyond algorithms and models. From legacy systems and fragmented data ecosystems to governance, compliance, and workforce readiness, the path to AI maturity is proving far more demanding than many anticipated.

In this exclusive conversation, Srinivas Reddy, Managing Director and Head of EPAM Systems India, shares his perspective on why scaling AI is fundamentally a technology and infrastructure challenge, how enterprises can rethink success metrics beyond productivity gains, and what it will take to build AI-native organizations for the future. He also outlines how EPAM is reimagining engineering, talent, and delivery models to stay ahead in an increasingly agent-driven enterprise landscape.

As enterprises move beyond AI pilots, what are the key barriers preventing large-scale, production-level deployment?

Pilots are not the problem; scaling is where things get hard. Once AI (Artificial Intelligence) moves beyond isolated use cases, it needs to work across data foundations, legacy ecosystems, compliance requirements and core business processes, all at the same time. That convergence is where most organisations struggle.

What we’ve found is that AI readiness is fundamentally a data and technology infrastructure challenge before it’s an AI challenge. Without modernised foundations, deployments don’t sustain at enterprise scale. But there’s also a gap in the execution methodology. Enterprises don’t yet have codified, repeatable ways of delivering AI at scale, and without that repeatability, pilots tend to stay pilots. To address this, we at EPAM, have developed AI/Run. Blueprints is a modular, structured delivery methodology covering orchestration, governance, change management, ecosystem strategy and workforce enablement. The goal is to provide a structured path from fragmented experimentation to enterprise-wide deployment.

AI is often expected to simplify operations, yet many organisations are reporting increased complexity. What is driving this shift?

This is worth addressing directly: AI is adding complexity to enterprises right now, not reducing it. Enterprises already navigate multiple dimensions simultaneously: strategy, data, legacy systems, governance, talent, vendor decisions, business processes and delivery models. AI is introducing new requirements across all of them at once.

We’re also moving into agent-driven systems, which are more dynamic than the linear, deterministic workflows most enterprises were built around. Agentic pipelines operate through orchestrated systems with verification and governance layers, and the same tools can produce very different results depending on the quality of the engineering behind them. This complexity isn’t a sign that AI is failing; it’s a natural consequence of genuine transformation. The simplification comes later, once systems are redesigned to be AI-native rather than just AI-enabled at the surface. Organisations that work through this carefully now will be in a stronger position in the years ahead.

Read the full article here.

Explore how EPAM can accelerate your #AI transformation initiatives and turn your technology ambitions into scalable business impact:  www.epam.com/ai