Cognizant CIO Points To Context-Driven Leadership As The Decisive Factor In Enterprise AI

Enterprise AI is pushing CIOs into a broader leadership role that extends well beyond technology oversight. As organizations scale from pilots to production, success increasingly depends on operating models that reflect how people share context, make decisions, and work together. CIOs must now act as organizational architects, responsible for engineering human understanding at scale so AI can deliver durable enterprise value.

Neal Ramasamy is the Global Chief Information Officer at Cognizant, with prior CIO leadership roles at New York Life Insurance Company and Fidelity Investments, and recent recognition on the Forbes CIO Next list. He said that when it comes to enterprise AI, outcomes are shaped by how effectively leaders connect human behavior, institutional knowledge, and execution.

"AI adoption isn’t about tools or infrastructure. It’s about people, context, and engineering human understanding at scale so organizations can create real enterprise value," said Ramasamy. For him, context is the defining factor behind any successful AI initiative.

• Unwritten intel: He described context engineering as the evolution of traditional process transformation, focused on capturing the informal, human elements of work that rarely appear in documentation. "Process transformation only captures what’s written down. Context engineering focuses on how work actually happens, including the informal decisions and interactions that never make it into documentation, and that missing context is where AI initiatives often break down," he explained. When that context is not intentionally designed and shared, AI systems struggle to operate effectively across real organizational conditions.

• A model with a mission: That emphasis on context carries through to how Ramasamy thinks about model design. Rather than relying on generalized intelligence alone, he argued that enterprise value comes from models built with a clear mission and a deep understanding of their operating environment. "As much as the generic models you can extract off the shelf will give you a lot of intelligence, to get to the next level, purpose-built models are going to be the answer. They also allow you to drive a lot of control and effectiveness."

• Context to capability: He pointed to a practical example inside the service desk to show how context engineering works in practice. "We gave our help desk a very specialized, context-rich set of tools that understand the specific domain. This allowed us to cut down on the noise and deliver high success rates in problem resolution versus things getting turned into tickets that require manual labor," Ramasamy said. It's a process that transforms the AI model from a raw tool into a reliable "enterprise skill" for the organization.