Artificial intelligence dominated boardroom discussions in 2025, but enterprise AI adoption has now shifted from experimentation to execution. According to Cognizant CIO Neal Ramasamy, context engineering will decide enterprise AI success as organizations move AI agents from pilots into live operational workflows.
Ramasamy argues that most enterprises still treat AI as a technology rollout rather than an operating model redesign. Deploying AI agents without defining decision ownership, governance boundaries, and escalation paths creates performance gaps once systems leave controlled environments. Successful organizations redesign workflows and accountability structures before scaling automation.
In a Fortune 500 environment, context engineering defines the operational framework in which AI agents function. Unlike prompt engineering, which optimizes single interactions, context engineering establishes persistent governance layers aligned to policies, data lineage, and business processes. When properly designed, agents behave consistently across teams and use cases.
For data and analytics teams, operationalizing AI agents exposes hidden challenges. Tacit knowledge (informal decision rules embedded in human judgment) must be codified. Without surfacing these rules, agents struggle in real-world scenarios. Ramasamy also highlights a “Velocity Gap,” where infrastructure deployment moves faster than operational value due to misaligned governance and cross-silo inconsistencies.
To measure enterprise AI adoption effectively, Ramasamy recommends tracking cycle time across core operational processes. Reduced cycle time indicates real friction removal, not just faster individual tasks. Traceability is equally critical. Leaders must understand why an AI agent produced a recommendation to ensure accountability and regulatory compliance.
Organizations preparing for agentic AI should prioritize building a context backbone. Codifying decision rights, runtime lineage, and governance into execution allows AI agents to scale sustainably. Enterprises that treat context as a strategic asset will unlock long-term AI value, while others risk rework and stalled initiatives.