The New Enterprise Stack: Why Process And Context Must Converge For Agentic AI

InnovationThe New Enterprise Stack: Why Process And Context Must Converge For Agentic AIByManish Sood,Forbes Councils Member.for Forbes Technology CouncilCOUNCIL POSTExpertise from Forbes Councils members, operated under license. Opinions expressed are those of the author. | Membership (fee-based)Jun 01, 2026, 11:30am EDTManish Sood is the founder and CEO of Reltio, a real-time context intelligence platform powering enterprise AI and innovation. getty​For two years, the enterprise AI conversation has been obsessed with models and infrastructure—the "plumbing" of the future. But this discussion is missing the point entirely, which is why there's a widening gap between ambition and execution: Although 91% of leaders believe agentic AI is the future, only 38% feel ready to deploy it, according to 2025 Harvard Business Review research sponsored by Reltio.The missing link isn't the algorithms or the machines—it’s that most AI systems lack the business context required to reason accurately. To bridge this gap, the next chapter of enterprise AI must move beyond simple workflows and embrace a dual architecture: a system of process and a system of context.The Context ProblemWhile the last generation of enterprise software perfected the system of record, context has emerged as a new opportunity. This shift toward the "systems of context" seeks to turn raw data from all of those siloed systems of record into the autonomous, decision-ready intelligence required for the age of agents.A system of process defines how work gets done, encoding workflows, transactions and operating steps. These systems are essential for scaling repeatability and control. However, a system of context defines what's true and how it all connects. It gives AI a unified representation of the concepts, entities and policies that matter to the business. Raw data tells an AI that a record exists; context tells the AI what that record means, how it relates to others and what...