Adding process context cuts operational risk but needs integration work

Small teams will spend time connecting models to their existing workflows; that reduces the risk of incorrect recommendations but does not come with a stated price.

What actually changed

MIT Technology Review reports that enterprise AI is shifting from standalone model outputs toward agents that understand the business conditions around a task. Devavrat Shah, chief scientist of enterprise AI at Celonis, frames this as "decision intelligence": AI that recognises the role each task plays inside wider workflows. The article says models operating without that operational context can miss constraints, make costly errors, or use excessive compute trying to infer missing facts.

Who this affects

This affects product managers, ops teams and anyone running AI pilots that are intended to move into day-to-day operations. Organisations that currently treat language models or automation tools as isolated components — feeding them prompts or data without mapping that work back to business processes — are the most exposed. The shift is relevant where decisions span multiple systems or require knowledge of explicit constraints embedded in existing workflows.

What it costs or what it replaces

The article does not include any pricing information.

What changes in practice is not a single paid product but an architectural step: connecting AI agents to the processes and data the business already uses rather than letting agents act "in a vacuum." That replaces a deployment pattern where models are deployed without process links and where teams expect models to infer operational constraints on their own. The cost to a small team will be time and engineering effort to map workflows, surface relevant data, and insert a contextual layer that feeds that information to agents; the source emphasises the integration need but gives no estimate of effort or money.

What we don't know

• The article does not state any vendor pricing or implementation costs.
• It does not specify technologies, vendors or reference architectures to provide the contextual layer.
• The piece does not quantify how much error or compute is saved by adding context.
• It does not describe step-by-step migration plans for teams with existing AI deployments.

What to do next

1. Inventory where your AI agents make decisions that depend on other systems or business rules; mark those as candidates for adding process context.
2. Allocate a short technical spike (a few days to a couple of sprints) to prototype passing workflow state and constraints to an agent instead of relying on prompt-only inputs. Use that spike to measure integration effort and failure modes.
3. If the spike shows fewer incorrect recommendations or less troubleshooting time, budget for integrating context into production workstreams rather than replacing models alone.