# Celonis chief scientist says enterprise AI needs operational context

Digest AI · Enterprise & Industry · published 2026-10-22T10:00:00Z

Canonical: https://digestai.news/story/celonis-chief-scientist-says-enterprise-ai-needs-operational-context

## Summary

In a Business Lab podcast, Devavrat Shah, chief scientist of enterprise AI at Celonis, explains that AI agents must be grounded in a company’s operational context to make reliable decisions. He calls this approach “decision intelligence,” arguing that without a contextual layer that links AI to existing workflows and data, agents can miss constraints, waste compute, or produce costly errors.

Shah suggests organizations start by picking a specific business decision, mapping its most difficult steps, and testing whether AI can improve speed or value. He emphasizes the need for governance, clear role‑based guardrails, and workforce education so employees can interpret AI outputs. Looking ahead, he sees context‑aware AI extending from routine tasks to enterprise‑wide planning in finance, workforce management, marketing, and sales.

## Key points

- Shah says AI agents need a contextual layer tied to enterprise processes to avoid costly mistakes
- He recommends starting with a single decision, mapping its steps, and measuring speed and value gains
- Workforce education and clear governance are essential for safe, scalable deployment

## Why it matters

Embedding operational context into enterprise AI could reduce errors, lower compute costs, and accelerate decision‑making across finance, marketing, and other critical business functions.

## Sources

1. [Making the case for context-aware AI](https://technologyreview.com/2026/10/22/1146812/making-the-case-for-context-aware-ai) (MIT Technology Review AI, 2026-10-22)

## Cite

Digest AI, "Celonis chief scientist says enterprise AI needs operational context", 22 October 2026, https://digestai.news/story/celonis-chief-scientist-says-enterprise-ai-needs-operational-context

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