Celonis chief scientist says enterprise AI needs operational context
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.
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
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.
The headline, key points and digest above were generated by Digest AI's editorial model from the linked sources. Automated summaries can contain errors: the sources are the record. Spotted a mistake? Tell us. Published by Martin K., who runs Digest AI and handles corrections.
More in Enterprise & Industry
All →- Revolut scales pragma for 80 million customers as European banks adopt NVIDIA open models · 1 src
- Nvidia powers European retailers with open models, cutting inference cost by 90% · 1 src
- EBU partners with NVIDIA to expand sovereign AI framework for European broadcasters · 3 src
- ServiceNow and Nvidia release Arc AI agent with security-focused guardrails · 2 src
- Opinion: former Microsoft HR VP outlines three ways to use AI at work · 1 src
Comments
via GitHub Discussions