Celonis scientist argues enterprise AI needs operational context
Original title: Making the case for context-aware AI
A Celonis-sponsored MIT Technology Review podcast says agents fail when they lack workflow constraints and human review.
Devavrat Shah, chief scientist of enterprise AI at Celonis, argues that enterprise AI agents need process and data context to support decisions safely. He says agents should start on one business decision, handle its hardest steps within guardrails, and leave recommendations for people to approve. The episode, produced with Celonis, also calls for workforce education before automating repetitive work.
- Shah calls the approach decision intelligence.
- He warns context-free agents can make costly mistakes or waste compute.
- Suggested domains include finance, workforce, marketing, and sales.
- The Business Lab episode is produced in partnership with Celonis.
- Organizations
- Celonis
From the original article
13 min read at technologyreview.com
Enterprise AI can generate recommendations, automate tasks, and help organizations make decisions faster. But without an understanding of how a business actually operates, even powerful models can miss the constraints and relationships that determine whether a decision is the right one. As companies look to move AI from experimentation into everyday operations, the challenge ahead…
By MIT Technology Review Insights · technologyreview.com
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