Making the case for context-aware AI

Devavrat Shah, chief scientist of enterprise AI at Celonis, argues that AI agents need operational context—an understanding of workflows, constraints, and the role each task plays—to support enterprise decision-making, since acting without it risks costly mistakes or excessive compute use. He advises organizations to start with a specific business decision, identify its hardest steps, test whether AI improves them, and measure gains in speed and value before scaling. Shah also says the shift toward automating repetitive work will require investment in workforce education so employees can interpret AI outputs rather than simply handing decisions to agents.