Organizations are shifting artificial intelligence from experimental stages to everyday business operations, aiming to enhance decision-making processes. However, deploying powerful AI models without a clear understanding of specific operational workflows can lead to significant inefficiencies. According to Devavrat Shah, chief scientist of enterprise AI at Celonis, AI agents must grasp the broader business conditions and constraints surrounding individual tasks to achieve true "decision intelligence."
When AI systems operate in a vacuum without context, they may overlook critical business constraints, potentially resulting in expensive mistakes or consuming excessive computing power just to solve simple problems. By connecting these agents to existing enterprise processes and data structures—creating a context layer—businesses can establish defined guardrails. This approach allows certified "roled agents" to produce reliable recommendations that human experts can then review, approve, or fully automate if appropriate.
Implementing context-aware AI agents could fundamentally shift how employees spend their time. As routine and highly manual tasks become automated, human workers could have more bandwidth to focus on complex, strategic decision-making. Shah suggests that organizations start by identifying difficult steps in specific business decisions and testing AI's impact on speed and value. Over time, this could extend to enterprise-wide resource planning across areas like finance and marketing, helping companies evaluate options much more efficiently