Moving AI beyond experimentation requires connecting agents to operational context and identifying the business processes where they can deliver the greatest value. 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.
For Devavrat Shah, chief scientist of enterprise AI at Celonis, that context is central to what he calls "decision intelligence." AI agents can support individual tasks within larger decision-making processes, he argues, but they need to understand the role each task plays and the business conditions surrounding it. Without the right operational context, agents risk overlooking important constraints, making costly mistakes, or consuming excessive computing resources as they try to work out what they need to know.
The solution begins with connecting AI to the processes and data enterprises already use. A contextual layer can help agents understand workflows, operate within defined guardrails, and produce recommendations that people can review and approve. Shah advises organizations to start with a specific business decision, identify its most difficult steps, and test whether AI can improve them. Measuring gains in speed and value can then help determine whether an application is worth scaling.
This approach could also change how employees spend their time. Automating repetitive, manual work and coordinating tasks across teams could free people to focus on more complex strategic decisions. Realizing that benefit, however, will require investment in workforce education, so employees can understand AI outputs and their implications. Looking ahead, Shah sees opportunities extending from routine operational tasks to enterprise-wide planning in finance, workforce management, marketing, and sales.
Source: MIT Technology Review AI · Summarized by HeadlinesBriefing