Enterprise AI can recommend actions, automate tasks, and help organizations make decisions faster. Yet a capable model can still make the wrong choice if it does not understand how the business operates. The challenge in moving AI beyond pilots is giving agents enough operational context to work usefully within real processes.
Why context changes what an agent can do
Business decisions are made through processes: a series of steps and responsibilities that lead to a choice. Some decisions are strategic, while others are operational and happen frequently. AI agents may support or automate individual tasks within those processes, but they need to know what each task is for and what conditions surround it.
Devavrat Shah, chief scientist of enterprise AI at Celonis, describes this combination of AI and context as a route to “decision intelligence.” An agent acting without context may miss a constraint or guardrail that matters to the business. Even if it eventually reaches a useful answer, it may spend excessive computing resources asking questions to work out what it needs to know.
The stakes vary. A recommendation that misses the mark in a low-impact situation may be tolerable; an error tied to a financial forecast could be far more costly. In either case, general capabilities alone do not ensure that an agent understands the specific decision it is helping with.
Connect agents to the work already happening
A context layer can link AI to enterprise processes and the data generated by workflow tools and systems of record. Shah describes a structure in which the systems below provide workflows and event logs, while people or agents above use the resulting context. Agents can then be assigned to particular parts of a process and operate within defined guardrails.
That arrangement can also give people a chance to review, assess, and approve an agent’s output before it is used. Some tasks may eventually be automated, but the process should make clear what the agent is doing and where its role fits. The aim is to connect AI to how the organization actually works, so its output reflects more than a generalized answer.
Organizations do not necessarily need perfectly clean or complete underlying systems to begin. Shah says the context model is designed to connect with existing workflow tools and make sense of them. But connecting enterprise systems to a vendor’s software requires a security review, which he describes as a key step in implementation.
Begin with a decision, then measure the result
For organizations considering where to start, Shah recommends choosing a specific decision and mapping the process around it. From there, they can identify its most crucial or difficult step and test whether an AI agent can help with that part of the work.
After the trial, the organization can measure whether the process became faster and whether it delivered value. Those results offer a practical basis for deciding if an application is worth expanding. A focused trial also helps keep attention on a concrete business need instead of treating AI adoption as a goal by itself.
Governance belongs in the discussion as organizations consider how agents will work within processes. Clear roles and guardrails help define how agents fit into the work, while human review can keep decision makers involved in judging recommendations. The source describes this as a way to support people and, in some cases, automate tasks—not as a reason to hand every decision over to an agent.
People’s work may shift toward strategy
Context-aware agents could take on repetitive manual work and help coordinate tasks across teams. That could give employees more time to consider strategic choices, compare options, and plan. The opportunity is especially relevant where manual work and coordination are important parts of making a decision.
The shift will require workforce education. Employees need to understand what AI outputs mean and how those outputs affect decisions. Shah also points to the possibility that new kinds of work may emerge as technology changes, though their form can be difficult to predict in advance.
He sees potential applications across finance, workforce management, marketing, and sales, from routine operations to broader planning. The practical starting point remains the same: understand the business process, place an agent where it can help, and evaluate the result. With context, AI may help organizations reach consequential decisions sooner while leaving people better equipped to assess the choices.