Making the case for context-aware AI

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 is giving technology the context to act usefully within complex business processes.

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. “You can't just do that in a vacuum,” he says. 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.

Devavrat: It's again, a crucial question to make this real. We can imagine an ideal world, but if we cannot reach the ideal world, then it's just ideal. So now in this setting, as we discussed the three layers, one being the layer which is the layer below system of record and workflow tools. In some sense, those things already exist. They don't need to be complete. They don't need to be in the cleanest possible manner. The way context model is designed with agentic's architecture, it manages to connect to those things and make sense of it.

Devavrat: That's a fantastic question, Megan. The layer below primarily is, if we call it system of record, primarily it's the workflow tools that are there and then the event logs that they generate around it. The layer above is either business user experts consuming information, or they're consuming that information maybe through agents working on top of that context layer. And this is where the way context model, as Celonis calls it, is designed is to do three things simultaneously. One is to get the right abstraction of the layer below, so that the right pieces of the process can be agentified. Two is to bring the rolled agents for each of these pieces in a structured guardrailed manner. And then the three is to be agent first so that if there are other agentic infrastructure that are already talking to it, they can naturally talk to it so that then they can bring the right output for humans or experts to review, look at it, approve, and maybe then at times automate.

And then things on top is very much in control of Celonis's infrastructure, so it's not that complicated. The crucial thing here though is when one goes to enterprise, when one opens up, let's say a system of record or workflow tool, event logs or things like that, one has to go through, what I would call, classical InfoSec to make sure that you can trust a vendor's software that's coming into your organization. And I think that is the primary process that is a step between, before and after, if I may call it.

Now, it's those tasks, or what I would call roles, that's where AI can bring in an atomic manner agentic power so that we can at least parts of decisions, if not all of the decisions, we can augment support and maybe sometimes when it's a painful automate. And to get to that, to know which role to do, which task to do, we really need to know the context. You can't just do that in a vacuum. And that's why context with AI put together in the right way would bring what I would call decision intelligence to enterprise. And that's where I think there'll be the biggest transformation that would come.

Devavrat: Fantastic. Let's just take the setting I was describing. We are trying to automate a role or support a role or augment a role depending on the type of decision, depending upon type of task. And let's for a second imagine that all these, as they're called, frontier labs have built these amazing models and amazing AI infrastructure so that everything that's happened in the universe and it's likely to happen in the universe is all there.

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 instead of just handing decisions over to agents.

Looking to the future, Shah sees opportunities extending from routine operational tasks to enterprise-wide planning in areas such as finance, workforce management, marketing, and sales. With the right context, AI could help organizations evaluate options and make consequential decisions more quickly. As Shah puts it, “Now, maybe [resource planning] should not take that much time, and people are actually making the right choices quickly.”

Megan Tatum: From MIT Technology Review, I'm Megan Tatum, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.