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. 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.” This episode of Business Lab is produced in partnership with Celonis. Full Transcript 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. This podcast is produced in partnership with Celonis. Now, enterprise AI is at something of a crossroads. After years of experimentation, demos, and pilots, leadership teams want clear proof that scaling up will positively impact their bottom line, but many are missing an integral part of the puzzle. By acting without operational context, enterprise AI and AI agents are performing at a significant deficit. They’re applying generalized principles to specialized scenarios and coming up short, leaving business leaders understandably unconvinced and AI’s potential unrealized. Two words for you: context matters. My guest today is Devavrat Shah, chief scientist of enterprise AI at Celonis. Welcome, Devavrat. Devavrat Shah: Hello, Megan. Thanks for having me here. Megan: Thank you so much for being here with us today. Devavrat, just to start, why is it so crucial that organizations are thinking about operational context as it relates to enterprise AI right now? What makes it such a critical part of that transition I was talking about from experimentation to scale? Devavrat: It’s a fantastic question. And as you correctly noted, we’re at the cusp of becoming experimental to mainstream as far as AI is concerned. As you know, I’ve been teaching AI at MIT as a professor for more than the last two decades, so it’s no surprise that I’m very bullish about AI’s future in enterprises. The way I think about where AI can make the biggest difference in enterprises is in decision making, helping enterprise make decisions, or putting it other way, how do we bring decision intelligence to enterprises? As we think about decisions, how do we make decisions today? Take MIT Tech Review, take Celonis, take MIT, take any large organization. We have different types of decisions we want to make in very broad strokes. There are decisions which are strategic decisions like episodic, other decisions which are operational that need to be done with a high frequency. In either of them, we have some processes in place. You do this first, you do that first, you do that first, you do that first. Many of us help coordinate those things. Many of us play roles within it, what Jensen Huang calls task. And once you get those things done step by step, then you reach decision. 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. Megan: Right. To drill down into that into a bit more detail, we know many organizations are, as you say, embracing identic AI right now with this potential for increasingly autonomous decision making. If context isn’t introduced in parallel, what are the commercial risks at play? 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. Now, we have a small decision that we want to make for that very specific context is there. And without having access to that, this massive system, this extremely bright person would figure that out eventually by asking lots of questions, the way we do it as humans, and then finally do the right thing. The problem is to get to that, it will do so much compute at some level that to get simple tasks done, we are spending massive amount of compute. So that’s scenario one. In reality, that’s not going to be the scenario, and we’ll miss some important context, important constraint, important guardrail, and then we’ll make a mistake. And sometimes that mistake can be okay. It’s like me giving you a recommendation saying, go restaurant A, and maybe you may not enjoy the dinner, versus sometimes mistakes will be very, very expensive. That is me telling you what the financial forecast is and somehow that leads to the company’s doom. Megan: Right. So in an enterprise setting, these are really business critical decisions that are being made. So the impact is that much more if we get this wrong, isn’t it? And on a practical level then, what are the different ways that organizations can add in this context layer we’re talking about? How does Celonis, for example, deliver that operational context via a real time digital twin of an organization? Devavrat: Absolutely. I think if it might be useful just to double click on the history of Celonis. As you know, Celonis, as an organization, came into existence with the mission, with the goal of helping organizations adhere to their ideal processes, build better processes, enable people to work with multiple processes in an intertwined manner. And that’s where their primary, let’s call it a DN