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. As companies look to move AI from experimentation into everyday operations, the challenge ahead is giving technology the context to act usefully within complex bu…
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,…
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 w…
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.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 …
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…
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…
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 co…
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 DNA, has been about what I would call process intellige…
Now, if you want to bring decision intelligence into this, each atomic step of this process that was potentially was done before by experts, people, now we want agents to help us enable that. And that’s where knowing this context of a process within enterprise and then bringing AI agents along with that, what I would like to call it as a roled agents, exactly provide the right way to bring decision intelligence to enterprises. And this is how Cel…
Megan:Right. And then in terms of how this works as well, how does the context layer relate to both the layer below, that’s the systems of record layer, and also the layer above, that identic layer?
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 designe…
Megan:Fantastic. And for all the benefits that we’re talking about with this, are there also challenges when it comes to implementing a context layer? For example, do most organizations have the right data in place to provide AI with the context it needs? And if not, I wonder what some of the practical steps are that they might need to take to address that.
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 des…
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 pr…
Megan:Right. So that’s the real crucial step for people to be taking here. And as AI agents with context are able to make more complex, more accurate hopefully decisions, I wonder what you think the role of human intelligence and input will be moving forward, and how can organizations perhaps strike the balance between the two?
Devavrat:There are the benefits and then there are the challenges. Benefits are clear. If you have right AI agents in a right role manner going through your decision processes, hopefully they’re doing it quicker and hence your decisions are taken earlier so that your opportunity cost is not lost. You are taking decisions in time, you can plan much better, et cetera, et cetera. This is especially useful when you have to take very manual, repetitiv…
Megan:For any decision maker listening to this, how should they start implementing these changes? For example, how can they prepare their workforce? What role should governance play as well?
Devavrat:Okay, that’s a great question. If you want to start, the first thing you need to do is identify a decision where you want to bring intelligence. Let’s identify the process around it. Let’s identify each step of the process. Let’s take the most crucial, difficult step in that. Let’s try to bring AI agents to help you to do that. And then from there you go and then let’s measure how much speed are we getting, how much value are we getting?…
Second is governance. Well, we’re going to bring AI in this prescriptive manner for each task in a role. That means that for that role, we can tell what it can or cannot do very clearly. And Celonis prescribes certified role agents for these things. This helps you clearly have an understanding of what sort of contract would be for this.And the third thing, which is people, and now this is where it’s really important, is a part which gets automate…
Megan:Yes, absolutely. The pace is incredible, isn’t it, in this space? And speaking of, I suppose if we could close with a look ahead, given the right context, of course, what are the use cases perhaps for enterprise AI that are on the horizon that you think could be most impactful or perhaps what you’re simply most excited about?
Devavrat:I think I would categorize them into three types. They’re all decision intelligence, decision-making, but there are three types. One, what I would call many repetitive manual labor-intensive, so that’s one.Two, what I would call coordination style tasks. A lot of work that we do right now as an information workforce that’s spent in coordination, that’s why email spreadsheets, et cetera. A lot of that would be done much better, much quick…
Megan:That’d be fantastic, wouldn’t it? Fantastic. Thank you so much for that brilliant conversation, Devavrat. That was Devavrat Shah, chief scientist of enterprise AI at Celonis, whom I spoke with from Brighton in England.That’s it for this episode of Business Lab. I’m your host, Megan Tatum. I’m a contributing editor and host for Insights, the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Ins…
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