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.<\/p>\n
For Devavrat Shah, chief scientist of enterprise AI at Celonis, that context is central to what he calls \u201cdecision intelligence.\u201d 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. \u201cYou can\u2019t just do that in a vacuum,\u201d 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.<\/p>\n
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.<\/p>\n
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.<\/p>\n
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, \u201cNow, maybe [resource planning] should not take that much time, and people are actually making the right choices quickly.\u201d<\/p>\n
This episode of Business Lab is produced in partnership with Celonis.<\/em><\/p>\n Full Transcript<\/strong><\/p>\n Megan Tatum:<\/em> From MIT Technology Review, I\u2019m 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.<\/p>\n This podcast is produced in partnership with Celonis.<\/p>\n 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\u2019re applying generalized principles to specialized scenarios and coming up short, leaving business leaders understandably unconvinced and AI\u2019s potential unrealized.<\/p>\n Two words for you: context matters.<\/p>\n My guest today is Devavrat Shah, chief scientist of enterprise AI at Celonis.<\/p>\n Welcome, Devavrat.<\/p>\n Devavrat Shah:<\/em> Hello, Megan. Thanks for having me here.<\/p>\n Megan:<\/em> 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?<\/p>\n Devavrat:<\/em> It\u2019s a fantastic question. And as you correctly noted, we\u2019re at the cusp of becoming experimental to mainstream as far as AI is concerned. As you know, I\u2019ve been teaching AI at MIT as a professor for more than the last two decades, so it\u2019s no surprise that I\u2019m very bullish about AI\u2019s future in enterprises.<\/p>\n 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.<\/p>\n Now, it\u2019s those tasks, or what I would call roles, that\u2019s 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\u2019s 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\u2019t just do that in a vacuum. And that\u2019s why context with AI put together in the right way would bring what I would call decision intelligence to enterprise. And that\u2019s where I think there\u2019ll be the biggest transformation that would come.<\/p>\n Megan:<\/em> 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\u2019t introduced in parallel, what are the commercial risks at play?<\/p>\n Devavrat:<\/em> Fantastic. Let\u2019s 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\u2019s for a second imagine that all these, as they\u2019re called, frontier labs have built these amazing models and amazing AI infrastructure so that everything that\u2019s happened in the universe and it\u2019s likely to happen in the universe is all there.<\/p>\n 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\u2019s scenario one.<\/p>\n In reality, that\u2019s not going to be the scenario, and we\u2019ll miss some important context, important constraint, important guardrail, and then we\u2019ll make a mistake. And sometimes that mistake can be okay. It\u2019s 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\u2019s doom.<\/p>\n Megan:<\/em> 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\u2019t it? And on a practical level then, what are the different ways that organizations can add in this context layer we\u2019re talking about? How does Celonis, for example, deliver that operational context via a real time digital twin of an organization?<\/p>\n Devavrat:<\/em> 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\u2019s where their primary, let\u2019s call it a DNA, has been about what I would call process intelligence. Take how enterprises help put multiple things together, digitize them, enable people to get their processes better, understand where the issues are, and then how do you re-engineer the process within organizations so that things can happen better.<\/p>\n 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\u2019s 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 Celonis brings context to help enterprise make better decision.<\/p>\n Megan:<\/em> Right. And then in terms of how this works as well, how does the context layer relate to both the layer below, that\u2019s the systems of record layer, and also the layer above, that identic layer?<\/p>\n Devavrat:<\/em> That\u2019s a fantastic question, Megan. The layer below primarily is, if we call it system of record, primarily it\u2019s 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\u2019re 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.<\/p>\n Megan:<\/em> Fantastic. And for all the benefits that we\u2019re 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.<\/p>\n Devavrat:<\/em> It\u2019s again, a crucial question to make this real. We can imagine an ideal world, but if we cannot reach the ideal world, then it\u2019s 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\u2019t need to be complete. They don\u2019t need to be in the cleanest possible manner. The way context model is designed with agentic\u2019s architecture, it manages to connect to those things and make sense of it.<\/p>\n And then things on top is very much in control of Celonis\u2019s infrastructure, so it\u2019s not that complicated. The crucial thing here though is when one goes to enterprise, when one opens up, let\u2019s 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\u2019s software that\u2019s 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.<\/p>\n Megan:<\/em> Right. So that\u2019s 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?<\/p>\n Devavrat:<\/em> 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\u2019re 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, repetitive, grungy tasks where access to workforce, coordination between people become crucial parts of the decision process. In a sense, that\u2019s where the huge value can come in. The flip side of that is that means that people won\u2019t be doing that.<\/p>\n On the other hand, the other type of decision that we always make, which are strategic decisions. It could be a small strategic decision, it could be large strategic decision, but the strategic decisions that typically most organizations don\u2019t have bandwidth to take to think, that opportunity would open up and that\u2019s where likely lots of humans and experts would engage more. In a sense, now they would have better support helping them rationally think through what are the choices that they have, how do different choices compare? And then at the end of the day, it\u2019s organization\u2019s role to decide which of the option to take because it belongs to their interest in mind where they want to go.<\/p>\n And so in a sense, there will be what I would call shift of workforce in terms of instead of doing a lot more repetitive, a lot less repetitive, but a lot more strategic work and that\u2019s where people will have to spend time.<\/p>\n The other place where I am personally very optimistic is like every technology change creates new work that it\u2019s difficult to predict at the time of change. My hope is that there will be another type of opportunity and work that will be created where we\u2019ll be engaging our workforce. Goes without saying, this would mean that we will have to carefully think through reeducating or educating some of our workforce thoughtfully. And I think as an academic in me, as an educator in me, I really feel that the timescale at which these changes are happening are so fast. So we need to also act in a similar manner.<\/p>\n Megan:<\/em> 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?<\/p>\n Devavrat:<\/em> Okay, that\u2019s 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\u2019s identify the process around it. Let\u2019s identify each step of the process. Let\u2019s take the most crucial, difficult step in that. Let\u2019s try to bring AI agents to help you to do that. And then from there you go and then let\u2019s measure how much speed are we getting, how much value are we getting? And that\u2019ll help us understand whether this was worth it. So that\u2019s one.<\/p>\n Second is governance. Well, we\u2019re 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.<\/p>\n And the third thing, which is people, and now this is where it\u2019s really important, is a part which gets automated, but then there\u2019s a part where experts will be in the loop and this is where they need to understand the outputs of AI, how do you work with that and what its implication would be? And this is where some AI education is needed and then some tool education is needed.<\/p>\n Megan:<\/em> Yes, absolutely. The pace is incredible, isn\u2019t 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\u2019re simply most excited about?<\/p>\n Devavrat:<\/em> I think I would categorize them into three types. They\u2019re all decision intelligence, decision-making, but there are three types. One, what I would call many repetitive manual labor-intensive, so that\u2019s one.<\/p>\n Two, what I would call coordination style tasks. A lot of work that we do right now as an information workforce that\u2019s spent in coordination, that\u2019s why email spreadsheets, et cetera. A lot of that would be done much better, much quicker and less boring, so to speak.<\/p>\n And then the third is where, especially as a support to human decision-makers to provide strategic support augmentation, and that\u2019s a third type of decisions that would be done. If I think of any planning process in enterprises, so financial planning, workforce planning, marketing, sales, product, anything, all of those are business critical decisions that we make that take annual planning process, take annual time. Now maybe it should not take that much time, and people are actually making the right choices quickly.<\/p>\n Megan:<\/em> That\u2019d be fantastic, wouldn\u2019t 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.<\/p>\n That\u2019s it for this episode of Business Lab. I\u2019m your host, Megan Tatum. I\u2019m a contributing editor and host for Insights, the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology, and you can find us in print, on the web, and at events each year around the world. For more information about us and the show, please check out our website at technologyreview.com.<\/p>\n This show is available wherever you get your podcasts. And if you enjoyed this episode, we hope you\u2019ll take a moment to rate and review us. Business Lab is a production of MIT Technology Review, and this episode was produced by Giro Studios. Thanks so much for listening. Goodbye.<\/p>\n This content was produced by Insights, MIT Technology Review\u2019s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.<\/em><\/p>\n \n<\/p>","protected":false},"excerpt":{"rendered":" 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 \u201cdecision intelligence.\u201d 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. \u201cYou can\u2019t just do that in a vacuum,\u201d 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, \u201cNow, maybe [resource planning] should not take that much time, and people are actually making the right choices quickly.\u201d This episode of Business Lab is produced in partnership with Celonis. Full Transcript Megan Tatum: From MIT Technology Review, I\u2019m 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\u2019re applying generalized principles to specialized scenarios and coming up short, leaving business leaders understandably unconvinced and AI\u2019s 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\u2019s a fantastic question. And as you correctly noted, we\u2019re at the cusp of becoming experimental to mainstream as far as AI is concerned. As you know, I\u2019ve been teaching AI at MIT as a professor for more than the last two decades, so it\u2019s no surprise that I\u2019m very bullish about AI\u2019s 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\u2019s those tasks, or what I would call roles, that\u2019s 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\u2019s 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\u2019t just do that in a vacuum. And that\u2019s why context with AI put together in the right way would bring what I would call decision intelligence to enterprise. And that\u2019s where I think there\u2019ll 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\u2019t introduced in parallel, what are the commercial risks at play? Devavrat: Fantastic. Let\u2019s 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\u2019s for a second imagine that all these, as they\u2019re called, frontier labs have built these amazing models and amazing AI infrastructure so that everything that\u2019s happened in the universe and<\/p>","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"pmpro_default_level":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"_pvb_checkbox_block_on_post":false,"footnotes":""},"categories":[52,5,7,1],"tags":[],"class_list":["post-126244","post","type-post","status-publish","format-standard","hentry","category-ai-club","category-committee","category-news","category-uncategorized","pmpro-has-access"],"acf":[],"yoast_head":"\n