# How Open Context Layers Help Enterprises Build, Govern, & Scale Agentic AI - with Prukalpa Sankar of Atlan

- **Show:** The AI in Business Podcast
- **Published:** 8 Jan 2026
- **Source:** https://podcast.emerj.com/how-an-open-context-layers-help-enterprises-build-govern-scale-agentic-ai-with-prukalpa-sankar-of-atlan
- **Transcript:** https://podcasts.opennash.com/shows/ai-in-business/how-open-context-layers-help-enterprises-build-govern-scale-agentic-ai-with-pruk-854f5d/
- **Provider:** OpenNash Podcasts (machine-generated)

## In brief

Enterprise AI pilots mostly fail to reach production because the AI lacks business context, not because data is scarce or models are weak. Prukalpa Sankar of Atlan argues that a living 'context layer' is needed. It combines data, business meaning, domain knowledge and governance so AI agents can reason reliably about what a company's terms and data actually mean.

The tension is between old-style governance programs, which take years and are built once, and the need to get AI live quickly. Sankar proposes starting with a few high-value use cases, testing against a verified benchmark, and continually enriching context with business experts. Her pitch has a commercial angle, as the episode is sponsored by Atlan.

## Key insights

1. **AI pilots fail from missing context, not data** Sankar says the gap has three parts: finding the data, teaching AI what business terms mean, and governing both data and AI. Her example is 'top ten customers', which means different things to sales and customer success.
2. **Context layers must be dynamic, not one-off** Sankar argues that old governance programs that took three years to go live fail for AI. Her alternative is a feedback loop in which each user clarification, such as defining 'top ten' by adoption, is saved back into the context store.
3. **Business teams must co-own AI readiness** Unlike earlier IT projects, which could reach production with only business sponsorship, Sankar says AI needs business experts working as 'context engineers' because they hold the domain knowledge. This changes roles and operating models.
4. **Start with a few use cases and an eval set** Sankar recommends picking business use cases with measurable ROI and first building a test set of common questions with verified SQL answers. Humans then keep verifying the AI's output against it, and she says accuracy rose ten times within a month for some customers.
5. **Context work lifts accuracy and adoption** Sankar cites Workday improving accuracy by over five times after adopting a context layer, VMO2 onboarding over 6,000 employees, and Mastercard building an agentic governance framework. These are customer results she reports from Atlan's conference.

## Questions this episode answers

### Why do so many enterprise AI projects fail before reaching production?

Sankar says the cause is a 'context gap': companies often can't find their data, the AI doesn't know what their business terms mean, and data and AI use aren't properly governed. She cites surveys putting the failure rate between 75% and 95%.

### What is a context layer in plain English?

It is a continually updated store of knowledge about a company, covering its data, the meaning of business terms, its domain and its processes, that AI can consult. Sankar says it is different from a traditional semantic layer, which mainly defines data terms.

### What does the 'top ten customers' example show?

A customer's AI data analyst failed because 'top ten' meant different things to different teams. Sales meant by revenue, while customer success meant by ratings or NPS (a customer satisfaction score), so the AI needed that business meaning spelled out.

### How should a company begin building context for AI?

Sankar advises starting with a few business use cases with measurable ROI rather than a huge multi-year project. First build a test set of common questions with verified answers, then have business experts refine the AI's results over time.

### Why does the business side need to be involved in AI projects?

Sankar says the domain expertise sits with business teams, and AI can't reach production without them. She calls them 'context engineers' who supply the meaning that technical teams can't.

## Facts

- **Prukalpa Sankar** [0:04:35]: "over $250 billion was spent on data and analytics. And 75% of those projects used to fail."
- **Matthew DeMello** [0:00:17]: "why seventy-five to 95% of enterprise AI pilots fail in production, at least according to MIT."
- **Prukalpa Sankar** [0:05:29]: "We call it the AI wild gusm."
- **Prukalpa Sankar** [0:06:48]: "we are deploying an HR chatbot that can use my payroll data, nothing else is allowed to use my payroll data."
- **Prukalpa Sankar** [0:06:48]: "AI governance regulation, EU AI Act."
- **Prukalpa Sankar** [0:07:51]: "Palantir talks about this idea of an ontology. We've talked about ideas of knowledge graphs."
- **Prukalpa Sankar** [0:08:48]: "if the sales team asks for top ten, it means top ten customers by revenue. If the success team asks for top ten, it means top ten customers by ratings or NPS."
- **Prukalpa Sankar** [0:12:21]: "We had a customer that was trying to get AI into production for their C E O for about fifteen months"
- **Prukalpa Sankar** [0:13:15]: "Forty five years ago, the average company on the S P 500 used to stay there for roughly seventy five years."
- **Prukalpa Sankar** [0:13:15]: "In the last decade, the average company in the S P 500 stays there for fifteen years."
- **Prukalpa Sankar** [0:15:01]: "we just got off our customer conference. And we had some phenomenal customers, customers like Workday and Mastercard and Virgin Media O two"
- **Prukalpa Sankar** [0:15:01]: "their perfectly governed BI data wasn't consumable by AI."
- **Prukalpa Sankar** [0:15:01]: "they were able to improve accuracy by over five times, which is the difference between an AI and pilot and AI in production."
- **Prukalpa Sankar** [0:15:38]: "VM O two talked about how they onboarded over 6,000 employees"
- **Prukalpa Sankar** [0:16:07]: "they're building an agentic governance framework for, uh, you know, Very cool, as they have over, uh, one thirds of their products are AI first products today."
- **Prukalpa Sankar** [0:16:07]: "you would think we're a we're a finance company, but we're actually a data company."
- **Prukalpa Sankar** [0:20:05]: "we can pull metadata from your Slack where people are asking you questions."
- **Prukalpa Sankar** [0:20:52]: "within a month, their accuracy has gone up ten times on that test set."
- **Prukalpa Sankar** [0:21:36]: "the metadata architecture needs to be iceberg native, and it needs to be open and interoperable"
- **Prukalpa Sankar** [0:21:12]: "I think golden approaches fail."

## Transcript

**[0:00:17] Matthew DeMello:** Welcome everyone to the AI in Business podcast. I'm Matthew Demello, Editorial Director here at Emerge AI Research. Today's guest is Prukalpa Shankar, co-founder and CEO at Atlan. Atlan is a software provider whose metadata platform provides enterprises with a context layer that stitches together their data and AI infrastructure. With these tools, teams of human and AI agents enrich business context with nuance and security measures that make data easier to find, trust, and govern across their organizations. Prukalpa joins us on today's show to unpack why seventy-five to 95% of enterprise AI pilots fail in production, at least according to MIT.

**[0:01:00] Matthew DeMello:** And why solving for context, rather than just data volume or model complexity, is becoming the decisive factor. Our conversation also highlights how leading enterprises are closing this context gap from reshaping operating models so business teams can co-own AI readiness to using dynamic context layers that boost accuracy and accurate production deployments. Leaders will hear what's driving real ROI in the enterprise and what context readiness means for any organization aiming to operationalize AI in 2026. Today's episode is sponsored by Atlan, but first, are you driving AI transformation at your organization?

**[0:01:39] Matthew DeMello:** Or maybe you're guiding critical decisions on AI investments, strategy, or deployment? If so, the AI in Business podcast wants to hear from you. Each year, Emerge AI Research features hundreds of executive thought leaders, everyone from the CIO of Goldman Sachs to the head of AI at Raytheon and AI pioneers like Yoshua Bengio. With nearly a million annual listeners, AI in Business is the go-to destination for enterprise leaders navigating real-world AI adoption. You don't need to be an engineer or a technical expert to be on the program.

**[0:02:13] Matthew DeMello:** If you're involved in AI implementation, decision making, or strategy within your company, this is your opportunity to share your insights with a global audience of your peers. If you believe you can help other leaders move the needle on AI ROI, visit Emerge dot com and fill out our thought leader submission form. That's Emerge dot com and click on Be an Expert. You can also click the link in the description of today's show on your preferred podcast platform.

**[0:02:42] Matthew DeMello:** That's Emerge dot com slash expert one. Again, that's E-M-E-R-J dot com slash expert one. And without further ado, here's our conversation with Prukalpa. Prukalpa, thank you so much for joining us on today's show.

**[0:03:07] Prukalpa Sankar:** I am so excited to be here. Thank you for having me.

**[0:03:10] Matthew DeMello:** Absolutely. We've seen years of investment in data platforms, yet most enterprises are struggling to operationalize data at the speed and reliability demanded by today's environment. Long-standing issues in this space include fragmented metadata, static catalogs, unclear lineage. Uh. And these problems block AI analysts and agentic systems, or at least emerging agentic systems, from succeeding in production, much less providing that that business value and ROI that we're all looking for. We have organizations like General Motors and Mastercard, they're proving that the problem isn't the data itself.

**[0:03:48] Matthew DeMello:** What's missing is a very, very big word that that escapes some definition, but it is context. Uh, and without machine readable meaning, connecting data to business logic, even the most advanced AI systems can't reason reliably. Which led to that recent MIT study citing that 95% of AI pilots stall in the production phase. I was at a I was actually at a a manufacturing conference very recently, and that was that was all the buzz. That's all anybody really could talk about.

**[0:04:17] Matthew DeMello:** But being able to engineer that context directly into data operations is beginning to show promise. Uh, Prukalpa, you've been in this space of enterprise data for the better part of fifteen years. What are the reasons you're seeing most AI pilots fail in the enterprise? And what does this say about traditional governance models?

**[0:04:35] Prukalpa Sankar:** Yeah, it's kind of interesting because I feel like we're on the brink of of history repeating itself, but just at a larger scale. You know, think about the world of data and analytics over the last decade, over $250 billion was spent on data and analytics. And 75% of those projects used to fail. Uh, I remember actually when we used to be like, our first slide, like 75% of these projects fail.

**[0:05:01] Matthew DeMello:** It's getting worse.

**[0:05:02] Prukalpa Sankar:** And there's a gap between, you know, like, where your where your data is and your business context, and that's that that's what we used to talk about. And what's become really interesting is that it's getting worse. We're spending a lot more money, hundreds and billions of dollars on AI, and now, you know, 95% of them are failing. And you know, roughly whichever survey you study says something slightly different, but it's somewhere between 75% and 95%, right?

**[0:05:29] Prukalpa Sankar:** Like, it's not, we're not talking about every AI pilot that's going to production. Uh, earlier this year at Atlan, we, uh, ran this massive survey and we interviewed a bunch of data and AI leaders trying to figure out what was going on. And uh, we realized that there's kind of this chasm between AI in pilots and AI in production. We call it the AI wild gusm. And as you start crossing this chasm, you start hearing these things, right?

**[0:05:57] Prukalpa Sankar:** For example, we we call it this context gap between the left and the right. The first lack of context, which is data. I had a CIO the other day tell me, I have a thousand AI projects in my roadmap, and I don't even know where to find my data. Uh, that's my first problem. Uh, the first thing that an AI agent does, it runs a search. What is it searching, right? And that's problem one. Second problem is business meaning.

**[0:06:21] Prukalpa Sankar:** The beautiful thing that AI has done, actually, LLMs have done, is that LLMs understand language. But who's going to teach LLMs business meaning? I had a CIO the other day tell me, TAM on the open internet can mean a hundred things, but in my company, TAM means total addressable market. How do we take that enterprise context and feed it into AI? And the final step is governance, and this is governance of the data and now governance of the AI too, right?

**[0:06:48] Prukalpa Sankar:** From, you know, the I I I had a CIO the other day say, we are deploying an HR chatbot that can use my payroll data, nothing else is allowed to use my payroll data. I don't want, you know, someone asking a question and it spits out the salary of my CEO, right? Now, that sounds like an easy thing to do. It's really hard to do in practice. And now you have now this whole new plethora of sprawl of AI, AI agents, AI applications, AI lineage, what data is feeding it, AI governance regulation, EU AI Act.

**[0:07:18] Prukalpa Sankar:** Uh, what policies do I manage my AI around? And that's kind of like the third last step of how do you get this final? And this is what we call the context stack, as what's preventing us from being able to take AI in pilots and into AI in production.

**[0:07:33] Matthew DeMello:** Yes, yes, and a context can be a very big word, but I think even trying to, you know, anchor what we mean here with business meeting, I think goes a long way. How can enterprises close that context gap and build AI systems that truly understand business meeting? What does this look like in practice?

**[0:07:51] Prukalpa Sankar:** Yeah, it's interesting. I think there are, like, many different layers of how people are starting to talk about this. There's, you know, this idea of in the data world, we've talked about this idea of the semantic layer. Palantir talks about this idea of an ontology. We've talked about ideas of knowledge graphs. We at Atlan are beginning to believe that the answer is actually in some of this, but is kind of fundamentally different. Uh, we think the answer is in this broad context layer.

**[0:08:19] Prukalpa Sankar:** And the context layer for AI looks very different than the context layer for data. Let me give you some examples. We, uh, a customer that was trying to roll out a AI data analyst. And, uh, it was an agent that was meant to look at their data and answer questions to their business. Now they roll it out, and they have a business user that asks them a question. Tell, out my top ten customers. Now, if you were looking at a traditional semantic layer, your end users, you you would basically say, we don't know how to define customers.

**[0:08:48] Prukalpa Sankar:** We have four different definitions. But actually, in this case, the problem was that, uh, they didn't know how to define top ten. Because if the sales team asks for top ten, it means top ten customers by revenue. If the success team asks for top ten, it means top ten customers by ratings or NPS. And so there are layers of enterprise context, which we think of as there's your data context, there's your business meaning context, there's your domain context, there's your process context.

**[0:09:18] Prukalpa Sankar:** All of this needs to be coming together. It ideally is a dynamic layer because enterprises are changing every day. You don't just build this context layer one off, you build this context layer and in a way that is dynamic, it changes alongside your business. And then it keeps getting feedback loops. The worst mistake that we can do in this world is to work governance programs just like in the old days. Where, you know, you start a governance program and then you'd say, Hey, it's going to take three years to get this live.

**[0:09:44] Prukalpa Sankar:** The reality is like, we need to get AI live, like, you know, tomorrow. And so, how do you think about the context feedback loop? So, for example, now if a business user is asking this question, what are my top ten customers? You can have AI respond and say, in your company, top ten customers is defined by adoption and by revenue. Which of the two do you think is important in this case? The user responds adoption. Then you say, hey, that makes sense.

**[0:10:10] Prukalpa Sankar:** Do you think for your entire team, this is how they would define it? Should we take that and feed back to the context store? Yes, it goes back and feeds back into the context. So, how can you take every live interaction that your humans are doing and codify it into this context layer that helps AI start to act autonomously? I think that's kind of what we have to find a way to do as enterprises.

**[0:10:31] Matthew DeMello:** Absolutely. And you talked about those differing layers, the B, the business meaning layer, the the domain layers, et cetera, within this context layer. Just want to get a sense of kind of the silos here, just because always the promise with AI is that, you know, the silos are coming down. To what extent are these layers distinct, and W why put them in these categories to build up this this concept of of context, uh, as a as a layer in the data?

**[0:10:58] Prukalpa Sankar:** Yeah, I mean, that's interesting. I think it's not siloed, but it's different, and it's important in this time.

**[0:11:06] Matthew DeMello:** Differentiated, right?

**[0:11:07] Prukalpa Sankar:** You can't have just one kind of context, you can't just have technical context, you can't just have business context. You have. You need to have all these layers to operate together for AI to be able to operate effectively. And it needs a effort for the enterprise to bring the business and your technical teams together to be able to build the context that needs AI to work. I think the most important thing around making AI work, unlike any other IT project in the old world, where the IT, you could actually take the IT project to production just with business sponsorship.

**[0:11:41] Prukalpa Sankar:** Now you cannot take AI to production without business being involved. They have to be context engineering with you because the domain expertise is with business. And I think that changes fundamentally the operating model that we need to operate on and the roles and responsibilities that organizations take in being able to make sure that we're actually able to get a living, living brain. Uh, they call it a digital twin, sometimes of the of the organization.

**[0:12:05] Matthew DeMello:** Absolutely. And and what's at stake here, especially if organizations, enterprises, are going the more traditional route, the semantic layers, as you were defining before. What is what stands to be lost if they don't pursue a context layer, as you're describing?

**[0:12:21] Prukalpa Sankar:** I mean, these projects are going to fail. Like, there is, uh. We had a customer that was trying to get AI into production for their C E O for about fifteen months, and it's existential. At this point, here's the challenge, right? There's this big hype cycle, this insane hype cycle. Everyone wants AI, everyone looks at ChatGPT, and ChatGPT is this beautiful moment of democratization. And then your business users are like, I want that. I want that on my enterprise data.

**[0:12:50] Prukalpa Sankar:** I want that to work on my business. And enterprises are not ready. And it's going to take foundational investments to get us to ready. The bar is higher. Uh, the challenge is, I I like to say this, uh, people treat AI to a bar that's higher than the bar they have for humans. And so the accuracy bar is also high. Uh, this is a change management exercise. And so, how do you actually find a way to get this AI into production?

**[0:13:15] Prukalpa Sankar:** And I think the real challenge, why does everybody, why is everybody bored, care about AI? I I truly believe that most of our foundational business models are at an existential threat scenario. Forty five years ago, the average company on the S P 500 used to stay there for roughly seventy five years. In the last decade, the average company in the S P 500 stays there for fifteen years. Today, in the AI world, I don't know, maybe you stay there for three years.

**[0:13:42] Prukalpa Sankar:** And the so the cycle of rapid innovation is existential. Our customers, like how custom how customers are interacting with technology, with your website, with your digital storefront, with all of this, it's changing dramatically. And I think that's the existential threat that every business has today. This is an opportunity. Great companies will use this as an opportunity. It's a super cycle. Uh, and then there will be a bunch of companies that get left behind. I think that's the existential.

**[0:14:09] Prukalpa Sankar:** And then for the leaders, I mean, one of two things is gonna happen. Like, they're going to either figure this out and they're gonna be the heroes of this transformation, or they're going to, quite frankly, not have a job in three or four years. And what I'm most excited about is the leaders that are the change agents that are taking this on and are having a business first approach to getting. And there's a lot of education here, cause, quite honestly, a business, like, the business doesn't understand context layer or auto, like, they just like, solve my business problem.

**[0:14:38] Prukalpa Sankar:** And I think that's a that's a great opportunity for techno-business leaders to just take a much larger role in this transformation that businesses are going to go through in the next decade.

**[0:14:49] Matthew DeMello:** Absolutely. Uh. W What lessons are we seeing from organizations that are already building context driven systems in terms of, uh, impact on the ground in that ROI we were talking about before?

**[0:15:01] Prukalpa Sankar:** Yeah, I mean, uh, we just got off our customer conference. And we had some phenomenal customers, customers like Workday and Mastercard and Virgin Media O two, all talking about how they're building context layers and how they're building the context layer to help their AI future. We have found that, you know, customers like Workday, for example, their chief data officer talked about how, you know, their perfectly governed BI data wasn't consumable by AI. And when they started moving towards this context layer approach, they were able to improve accuracy by over five times, which is the difference between an AI and pilot and AI in production.

**[0:15:38] Matthew DeMello:** Of course.

**[0:15:38] Prukalpa Sankar:** We had, you know, VM O two talked about how they onboarded over 6,000 employees and achieved over a million platform. In terms of, you know, business starting to use data for their end user use cases, uh, through this idea of like a data product and a context product inside their organization. So, I think what what we're seeing across the board is right from organizations that are using this for business to start making better business decisions to.

**[0:16:07] Prukalpa Sankar:** We had the chief data officer of Mastercard recently talked about how they're building an agentic governance framework for, uh, you know, Very cool, as they have over, uh, one thirds of their products are AI first products today. And, you know, he started by saying, well, you know, you would think we're a we're a finance company, but we're actually a data company. And I think we're seeing that massive transformation that is driving usability and adoption and ROI for these end use cases.

**[0:16:38] Matthew DeMello:** Absolutely. And I I think you're you're bringing up a lot of much more than just short-term wins to to really sell that board conversation as, as you were mentioning before. But thinking a bit more long term, how can business and data leaders use a context layer to drive sustained business value? How are we seeing this, uh, play out as plans for 2026 come together?

**[0:17:01] Prukalpa Sankar:** Yeah, it's super interesting. I, uh, the the chief data officer of DGT said something really interesting at our conference. And he said, I talk about context readiness before AI readiness. And uh, it's interesting that context actually is something that can bind data and business together. Like, actually, that's missing between the two, right? Data is foundationally technical. Context makes it business ready, and business is the one that has context. Like, that's the context that's always been missing in the AI world.

**[0:17:33] Prukalpa Sankar:** So, the best operating models that we see in this, you know, we have something we call a blueprint around the operating model of how to make this work inside organizations. But we, we, we always say, start with the use case, get a few AI use cases to production. Like, let's not sit in a room and let's not say, like, we are going to build this large, complex graph and it'll go into production in four years. Like, that's not how.

**[0:17:55] Prukalpa Sankar:** So, let's pick a few use cases, and those should be business use cases that drive measurable ROI. In these use cases, let's basically now build your context foundation. And let's keep context engineering on these use cases. Let's use, let's make sure we build your foundational technology stack in a way that you are able to build this in a way that's reusable and reproducible. And ideally, the context engineering that you're using for this one use case can be leveraged by other use cases in the world.

**[0:18:21] Prukalpa Sankar:** And in this process, bring the data leader, own the development of the context. They need the business to be involved as context engineers so that they can get the domain context in and they're ultimately shipping towards production. Uh, and it's a beautiful moment because context now can be embedded in the process of getting an AI use case to be live and not an afterthought. Like, you know, we're not going back and trying to drive documentation in the past, right?

**[0:18:48] Prukalpa Sankar:** Like, we're we're basically just getting all our context in for the use case that's driving business value. But we're doing it in a way that's reusable and reproducible and part of this complex living graph that can be reused in the future world.

**[0:18:59] Matthew DeMello:** Yeah, absolutely. And just to really articulate the difference between, uh, kind of the status quo way we're seeing with, you know, just the semantic layer approach and the context layer that you're describing. You know, for businesses that are just beginning this process of of building the context layer, getting that context readiness prepared first before AI, as, uh, as, uh, the guests at your conference were were describing, the the gentleman from, uh, the, uh, the folks from GKP, really getting a sense of, oh, how will they know that they are, uh, that they're getting the business results, or that the systems are working appropriately with that context layer in those short-term wins?

**[0:19:37] Prukalpa Sankar:** Yeah, so what we typically recommend, we have a framework for this, but we actually recommend by starting by creating a a great testing eval framework. So that we know what does it take for this AI to succeed in production, right? Uh, and the eval framework is a really important part. So what we do is we start by saying this is the eval framework. So, for example, if you're shipping an AI analyst for this use case, this domain, uh, what are the top questions that your business users are already asking about this?

**[0:20:05] Prukalpa Sankar:** We can actually bootstrap this because we can pull metadata, your business intelligence tools, we can pull metadata from your Slack where people are asking you questions. We can say, these are the typical questions. Uh, against this, we can also create your your golden verified SQL query. So you can say, hey, the verified way of answering these these questions is like this. Because we already know from your BI that, you know, this is how you measure ARR, for example, right?

**[0:20:30] Prukalpa Sankar:** Like, we can create that. Then what we do after we create that foundation, we call it bootstrapping, is when we have humans start to get involved in more context engineering. And ideally, you are basically presenting them with options. So you say, this is what the LLM did when we just put the LLM on it. This is the verified, like, keep verifying it. So there's a human in the loop process that we run there, and you keep improving the benchmark of the accuracy.

**[0:20:52] Prukalpa Sankar:** And so we've seen with customers from the time we start with them within a month, their accuracy has gone up ten times on that test set. And then we say, you can now like, ship, you know, uh, let's ship it to business users. And this is where my biggest realization has been that this is not a one off process. It is a always on enrichment process. You're never going to have all the context, like that's never going to happen.

**[0:21:12] Prukalpa Sankar:** Also, because your context keeps changing, your business context also keeps changing. Uh, and I think that's the biggest change, I would say, in the context layer versus the kind of like traditional semantic layer approach, where I think a lot of people try to build this. I mean, there was a lot of hype about this in the industry in in 2020. You know, they they tried to build, people tried to build this golden approach, and I think golden approaches fail.

**[0:21:36] Prukalpa Sankar:** Like, they're just, it's too hard to get off the ground. You don't get business behind it, you don't get business value, you lose funding. Uh, and so I think the approach of being able to solve, embed this in the way you get a use case live is what we see as the biggest differentiator in the operating model. And then, you know, we can talk about the tech, how do you get, you know, archit like the metadata architecture needs to be iceberg native, and it needs to be open and interoperable, and it needs to be ready for real time workloads.

**[0:22:04] Prukalpa Sankar:** Like, there's a lot of things about the tech, but more than that, the just the tech, I think there's also the operating model here is important.

**[0:22:10] Matthew DeMello:** Absolutely. And, uh, I think just really even differentiating the benefits, uh, of a context layer first, and then kind of working backwards towards, you know, what is the difference within the systems is is gonna bring a lot of clarity to what we're seeing. Um, especially in in that, uh, MIT report, uh, we were talking about at the beginning of the show, which sim seems to have, uh, really sent, uh, uh, some some, uh, waves through through the entire industry.

**[0:22:38] Matthew DeMello:** Of course, yes, uh, as I was saying before, but Prukalpa, thank you so much for being with us on today's show. It's been very insightful.

**[0:22:45] Prukalpa Sankar:** Thanks, Matthew. Really appreciate it.

**[0:23:00] Matthew DeMello:** Wrapping up today's episode, I think there were at least three critical takeaways for enterprise data analytics and AI leaders from across industries to take from our conversation today with Prukalpa Sankar, co-founder and CEO at Atlan. First, AI fails in production when systems lack business context. Models can't deliver reliable outputs without shared, machine-readable meaning embedded into the data they act on. Second, context isn't a static governance artifact. It requires an ongoing operating model where business and technical teams continually refine definitions, logic, and feedback loops.

**[0:23:35] Matthew DeMello:** Finally, real ROI comes from starting with high-value use cases, building context around them, and reusing that foundation across the enterprise to accelerate accuracy, adoption, and deployment. Interested in putting your AI product in front of household names in the Fortune 500? Connect directly with enterprise leaders at market-leading companies. Emerge can position your brand where enterprise decision makers turn for insight, research, and guidance. Visit emerge dot com slash sponsor for more information. Again, that's E-M-E-R-J dot com slash S-P-O-N-S-O-R.

**[0:24:13] Matthew DeMello:** I'm your host, at least for today, Matthew Demello, Editorial Director here at Emerge AI Research. On behalf of Daniel Fagella, our CEO and Head of Research, as well as the rest of the team here at Emerge, thanks so much for joining us today, and we'll catch you next time on the AI in Business podcast.