In this episode, Dana Gardner sits down with three industry experts from Snowflake: Rosemary DeAragon, Rinesh Patel, and Tim Long to explore how AI will transform retail, financial services, and global manufacturing in 2026. Together, they break down the forces reshaping consumer behavior, enterprise operations, and competitive dynamics across these sectors. Across all three industries, one theme is clear: in 2026, AI will no longer be a side experiment. It will be a foundational driver of growth, efficiency, and competitive advantage.
In this episode, Dana Gardner sits down with three industry experts from Snowflake: Rosemary DeAragon, Rinesh Patel, and Tim Long to explore how AI will transform retail, financial services, and global manufacturing in 2026. Together, they break down the forces reshaping consumer behavior, enterprise operations, and competitive dynamics across these sectors. Across all three industries, one theme is clear: in 2026, AI will no longer be a side experiment. It will be a foundational driver of growth, efficiency, and competitive advantage.
[00:00:00] Producer: Hello and welcome to the Data Cloud Podcast. Today's episode features 2026 industry retail predictions with experts from Snowflake, Rosemary DeAragon, Global Head of Retail and Travel, Rinesh Patel, Global Head of Financial Services, and Tim Long, Global Head of Manufacturing. Our guests explore how AI will transform retail, financial, services, and global manufacturing in 2026.
[00:00:28] Producer: Together, they break down the forces reshaping consumer behavior, enterprise operations, and competitive dynamics across these sectors. So please enjoy this discussion between these three vertical industry experts at Snowflake and your host, Dana Gardner, Principal Analyst at Interarbor Solutions.
[00:00:45] Dana Gardner: Welcome to the Data Cloud Podcast everyone. We're delighted to have some vertical industry experts here with us to make some insightful AI predictions for 2026. Welcome to you all. Rosemary, looking ahead to 2026, and how specific industries will be impacted by AI, how will agents begin to automate shopping for consumers, especially retailers of all stripes as they compete for attention across all sorts of domains and across all sorts of media?
[00:01:14] Rosemary DeAragon: Yeah, we see it happening already today. The way that the internet is being used is fundamentally changing. In the past, a lot of people would search using a kind of traditional query. Typically, that would be very little use of kind of words, not as verbose as it is today, and people would search through the search index.
[00:01:36] Rosemary DeAragon: So the first search result page, the second page, the third page. Now with AI summarization, a lot of people aren't even visiting those websites anymore. They're getting the answer right at the top. They're getting a recommended product right at the top, and it's already changing the game. So AI has already changed how products are being discovered and how people are shopping for different products.
[00:01:59] Rosemary DeAragon: To your question of how agents are, are going to change shopping, I think hugely, especially in the world of consumable products that you would typically use kind of an auto replenishment feature for, that's where you'll see a little bit more trust from a B2C standpoint in shopping agents, we haven't yet seen it.
[00:02:19] Rosemary DeAragon: I mean, we're, you know, at the end of 2025, we haven't yet seen large scale consumers trusting AI to make, to transact and make purchases on behalf of them. But I think in 2026, you can expect to see that more and more, especially for quote unquote boring items like toilet paper or things that you know, brands that you know and love, products that you know and love, and you know that you need to replenish at a certain cadence.
[00:02:45] Rosemary DeAragon: I think a lot of those are ripe for agents to be able to actually transact. There are other types of products, luxury products, et cetera, that are maybe less prone to agentic automation and shopping. But certainly we'll see a rise in that from a consumable standpoint.
[00:03:02] Dana Gardner: Right. And you know, a lot of companies, and not just end users, but in the B2B space, have purchasing agents already, sometimes entire organizations and teams set up to help people with procurement. That is perhaps something that can be largely automated.
[00:03:17] Rosemary DeAragon: Totally. I think the future within a retailer is a multi agentic future. You know, Snowflake's MCP server and the use of MCP servers I think is going to increase dramatically. I think in the future you're actually gonna see enterprises having agents, for example, multiple agents within supply chain, multiple agents within merchandising, buying, procurement, et cetera.
[00:03:40] Rosemary DeAragon: Multiple agents, even within customer 360 advertising and, and all of that. So I think it, we are going to be in a multi agentic future. I think that retailers that have a good grasp of their data, and not only that, but the, the ones that are actually doing deep semantic modeling on their data, where AI can actually understand what that data means, that is gonna be super important for them to ensure that they're not left behind in this world of AI.
[00:04:07] Dana Gardner: Yeah, and some of the more sophisticated individual shoppers as well as individual companies, businesses can use all sorts of ways to improve pricing and delivery times and create incentives for certain events, and so it can become rather complex. But in doing that, the savings and the, the value of the productivity and the just in time nature of, of, of consumption improves a lot.
[00:04:31] Dana Gardner: So I guess maybe we're gonna democratize that sophistication, you know, bring from what the bleeding edge does down to the, to the base.
[00:04:39] Rosemary DeAragon: I think so. I also think that there are traditional machine learning methods that have been used for several decades in the world of dynamic pricing, smart pricing.
[00:04:49] Rosemary DeAragon: You can use linear aggression models for things like that there. Obviously, there are also cases where traditional machine learning models are being replaced with generative AI. So that would be like you were talking about just in time merchandising, we're seeing that, you know, at a fast pace, especially in fashion and apparel where you have, you know, these fast fashion trends that are coming about a little bit in competition with the sustainability trend as well.
[00:05:12] Rosemary DeAragon: But you have that fast fashion trend and in that you have, you know, generative AI taking over a lot of the more traditional machine learning use cases where you can then understand human sentiment from, you know, social media hoses. You can understand what the trends are as they're occurring and actually manufacture things just in time.
[00:05:30] Rosemary DeAragon: And, you know, it's a, it's actually a big threat to brands because you know that you're able to manufacture so quickly and produce these fast fashion goods so quickly, you know, especially with the rise of social commerce, TikTok shop, and all of these e-commerce methods of, of delivery. And so. Again, I think brands need to really understand their product data and tie the product data to human sentiment as opposed to kind of the more traditional product catalog, you know, red sweater, right?
[00:06:02] Rosemary DeAragon: What does a red sweater evoke is, is it fall? What kind of red is it? Is it Valentine's red or is it more like a fall red? And a lot of those types of additional products, data enriched product data attributes traditionally were overlooked and maybe you didn't have the manual capacity to be able to add that onto your product catalog.
[00:06:22] Rosemary DeAragon: Now with generative AI features, you're able to augment your product catalog and with semantic modeling be able to understand exactly what the, the products item data, and how it ties to human sentiment. I think that is super important in this new age of, of discoverability within ai, and when we were talking about just in time merchandising, it's gonna be about how do you actually decipher human trends through social media, data, content reviews, et cetera, and translate that into the manufacturing.
[00:06:53] Dana Gardner: As the sophistication increases and more organizations can disrupt brands, for example, wouldn't it perhaps start blurring the lines and distinctions between digital and brick and mortar? Does it really matter where and how people intercept this information and then make a decision to purchase?
[00:07:13] Dana Gardner: It seems like this might give the brick and mortar companies if they, you know, get into agentic and make that sophistication part of their process that the, the distinction of that maybe they'll be able to hold onto their turf better, I suppose.
[00:07:27] Rosemary DeAragon: Absolutely. It's actually a great point and one that I loved talking about, because traditionally, you know, back in the day you had brick and mortar shops.
[00:07:37] Rosemary DeAragon: That and retailers really revolutionized the ability for consumer products, brands to reach the customer by putting all of those brands next to each other in a, in a kind of physical store. Then you had the dawn of the internet, the dawn of the internet caused an, what we call, endless aisle. Where you now have an endless amount of assortment and endless amount of products in your quote unquote online catalog.
[00:07:58] Rosemary DeAragon: Where you could shop for all of these different items, and that was still happening in the confines of a website. So you still had kind of a digital brick and mortar feel where you're landing on a website and you're seeing and browsing through all these products..
[00:08:18] Rosemary DeAragon: Now with AI and large language models, you've actually now expanded the, the, even the digital four walls in that you are able to speak to a large language model and discover products that you don't even know if they exist. For example, if you're building something, if you're a DIYer, you're woodworking, you're needing to build a hinge. You don't even know if this particular hinge exists, but you can describe the problem to a large language model and they can then crawl because they're trained on the world's data on the internet and the public internet, they can then crawl all of the different websites.
[00:08:48] Rosemary DeAragon: And tell you whether that that product exists or not, and then obviously be able to transact. So really, the disruption occurred, yes. When in, in the 2000s with the dawn of the internet. But it's occurring now again, in that it's expanding the, the product's catalog. It's beyond an endless aisle.
[00:09:05] Rosemary DeAragon: It's beyond the digital four walls to be really a true, endless aisles of products catalog data. That's actually a really big threat to retailers because if you think about it, the whole value of a retailer is to bring together these interesting products that are kind of similar, that customers can then compare and contrast as they're browsing through the physical shop or through a online website.
[00:09:28] Rosemary DeAragon: Now that value is called to question, right? Because you can actually break through that by just having a conversation to find the exact item you're looking at. You don't necessarily need to browse through similar items. So it ranks the question what the value of the retailer is bringing to the table.
[00:09:45] Dana Gardner: And it's it exciting because when people can use AI agents as an advocate for them as shoppers. They can be more intelligent, they can get the data that they need, and it makes, I suppose more, puts more pressure on the retailers to step up because we could have dueling agents on the, on the buy and the sell side.
[00:10:09] Rosemary DeAragon: Right. It's both exciting and also a little anxiety ridden because agents are gonna be the one scouring through really objective data, whereas retailers used to reach customers through emotional means, you know, the look and feel of the website, the font, the logo, the packaging, right? All of that is kind of out the door when the agents are the ones doing the research.
[00:10:32] Rosemary DeAragon: Unless in the prompt you're saying, okay, make sure that the packaging looks sustainable. It's, you know, using compostable materials and things like that. So, so the agent is really using the hard data, the objective facts to be able to find the best product for the customer. As of right now, OpenAI hasn't yet brought in the advertising model into ChatGPT.
[00:10:52] Rosemary DeAragon: So you are pretty much getting the true recommendation of a product without influence of retail media, which again, is very anxiety ridden for retailers that typically depend on that sort of monetization system to be able to push the products to the end customer. So again, a lot of that emotional draw is taken out of it and replaced with a very objective look at your product data.
[00:11:17] Rosemary DeAragon: Using that as the main source of truth for recommending the product to the end customer.
[00:11:23] Dana Gardner: Now, if we're doing more machine to machine, the, the algorithms, the machine learning, the AI, the agents are all beavering away to, to make the utmost of this transaction between a buyer and seller. Again, as in many cases, it's gonna be the quality of the data.
[00:11:42] Dana Gardner: At the end of the day serves this, this purpose or this outcome best. But as you say, the areas and and ways in which we've gotten data are changing and being disrupted rapidly. So it seems to me that all sorts of data structured, unstructured personal inference may be coming off of your smart watch, whatever, you know, we all have to bring that together. And, and so tell me a little bit about this new era of all sorts of interesting data from all sorts of places being brought into this equation.
[00:12:13] Rosemary DeAragon: Absolutely. Another super exciting topic for me is talking about how customer 360 as a topic is fundamentally changing. It's exciting for me because for many years, customer 360, customer data platforms, how do you reach the customer? How do you build a well of data? It was all kind of, I mean, it was getting more sophisticated, sure.
[00:12:34] Rosemary DeAragon: But it was all kind of in the same box and you could kind of just, once you really understood it, you really understood it. And, and now what's exciting for me is that customers are sharing much more intimately with large language models than ever before. So traditionally in the past, you would stitch all these third party data sets together.
[00:12:51] Rosemary DeAragon: You would try to figure out the identity resolution of a household. You would try to kind of enrich the the data by saying, okay, this customer, you know, has this partner, has a dog lives in an apartment based on all of these different third party data sources. Now that is being completely flipped on its head because customers are telling you.
[00:13:08] Rosemary DeAragon: I have a pimple on a Friday morning and I'm going on a date with these three people and it's cold outside and I'm, this is what's in my wardrobe. Never in the history of the internet have we had customers so willing to share these extremely personal bits of information without the retailer being the one that has to stitch all of that data together from third party partners.
[00:13:29] Rosemary DeAragon: So it is just such an exciting time because now you're having customers willing to divulge that information in exchange for the value of getting the right recommendation on the product. Now that is true customer 360 and we're finally landed in this place where we have that interaction digitally with the customer.
[00:13:52] Rosemary DeAragon: We have that trust as well. And how do you, how do retailers take advantage of that to understand the customer in depth? And be able to serve them better. And at the end of the day, it's better for all customers, especially if they're willing to divulge that information in exchange for that value.
[00:14:07] Rosemary DeAragon: And so, you know, I think your question was more around also all of the unstructured data as well, right? So the voice data of customer care agents, when you're speaking on the phone right now, you're able to bring that data in as part of the customer data profile. Whereas before it was much more difficult or much more costly.
[00:14:24] Dana Gardner: And even analyze the sentiment of, of that conversation.
[00:14:28] Rosemary DeAragon: Absolutely. Yeah. And, and now with more and more video data, also in the unstructured world, you can also use video data as well as audio data. You could also understand based on how someone speaks and the slang that they're using, the age probably of where they're from more, or this particular regional location that they're from.
[00:14:47] Rosemary DeAragon: And it's really, you know, we're in such a world where you get such a rich look at the customer and they're benefiting from that by being able to get, you know, really, really good product recommendations as opposed to, you know, it's kind of, you know, not really sure, like this might be a probabilistic kind of determinant of what you would want to see.
[00:15:07] Rosemary DeAragon: No, now you're actually able to get down to the specifics of what these customers actually want.
[00:15:12] Dana Gardner: Hmm. Now your purview includes travel as well, and we've been focused on retail and products. But this all applies to services too, right?
[00:15:20] Rosemary DeAragon: Absolutely. I mean, anyone who's listening that's planning a trip, right?
[00:15:24] Rosemary DeAragon: Like in the, in the past you would like go to Google and look up, you know, San Francisco to Tokyo. But now you can tell, again, diluting personal details and, you know, I have a toddler and I wanna go to private dining. And I, and I wanna avoid these routes and I want the cheapest flight here, but maybe I want business class here.
[00:15:44] Rosemary DeAragon: And you know, providing that level of detail in exchange for this very personal response of, you know, this is the best. Right? And that whole thing is changing. I think next year we're gonna see that be so much more in commonplace and again, how does that actually disrupt the entire journey from an airline standpoint or a hotel standpoint?
[00:16:04] Rosemary DeAragon: Which often also use more emotional tactics to reach their customers. Now we're actually looking at the hard data to figure out, okay, what is the optimal path to figuring out my my plan,
[00:16:15] Dana Gardner: And we're talking about massive portions of the economy, right? So consumer spending 70% of the economy in the US services add onto that.
[00:16:24] Dana Gardner: We're talking about, you know, a multi-trillion dollar global market here. So when you look at the economics, it sounds like we've been talking about agents as almost an intermediate, intermediate, intermediary, third party who can extract revenue for services. And value in both directions, buyer and seller.
[00:16:45] Dana Gardner: Isn't that rather disruptive of the economics here too?
[00:16:48] Rosemary DeAragon: Yeah, absolutely. I mean, I think when you, you know, just thinking about those numbers, it's, it's insane. And, and I do think that, you know, there's, it's always gonna land somewhere in the middle. I think you'll have people who say everything's gonna be agentic.
[00:17:02] Rosemary DeAragon: But, in my opinion, until we get the main device manufacturers so Apple, that has a true agent and also true AI integration into the platform, until we meet that moment, consumers I think will not be used to offloading transactions to an AI until the devices that they're using has that infused into the, the device journey.
[00:17:30] Rosemary DeAragon: And, and I think that, you know, Apple, some say they're late to the game, some say it's all in good time, but, but given where they are today, we still don't, we still haven't yet unleashed the power of AI and the personal device within the Apple ecosystem. Because if you think about it, Apple knows everyone you're talking to.
[00:17:48] Rosemary DeAragon: They know your calendar, they know where you work, it knows, you know, which social media asks. I mean, it knows everything about you. You think ChatGPT is good, but imagine not even having to feed it that much information because your device already knows when your nephew's birthday is coming up and what, what they got last year and, and, you know, offloading the, the, the mental load of having to purchase a birthday gift for them, or knowing that you have a trip coming up and offloading the mental load of tracking the cheapest flight.
[00:18:18] Rosemary DeAragon: Like all these things, right? I think once it reaches the personal device, that's when customers from a B2C standpoint are gonna be much more comfortable offloading agentic workloads and transactions actually happening to an AI
[00:18:33] Dana Gardner: Rinesh, you're up next. From your vantage point, how do you foresee financial services specifically being impacted by AI's advances and the prospects for greater visibility into ROI over the next year?
[00:18:47] Rinesh Patel: It's a great question and one that's certainly topical at this moment in time, Dana. Look, I think there's a broad and universal acceptance that AI is clearly a powerful technology and that's something that we don't need to prove anymore. That said, what we do need to prove is the ROI. So certainly the biggest shift that we're seeing in financial services is really this change in mindset towards the commercial outcomes rather than the technical wins.
[00:19:09] Rinesh Patel: And I expect next year financial services organizations to really be focused on measuring business impact of every dollar spent on their AI investments.
[00:19:21] Dana Gardner: Mm-hmm. Is there something about the metrics and key performance indicators and the ability to analyze in greater detail that's going to help solidify that understanding of the ROI in, in real brass tack terms?
[00:19:37] Rinesh Patel: Yeah, look, I think there's gonna be, there certainly is and is gonna continue to be a shift in metrics I that greater focus on ROI value. You know, really focus on those business outcomes for an organization and to be measured through the lens of metrics like experience, for example, hyper-personalization and things that real time insights, retention through the lens of, you know, best in class analytics, for example, ship managers and their client engagement activities.
[00:20:02] Rinesh Patel: Growth, you know, revenue outcomes tailored towards things like product recommendations and a variety of others like risk as an example, and, and, and, and, and digital adoption as another example. So that's kind of where we're gonna see a level of consistency around the metrics that this industry's really starts focusing on and adopts more pervasively.
[00:20:22] Dana Gardner: And this productivity and value and increase that you're measuring, you can do it focused on external factors like customer retention, the bottom lines impact to greater addressable market and share of wallet and some of those key metrics. But there's also what AI is bringing internally to operations.
[00:20:42] Dana Gardner: Do you foresee the ability to measure that in terms of how it, it doesn't necessarily impact the top line, but more the bottom line, the productivity part internally?
[00:20:51] Rinesh Patel: Yeah, absolutely. I think if I think about where there's gonna be a, a focus, it's both gonna be, as I said, downstream in, in solving some of those areas that I just mentioned, specifically lines of business and supporting the business cases.
[00:21:03] Rinesh Patel: But absolutely, it's really gonna be focused also upstream. You know, unlocking the data management efficiency gains across the entire data life cycle because AI is fundamentally gonna be infused across the data life cycle, really driving those productivity, those sufficiency gains across the enterprise and across the, the, the organization.
[00:21:24] Dana Gardner: And of course, financial services organizations are often a bellwether for other verticals, that they're an early adopter and an aggressive adopter of technology, especially when it can impact their, their productivity. And so I would think that 2026 is an important year and that financial services might be able to establish some common rules and expectations around AI's productivity that will permeate into other parts of the economy.
[00:21:50] Rinesh Patel: I certainly see that when you, when you look at any of the recent surveys that are out there, it, it certainly validates that hypothesis in that financial services or, you know, the industry itself and its customers are very much leading the way in terms of adopting AI, in terms of implementing AI, and with that will cover some of best practices.
[00:22:08] Rinesh Patel: I, you know, things that other industries will certainly be paying attention to in the hope that they can learn from and evolve from. So I certainly see that. And so I think there will be some social benefits to some of the, some of the work that's taking place in financial services across some of the broader industries that my colleagues, Rose and Tim are certainly working through.
[00:22:28] Dana Gardner: Yeah, so financial services clearly a, a sector to keep an eye on. And while financial services is an early adopter, it's also somewhat unique in that it has to be very conscious of risk security and governance. And so, how is AI risk management evolving in 2026? It might also be a bellwether for other vertical industries.
[00:22:50] Rinesh Patel: Yeah. Look, this is a, a, a highly regulated market and trust is important and more so essential. So if you think about, you know, you know, AI agents and you think about large language models as they become embedded in critical financial operations there is going to be, and already is a greater focus on mitigating risks and the heightened focus on responsible AI.
[00:23:14] Rinesh Patel: We're certainly seeing that and you know, when I think about, you know, some of those AI risks, you know, it's gonna evolve from hallucination, safety, ethics, but also you also have to factor in, you know, how they collide with data risks. Things like data residency, operational resiliency and so forth. Things have become clearer, that AI, data, large language models and agents can't be disaggregated, you know, a unified approach to, to data governance and AI evaluations is gonna be essential to support risk management across financial services.
[00:23:41] Rinesh Patel: So we certainly expect that by already seeing signs of this industry taking, you know, some thoughtful steps in terms of putting those processes and practice in place.
[00:23:55] Dana Gardner: Is there anything in particular about data management and governance that you think 2026 will advance?
[00:24:04] Dana Gardner: That is to say if you take care of your data lifecycle, you're in a better position to forestall any issues and risks. How important is the data management phase of this overall risk management process?
[00:24:18] Rinesh Patel: I think, I think the data management is going to be essential. I think there is no, you know, a a as per the phase, there is no, you know, AI strategy without a data strategy.
[00:24:31] Rinesh Patel: And so I think, you know, a couple of things. I think as an industry we've already evolved to accept that it's no longer about AI first, but data architecture incorporated with AI as part of the strategy. That's the first thing that we're certainly course correcting, you know, as, as an industry. The second is, as I mentioned a few moments ago, a unified approach to data governance and AI evaluation is now becoming essential.
[00:24:56] Rinesh Patel: The ROI just won't be there when siloed data erodes the trust in AI systems. And I think, I think the third area is really, you know, being very much focused on the evolution of, of, of governance broadly from traditional data governance to modern governance and usage governance. So I think governance is gonna be essential both across, you know, the data space, but also the large language model and the AI space. And I think organizations are certainly focusing on it in that way.
[00:25:31] Dana Gardner: Hmm. Well, we've certainly seen a very rapid advance and dynamic landscape when it comes to AI and technology itself, but there are other very fast moving aspects to this point in time, and so regulators are moving, changing regimes or changing in terms of oversight, and then we just don't know about what risks may or may not appear.
[00:25:55] Dana Gardner: The so-called unknown unknowns. And so I suppose from what you mentioned about unification, that being agile in fleet and how one can adapt and adjust would be an important factor. Is there anything about financial services organizations that you predict will be required in terms of being able to pivot as needed in order to keep up with a changing world?
[00:26:18] Rinesh Patel: Yeah, look, I think, I think that changing that need to pivot and to be agile is gonna be coming from, you know, a variety of stakeholders. I fully expect regulators to continue to signal tighter oversight. And for a global bank, that will mean global regulators overall from the US to EMEA to a PJ that they will have to engage with and, and, and, and, and, and make sure they are, they are working with.
[00:26:42] Rinesh Patel: But it also will be the boards. Boards of, you know, organizations, of listed organizations, which we, which will demand comprehensive risk frameworks that treat AI deployment as seriously as any other mission critical system. So it'll be coming from both the internal stakeholders but also the external stakeholders like regulators.
[00:27:01] Rinesh Patel: And as I said, you know, it's, it's really gonna, it's really gonna force organizations to take a more strategic, a unified approach to understand data governance and AI evaluation a bit more, more seriously, and certainly more practically.
[00:27:13] Dana Gardner: Yeah. The one thing that we can count on as being consistent is more change.
[00:27:18] Rinesh Patel: Absolutely. Absolutely.
[00:27:20] Dana Gardner: Alright, let's move on to how the adoption patterns and the culture of using AI should change coming over the next year or so, so we expect more of an agentic experience. Can you explain how agentic AI will manifest itself, particularly in financial services and why that's an important leap in, in how productivity will will deliver results?
[00:27:46] Rinesh Patel: If you think about providers, providers of data typically been, you know, providers of structured data that's now evolved to structured and unstructured data, and consumers have typically been humans and applications that now very quickly will become agents and the way information is distributed from providers to consumers is going to be very, very important to enable that true agentic experience.
[00:28:14] Rinesh Patel: And what I mean, agentic experience, I mean the ability to surface up insights to business leaders through natural language capabilities that allows, you know, enterprise intelligence to be pervasive that every user effectively at their, at their fingertips. And what you're gonna see is effectively, you know, a natural language interface, an agent workflow orchestrator, that enables as an enterprise to truly extract meaningful value from the data that it has and insights that it holds within an organization.
[00:28:46] Dana Gardner: So it sounds as if you believe that agentic AI will be an accelerant or a catalyst to the more of that democratization that more people will be able to take advantage of, interact with, and perhaps optimize the AI value chain.
[00:29:02] Rinesh Patel: Yep. And we're already, we'll, we're already seeing some of this already done out.
[00:29:06] Rinesh Patel: We'll, we'll, we'll see an agentic experience quickly become reality, if not the norm for most businesses. I expect, you know, financial services to start introducing, you know, AI agents into core business processes, from risk monitoring, to surveillance, to customer reviews, to to portfolio operations.
[00:29:23] Rinesh Patel: And, you know, as, if you think about these systems, they take on the multi-step work traditionally handled by teams of analysts, analysts, you know, and this will, this will, you know, create a, you know, a very different set of challenges for leaders, if you will, to navigate. When you think about the agentic experience, I mean, the first will be how do you measure productivity in a world where humans and AI collaborate? As an example.
[00:29:47] Rinesh Patel: The second, how do you govern agents that make decisions and take action? So there's gonna be a change, certainly a major change, I say in the operating model of organizations as they, as they straddle across these two things.
[00:29:59] Dana Gardner: Yeah, it sounds really like an advance and a whole new capability when it comes to process re-engineering.
[00:30:05] Dana Gardner: You really have to look at this top down and strategically to refactor how the individuals, the teams, the culture and the technology all interact. That's, that's a big undertaking.
[00:30:18] Rinesh Patel: Yeah, absolutely. You know, if, if you think about, you know, what we're we're gonna see is firms have moved from measuring tasks handled by people, as I said, to evaluating the performance of blended human AI workflows and things like speed of detection, accuracy of decisions, consistency with policy, and just over overall business impact and gonna be very much kind of things that become topical and, and, and part of the evaluation, if you will.
[00:30:42] Dana Gardner: Tim, from your vantage point, how do you think global manufacturing specifically will be impacted by AI's maturity over the next year or so?
[00:30:51] Tim Long: Well, we're, I think we're gonna see tremendous impact, and a lot of this is gonna be fueled by the challenge that manufacturers are facing today, where there's more work to do than there is skilled labor to meet the needs.
[00:31:03] Tim Long: And so manufacturers, we see this across the board, not just in the US but really globally. Where AI is going to play a role is really to help augment these skilled workers, to give them new capabilities to not only help them be more efficient in their work, but also to help them level up so that their organizations can innovate faster, they can produce better quality products, ultimately leading to the growth of their business.
[00:31:28] Tim Long: So it's an exciting time in manufacturing as the world is sort of rebalancing where products are made. Also the role that AI will play is gonna be a tremendous transformative force.
[00:31:40] Dana Gardner: So for all those pipefitters out there, their tool belts, they're gonna have to make another little slot for their AI companion. Is that it?
[00:31:46] Tim Long: That's right. AI can really help everybody. You know, we see it in our personal life. There's not an hour that goes by where I don't see the opportunity for AI to help me personally. And of course it'll help those that are doing more traditional blue collar, you know, work that we would think of.
[00:32:02] Tim Long: We're seeing this already with the ability to tap into large databases of historical, you know, records of how different projects were completed or how different repairs have been made in equipment. And these AI tools can really answer questions much more efficiently than thumbing through a repair manual or even go into the web and do a simple search.
[00:32:25] Tim Long: So this is really a, a transformative, you know, what I would call a step function improvement in the efficiency of our skilled workforce.
[00:32:33] Dana Gardner: Sure. Now manufacturing has long been a source of great data at the edge, whether it's sensors on a factory floor or logistics and tracking shipments and deliveries for just in time.
[00:32:45] Dana Gardner: But maybe that data hasn't been as well used as it could have. Is this a, a function of using the data that's existing better or more data and more int intelligence?
[00:32:54] Tim Long: Well, I really love that question because what we're seeing in the manufacturing world is this transition from where manufacturers were very comfortable operating in, in just on-premise, at the edge software solutions exclusively.
[00:33:09] Tim Long: In fact, many manufacturers purposefully close off the network of their shop floor from any external networks such as the cloud or the internet for all kinds of great reasons, reliability, cyber threats, and so on. Now manufacturers are really recognizing that the cloud is essential and not everything's moving to the cloud, but the cloud plays an essential role, and specifically in the place where all of this data that was managed in the siloed systems at the edge is now coming together in the cloud.
[00:33:41] Tim Long: And this is more commonly being called the Unified Namespace, it's a term that was coined by Walker Reynolds. For those in the manufacturing world have probably heard of him. And the concept here is how do I bring all events from the business, whether it's a transactional event in my ERP or it's a manufacturing event for the shop floor together in one place, tying together the entire stack of systems that runs a manufacturing organization.
[00:34:07] Tim Long: So I can really see end to end from shop floor to top floor, what's happening in my organization. And that trend has really taken off over the last year and will accelerate into next year converging IT data with the operations technology or OT data.
[00:34:24] Dana Gardner: Yeah, this has been sort of the nirvana since process re-engineering 30 years ago, and maybe AI has certain great capabilities on its own, but it seems to be an accelerant or a catalyst to doing this end-to-end unification, which is long overdue.
[00:34:40] Tim Long: I completely agree with you. If we look at sort of this, this evolution of manufacturing, we can look at the, what's been known as the Third Industrial Revolution where computerization and automation really made a big impact to manufacturing processes being more efficient and and improving quality. But that data always rested in these siloed systems.
[00:35:02] Tim Long: And so now with AI, if I tie that data together in platforms like Snowflake, I can now ask questions that can work across the organization, across each of those silos. Bridging data from orders to the the bill of materials to create that order to the raw materials to my supplier base, as an example in supply chain, where all that data can now be tied together and insights can be delivered simply by asking questions.
[00:35:30] Tim Long: And I think that's what AI is offering as a value incentive. For manufacturers to bridge those data gaps.
[00:35:37] Dana Gardner: Yeah. I suppose in another area, whether it's just in time or whether it's build for manufacturability and design for manufacturability, that AI can have an impact there as well. And so just in time faster and manufacturing with, you know, fewer seams, fewer components, is that something that you're seeing as well in terms of this overall productivity that AI can increase by this unification the, the whole life cycle of manufacturing?
[00:36:04] Tim Long: Absolutely. So a, a great place that AI is being leveraged is really in improving supply chain speed. How do I sense opportunity? How do I sense risk, and how do I make decisions that better position my organization to execute in whatever changes are happening within my market, whether those are geopolitical economic impacts, or even, you know, impact traditional supply chain disruption.
[00:36:28] Tim Long: So we all became very familiar with, so, you know, a few years back, all of those are events that when manufacturers are equipped with the right data and tools can make decisions faster that position them a little bit further ahead of their competition.
[00:36:48] Dana Gardner: Now we're all interested in finding ways of measuring the productivity. Measurement in technology has been a difficult thing for quite some time, but is there something in the manufactured control environment that you're describing that will enable more disciplined IT investment validation?
[00:37:05] Tim Long: Yeah, so I really think of manufacturing as an ideal place to prove the value of AI engineers in within a manufacturing process.
[00:37:15] Tim Long: Operate using the scientific method every day. They can create controlled experiments to say, if I adjust my process with this change, can I improve efficiency or quality? And really implementing AI, I think should be thought of in the same dynamic. Can I create an experiment that proves the value of this new approach to doing my business process?
[00:37:38] Tim Long: So manufacturers are accustomed to this way of learning from trial and error. I think to answer your question, absolutely, manufacturing well positioned to do this and the dividends are huge. If you think about the cost of software coming in and improving your ability to deliver more products with better quality, that cost compared to new capital equipment, expenditures, or expansions of your factories or purchase of more materials, that cost is much smaller in the big picture.
[00:38:10] Tim Long: So the ROI is there. And I think manufacturers are figuring out how to prove that as they're, you know, delivering on their AI roadmaps.
[00:38:19] Dana Gardner: Is there any specific way that because of the large investments required for many manufacturing environments, automotive comes to mind, where you've got tooling and long-term process involvement with highly complex supply chains?
[00:38:33] Dana Gardner: Is there some way that you think AI can help in moving the risk around the cost of, of investment, and then the payoff in, in terms of getting the manufacturing and products that, that you wanted in the first place?
[00:38:47] Tim Long: Absolutely. So any efficiency gain that you can drive back down to the floor, either making your equipment available more often more, or improving their uptime so they're available 24/7.
[00:39:00] Tim Long: That's a big way to make an improvement. If you can make your equipment produce products at a faster pace, increasing the throughput, that's a big improvement. And lastly, if you can improve the quality. That's a big improvement. In fact, those three dimensions I just described make up the manufacturing king metric known as OEE or Overall Equipment Effectiveness.
[00:39:23] Tim Long: And so to do that though, you really need to bring data from your equipment management systems, from your industrial IOT sensors that are monitoring that equipment, you need to bring data together from the performance of the equipment itself, how many widgets per hour is it able to produce? And, and, and then lastly, you need to understand all of your quality, whether you're measuring that physically or virtually with things like computer vision, having that data tied all together really is empowering manufacturers to improve efficiency and overcome all these challenges that they've experienced within the markets.
[00:40:00] Dana Gardner: Mm-hmm. And how about AI for simulations and experimental pilots so that you don't have to take the risk of building out large and complex factories, but can perhaps simulate and determine beforehand where your strengths and weaknesses are?
[00:40:15] Tim Long: Absolutely. This is in manufacturing world known as the digital twin. How can I model any part of my factory or that my actual end product to understand its performance in different conditions? Can I capture enough data over time, both in the context of the floor itself as well as the, how the equipment is performing and understand relationships that are otherwise difficult to maybe to appreciate?
[00:40:41] Tim Long: But if you can do that, you can capture that in, think of it as a physics model that then you can experiment with, and those simulations can really unlock the true potential of your, you know, shop floor on the connected product side. So if you think of manufacturers, you mentioned automotive. These vehicles that are shipping every one of 'em is essentially a supercomputer on, on wheels now.
[00:41:04] Tim Long: And all the data that they generate and collect has value not only to inform your, your dummy lights on your dashboard, but really to provide a insight back to the engineering and design organizations to say, this is how our product's actually being used. These are the challenges our, our customers might be facing with it in terms of reliability or performance, and how do we learn from that to feed back into the design process, to create a better product, excuse me, better product for the future?
[00:41:33] Dana Gardner: Yeah, that lifecycle can be a huge benefit. That historical data, we've seen it in other industries, but now going to manufacturing where the costs you say are so high, it could be, it could be huge. So we've obviously established that there's a great deal of opportunity for AI to come and prove itself rapidly and significantly in manufacturing.
[00:41:53] Dana Gardner: But how do you expect the AI and agentic AI in particular to manifest itself in manufacturing organizations in order to, to prove its, its its importance and, and value?
[00:42:04] Tim Long: Yeah, I mean, I think that agentic AI is really just an extension of the investment and automation manufacturers have been making for a long time.
[00:42:13] Tim Long: And what these agents are really doing now are they're able to take on repeatable decisions that an organization would otherwise need to make. And they're able to do this with rich data sets and with the guidance, and they're able to make decisions at a high speed. So again, it's all about speed within manufacturing while not introducing new risk.
[00:42:39] Tim Long: So manufacturers are looking to these agents to do things like automatically reorder when materials are low. Figuring out which products should be expedited in the in the line to achieve their commitments to their customers for shipping things on time, how to optimize the logistics. So how do I get it from my factory to my customer in the most efficient and timely way?
[00:43:04] Tim Long: There are just so many opportunities where agents are starting to make an impact, and we really see that accelerating over the next year.
[00:43:12] Dana Gardner: All right. How do early adopters perhaps put themselves at a competitive advantage by doing this sooner rather than later? I should think that becoming a digital-first manufacturer when you're out there in the field competing with a non-digital-first manufacturer, that you should get some pretty good benefits.
[00:43:29] Dana Gardner: So this isn't a let's crawl, walk, and run type of an affair, right?
[00:43:34] Tim Long: Yeah, Dana, I get that question a lot, and what I see are those manufacturers who have invested in the physical automation of the shop floor stand to benefit the most. And the reason why is they have now these rich data sets. And now the question is how do I synthesize these data into insights?
[00:43:54] Tim Long: And that really comes in the form of integrating those data across the silos in a platform like Snowflake. Creating that unified namespace where all business events are tied together and doing it in a way that's not just fit for dashboarding, as was the previous goal. Creating these data marts to, to run these dashboards.
[00:44:15] Tim Long: But really thinking beyond that and and providing things like semantic models that help AI understand how to relate data from one data object to another. That is the key to success of using AI to really unlock the value of the data is to position the data in the best way on a strong data foundation with strong governance, and of course all the guardrails that are necessary to deliver the value from AI while mitigating the risk.
[00:44:47] Dana Gardner: Well, thank you very much for that, Tim. Let's now move on to our round robin portion of our discussion today among all of our experts. Tim, now that we've heard about AI's potential across several important industries, do you agree that the ability to be precise but agile and customized to specific factory floors is, is a big improvement over what we've seen in the past from software and when it comes to, to optimization?
[00:45:16] Dana Gardner: Are we, does AI give us an opportunity to fine tune at a much more granular level our efficiencies?
[00:45:24] Tim Long: Yeah, it's an interesting question. Manufacturers tend to have a, a very diverse set of systems from one factory to the other. They generally grow through merger and acquisition, and as a result, it's really hard to take one solution from one location and replicate that across the network.
[00:45:40] Tim Long: And I think AI is gonna help with that. We're already seeing AI help with things like data migrations, mapping source system to target system. Rather than doing that manually across thousands of data objects, AI can take the lead and get you 90, 95% of the way there. And so I do see that that complexity simplifying over time with AI playing a big role.
[00:46:05] Dana Gardner: How do you think the adoption patterns in 2026 will help organizations value AI, establish its benefits, and perhaps remind people that they may be undervaluing AI? I mean, we often hear about a wide variety of perceptions in the market as to AI's performance and impact. It seems to me that there's still quite a few people that are underestimating AI's impact.
[00:46:30] Dana Gardner: Do you think we'll move past that in in the next year or so?
[00:46:34] Rinesh Patel: I think we will, I think we are already, as I said, you know, a few moments ago, I think there's a broad acceptance that AI is clearly a powerful technology, and that's not something that we need to prove anymore. I think it's now really about how do we leverage it strategically and how do we make sure we, we put the right focus on the metrics.
[00:46:51] Rinesh Patel: So I, I, I fully expect next years to be an acceleration of adopting the technology, of implementing the technology, of measuring some of those outcomes. And we're gonna see, you know, a lot of winners and we're gonna see a lot of people having to learn very, very fast.
[00:47:05] Dana Gardner: Okay, well, we're about out of our time, but I wanted to ask you a question that will be addressed across these different vertical industries and, and that's, you know, who's your poster child?
[00:47:14] Dana Gardner: Who's doing this well now that is a harbinger for others to, to look at, even if you can't name them. Describe a use case or, you know, a, a situation that you're aware of that that highlights and exemplifies the potentials that we've been talking about.
[00:47:30] Rosemary DeAragon: Yeah, I mean one of the most advanced retailers that is a public reference for us is Under Armour.
[00:47:37] Rosemary DeAragon: They are super advanced credit to Patrick Duroseau, the the Chief Digital Officer, I think Chief Data officer there, but CDO at Under Armour for really pioneering a lot of the AI efforts at Under Armour. They have done things like, you know, data sharing with their logistics providers. They've gotten into clean rooms and now they are actually building out fully agentic workflows between internal and also with their customer service use cases as well.
[00:48:04] Rosemary DeAragon: So when somebody has, even internally, if somebody has a question about, okay, how do I download Zoom or something like that. It is all automated. So being able to replace a lot of the more manual effort of looking through documentation, they're using the RAG method, using AI on top of all of their internal data sets with Snowflake to be able to automate those workflows.
[00:48:26] Rosemary DeAragon: So big shout out to Under Armour for being a really kind of a pioneer and, and really a good partner for us as well in testing out a lot of our AI features.
[00:48:36] Tim Long: Yeah, there's so many, but if I had to pick one, I'll pick one that just illustrates the, the effectiveness of AI at restoring high throughput on a shop floor.
[00:48:47] Tim Long: We have a, a semiconductor customer who is leveraging, you know, years of history, of equipment, maintenance logs, and all of the manual, the repair manual information about these very, very expensive and complex equipment. And they're loading that within Snowflake and using Snowflake Cortex and Snowflake Intelligence to really help them maintain peak level performance across their shop floor. There's no better example of how AI can help manufacturers than to make sure the shop floor is running at peak performance.
[00:49:22] Rinesh Patel: The one that really stands out to me is the, the real focus on unstructured data. Now, this industry has a lot of unstructured data that resides across an enterprise, across an organization, and I think the one that's really compelling right now is our organization really uncovering new potential sources of alpha by leveraging gen AI in large language models to really uncover untapped alpha.
[00:49:50] Rinesh Patel: The unstructured data beyond traditional natural language processing. I think that's gonna become very much a theme, a pervasive theme of, of opportunity for financial services. There's a lot of organizations, specifically the buy side, that really focused on that, and I think that's a really interesting area of exploration going to 2026.
[00:50:09] Dana Gardner: Well, thanks so much to our latest Data Cloud podcast guests. We've been here with Snowflakes, Rosemary DeAragon, Global Head of Retail and Travel, Rinesh Patel, Global Head of Financial Services, and Tim Long, Global Head of Manufacturing. We so much appreciate your sharing your thoughts, expertise, and experience with us all.
[00:50:27] Rosemary DeAragon: Thank you.
[00:50:27] Rinesh Patel: Thank you so much, Dana.
[00:50:27] Tim Long: Thank you, Dana.
[00:50:30] Producer: Want to see what's next for apps and generative AI and the data cloud? Check out BUILD, Snowflake's annual developer event. Dive into the latest innovations like Snowflake Intelligence, now GA, and explore how developers are building powerful apps, data pipelines, and machine learning workflows for the LLM era. Watch on demand at snowflake.com/build.