In this episode, Dana Gardner, Principal Analyst at Interarbor Solutions, is joined by Vikrant Bhan, Global Head of Analytics, Data, and Integration at Nestlé. They discuss the transformative potential of agentic AI in reimagining end-to-end business processes. The conversation also delves into the evolution from BI to AI, the advantages of using Snowflake's modern data cloud environment, and the necessity of change management and adoption for realizing AI initiatives.
In this episode, Dana Gardner, Principal Analyst at Interarbor Solutions, is joined by Vikrant Bhan, Global Head of Analytics, Data, and Integration at Nestlé. They discuss the transformative potential of agentic AI in reimagining end-to-end business processes. The conversation also delves into the evolution from BI to AI, the advantages of using Snowflake's modern data cloud environment, and the necessity of change management and adoption for realizing AI initiatives.
[00:00:00] Producer: Hello and welcome to the Data Cloud Podcast. Today's episode features an interview with Vikrant Bhan, Global Head of Analytics, Data and Integration at Nestlé, hosted by Dana Gardner, Principal Analyst at Interarbor Solutions. They discuss the transformative potential of agentic AI in re-imagining end-to-end business processes.
[00:00:21] Producer: The conversation also delves into the evolution from BI to AI, the advantages of using Snowflake's modern data cloud environment, and the necessity of change management and adoption for realizing AI initiatives. So please enjoy this interview between Vikrant Bhan and your host, Dana Gardner.
[00:00:38] Dana Gardner: Welcome to the Data Cloud Podcast, Vik, we're delighted to have you with us.
[00:00:41] Vikrant Bhan: Hi Dana, and nice to be with you all as well.
[00:00:38] Dana Gardner: Tell us about your role at Nestlé, your background, and what excites you about the future of data analytics and AI.
[00:00:44] Vikrant Bhan: Yeah, so my role at Nestlé is quite a complex one. I am managing what is called analytics, AI, data and integration globally, across the scope of the 180 plus countries that we operate in.
[00:01:04] Vikrant Bhan: And, and I'm managing all the platforms. I'm managing all the data assets and our data domains at an enterprise level, and then all the AI analytics services and products as well. To your question on what excites me about the future, Dana, I think we have been doing AI to optimize a lot of our functional capabilities over the last many, many years, and obviously having a very
[00:01:28] Vikrant Bhan: value-driven approach to choosing which problems to solve for the business. But I think now with what we are seeing, especially with agent AI, I think the big opportunity that we all see within our company and generally across the industry as well, is that it's not just about a particular capability, it's about the, the whole process, the end-to-end process, as we call it, at least within Nestlé.
[00:01:51] Vikrant Bhan: That can be not just optimized because that's been something done before, but I would say completely reimagined with the help of AI. And that re-imagination has a massive implication on the operating model of that process. And it also has a massive implication on potentially how the structure. Our interactions with our consumers, customers as well internally within the company employ from an experience perspective gets impacted.
[00:02:18] Vikrant Bhan: So I think that's the thing that excites me the most about what's coming in the future.
[00:02:22] Dana Gardner: Sure. So not only process re-engineering, but cultural re-engineering, which sort of really gets to the heart of, of making things better in, in holistic ways.
[00:02:33] Vikrant Bhan: Absolutely. And I think what we say here at Nestlé as part of our enterprise digital transformation is while the processes will be reimagined, there are four key fundamental enablers that always need to be thought about.
[00:02:45] Vikrant Bhan: And as you rightly said, the people, the capabilities, the culture, as an aspect is very important, but so is operating model, which I think sometimes, you know, in the past when we have done optimizations or various kinds of analytics and AI use cases sometimes gets lesser importance than maybe the, the tool and the data and AI capability itself.
[00:03:07] Vikrant Bhan: But at, at also, you have to keep in mind that data, AI, is a key enabler as well along with technology, and I think until these four ingredients don't come together, I don't think that re-imagination is going to work.
[00:03:21] Dana Gardner: Okay. So for many large, diverse and complex businesses like Nestlé, end-to-end and ubiquitous data flows are essential, as you were saying, to enabling these better and faster decisions and deliver a real difference to the business.
[00:03:34] Dana Gardner: So after 18 years at Nestlé shaping the IT and the data strategy, Vik, how does this point in time where we are right now stand out in terms of reaping the rewards from all the past data maturity efforts? Are we actually seeing an acceleration in, in an additional business value now?
[00:03:53] Vikrant Bhan: I think so. I think one aspect that has been different over maybe I would say the earlier part of my career is, we started, I think on this journey, obviously looking at a lot of our internal processes, at least in Nestlé, and, and hence a lot of focus on business performance, metrics, how that,
[00:04:12] Vikrant Bhan: it requires a lot of internal data from the company.I think what we are now seeing is the end-to-end view also from a business architecture perspective, because not only are you looking at data that is impacting your consumer journeys, your channels, as well as the back office processes that you have within the company like
[00:04:30] Vikrant Bhan: operations and finance and HR and everything. I think what has changed quite a lot is that data has gone from being just focused on your internal data to a lot of external data and signals, and this has been a journey that I've seen over the last four or five years where we have matured because that data is not easy.
[00:04:50] Vikrant Bhan: I mean, a lot of the external data that we acquire has a lot of art and science to manage that. Then also what has changed is the interplay of all of that data. So in the past you would require maybe for an individual use case or a project data that is actually required for that. So I always think that we were pretty much a data as a service organization.
[00:05:13] Vikrant Bhan: So what data do you need? We give it to you. I think what we have now seen is a very, very mature data product organization. So how do we actually treat data as a data product itself, and I think that's been one major shift that I've seen. The external data and internal data combination has been another shift that I've seen.
[00:05:35] Vikrant Bhan: What I have also seen is that the importance of master data, which used to be pretty much focused on your key internal things like core product, core customer, core vendor or business partner type definitions. With things like all the complexity of external data and other data, other nature of data that is coming in, that master data has also become very rich, and you have to think about all kinds of relationships and that
[00:06:01] Vikrant Bhan: critical master data in the company has also increased. And then I think now with Gen AI, Dana, what I'm seeing is something, to be honest, which I didn't ever have to think about in the past, is how to deal with unstructured data and, and especially data that is maybe what we would term as knowledge within the company, not necessarily structured data as such.
[00:06:23] Vikrant Bhan: I think that has become extremely important. And, finally, I also see, although, in very limited spaces where we had data gaps, how can we also start thinking about synthetic data creation? So I think what we have done at Nestlé, Dana, with the help of modern data cloud platforms is create our enterprise data domains.
[00:06:45] Vikrant Bhan: And essentially these domains are a combination of master data, internal and external structured data, and unstructured data. Because at the end of the day, AI needs all of it for getting the best for the company.
[00:06:59] Dana Gardner: And finally, after many years of standing on the shoulders of other technologies and bringing together more and more efficiencies, we're finally at the point where we can bring all of the data, resources and increasingly knowledge, resources together to be optimized, to be accessible, and to be managed.
[00:07:18] Dana Gardner: And that truly does mean a whole greater than the sum of the parts, and that is quite new and interesting.
[00:07:24] Vikrant Bhan: Absolutely. And also an element there is about reusability and interoperability because I think there were cases in the past where the same data might have been replicated multiple times across the ecosystem of the company in different digital initiatives.
[00:07:39] Vikrant Bhan: I think what the enterprise data domains have allowed us in Nesta to do is to genuinely create data Products that are reusable, are used by multiple products, and actually one of the ways we actually measure the success of our data strategy apart from many other things is the fact, how often are we reusing the same data product?
[00:07:56] Vikrant Bhan: Rather than creating new ones. And, while it helps the company on the total cost of ownership and efficiencies and FinOps and various other dimensions, I think it really gets more and more in intangible and tangible value out of the same data products.
[00:08:11] Dana Gardner: Sure. We're using data and analytics and now AI to actually do data and analytics and AI better, faster, and cheaper, right?
[00:08:19] Vikrant Bhan: Absolutely, absolutely.
[00:08:21] Dana Gardner: Yeah. Okay. You know, Nestlé is one of the best known brands in the world, but I'm gonna guess that many of our listeners and readers and watchers don't necessarily know the full extent. So can you give us a quick encapsulation of what Nestlé is, what Nestlé does, and how your organization is a pervasive value across all of those businesses?
[00:08:41] Vikrant Bhan: Absolutely. So Nestlé is, I'm pretty sure most of your viewers would at least have had a touch point with Nestlé at some point in their life. I always think about calling it out, like, you know, if you're born, you are obviously into a baby food business, and if you are potentially old, you are still using some of a health science and life science products.
[00:08:58] Vikrant Bhan: And in the middle, you are actually indulging in a lot of amazing food products from our company. So it's one of the biggest CPG companies in the world. It's the biggest food company in the world. We have more than over 2,000 global brands, close to around $90 billion of revenue. And some of the brands, which I'm sure you would have heard about is NESCAFÉ, Nespresso.
[00:09:19] Vikrant Bhan: You know, Maggi, if you are a, a listener somewhere in Asia, and things like Milo, or Nesquik, or you have, obviously, NAN, and some of the products that we have within the nutrition space, and the list goes on. KitKat, if you love your chocolate, and as I said, 2000 plus brands, right.
[00:09:41] Dana Gardner: And such diversity means, you know, one solution doesn't fit all. So you need to be perhaps centralized when you can, but you also have to be very custom and I suppose focused at the edge as well.
[00:09:54] Vikrant Bhan: Absolutely. And, also what adds to the complexity is of a business operating model. Maybe to compare to some of the businesses in CPG where you are structured by categories because you are only playing in certain categories.
[00:10:08] Vikrant Bhan: We are actually playing pretty much across the value chain of food and beverages businesses, and now life sciences as well. And you can imagine multi-category set up like that. And the need for localization in a category like food, because food is very personal to people at a consumer space, means that our business actually from a P&L responsibility is decentralized by nature.
[00:10:33] Vikrant Bhan: So, having said that, I think we in Nestlé have always understood the importance of a very strong core digital, what you would call the core digital or digital core, as part of your enterprise architecture. And for that many, many years back being very foresighted, we created what is called our ERP standardization program.
[00:10:55] Vikrant Bhan: So we actually run our entire business. As far as the back office is concerned, is one single ERP. It's actually one of the biggest SAP implementations in the world, and on top of that, digital code allows us to create enterprise data domains, but we can never be having enterprise data domains cover all the needs of every single market.
[00:11:13] Vikrant Bhan: Just imagine in China you will have some very specific media. And consumer journeys, you will have them very different. In Latin America, you have different channels of trade. You have mom and pop shops in India or in some of the South American countries. While the world of grocery looks very different in a US or a European market.
[00:11:33] Vikrant Bhan: So I think keeping that localization, whether it's about recipes, it's about consumer journeys, whether it's about channel management means that we have to be flexible and that's why our data and analytics model, from an operating model perspective, is hybrid in Nestlé. We try to centralize what we need to centralize and we also allow the flexibility on the edge and
[00:11:56] Vikrant Bhan: we have teams that are working very close to our, what we call our markets. We have 40 plus markets across the world, which are running the P&L responsibility, and we have teams that are based in those OpCos or other markets who are actually helping with that last mile integration of data. But at the same time, they're not duplicating the efforts of what’s
[00:12:17] Vikrant Bhan: standard across the entire company. And they use the digital code that is coming from our enterprise data domains and how to make it all work together is the art of working in a matrix organization like that.
[00:12:30] Dana Gardner: Right, right. Now I wanna go back to a point you made, Vik, about trying to eliminate replication when it comes to data.
[00:12:37] Dana Gardner: I suppose there's also the need to remove replication when it comes to applications too. When it comes to making a shift from a BI focus to an AI focus, is that about removing replication or is that a different type of journey and, and how would you characterize it?
[00:12:53] Vikrant Bhan: Yeah, and you use the example of BI to AI, but there are many countless examples.
[00:12:58] Vikrant Bhan: Obviously, in the past we had monolithic data warehousing technologies. We moved to a modern data cloud environment, and essentially the use of the monolithic data warehouse becomes less. So we are always in our strategy, and I own the platforming strategy along with the data and AI services. We are always thinking about what is the sunsetting strategy or where can we actually be efficient?
[00:13:21] Vikrant Bhan: And Nestlé being quite a big company, we have lots and lots of applications that we have within our application management platform recorded, but we are always looking and we have a product management mindset to this on what are the things that we are conceptualizing, developing, further industrializing, scaling.
[00:13:38] Vikrant Bhan: But we are also looking at a very important aspect of sunsetting. But the reality is I do see, for example, business intelligence starting to maybe become more niche and not as expanded across a company like we see today. We have a massive use of tools like Power BI and other such business intelligence tools.
[00:13:58] Vikrant Bhan: Actually, some of the highest consumption across the industry is in that. But then I think now with AI, we can talk to our data. In a more AI-centric way. So you are right. I think we will start seeing that having an impact on the consumption of business intelligence tools. But there are many other such things.
[00:14:15] Vikrant Bhan: For example, I do think that there will be also an impact on robotic process automation, which was maybe the earlier part of deterministic automation that was happening, and that is a part of my portfolio as well. And we'll see maybe some of the agent capabilities to come and start taking a few of those capabilities into semi-autonomous, if not autonomous, autonomous identification that we are starting to see, and the list goes on.
[00:14:41] Vikrant Bhan: I think that we are always constantly looking at that roadmap and how do we sunset what is maybe not relevant anymore.
[00:14:48] Dana Gardner: While you've raised the subject of agentic AI, how do you see that evolution manifesting when it comes to optimization of people, process, and technology? I suppose BI is very good at telling you what's already happened.
[00:15:02] Dana Gardner: Perhaps agentic AI can tell you what to expect with high probability. And then also, how to get there and execute on it. Is that the case in your thinking?
[00:15:12] Vikrant Bhan: Yeah. I think when we started this journey, like every other company, two or three years back, we were obviously starting with a lot of what I would call AI assistance and still having the human in the loop.
[00:15:24] Vikrant Bhan: I think that gave us a lot of learnings on where can we augment within our processes, people with capabilities that are essentially making their life easier. And you had to create some focused assistance for that. And then I would say maybe not autonomous agents, but we started delving into agents and those agents were of different nature.
[00:15:43] Vikrant Bhan: Like for example, Dana, you just talked about the talk to data kind of agent, or they might be an agent that is allowing you to write back data into your ERP system, or there's an agent that is actually following a step that potentially is multiple things that you have to do in different systems, and it's orchestrating across all of that.
[00:16:02] Vikrant Bhan: I think what we are now seeing is that because we have learned in that journey across these capabilities, which are like steps or tasks or activities within a process, we, I think, have understood what the building blocks are and which ones are working well and which ones potentially need a bit of work to do.
[00:16:21] Vikrant Bhan: Because to be very honest with you, they're also learnings like, you know, what is really maybe advertised by sometimes the platform and product companies versus what's the reality when it hits our data, our ecosystem. And essentially, what we are starting to do now, in line of that re-imagination that I was talking at the very beginning of this call, we are starting to see how we can maybe take something like an idea to launch process within Nestlé.
[00:16:49] Vikrant Bhan: Essentially think about orchestrating across the different steps and different platforms and different business workflows and identify parts of that so we can reimagine that flow. And, that is a very interesting exercise, Dana, because sometimes we also realize through it that maybe that step is not required anymore.
[00:17:08] Vikrant Bhan: And we are going at it from a very outcome lens. We obviously are having outcomes lined to a business strategy. So we are focusing on the outcomes that we want to achieve, and then kind of drilling down from there into the processes that need to be changed. I wouldn't say we have cracked it, but I think clearly from a strategy perspective, that's where we want to go rather than do a plethora of lots and lots of
[00:17:32] Vikrant Bhan: assistance and agents. We want to really focus our efforts on how do we completely change the process itself.
[00:17:38] Dana Gardner: Yeah, interesting. It almost sounds as if you want to elevate project management to an agentic activity and then perhaps organize so many different projects happening simultaneously. In such a way that the people, the best knowledge, and human resources can then execute on those project elements.
[00:17:58] Dana Gardner: But orchestrating it has always been very difficult. So is that a part of what you're seeing as for the potential for agentic AI?
[00:18:05] Vikrant Bhan: Yeah, absolutely. I think we are actually now even looking at a portfolio of projects, which used to be use cases to help us in specific functional capabilities and starting to think that how can we not just prioritize based on value amplification complexity, but also how is it contributing in making that end-to-end process.
[00:18:28] Vikrant Bhan: I won't say completely reimagined, but optimal to the degree that you know, it starts unlocking the maximum amount of value. So our big bets and our projects are actually now really taking shape based on all, all those factors.
[00:18:44] Dana Gardner: Fascinating. Of course, bedrock to all of that foundational is to use as much data resources as possible.
[00:18:51] Dana Gardner: We mentioned earlier that knowledge and unstructured data of all sorts is part of that. So I'm wondering how Snowflake is helping you bolster that foundation in order to enable some of these higher order agent values over time?
[00:19:05] Vikrant Bhan: Yeah, so when I was mentioning in the past commence, the use of the modern data cloud environment.
[00:19:11] Vikrant Bhan: I mean Snowflake is that for us in Nestlé. And when I was talking about the 15 enterprise data domains that we have actually been working on, that's what we are using as a technology. So, I think what certainly Snowflake has helped us achieve up until now is the ability to start piecing together this data, which used to be in lots and lots of systems of record and systems of engagement into how we want to bring them together as data products.
[00:19:37] Vikrant Bhan: For consumption, for multiple use cases and the use case patterns that we have obviously been very successful, have been scaling machine learning use cases, scaling business intelligence use cases, scaling some of the capabilities that you would typically associate with assistance and agents.
[00:19:56] Vikrant Bhan: But maybe in more specific zoomed in areas, I think we are still trying to do some groundwork when it comes to the unstructured data field because we do understand the importance of it. And obviously when we are working on a particular use case, like say we are focusing on source to pay, we are focusing then on the contracts within our company. Or if we are looking at our factory operators, then all the SOPs and various documentation that is related to the different tasks.
[00:20:26] Vikrant Bhan: So we are going at it use case by use case, but there is still a lot of work to do to just clean up using clean rooms and things like that. The ability to bring it all together because I think one thing which you can imagine in a very big organization like Nestlé is we did not have, potentially with our decentralization, all of these components of unstructured data necessarily well-organized or well-defined or metadata around it.
[00:20:53] Vikrant Bhan: Well structured. So I think there is a bit of a groundwork that we have to do. Obviously after that for a particular use case, we are doing it in the priority of the use case that we are choosing to work.
[00:21:03] Dana Gardner: You also mentioned earlier, Vik, that talking to the data is important and that people can start to interact with data as a conversation.
[00:21:12] Dana Gardner: Natural language suiting the human rather than the machine, if you will. Is Snowflake an important element of making that possible?
[00:21:19] Vikrant Bhan: Absolutely. So I think we have obviously been using Snowflake a lot for the patterns that I mentioned, but when it comes to the agent side, one pattern that we are seeing is talk to data.
[00:21:28] Vikrant Bhan: I mean, obviously we don't want to have everybody going and creating reports in the company. I think they spend a lot of time on it, and we want to focus on getting the insights, and certainly we are using some of the capabilities that are coming from Snowflake. For example, Cortex AI, which is a product that obviously Snowflake is investing quite a lot in.
[00:21:47] Vikrant Bhan: Obviously working closely with our key account and architecture groups that are provided by Snowflake. We are starting to use the capabilities that are coming as ground running, and then we are starting to use them in those features like talk to data.
[00:22:02] Dana Gardner: And how about that other concept we touched on about being able to use analytics and AI to better manage and process and optimize analytics and AI. Is Snowflake a factor in that and being able to point the technology at the use of and cultivation of the technology in terms of analytics?
[00:22:24] Vikrant Bhan: Yes. I mean, there is a lot of metadata that we are generating and we talk about the active metadata.
[00:22:29] Vikrant Bhan: So we have many different measures for how we see the success of our data products. And I think just like you would want to see a successful business or a successful function or a successful anything, you need to have metrics to measure that. And I think we also need the measures for a successful data product.
[00:22:46] Vikrant Bhan: So we are using a lot of active metadata from the systems to see how the consumption patterns are, how people are querying things for financial operations, for seeing like, you know, what are the type of queries that people are asking and hence how can we tune them. So there are lots and lots of capabilities that we can use from the data about the data to make our data products even faster.
[00:23:09] Vikrant Bhan: We actually have a maturity score within the company, which we are measuring across five dimensions. And obviously the data about the data is helping us come to that maturity score as well.
[00:23:20] Dana Gardner: And Vik, I should think in an organization that's hybrid, as you described, that with a high degree of centralization and the need to go out to the edge in various markets, different geographies and jurisdictions and legal environments, that openness and standards whenever appropriate are important.
[00:23:38] Dana Gardner: Is there anything about the way Snowflake manages openness when it comes to data and repositories and cloud structures that allows an organization like yours to better accomplish the goal of a hybrid efficiency?
[00:23:53] Vikrant Bhan: I mean, obviously what we are trying to do is we don't replicate the data outside of Snowflake into fringe environments.
[00:24:02] Vikrant Bhan: So we have set some architectural patterns from our enterprise architecture as well as our centralized platform teams. And one of the things that we are starting to do within our edge, I would say, infrastructure is how do we, rather than people are exporting data or potentially sending data to some other environment, actually contributing to enhance the data domains themselves.
[00:24:27] Vikrant Bhan: So, which by the way comes back to capability building and training the teams in the edge, which is not easy because you have to deal with 40 different entities. But some of our OpCos and markets have very, very amazing skills. I would say even more mature than maybe some of the people in the central team.
[00:24:44] Vikrant Bhan: And we are always using them as a lighthouse markets to try these things out and then see how can we learn from that and potentially spread that across different markets that we have. And essentially everybody contributes to the same ecosystem. But keeping in mind that there are some guardrails, we call it within Nestlé, and I'm sure it's called in the industry, something similar, freedom in the box.
[00:25:07] Vikrant Bhan: The box has to be very well defined and the box has to be defined through some standards and some guardrails, some guidelines, some, you know, automation scripts and various other capabilities. But at the end of the day, it's more a governance topic and not just a platform topic.
[00:25:22] Dana Gardner: Right. I have to say in our discussion, it's become evident to me that you're very advanced in how you're using analytics and are not just creating a vision around agentic AI.
[00:25:33] Dana Gardner: But a true path that you're walking on and that gives you, I think, insight into the relationship between people and their culture and their knowledge and what agentic and AI can do. So can you share with us what you may have learned about what's the right mix or the right approach to enabling the best of what
[00:25:56] Dana Gardner: people and machines or machine learning or AI can do? You know, what is the cultural path? Because this is quite unprecedented. This entirely new and human evolution even where the opportunity to combine the best of digital and the best of organic come together.
[00:26:13] Vikrant Bhan: Yeah. I think, my only take on that would be, and I'm sure many people will relate with what I'm just gonna say, I think a lot of focus when you talk about AI goes on technology and, you know, the delivery models, and how to deliver the architecture, and all of that about AI. But honestly, what is the most important thing, and I think most people would relate to this, is you can have the best agent, the best AI tool, best machine learning model, or
[00:26:41] Vikrant Bhan: the best application at the end to solve a particular business problem. But unless and until you haven't got the people who are going to use it with you on the journey, it's never going to happen. So there are various steps that we have obviously embarked on and that is our functional capabilities.
[00:26:59] Vikrant Bhan: We are using functional excellence within the company. So our products, which we build, are owned by our business counterparts who take a proxy across all of our markets. And there, one of the big roles of theirs is to actually make sure that they are doing a lot of learning capability, building programs when it comes to change management and adoption. And our business entities, when they are deploying these capabilities actually have a very clear focus on change management and adoption because we can see, like, there are tools that we have created which have been extremely successful in one market.
[00:27:35] Vikrant Bhan: But in the other market, it doesn't work, and it can't be about the tool because the tool obviously is working for many markets. It really comes down to that change management and adoption capability. So that's very important. But the other thing, which I think is starting to become quite interesting is in AI.
[00:27:49] Vikrant Bhan: I think the complexity that people had to deal with the different workflows, as an example, or learning new tools, because imagine what used to happen. Like you had systems of record, systems of engagement, systems of data, multiple reports, multiple applications that people had to be taught on. And at the end there is this one guy in the market or one end user who has to learn all of this.
[00:28:11] Vikrant Bhan: And I think what is now happening, and this is what they see in their consumer day-to-day life is when they are operating maybe some of the Gen AI tools. As a consumer, I think we are trying to bring that experience to our enterprise as well, where maybe through chat-based interfaces, we are creating the experience layer for all of these products so they don't have to essentially learn a workflow in a tool X or a tool Y, or a simulation capability in a particular report or an application.
[00:28:42] Vikrant Bhan: But at the end of the day, the experience layer is a common one. The complexity of the agents and complexity of the assistance and orchestration and data products is kind of hidden from them. So I think that's the other advantage that we are actually now seeing with this technology, which is going to make it hopefully easier for that change management and adoption as well.
[00:29:04] Dana Gardner: Wow. It sounds as if we maybe have a new equation for managing and dealing with complexity that is a combination we haven't quite figured out the right mixture perhaps. But, certainly bringing the best of what agentic AI and what human creativity can do will allow complexity to continue but not become a barrier, but perhaps allow for a path to even better complexity if there's such a thing, right?
[00:29:30] Dana Gardner: We always think of complexity as a bad thing because it's so difficult, but perhaps this combination, as you're describing it, will allow complexity to become a good thing.
[00:29:40] Vikrant Bhan: Yes, and maybe eventually I think it'll also help us imagine different operating models, which today I think is not the case because you're kind of limited by the way
[00:29:50] Vikrant Bhan: things work in a process, right? So I think if you start re-imagining a lot of the steps in the process, then you might start thinking about what roles do you need in a particular process and what are the humans in the loop in which process? And that actually will, maybe, will remove some of the complexity from that equation and really help people focus on decision intelligence and where we really need their mind to operate and work.
[00:30:19] Vikrant Bhan: We aren't there, Dana, just to be clear, but we obviously want to get there. Just to be clear.
[00:30:24] Dana Gardner: Yes, that's probably another good big topic for another day. But yes, I really appreciate what we've been able to get into and understand the level of accomplishment that you've made at Nestlé and I'm sure that's gonna help other people perhaps think about their direction and approach as well.
[00:30:39] Dana Gardner: So, I thank you so much for joining our latest Data Cloud Podcast, Vikrant Bhan. He's our Group Head of Analytics, Data, and Integration at Nestlé. We really do appreciate your sharing your thoughts and experience and expertise with us all.
[00:30:53] Vikrant Bhan: Thank you, Dana. Very happy to do so.
[00:30:55] Producer: Calling all developers, business leaders, IT execs, and data scientists. Snowflake World Tour is your chance to learn and network. Discover how Snowflake's AI Data Cloud can transform your career and company. Experience the future. Join us on tour. Join the Snowflake World Tour to experience the future of the AI Data Cloud with Snowflake. Hear from experts, engage in breakouts sessions, and network with peers. Transform your business and career with Snowflake. Register today for one of our 23 stops worldwide at Snowflake.com/world-tour.