Delivering AI in Professional Services: Managed Services, Platforms & Internal Excellence

Summary

This video features executives from EXL Service, Bain & Company, and EY discussing how professional services firms are using the Databricks Data and AI platform to drive internal excellence and deliver AI-powered managed services to enterprise clients.
Key themes include token budgeting and cost controls for agentic AI, workspace-per-client isolation for compliance, and the challenge of moving beyond promising pilots to measurable production outcomes with real business impact.
Panelists explore how professional services firms can package AI-powered data products for clients at scale while navigating governance requirements, multi-scenario AI automation, and the expanding regulatory surface area as AI reaches business users.

Delivering AI in Professional Services: Managed Services, Platforms & Internal Excellence

The market has predicted the end of consulting several times over. However, in an era of industrializing AI adoption, enterprises need professional and business services now more than ever. The most successful PS firms of this decade will be the ones that leverage AI to enable their clients to deliver on their AI bets. Hear executives share how they used Databricks to drive internal excellence, productize their IP and deliver managed services to their clients.
Talk By: Gregory Weeks, VP II Global Technology, EXL Service ; Pinaki Ghosh, VP Data Transfomation, Bain & Co ; Raghu Jakkampudi, Executive Director, EY ; Raj Suresh, Industry GTM Leader, Financial Services, Databricks ; Zach Rothstein, VP - Business Services, Fintech, Findata, Databricks ;

Chapters

FAQs

How are professional services firms using Databricks to serve clients?

Firms like EXL, Bain, and EY are building AI-powered data products and managed services on the Databricks Data and AI platform, using it to deliver analytics, automation, and AI capabilities to enterprise clients. This video explores how they package their methodology, models, and platform expertise into scalable client offerings.

What is workspace-per-client isolation and why does it matter for professional services?

Workspace-per-client isolation gives each client a dedicated Databricks environment, ensuring data separation and compliance independence between engagements. This video explains how EY uses this architecture to maintain security and enable rapid AI innovation for each client without risk of cross-contamination.

How do professional services firms manage AI costs when deploying for clients?

Panelists in this video discuss the importance of token budgeting, cost caps, and chargeback configuration when deploying agentic AI on behalf of clients. The governance surface area for AI cost management grows significantly when moving from technical to business users, making centralized controls essential.

What does it take to move professional services AI from pilot to production?

According to panelists in this video, the common denominator across successful deployments is having clients invest in their data foundation — governance, semantics, and centralized data — before layering AI on top. Without that foundation, AI pilots tend to produce inconsistent results and fail to scale into measurable business outcomes.

Full transcript

[00:08] Hi everybody. How's it going? Good afternoon. Um we're going to get things started. So to to kick off our session today is uh Raj Sesh. So Raj, why don't you come up and get started for us?
[00:30] >> Hey everyone. Uh very good afternoon and uh thank you for making it here. First of all, this is a very special session because this is the first time that we're doing an industry forum for our professional and business services um
[00:42] you know industry. It has one of our largest uh clients as part of this industry. So thank you all for being here to make this a success already. My name is Raj Suresh and I lead industry go to market for our professional and
[00:54] business services vertical and I'm a recovering consultant myself. I was a partner with BCG in our financial services and fintech practices. So we cross paths with many of the clients that data bricks serves and that's how I
[01:07] made the move to data bricks. Uh we have a packed agenda today. Um but let's maybe get started with a quick question. Right. Show of hands if you think 50 million is a is a big number. Let's get
[01:21] warmed up. Okay, I see two, three, four, 20. Okay, there you go. 50 million is for context is the total number of technical personas which is
[01:34] developers, data engineers, data scientists and enterprises all over the world. So let's let's hold that in our mind for a second. If we thought that was a big number, any guesses on what this is, not the population of a
[01:48] country. No, not agents, but that's a good guess. So, this uh tokens, no, but we're getting there. This is the total number of estimated knowledge workers in the
[02:01] world. And why is that relevant? It is the proxy for the number of business users for data and AI all over the world. So for every technical user you have 20 times the number of business
[02:13] users and it's important because the flip side of it is the regulatory surface area or the governance surface area to ensure that all of these people get to use AI responsibly and within
[02:25] cost controls explodes by 20fold. Now we had you know people saying agents but this is the number of agents that are expected to transact on behalf of humans by 2030 by the end of 2030.
[02:40] And the flip side of it is obvious right like it's agent sprawl but these are all big numbers and and there's the hype surrounding uh data and AI and all of the dollars that are going into it. But the common denominator across all of
[02:54] these numbers is what I get asked or what we get asked from data bricks when we meet IT data and business executives which is how do we get the first million dollars of business value? How do we
[03:07] make it start showing on our P&L? Right? This might be familiar to you in in your hats as client service uh uh executives, but you would also be seeing
[03:19] this in your internal organizations as well. Now, this is understandably a hard question to answer, right? And I apologize for slapping a chart there as a as a consultant. I couldn't help it.
[03:31] But if if you were if you were to think about why this is hard, it's because of a couple of cost curves in the industry. It's no secret that the cost of intelligence which is the unit cost of tokens is coming down. All of us know
[03:44] it. But is that really reducing our AI budgets that enterprises are committing? I don't think so. And if you were to look at the number of pilots that do not cross the chasm towards production, it's
[03:58] close by estimates vary, but it's anywhere between 85 to 95%. So the the cost of failed pilots, the value curve is kind of going up. And the
[04:11] reason why this is relevant here is because the folks here, the companies here are actually at the intersection of both these curves. Meaning the moat for organizations, everybody has access to
[04:24] the same models. It shifts from having the best model to having the best delivery model, which is how do we really industrialize AI? across the enterprise.
[04:36] Now this anxiety around
[04:48] the last few decades right so if you were to rewind even before the millennium starting from the dot bubble and the great financial crisis the wave of customer journey being digitized co jai
[05:01] agents you name it we've we've heard this narrative time and again. But in the same period, if you were to look at how professional services has performed as an industry,
[05:14] and I'm just taking the big four as a proxy for it just for a second, they've grown revenues by close to 5x. The takeaway here is not that these threats are not real. It is that
[05:27] professional services as an industry has found ways to reinvent its value model and its delivery model to drive durable revenue. And that is kind of the crux of what we're going to be hearing more of today.
[05:40] And we're seeing this in our largest customers as well. Just in the last 12 months, if you were to look at just our financial services customers putting AI into production, the revenue that we're
[05:52] seeing from that has grown 10x over 10x in just the last 12 months. I'm sure many of you have been seeing that as part of your end clients as well. But
[06:04] that is just to show the velocity of growth over the you know just a limited time frame um as recently as 12 months back.
[06:16] And we're seeing that as part of some of our largest customers. And I know you're very well represented uh in the room, but these are just a select few logos that we have. And and for each of the logos that you see here, there are several other uh customers that we work
[06:28] with. So I wanted to say a big thank you to all of you for being great customers and great advocates of data bricks. And we'll hear it firsthand from uh three of them today.
[06:41] But if you were to look at what these clients and what these customers are doing well from data bricks from our experience in serving this industry we've seen three pathways of success.
[06:53] The first pathway is essentially to use data bricks to deliver internal excellence. And what I mean by that is across firm functions be it finance be it HR be it
[07:05] internal knowledge management um be it the commercial excellence pricing using data bricks to build the foundation to democratize AI across the organization
[07:17] is one of the drivers of value once you've built that foundation it leads to the second pathway which is how do you differentiate your client value prop as you go to the market which is
[07:29] like I said everybody has access to the same technology but how do you make your firm stand out as you go to the market is through productized offerings productized services which could be self-service offerings how do you
[07:42] package data and insights and create reliable data products for your customer end clients and then finally the last part is how do you use that differentiation and scale it across your
[07:55] broader client base which is build once deliver it to several to clients. And the answer to that lies in how do you build robust platforms and manage services and ensure that your value prop is differentiated, but at the same time
[08:07] you scale it across several customers. And those are essentially the three flavors of successes we're going to be hearing today from uh some of our clients from from our top clients. We'll
[08:20] hear from and Zach will take the stage with him on how do we build for internal excellence and then he would be followed by Greg and Zach will stay on to talk about how do we use that
[08:33] foundation to deliver differentiated value prop to our to their clients and then finally it'll be Ragu and Henry on stage to talk about how do we build platforms to scale that delivery model
[08:45] across several clients and with that I'll invite Zach and to take the stage. U look forward to the session.
[09:17] >> Hi everybody. I'm Zach. I'm Zach Rothstein. I'm a VP at data bricks. I lead the go to market teams that support the professional services folks that serve um any organization that is a
[09:29] customer. So if you think of the big four or advisory firms and the teams that support those folks um that's uh the business that I get to lead alongside Henry who you'll meet in a little bit. I'm joined um today with
[09:43] Panaki Goch. Panaki um is the VP of data and analytics at Bane. Um I'm uh fun fact about Panaki is he holds a patent
[09:55] um which he can tell you more about after today around um what is it large uh amounts of data and uh how you secure it and it was really interesting to learn a little bit about that.
[10:08] So um today you know we we have a few things we we'd love to start but maybe to to start Panaki if you can tell us a little bit about your journey with data bricks like where um did you start what was the baseline what did you build and
[10:22] where you at today? >> Thank you. Hello everyone. Uh this is go uh from Bane and company. I joined Bane about four years ago and when I joined uh we have been doing
[10:35] analytics. Um but it was a platform where it is not ready to scale. Couple of years ago we were asked the question are we ready for AI right? We all probably had the same question in this
[10:47] room. Um the answer was no. Uh we were somewhat ready but we were not actually ready. And so last year we started our journey of data transformation, data platform transformation. One thing was
[11:00] very clear is that in order to have good AI, your data foundation has to be solid. So that's where our journey began last year. Uh the second thing we thought about was okay uh we can get a
[11:13] bunch of all the data into one platform uh but that doesn't solve a business problem. So let's look for the right business problem to solve it. Right? So we started building an app that actually
[11:25] serves about 5,000 people in the company and that's how we started our journey last year. >> Great. Thank you. Um when you think about patterns or um sequences around AI
[11:38] adoption, so when you look at like the different internal excellence um you know users of the platform, are there any patterns that you've seen on who's adopting and why? And is there any sequence that you can give advice to the
[11:50] to the audience on today? >> Yeah, I think uh within Bane I think everybody wants access to data and everybody wants access to AI and AI capabilities. Uh
[12:02] business at a leadership level they are servant leaders. They want access to the best and greatest information at their fingertips. A lot of them are probably on the planes. So they want access to it on their mobile devices, right? Uh and
[12:14] then as you start going down uh a little deeper, you start clicking down analysts they want access to their data but they want access to their cleanest possible data. And so again data bricks
[12:26] comes into play. They also want access to AI because they are not ready to go say to querying. They want access to tools like Genie, right? So we see
[12:39] across throughout the organization we see there is a demand for data and data and AI products. Great. Thanks. One uh one final question for you. So I uh I imagine everybody here has heard a
[12:51] little bit about like the buzz around the escalating costs of AI. Um, I'm sure that that's front and center for you. So, how do you balance the idea of being an AI first organization with ensuring
[13:05] that you're putting, you know, some cost controls around enabling people to use it? Um, and I'd love to to hear any advice you have around that alongside governance. Um, and how those things you
[13:17] kind of have to balance it as you deploy these products. Yeah, having uh built a lot of large scale cloud uh platform, one of the things I have had bad experiences with was runaway cost, right? Uh people I remember implementing
[13:31] Google cloud at Equifax uh with 10 pabytes of data in it and somebody wrote a query a bad query that costed immediately $45,000. Right? So uh I knew uh where to start in terms of data
[13:44] governance and cost governance. So uh couple of things we're putting together in place. One is understanding what is the consumption going to look like within the firm. Who should have how much budget? Um do we have all of that
[13:58] in place today? No. Right? That's the real answer. But those are all the areas that we're looking at. And the second thing is we're also looking from a budgeting standpoint. So annual budgeting standpoint. How should we think of modeling entire database stack
[14:12] the AI stack? because there's so many ways the tokenization is going to go with runaway cost and so that is definitely front and center for us. tok to token maxing. >> Token maxing. Sorry, not tokenizing.
[14:24] Token maxing. >> Token maxing. That's right. Um I you know f first off, thank thank you so much for being such an incredible customer. Um and uh and we really really appreciate it. And I think that's all I had for you today and from there I'll uh
[14:36] I'll shake your hand. We'll welcome Greg up to the stage. So thanks so much. Yeah. Thank you.
[14:50] So um hello so that was our first spotlight focused on internal excellence. So as we transition having Greg here from EXL on stage um you know first off Greg is a VP
[15:02] of global technology within EXL. Um he's responsible for their the technology that they bring to market for their healthcare business. Um, so as you can imagine, spotlight number one was about
[15:14] internal excellence and spotlight number two in the world of ProServe is how do you build something that's valuable to your clients and uh and we think that Greg and and what you've done at EXL in
[15:26] your healthcare vertical is incredible. So first, thank you very much for your business. Um, I' I'd love to start in the the same place we started with Pinoaki, but maybe tell me a little bit about your data bricks journey, right?
[15:38] What was the baseline like? what did you start with? What did you end up building and where you at today? >> Great. Thanks, uh, Zach. Very excited to be here, and thank you everyone for coming and showing attendance here. So,
[15:50] um, about 5 years ago, we had, um, a legacy product that had been running in an on-prem infrastructure that we started just hitting a lot of roadblocks and bottlenecks with. We had a a Hadoop
[16:02] cluster, a large Hadoop cluster, SQL server engines, disperate analytics engines, all tied together by literally like duct tape and just random connectors. It was something that we had
[16:14] um gotten to a very good point in our business strategy, but we knew in order to get to the next stage of our evolution, we needed to kind of unify our data strategy, standardize our um the foundation of what we wanted to
[16:27] build on. and then um in order to take advantage of AI and advanced analytics. So, of course, we moved to data bricks after a lengthy investigation of other products, but data bricks had um really
[16:40] gotten us to be able to bring that unified data strategy into our day-to-day operations, allowed us to get into a different business model and our prepaid uh data an our prepaid data
[16:54] auditing for medical claims and um bring in advanced um automation and reliability in our data um engineering platform. This allowed for easier access
[17:06] formemes for them to focus on their you know domain knowledge and applied knowledge on our data to build out models at a more um efficient and easier operational pace. Um and of course since
[17:19] we have um you know we work in healthcare and with PHI data the unity cataloges um governance and compliance was paramount for our success. We were
[17:31] really easy to manage everything from a single pane of glass versus having all these different systems interconnected where we had to run around and chase things to make to be able to meet meet
[17:43] our contractual SLAs. >> Cool. So quick uh kind of note, right? So of course you can use data bricks within professional services to improve your company, but building assets um is something that we think is really really
[17:56] great as well. So thanks for for choosing data bricks to build your healthcare asset on our platform. But the the next topic I want you to speak to is a little bit about like scale, right? So how do you kind of take um something that you probably built for
[18:09] maybe one client in the beginning, right? How do you isolate or identify the right people or the right processes and the right technology to really take that to the market to not just deliver value to your customers but you know
[18:22] perhaps generate some revenue for your firm as well. >> Yeah. So one thing we did was that so we had a lot of success in healthcare and that really revolutionized our payment um payment services business and quickly
[18:34] throughout the other parts of our company in insurance consulting finance they all understood or realized that how important data bricks was and it had that unified data strategy so and when I
[18:47] saying scaling it's kind of outside of our healthcare business is a different you know maybe not everyone here is working across an organization. It might be in their own silo bucket. So with health care um or with their other
[19:01] business units, they came to us and asked, "Hey, I need a unique catalog. I need a workspace. I need data bricks. How do I get it?" And soon enough, we just had a lot of random people and and business units coming to us asking for
[19:14] access, which became very chaotic. So we developed this thing called a a landing zone where we're able to spin up a Unity catalog in a structured man manner. We had the central governance of our technology team administering that and
[19:28] all the policy controls in order to keep sanity and then you know we would have some individualmemes on our team work with our business um uhmemes of what they're trying to do and
[19:41] then we would develop and just spin off um workspaces for them so they could innovate and just focus on the innovation focus on the data aspect without having to get weighed down by the administration of Unity catalog. bug
[19:53] or by any of the security and compliance and kind of tweaking all the wheels and behind the engine there and that really got them ability to be creative work and solve business problems and focus on the
[20:07] actual innovation. That was uh probably the most important thing where we try to get the folks that are front lines with the customers they need to find a business solutions to a problem customer might be having and focus on that.
[20:21] >> Awesome. So we start with one, you know, kind of business unit and uh and a bunch of others came and and could go and build some solutions and some assets for others. That's awesome. Um you know, one of the things I love about EXL and you
[20:33] know, I I think that it gives you a a bit of differentiation, right? It gives you something that's unique to the market. Um if you can just talk a little bit about that, maybe give some advice to leaders that are trying to figure out how they build some sort of practice or
[20:46] some sort of differentiation that they can then bring to their customers. I think I'll go back to the innovation is really focus on allowing the people that are focused on data or domain
[20:59] expertise give them the innovation sandbox where they can play where they can access data use the genie use the AI tools on top of that where they don't have to worry about coding or any um you
[21:11] know low-level engineering this is where they can bring the most value to the other um the other uh to our clients now what We also did which was very important is we you know we built them an environment where they can
[21:24] effectively tool and play around with any as much access as they want in our sandbox and research and envir uh research and innovation environments and then follow an SDLC process all the way
[21:36] up to production kind of mimicking a software process but in the data bricks environment so that they can play with um new features from data bricks without having concerns about um problems with
[21:48] the with any of the data. or contractual issues with um compliance. So it it you know that's really has been helpful for those businesses and it's all fully automated which you know we we struggled
[22:02] with at the beginning because we came from a legacy tech shop and we had very minimal automation in place when we started the whole journey. One of our like foundations was to make sure that everything was automated, everything's
[22:15] in code so that when you get through those pain points, you're really just doing push button deployments and being able to service our um folks that are in front of clients as fast as possible
[22:27] with workspaces and access to the data that they need. >> That's great. Yeah. I um I think what uh what what I'm so excited about the Excel is the growth that you've seen, you know, post deploying the service, how
[22:39] sticky it is for the folks that use it and the customers. And so it's a great success story. So I I appreciate you both as a customer and a partner, which I think is important you all leave today knowing that we are um hungry for both
[22:52] in uh in this uh business unit. So um Greg, thank you very much. That's my time as well. Yeah, thank you, Greg. >> Thank you. I'm going to turn it over to my partner
[23:04] in crime, Mr. Henry. Hi, and from EY Ragu. >> Thank you so much, Zach. Come on up, Reggu. So, why don't you take that seat over
[23:17] there? Uh, you got the clicker. Excellent. Yeah, I'm going to leave this with you. So, Reggu, thank you so much for taking the time to join us today. um why don't you tell us a little bit about you what you do at EY as well as kind of
[23:30] the managed services business. >> Yeah, absolutely. Uh Henry, thank you have for having me here. Um and thank you everyone for joining. Um I'm Ragu Jakampuri. I'm executive director in our
[23:42] financial services practice. Um and uh you know primarily I have sort of two roles. one supporting our clients in the financial services industry
[23:54] um you know in in building data and AI platforms um in sort of bringing the change and also I wear uh another hat which is uh leading enterprise data
[24:08] platforms supporting our managed services and solutions um in in our in our you know firm. Um so when it comes to our um oh sorry which one is this
[24:22] slow? >> Okay there we go thank you. Yeah. So our managed services business essentially uh if you look at it uh majorly we have like five
[24:34] um service lines uh tax, audit, cyber, consulting um and um you know uh risk and consulting and you know together. So we
[24:50] support about like 150 countries uh you know across the globe uh in these services and you know we have experienced professionals with
[25:03] decades of experience that have methodologies and models um that that we have honed in over over the period of time. Right. So that's kind of like our scale of the managed services business.
[25:17] >> Yeah. So thank you so much you know thank you for being such a great partner we you know and customer as well and you were one of the early adopters of data bricks right at EY one of the first folks to bring it on board I would love for you to tell the audience a little
[25:30] bit about your journey and some of the business outcomes that you've driven to date >> yeah absolutely um I think uh our journey with data bricks uh has started with trying to solve our own internal uh
[25:44] data management uh problems right So um I think one of the things that we've learned very early is um that data management is a very
[25:56] complex task right so um when you think of data management you know if you think of our scale globally um operating in multiple service lines uh it just
[26:09] multiplies right so there's so many scenarios that we have to deal with um and it gets very very expensive, very fast when especially we try to solve those things in a very siloed manner um
[26:24] and using different technologies um and then trying to like make connections between the two. It's it's too expensive and especially when it comes to our managed services business, you know, our clients expect us to be um
[26:38] you know, predictable in terms of cost, be efficient. uh but you know with the with the kind of the technology how it's evolving we now have requirements to build faster
[26:51] better outcomes um and and be able to scale um and for for that reason like we we wanted to have a unified ecosystem where data and AI are together um that
[27:04] we can unify and and uh you know bring bring it together so that we can actually have the ability to scale across the service lines and we're talking like globally, right? >> So that's what you're calling the fusion
[27:16] data platform. >> That's right. Yeah. So what we ended up building is um something like this um essentially where we are bringing data from our clients, third parties um you
[27:30] know different uh types of data structured unstructured um and be able to bring that unify that data together um in a very consistent scalable way and bringing governance to the front of it.
[27:43] Right. Um so as part of that journey we've built we built this where we bring curate and then you know uh bring those products together um and applying governance on top of it that we can you
[27:57] know then scale right for what you're seeing here is is is an instance of it where different service lines are operating in their own lanes using this unified service that's built on data bricks uh that then supports all the
[28:10] data and AI uh capabilities that each of the service lines needs. to solve their uh problems. Yeah. >> Yeah. Kudos to the team for the amazing visual. By the way, I know they're sitting here in the audience. So amazing looking. You know, one thing that really
[28:23] strikes me is the distribution point is that marketplace. So I'd love to hear how you thought about designing that to help address your regulatory and compliance needs that are global for EY and we understand there's a lot of restrictions globally on what kind of
[28:35] data you can actually, you know, distribute. >> Yeah. So essentially what we u ended up is building our own marketplace. Uh not that datab bricks marketplace is not great, it's pretty amazing. Uh but our
[28:48] our you know internal needs are kind of unique in in in ways how we operate our businesses. Um you know we would need an ability to uh apply policies dynamically
[29:00] depending on who you are, where you're accessing from um and what is the purpose of your products that you're accessing. um and we need to maintain that and we also have to keep in mind
[29:12] our global and local uh entitlements which is very important uh for us and making sure that we comply with those right so uh that brings a a unique um
[29:24] you know uh requirements for us uh to how we solve that internally for our data um and that's that that's where we ended up building our own uh data marketplace to achieve that >> and is unity catalog a big part of
[29:37] Yes, absolutely. So, you know, the front and center of this uh is Unity catalog. So, where are all our assets, you know, sit harvest and then like, you know, we apply all all services through the Unity
[29:49] catalogs policies. Yeah. >> So, now that you have all your data in a single kind of governed platform and managed, I'd love to hear some concrete examples of how you've applied AI and
[30:01] scaled it out. And I think everyone in the love to hear your experience in that. >> Um, yeah. So the way we think about ourselves like we we think ourselves as a client zero um by that we means we
[30:13] have our own uh unique uh challenges uh that we want to solve before we apply that to our clients. Um so we are embedding AI directly into our workflows
[30:26] uh not not not just as pilots but in a way we're driving re real measurable outcomes. um you know one of the I mean there are some some of the metrics here on what we what we're doing but one of
[30:38] the good examples of of that is like unstructured data uh so when you think of unstructured data applying uh any of the sensitive data uh you know identification is not a simple task
[30:51] right so it's not as as easy as structure where you identify your PI IPCI type of things it's it goes a lot more depth so we bring together our domain expertise um and our models and
[31:05] methodologies into it to be able to identify you know um that sensitive data and then apply AI as part of it to how how you can actually build those models that are smarter because the amount of
[31:18] data that we process in unstructured way uh you know historically is huge. It's not humanly possible to go through each document and try to do that and then be able to scale it globally. uh we we we
[31:30] have a need to use uh that AI at the center of uh how we approach it. So uh those are some of the things and you know one of the recent ones we we started doing in terms of achieving scale uh is build financial service uh
[31:45] statement analyzer uh which is one of our uh solutions within tax uh that's supporting 60 countries building 100 or uh plus products uh that we are able to achieve uh using this platform. That's
[31:58] amazing. That's amazing to hear. And you know to I'd love to hear more about the subject matter expertise. I think we've heard about the the fate of consulting multiple times. And as you look in your crystal ball, how do you see managed services evolving over the next two to
[32:12] three years? >> Yeah. Sorry, I just flipped the slide. Yeah. So I think uh uh one uh one thing that uh uh you know we see a lot of news you know I think uh Raj shared some you
[32:24] know slides where there's lot of um you know media about like you know AI and uh you know how it affects consulting to me I think it's a very positive thing the way I see it um so I just gave you an
[32:38] example of that unstructured data so you know AI comes you know very handy in in doing that um and you So the it it brings an aspect of increasing the productivity. So if you think of you
[32:50] know I'm sure a lot of the members in the audience would would relate to it like doing data management uh you know is not an easy task and with AI it it becomes
[33:04] terms of uh driving value is more on you know what what you want to deliver to our clients less about uh you know processing the data and you know getting it to the right shape where AI can come
[33:16] come pretty handy and that productivity gain can be used to drive better outcomes, faster outcomes to our clients. And I I see that being shaped into uh a more um I guess different use
[33:31] cases that we're going to be coming across uh different solutions that I think which were previously not quite comprehensible uh that that that that we'll start like you know exploring and
[33:43] and uh bringing it to the surface. So I I see the future in much more positive way with AI uh than less optimistic. >> Well, that was my last question. Reggu, thank you so much for joining us up
[33:55] here. Really appreciate Ragu Jakuri from EY. Um folks before you leave, please try to complete the feedback on the session and also if you want to see us, please visit us at the expo hall and we
[34:08] are at the industry hub. So if you want to see some of these demos and use cases, please come on down and visit us. So, thank you everyone for coming. Have a great summit.

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