Strategic AI Investment for the Enterprise — with Apurv Baviskar

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This transcript is based on the episode’s English auto-captions and has been formatted for readability. Please allow for occasional transcription errors in names, acronyms and specialised terms.

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Welcome to Enterprise Tech Talk podcast. Here we aim to demystify various enterprise technology topics and emerging technology trends through an open and honest discourse. Okay. So about today's uh topic. The topic of today is strategic considerations for AI investment for the enterprise. Now it is reasonable to assume that most of us have at least given some thought about um how to invest in AI in a more safe and scalable manner. In fact um the investment in AI is nothing new. Most of the organizations have already invested into um the classic machine learning AI models and with the advent of genai uh many organizations have already rolled out um some use cases for wider employee base but there are reports which suggests that most of the organizations still they still struggle to justify investment in ROI.

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Um so the these there are some challenges which organizations face. In fact most of the organizations have not taken um investment in AI in a more strategic manner. So these are some of the points which we are going to discuss today. We will discuss um how organizations should look at defining a strategy around AI. What are the governance and ethical considerations organizations should think of for a safe AI adoption? What is the technology backbone organizations should um consider uh for um for a scalable AI adoption? What are the skills and the rule new roles which are emerging that organizations should consider and plan for for AI adoption? And last but not the least um how organizations should approach the the next evolution in AI which is agentic AI. So to to discuss some of these all these points today I have with me Apura Baviskar. Apurva has almost 20 years of experience in IT industry. Um he's passionate about data strategy AI strategy and architecture.

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um he has wealth of experience in many verticals like healthcare, insurance, uh telecommunications and financial services. So I hope you find today's conversation enjoyful as well as insightful. Suru welcome to the podcast. >> Hello Somitra. Thanks for having me. uh really honored to be part of uh Enterprise Tech Talk as a first uh guest and looking forward to discuss this important topic with you. So it's my pleasure to be here and really look forward to to see Enterprise Tech Talk succeeds and scale in a wider as as uh you progress. >> Thank you. Thanks Aurora. you know much appreciate that and as uh as I mentioned at the start as well um the intent here is to have much more open conversation and benefit everyone.

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Cool. Um so before we get into the the the actual topic of today uh do you mind providing more uh more about telling more about ESF to our listeners uh what's your background and what excites you about data? How did you get into this field? Yeah, definitely. I mean uh thanks for providing that uh kind summary about my experience. Uh yes, it has been a long 19 years journey in technology. I've been been passionate about serving different geographies along with the different verticles. So that has been the one of the important thing I consider that uh I was able to serve different geographies along with Australia. So that makes me me proud. Uh coming back to why data and AI. So I have been working in technology same number of years as I was working in data and last 12 years I've been working with AI. So the passion comes from the numbers. I am a numbers person. I always loved mathematics as a subject and uh was quite fortunate to get good grades uh when I was in schools or engineering and that pursued that passion pursued as a career to focus on the intelligence and define driving the business value with the data. And then it came 13 years back when I got an opportunity in South Korea for one of the biggest client in the telecom to uh really drive the solid technology architecture and deliver end to end around the uh ML using the crosselling and upselling opportunity that got me >> into the uh ML and the AI field to see that how probabilistic determination can help to define a value for the business.

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So that passion continued and you can see the 13 years I've been involved in number of roles in the AI and data space after that uh in terms of setting up the foundations around the architecture around the holistic enterprise platforms for insuranceances, utilities, supply chain uh as well as financial services and the uh healthcare businesses which are enterprise level and the field has come long way the ML models then became foundational models then it became the the sort of uh with the with the likes of LLM agent KI which you're talking the major shift is with the compute the compute is getting cheaper and cheaper uh and because of that data processing is getting faster and faster as well as the the AI is able to learn faster and faster with the number of parameters you can Just this two days back Gemini I3 is getting released and >> the >> that's that's exciting isn't it? Yeah, >> fascinating. However, it sort of scares me as well. The technology development is happening this fast in the curve.

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However, the adoption are we there yet? So today will be interesting to see uh and have that discussion with you. >> Exactly. Exactly. And that's what exactly we want to discuss today. So let's move to the the the first point of for today's topic. Um as as you mentioned and I was also referring to in the introduction um while uh organizations have started investing in AI the they're still struggling to justify returns of investments many organizations have invested in AI organically in a way so they started with machine learning and then GI came then started piloting GI tool sets use use cases etc right um now agentic is knocking the door. Um but organizations are just responding to this um uh to this AI innovation. Um but do you think the time has come for organizations to start looking at at uh investing in AI much more strategically for longerterm gains for value differentiation etc. And if so what what how they should approach this? >> Yeah, I think you're spot on Somitra on that. But uh I think it's no longer a fringe of innovation right it is becoming the core backbone of organization to operate some of the researches uh from the universities which came like 70% of the pilots are not going into the production and that's sort of tells that it is uh not having a proper strategic direction or right foundations applied to that. uh so the number of experiments are getting carried out so they are not adding the value or tying back to the so I think well definfined strategy is required probably I will touch couple of three four things uh firstly AI strategy gives an opportunity to align to the business outcome uh based on the what the business strategy is that towards going towards improving customer experience or operational cost by 20%. It gives an opportunity that to see where AI will really add the value and tie back to that business strategy going ahead. So that's the that's the fundamental thing we can think of where AI can be critical and not critical in the business. That that leads a second point is around minimizing the duplication and fringe investment from some of the from the you must have experienced and I have also experienced like 20 chat co-pilot chats 20 SEO AI >> yeah around enterprise and that sort of creates the not only the lack of ROI but increases the cost. It increases the compliance and the risk issues and not having the cons consistency across the technology landscape [snorts] and which creates the risk around the data. Also, it gives opportunity around the foundations of the governance about the culture and the data. Right?

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AI cannot succeed without having the right data foundations around the data quality, data management, ability to get real time data and the productization of data. So these are the some of the things uh I think would be required uh from the AI strategy perspective which which is gives an opportunity to drive AI investment strategically. >> Yeah. Yeah. Good. Good. Um yeah so go >> there some of the things I think probably would consider is uh as Gartner suggests the the framework around the strategy around the value vision and the stakeholder outcomes focusing on that and then defining the capabilities around the structures governance and the systems and the uh the assets around the data and AI. I would say that these are some of the core ways to think about the AI strategy and developing that strategy. >> Yeah. Yeah. No, good good great points.

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I would also say um I mean even most of the boards um uh they have started asking about or at least inquiring about what our their organizations are doing about AI. So there is also a kind of top-d down um uh push from from across the organizations to to develop a formal AI strategy. And and the point you made also is important that um you also need a to to have a successful air strategy, you need to have a a good data strategy as well because the air strategy will not be successful if there is no clear um backbone about data right from technology to govern as everything. [snorts] Okay. So let's suppose move to the next next point of of our discussion which is around AI governance and AI ethics right um so it goes without saying that and everyone would agree that AI investment comes with its own risk right um because there's lot of automation involved um uh not necessarily there is a clear understanding of how AI will predict something right um uh There is always this argument around human interloop when we talk about AI implementation. Um and organizations are generally a bit cautious in implementing AI for more customerf facing use cases. Um or in the industries where there is lot of regulation like healthcare, insurance, uh etc. um which means that there needs to be a good consideration around governance and there is an ethical component to that as well. So what are your thoughts as to how organization should approach governance around AI and what are what should be the ethical considerations for any organization to implement AI in a safe manner?

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Yeah, I think uh that's a great question and uh to have successful adoption of AI, a scaled AI, I would say that governance forms a solid uh fundamental part of any enterprise, right? So I would say three probable areas which AI governance can focus on. uh is the one is probably the model governance around uh if the models are getting inbuilt or embedded or getting fine-tuned in case of LLM then what's the documentation around the model have you have model catalog any risk profile rating and management of that periodic performance testing explanability standards and getting that visibility of observability of that model performance and then the model lineage around how the data is getting transformed and predicted across the board. That would be really important and that can be critical uh when the models are developed uh internally to manage those risk.

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However, [snorts] uh it will be important for enterprises to ask those sort of questions when they are buying products and embedding into the enterprise landscape. The second is uh I think the data governance the importance of data governance also goes up as uh as we discussed previously >> having that right strong data privacy data ethics embedded into the organization culture. [snorts] It's quite important to set up that right framework and to be able to >> create the catalog of the data domains and profiling and to be able to uh define the right data quality rules uh potentially use AI to uh drive the data quality around that proactive data quality uh findings because there have been surveys that uh the most of the model failures happen because [clears throat] of lack of proper understanding of the data uh proper management of the metadata and the data quality failures. [snorts] So that's the second aspect and then the third is the AI and the data ethics governance. uh privacy is fundamental for any sort of uh AI or nonAI related initiatives but then the ethics comes into pretty good play when you're talking about AI and um as we have been discussing quite a lot is the some of the standards specifically here in Australia AI ethics principle which released few years back and now then there are voluntary AI safety standards which clearly really outlines the 10 guard rails which can be considered and embedded within the within the enterprises. So uh it's not a policy is just the one part of the the ethical AI implementation but then understanding some of these regulatory standards or the compliance standards as well as what's happening if you are in the different geographies within your geography what is happening uh with the frameworks uh if I study there are for us specifically there are every state has their own their own sort of AI laws. So that getting >> implemented and in that ethical standards the bias is quite critical from my perspective explanability >> uh auditability human in the loop and is it going to make any critical decision making so risk management around that >> and what sort of >> escalation pathway are there? Yeah.

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>> So it is important to have that trust. I see that um trust layer embedded within the every layer of the application integration data layer and the AI service layer uh and helping to drive that trusted AI across the organization. So these are three I would say the model governance, the upliftment in the enterprise data governance and the ethical data and AI governance. >> Yeah. Yeah. No, it's they're good points and uh uh as you said the the AI regulations across the globe are still kind of emerging I would say. Um as you said correctly US has state specific and then um I think in EU they are potentially leading the way in a way uh to come up with the first AI regulations which are much more risk averse um and in Australia yes it's mainly the new uh voluntary standards and most of it otherwise we leverage based on privacy act >> right so so it remains to be seen how whether there will be convergence of those regulations eventually across the group just as uh the GDPR did for for every other country right um >> um and the other thing potentially we can also add on the governance uh also is around observability in the runtime >> uh right so that is also becoming more and more important uh as well >> yeah I think on the observability side uh uh yes the governance is can majority focus on the ethical use of AI responsible use of AI but governance can play critical role in terms of the fin ops perspective as well because that's also a governance >> and with the cost of the model inbuilt or the embedded if the they are not watched properly and right uh guardrails and the controls are not there around >> in observable capability of the models it can spiral the cost quite quickly. So it is important to apply that governance around that financial operations.

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>> Yeah. Yeah. Cool. Um so governance and ethics is definitely one important pillar or dimension for your whole strategic consideration. The other equally important is the the technology side. um in my view uh and this would be this is become more and more relevant and apparent as organizations are at least started thinking of moving towards agent care. Uh so uh it seems we organizations now need to start thinking about what critical capabilities from technology perspective are required to enable acceleration of AI in a scalable manner right uh whether it's a resiliency of of infrastructure whether it is data backbone whether it is the security of AI agents themselves etc so what can you summarize for listeners as to what that technology backbone would look like at a high level and how organizations think of implementing that. Yeah, I think um it's a pretty broad area and quite evolving as we have seen in last two or three years. I would say uh let me try to summarize as much as I can uh the best of my knowledge and this is at this point you never know it can be >> yes yes that's one thing about AI it's so overwhelming the speed of change is so overwhelming that every 3 months you see entirely new models and new maturity of models and you need to think of it differently >> yeah I mean the first I would say coming from the data background uh the fundament fundamentals of data architecture has changed in a way that uh it is now called as AI ready data right that's that's fun to understand from because I've been in 19 years in the data space and >> d we used to have data as a asset data as enabler datadriven organiz now it's AI ready data that's the fun concept so >> I would say that evolution of the vector data sets uh the real time pipelines uh streaming feature stores uh knowledge repositories or the knowledge graph which are heavily scalable and more and more sort of jai solutions are based on the vector data sets and vector search then uh what's sort of uh how you're going to create are you going to create the knowledge graft or are you going to host the vector data assets because there are >> lots of open source are there as well as it could be with AWS or Azure. So what the technology of choice and what sort of uh uh or are you going to buy from the market and then embed that in your landscape and then how you going to integrate and the controls around that.

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So that's one of the fundamental change I see the rag is going to rag is we all know so how do you set up that rag architecture efficiently what sort of embedding models you use which sort of use cases you drive out of rag within your organization which models you use for embedding models as well as for the output models as well as uh then how do you integrate uh in your system Right. So that whole rag architecture becomes uh really important and then how do you manage that rag architecture going ahead with the cost perspective security perspective. So that's the second pattern evolving and then the third is as we are talking about now we have moved from the ML to the deep learning to the uh the LLMs of the rags and then the agent now we are talking about a number of agent single agent or multi- agent systems then we got to really think about the AI orchestration platforms right?

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whether uh how it can be able to have that orchestration integration between number of agents and the workflow management the API connections whether you yes yes MCP is there the protocol but then whether do you create the MCP servers for which sort of your applications and then able to integrate that the analytics around that uh personaliz uh use case specific agents or you go domain specific agents. So that sort of thinking and then the modularization of that. I really like your concept around the >> composible enterprise. So if the enterprise starts going in that that means more and more agentic AI starts coming then how do you >> not lose that value. So that potential fourth flavor.

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>> The fifth probably as we discussed AI governance platforms coming into the >> Uh >> which can gives us the ability to really have streamline AI implementations and then monitoring of that observability of that AI across the board. And potentially the other would be the modularity around different uh sort of models uh because what's going to happen the the there will be some sort of drift in the models because of the data quality change in the business processes. So how do you ensure that there is a modularity one certain models or the agents are not performing then how vote and have a different >> sort of um agents performing for you. >> Yeah >> and I think security is going to be pretty critical. I see that AI governance probably come forming that key part like cyber security. So >> going ahead. So then security right from the infrastructure network application and the integration [snorts] where do you embed AI to drive certain security features >> and be able to provide advanced threat >> Yeah. >> Yeah. [snorts] No, I agree. Now good points. Um one thing uh as you mentioned things going in my mind where um and what you mentioned but to summarize um even we think of AI enablement from technology backbone perspective some of the the the traditional fundamentals about technology whether it's a the strong data strategy so data is a product or or uh good data governance uh technologies um reusable modular APIs.

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All right. Um the the cloud infrastructure, right, for better scalability and resilience, etc. Those fundamentals are still equally important, right? You you build on top of that, right? The the specifics of um uh AI, AI capability that you need, but these fundamentals are still very much important for any organization. That's important thing everyone should consider. Um the the couple of things uh probably I'll just add on what you said where from security perspective and we'll come to agentic AI a little bit later but agentic AI comes with its own expectations on security um um particularly these agents will have their own identities. So how do you make sure there is a robust access management controls um that you implement for these kind of agents. All right, that's important. Okay, so Apur let's let's move to the next topic in our conversation. Uh we talked about ethics, governance, we talked about technology etc.

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One one thing that emerging more and more for that organization should start thinking of is AI at the end of the day is is a is a new technology right and it with the any new technology comes uh expectations or requirements for uh new skills potentially uh and AI is a step change in the way organizations would function right it's just not a not really just a new technology it's it's a different way of doing the business as to certain extent. So it will necessitate consideration for different types of potentially even operating models, different roles etc. Right. So how I mean do you want to can you summarize for listeners again um what kind of skills organizations should start thinking from let's say from developer engineer perspective or end user perspective um and what kind of roles potentially might even emerge in the future that organiz should start thinking about and plan ahead.

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Yeah, a great question. Again I think as we touched upon in the initial you know question around the AI strategy and AI strategy can really unlock that sort of uh understanding that based on your enterprises culture and the vision and the current skill set you have what's the best way to operate organize and reskill or redevelop the the organization when I talk about uh some of the uh roles which will come come into play is uh is uh senior level is pretty critical would be the AI product managers. Uh I strongly feel that as the demand for data products and AI products starts going up in the agentic enterprise then the AI product manager becomes a really key to be able to drive that vision around the uh AI AI initiatives and the ambition around the business and then how that product can evolve and get developed end to [clears throat] end. The second senior role I would say as a governance lead, AI governance lead potentially a dedicated person uh defining the overall framework approach, vision, the road map and to be able to take the overall accountability and responsibility to drive the AI governance as it becomes critical as critical in the organization.

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Then the um third thing uh probably in the some of the leadership role can come around the human in the loop uh sort of uh the officers probably which they can identify opportunities and work uh with the relevant AI implementations to be able to set the standards and the framework and to be able to provide that the checklist tick marks in terms of uh assessing the uh overall implementation around the AI and then some of the core around the implementation and uh sort of uh engineering level. I see the prompt engineer, context engineering and the AI engineering are emerging as a role where they specifically focus on the rag architectures, the agentic architectures to be able to uh or the languages which are like sort of graph crew able to define and create those the frameworks and implement those frameworks based on the architecture.

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The second is around the [snorts] uh ML ops engineers. Not most of the organization has the ML or data scientist but MLOps or the AI ops engineers which are in the different operation management uh sort of area of the AI where uh when we started to have the DevOps uh engineers coming into play. Now the AI ops and the MLOps engineers can come into play. And then the agent orchestration or the agent um orchestration managers or the designers also can be one of the important things to understand those frameworks and to be able to uh really manage that across the enterprise to take that responsibility of ensuring that agents orchestrations and the agents are performing. [snorts] So these five or six key roles >> uh changing probably another is uh whether AI architecture emerges as a dedicated along with the data architecture [snorts] >> and I think what I've uh some of the gartner predictions or CDAO sort of roles would always have will AI will embedded into the data uh responsibilities as well [snorts] and not just the data uh executive leadership but the AI embedded as per the Gartner and >> to drive that holistic responsibilities.

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>> Yeah. Yeah. No, sure. So some of Yeah. [snorts] Sorry. Go ahead. >> Yeah. No, thanks. I think that this is what I I think uh >> critical changes are being into the >> organization. I think uh these are from my perspective of course it's based on the culture of the organization and how >> so >> how what sort of best operating model suits for for an organization. >> Yeah I agree. I mean these are more of a generic uh trends I would suggest which might be emerging all right but it for every organization they will have to take that into consideration and then accommodate that in their operating model which means that the roles may not be the the same roles potentially within their organization but uh but the responsibilities are more important and those responsibilities could be covered under some other roles etc. Right? No, good. Um, let's move to our next uh conversation topic. Uh, and this is uh this excites potentially everyone nowadays. Um, which is this shift u uh towards the agentic AI, right? And this seems and everyone thinks that this is potentially the promise the final the eventual promise from AI where when organizations are struggling for ROIs etc. This is where potentially they will start realizing their ROIs they will start realizing the much strategic use cases real value differentiation etc. So and it's early stages um but uh there's lot of excitement and buzz around it um within the industry. So can you summarize every again for everyone what is unique about agent care what is why this shift is so important and uh how organizations should potentially consider approaching this uh at a high level at least. Yeah, I I like Samitra what what you said there that uh it's a new buzzword and clearly two years back everyone was talking about LLM and >> Chad GPT and now by the end of last year it is all agent KI >> and some of those chat GPD feels like >> you think from the schools >> all of a sudden two years back technology has become legacy. Yes, exactly. And that's that's fascinating and we have been discussing in this forum as well quite quite a lot. I think >> um I see agentic AI is sort of one of the evolution because we are looking already at the quantum and the AGI going ahead. >> So this is midway as as per the Gartner hype curve. the hyper niching the normalization >> the the proper adoption >> I I would say see let's say traditional AI was around the ML and the deep learning uh for detection churn risk next best action then come the jai which is able to predict the next sequence of the words or the tokens based on the uh context you give >> and then agentic AI uh is the next level which can act autonomously within the guardrail to complete the task end to end with the help of LLMs or the ML models or talking to your core systems or systems of the uh or to be able to talk to uh call external systems and get the insights from that. So I would say four or five things agent KI can do is it can plan the sequence steps like you are booking a flight it can literally >> plan for you it can call those APIs and systems like with the P protocols >> able to interact with multiple uh let's say flight booking servers collaborate with other agents uh if uh let's say you want any special deals along with the flight booking and you have a holiday planning agent then it can plan that for you. Self-correct if it fails uh escalate if it requires and working step by step uh with other agents or other systems.

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It gives you the recommended fl best way to book your flight or best deals even though [clears throat] you don't know but it can offer you the the sort of best of the holiday experience. Uh that's sort of I see the agenti potentially can be giuh to be able to drive that enterprise fully. I was reading about what could come in terms of the contact centers. Let's say for example by 2030 it probably we would have the bot economy where agents are talking to agents and >> to be able to order for example uh your fridge agent is able to understand what's is getting empty and able to talk to relevant super >> order things for you and get it here >> without >> it al it it also means There would be increasing need for orchestration of multi multiple agents because you will have that economy of agents uh ecosystem of agents which are actually doing work but then if someone needs to orchestrate and coming back to our previous discussion on the roles etc. uh there could be potentially emergence of a human role called orchestrator right within different organ different parts of the business which is responsible for set of AI agents within that operation right and which is more um can orchestrate an agents for specific business outcomes. So this a fascinating new new um considerations right um yeah and agentic AI in my view >> it is yeah go ahead go ahead sorry >> yeah it is fascinating uh however then it comes with the consideration around the solid foundations of your data >> yes around security >> and protection because if it is going to external agents or your enterprise is going to provide the MCP servers then what's the >> around that so it >> comes back to lot can be done but having the right foundation >> having the right foundations and all the points we discussed previously governance technology backbone skills they there is specific implication on all of those for agentic care right there are specific consideration you need to make for governance perspective for agent care because the if the systems are completely autonomous right then the the need for governance and um auditability and observability that elevates much higher >> right u and so on and so yeah >> okay so Apura now let's think a little bit ahead in the future right uh we talked about how um what are the key considerations for developing strategy for organizations etc. Uh um so if we now think 2 years, 3 years, four years and four years is yon in in a word. Um u what kind of major shifts you are you are anticipating which organizations uh should uh um be prepared for at least plan ahead.

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>> I wish I had a crystal ball and I could answer this question. the way things are moving so fast probably five years is too much >> maybe two years >> it's too much >> I'm think of these days and you know right uh it's it's not just the data AI but other aspects around the infrastructure security as well how things are moving faster so probably let's say uh next two or three years I see uh every employee will have some sort of co-pilot given to them. Uh and the assumption would be that they have been trained to use it and that's driving the lot of efficiency and productivity. Uh the end to end process uh redesign right uh the AI was just automating some of the task in the entire process. For example, [snorts] uh you are trying to summarize the conversation or summarize uh the the clinical outcomes after the consultations or pro able to provide you the recommendations uh after you log to portal let's say but uh how it can really drive that end toend workflow uh using AI embedded in each step that would be the key. The third thing probably the data quality might get embedded in the one of the top uh level board level or CEO level KPIs where the data quality dashboards are getting embedded. This is a bold claim but it can happen uh to really see uh in order to AI to perform what's the data quality across the board along like the six principles of the data quality >> are getting met or not uh specifically around the structured data at least but then if you are for uh the enterprises which are able to tap on to unstructured data and uh you can't guarantee the quality but at least have some of the principles applied on the unstructured data that we could [snorts] >> uh AI governance and the risk dashboards with the real time accessibility with the model inventories and what sort of [snorts] uh uh risk AI has and that gets elevated as potentially at the level of cyber security but that can be also really important in terms of driving the right decisions from the enterprise >> and I think this the final would be whether the system of record will stay system of record or because every you have seen the core systems as well coming up with the uh the semantic AI layer whether they can create the context aware recommendations and the searches >> so and able to drive the insights directly from the code systems >> which then can be embedded with the agents. So these sort of five or six changes >> having the widespread co-pilot adoption process redesigning with the AI embed embedding AI into the systems prioritization of data quality along with the unstructured and the metadata management uh elevation of AI govern the governance across the board and probably the shift in the uh the way core systems operate from the systems of record, systems of intelligence. What do you think from your >> Yeah, I mean these are really good. U maybe I can add a couple of and probably you might have touched base those implicitly. uh one is uh I can add is in the future uh the the the business operations like uh individual business operations you can start looking at those as is a hybrid model of humans plus agents >> right uh so let's say um a back office operation which has certain people uh doing insurance uh claims operations but you might have some claims related agents and the when you look at it operation holistically it's a it's a hybrid of some agents and some people working together. So it's a different way of looking as a as an operational team. >> Um um second is which is emerging and I was reading an article yesterday or day before itself is uh uh the how the the the global the thread actors are they started weaponizing AI. Right. So this there is lot of implication of how enterprise are going to look at security because we are using most of organizations are using AI for the betterment of course right but there are global trade actors in in the dark webs and everywhere they are using LLMs and AF for an entirely different purpose right and then the the way the ransom wares happen the way fishing attacks happen etc they are going to be dramatically different and very high at a very high scale um using AI um automated AI capabilities right so how organizations can prepare themselves and respond for that that is still I'm not sure how that's going to work out um so probably that is one important thing organizations need to worry think and potentially worry about it >> right but yeah >> yeah so these are the ones uh anyway I think we are running out of time But uh Apura thanks thanks for coming here.

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Thanks for providing your insights and this this I'm I'm I'm sure listeners are going to uh take away many key learnings um in the if you are okay I will drop uh uh your LinkedIn profile reference LinkedIn profile in this video when I upload this um if people anyone wants to reach out to you they can connect with you they can connect with you on LinkedIn right um but again it was a great chat thanks Thanks for joining. >> Thanks Amira. Thanks for having me and I also really enjoyed discussing this fascinating question and talking to you. Look forward to next one. >> Thank you. See you. Bye. >> Hope you like today's episode. Please subscribe us on YouTube, LinkedIn and X platforms. Also, if you are passionate about any such enterprise technology topics and want to participate in the discussion, [music] please reach out at inquiry@ enterprisette.com. Also, please visit the website www.enterprisete.com Enterprisetto.com for more details.