Unlocking AI’s Transformative Power: Data Governance and Industry Insights

Welcome to the Hitchhiker’s Guide to it, brought to you by Device42. On this show, we explore the ins and outs of modern IT management and the infinite expanse of its universe. So buckle up and get ready to explore the ever changing landscape of modern IT management.

Michelle Dawn Mooney

Hello and welcome to The Hitchhiker’s Guide to It, where we explore the most transformative trends and best practices in information technology. I’m your host, Michelle Dawn Mooney, and today we are diving into the evolving landscape of artificial intelligence, from generative AI to traditional approaches. Ai is revolutionizing data management, governance and innovation across industries. We will discuss how businesses are adopting these technologies to solve real world challenges. Enhance data driven decision making and shape the future of sectors like health care and software development. I am pleased to bring on today’s guest. Aurora is CTO of Win Wire. Vineet, thank you so much for being with me today.

Vineet Arora

Thank you for having me, Michel. Great to speak with you.

Michelle Dawn Mooney

Yeah, great to have you here as well. And I’m looking forward to getting to this conversation. I feel like we could talk all day about artificial intelligence and still not have enough time basically to scratch the surface, but let’s start off here. Can you start by sharing your thoughts on how you have seen customers adopt generative AI versus using traditional AI to address their business problems? How has it influenced your approach to various technological initiatives and solutions?

Vineet Arora

Absolutely. I think generative AI, if you look at it, it’s been a real game changer when you compare it to traditional AI. You know, traditional AI, of course, is very good at specific tasks, which are more predefined rules, patterns and then addressing business problems that are a little bit more, you know, defined. And it has been already many, many years and decades, actually, that AI has been used in several scenarios. Uh, generative AI, of course, the way we have seen in the last 2 to 3 years and even it was, you know, sort of under wraps for many years, uh, before OpenAI released ChatGPT. And then it became such a common term. And then, of course, it’s been the last two years of whirlwind with generative AI. I think the generative AI situation that I see is that the massive data set and the sophisticated algorithms that the GPT is the, you know, the the really real technology behind generative AI, they help you create new and innovative solutions. Traditional AI has, you know, a lot of, um, capabilities in solving business problems, right? It has been doing it for many years. Uh, generative AI, on the other hand, is the next, uh, level of looking at how do don’t we just look at solving existing problems but also identify new patterns, innovative solutions? Right.

Vineet Arora

The way I typically talk about it is, you know, it’s more like a gas. Um, gas is generative. Uh, analyze and summarize. Right. So if you if you just look at that, those three key things that generative AI is able to do across different business problems across different industries, um, is really, really powerful. You know, we are anticipating the problems. We are not just solving the problems. We are also generating new content and new kinds of solutions that have not been even created in the past. And that is giving us an opportunity to not only solve the problems that problems that we know of, but also start thinking about problems that are going to come in the future, but also find patterns and solutions that can add newer solutions altogether for businesses. It’s an exciting time, and we should be seeing a significant leap in innovation and efficiency for any businesses using generative AI that some of them have already been using.

Michelle Dawn Mooney

Let’s talk about the role of data in AI transformation. Enterprise data governance and security is understandably so, becoming a crucial area of focus for all business leaders. What are you seeing when it comes to how companies are aligning their overall AI strategies, and especially generative AI solutions with those critical needs?

Vineet Arora

Yeah, I mean, data governance, security, it has always been critical. You know, it has become, I think, more critical Cool and absolutely crucial for all IT leaders and some business leaders also that own and and work with a lot of data to make sure that the data that you are using to both train and you’re using it in your solutions, that is, leveraging AI or even generative AI, right, are properly organized, is properly secured. Um, and then, of course, you know, there is a need for, uh, compliance issues also to be taken care, right? Those are specific to certain industries. But when you combine all of these basic issues like the security and the data governance and compliance, uh, you know, the stakes are very high, right? Uh, you may have you may be dealing with, of course, sensitive data, um, or operating in the regulatory industries like healthcare, where compliance is a lot of need. What I’m seeing is that, you know, organizations as they have had or they are starting to build their AI strategies, they are making sure that the right tools and technologies for securities, um, are incorporated into the strategy itself. It should not be an afterthought that you are incorporating some kind of a patching and some kind of a security, right? So from the outset, you have to think about having the right tools, technologies to secure your data. Uh, you know, making sure that you use technologies like data anonymization. Right. What that does is, you know, the source data can still be processed by generative AI, but it doesn’t need to have, you know, all the PII, personally identifiable information.

Vineet Arora

And that requires, uh, some tools to be used. Some organizations are using it, some folks that are starting to employ solutions where they are not doing that are seeing the, uh, unfortunate, harmful effect of data getting exposed to people that should not be not be seeing it. Right? And that’s where anonymizing data is, is super important. And making sure that, you know, you’re you’re taking care of that. Not only that, I think there are also, um, ethical guidelines that have to be considered. Right. There are there are information that is in data that is going to be surfaced by these technologies that you may, uh, a normal human, even if they are looking at, you know, a lot of data, may not be able to make out very clearly. So, uh, bias, bias towards a particular type of pattern. Right. How do you identify that and implement, uh, tools to mitigate it? Um, one of the things we recommend to many of our customers is start looking at some kind of a chief AI officer as a role within your organization. Right. And that individual he or she should be responsible for first, of course, this data itself. Now there is a chief data officer in almost all organization nowadays. Uh, right. Chief AI officer, I think, should work very closely with the Chief Data Officer in ensuring that the current data state that exists in the enterprise.

Vineet Arora

Um, you know, has the right level of governance and security, right, but also leverage AI itself in certain cases to implement some of that security, like the like the identifying person, you know, personalized information. Uh, we have, uh, used tools where you could use AI itself to operate on a data and then remove all the personal information so that it can be used by other AI algorithms for, you know, providing actual business solutions. Right? Healthcare is the perfect example. Uh, if you look at healthcare, that’s where patient data has to be secured. But you still need to be able to analyze all the other information to find patterns and solutions. Uh, that AI is really good at without exposing, you know, the know, the individual’s information to any other solution. So it’s a it’s a delicate balance, um, that you have to establish while, uh, you know, if you ask a typical data scientist, right, they will want a lot of data and a lot of good data, right? That doesn’t exist all the time in enterprise. Right. So how do you focus on spending time in enhancing the data while you can also deliver the solution? So, uh, it’s it’s it’s very critical and important, uh, no doubt as the first step in any organization that you look at, all your data sources, all your data estate, and invest the time and energy that is required to enhance it so that it can be used very well with the AI.

Michelle Dawn Mooney

I want to talk a little bit more about that delicate balance, because as much as we have a lot of good things to talk about when it comes to AI, there are definitely some pain points, right? So what challenges do companies face when adopting generative AI into their operations, and how have you seen some of them overcome these challenges? And additionally, to what level of success are they seeing here?

Vineet Arora

Yes. Um, I think, uh, as you were saying in the beginning, I could talk about that particular topic for a long, long time, because just in the last two years since ChatGPT and all the other technologies have come in, I think it looks like more, more like 20 years of, uh, you know, uh, progress that has happened and the challenges that has been seen. Uh, one of the things that we already talked about, I think it’s data quality. Right. Because organizations have been living with, you know, data that they don’t sort of review it on such a regular basis that they know what are the issues in that they have been able to build good kind of analytics on top of it dashboards and, and reports. And, you know, that has been happening for the last, couple of decades, I would say with so many tools out there, but the data quality level that is required for AI to operate on it, I think is, is is at the next level. So that’s one of the challenges that we see. And as I mentioned earlier, also having a dedicated plan and it’s not going to be a one time activity because new data is going to be getting generated both from your source systems. But AI itself is generating data, right? How do you make sure that that is clean and that is of quality to give you better output? So it’s a very iterative process on the data quality checks and then usage.

Vineet Arora

Second, we see of course is a lot on integrating all these new technologies into the existing systems, right. Some you know companies of course, in the last I would say ten years or so, have been incorporating a lot of machine learning and algorithms into their systems, and they’ve been successful as the as the technology has advanced. When you make a jump from those long cycle machine learning and AI to be incorporated. Now generative AI is is like a gray box or a black box. How do you incorporate that successfully into your existing applications is a challenge. So many organizations have tried it. Some of them, of course, are trying to say, okay, let’s do a net new development. I think it’s again a balancing act. The way I recommend it is, there are ways that you can look at, um, incorporating uh, some of that, uh, capabilities in the existing applications. And that’s a change in design and architecture. Uh, many product companies are doing that, uh, right, rather than developing a completely new product itself. So it requires going back to the design and architecture and finding if you had designed it already in a modular fashion, you should be able to incorporate these technologies. And the related point that I see many organizations face today is to implement many of those new technologies. You need talent. You need talent that is up to speed with the capabilities of the new technologies.

Vineet Arora

And that skill and talent is, is is rare, right? And companies are investing a lot, and we suggest them a lot into not only looking at specialized, uh, partners, but also looking at building their own capabilities. So there’s a lot of emphasis on readiness. Um, you know, trying it out and making sure that your existing, uh, developers, data scientists, data engineers, architects, all of them understand what is different when using generative AI and AI technologies. And that is something that I see is going to be a continued, uh, you know, challenge in the industry. Availability of skills and talent and how organizations are going to invest into training their own people to make sure that the entire spectrum right. Data quality changes that are required, incorporating the AI and the way I see it also is AI to be becoming the new kind of a user interface, right? You see, chatbots, of course, have been there for a while, but more intelligent chatbots agents, right. Incorporating those requires a different level of architectural thinking. So you do need to invest. But look at how can you leverage external partners and partner with probably multiple companies in this case, and use the best of the breed to build your solutions? Those are the two three challenges that we see. Security continues to be a evolving landscape, especially in the case of AI.

Vineet Arora

Um, the term that you will hear a lot and companies have been investing in is how to build guardrails in your AI solutions. Right? And there are startup companies, all the hyperscalers. They are building capabilities in their platform to implement that guardrails. So the data that is coming in also requires checks and balances to make sure that it meets the standards that the companies have set up. Right. And if there is different levels of thresholds set up for either hate speech or, you know, unethical behavior, right. How do you filter those out? Now there are situations where we have worked with customers where, you know, let’s say a news establishment, right? A news article will have those elements and you can’t filter it out. So you need to be able to, you know, balance that out in the tools that you’re using so that it’s not a one size fits all. It has to be a customizable one. And the more important part is the data that is coming out of all these generative AI solutions. How do you ensure that that is not only accurate, but also meeting certain thresholds that you have set up for different kinds of parameters around, you know, be it hate speech or ethical or ethical guidelines that are relevant to your business. Right. That is very, very important to realize, because every business will have different kinds of, uh, thresholds to be established.

Michelle Dawn Mooney

Yeah. And the bottom line really is the bottom line. Right. So we’ve heard of the saying the proof is in the pudding. Let’s talk about some real world examples of how industries like healthcare, you mentioned software development. Some others are leveraging generative AI to drive better business outcomes.

Vineet Arora

Absolutely. Um, I think healthcare probably. And manufacturing, I would say another one. Retail to a certain extent have been using, uh, you know, the traditional AI and machine learning algorithms, uh, for for a long time in different shapes and forms. Uh, you know, there has been effort that has already gone in. Just to give you one example around, uh, you know, all sorts of medical imaging, uh, technologies. Right? So be it. X-rays, beat MRIs, scans. Uh, there have been systems that have been built over the last, I don’t know, 20, 25 plus years using traditional machine learning algorithms to identify patterns, to identify, uh, you know, cancer detection and any kind of other abnormalities in a much faster fashion. Right? Those are specialized hardware, specialized, uh, algorithms. And some of them are proprietary because organizations have invested millions and sometimes billions of dollars to build those solutions. What generative AI is doing that we are seeing is one, all the learning that has happened on that, uh, implementing those machine learning algorithms, the same knowledge and skill can be established. But now you have much more capabilities both on the hardware that is processing the huge amount of data. But also the capability to identify patterns much, much faster. So you are able to build solutions much faster, increase the accuracy of those solutions. One of the customers that we are working with has, you know, probably last 20 plus years of data around their patients MRI scans. Now, what can you do with that? Of course, you can find a lot of patterns about the disease itself.

Vineet Arora

But think about a new patient comes in and they want to get an MRI scan done. You could actually look at all the data that you have for similar kind of patients in your last 20 plus years of history, and actually almost in a matter of seconds, if not minutes, you know, be able to generate some kind of a recommendation that these are the areas that we should focus on based upon your age, based upon your lifestyle, whatever information you have gathered. Generative AI, I think, excels at that. Doing it in a much faster fashion, right? We look at automation of that process of, uh, onboarding a patient, you know, going through the life cycle of, uh, scheduling a patient, right, in a much faster fashion. If you have ever scheduled, unfortunately, a medical exam at any point in time, you know, it’s a back and forth, back and forth you discuss with the scheduler for so long. Uh, we implemented a solution where we looked at reducing the time it takes for a patient when they’re calling in to say, I’ve been recommended for so-and-so MRI or x ray. How can you reduce it down almost by, you know, 50% and the amount of time, money and the time it will save is humongous. And that’s what generative AI and conversational AI can assist a lot. Uh, we also see, you know, the, the whole sort of term that has not been used very often, but it was it was very popular for the last few years.

Vineet Arora

Is natural language processing right? Nlp right. So from the advent of Siri and Alexa and all of those similar ones, NLP was being talked about. Now NLP is such a such an integral part of this newer technology where over the last few months only all these generative AI platforms have voice based interaction, both input and output. I mean, that makes it much more accessible to a larger population and provides the benefit to a larger population for the same capabilities. So that’s what that’s what we see. Last but I will I will also mention one of the scenarios where it’s it’s more a little bit selfish in our industry itself where we do software development ourselves. We have seen tremendous. And not only us, you know, there are studies published out there where the traditional manual tasks of writing code. Testing code. Finding and or deploying code write that automation is helping us deliver. Our solutions faster to our customers, right? And thereby, you know, customers are able to. Take their products and solutions and services to the market and serve their customers. Much better if we are able to create those code and make sure that testing is done in a much faster fashion. And of tools out there that we use and many other companies use. And I think we will continue to see a lot of evolution on those environments to continue to enhance the usage of AI.

Michelle Dawn Mooney

I want to dig a little deeper with regard to the innovation, because we started this conversation talking about AI has been in play for a long time, but it seems like leaps and bounds. We’ve seen growth just in a very short period of time over recent months, Years, I’d almost say even minutes because it seems like it’s evolving so quickly. So any thoughts on what is on the horizon for IT solutions as AI technology continues to evolve and become more capable?

Vineet Arora

Oh yeah. You know, you’re absolutely right. Every morning I wake up, there is some other news. Some other company has released some other new feature of AI, and companies are scrambling over on how to, you know, make sure that they use it in the right manner and it has the capabilities. Um, I see two big patterns emerging. Um, and some of them have already been happening that listeners of this podcast would be familiar with or the last, I would say few months. Uh, the traditional, uh, process automation that has been established using a lot of tools. I think that is going to increase in its capabilities using, uh, autonomous agents, uh, generative AI Capability to be able to provide that kind of solutions, where you’ll be able to make decisions not just on an interactive solution where somebody is clicking buttons or somebody is typing something or somebody is speaking to the system. But behind the scenes, there are going to be AI based agents that are going to be able to optimize your processes, optimize your solutions, or even remedy certain issues that they occur without even a human involvement. And I think that will assist us to be more productive as a as a team, as a business. I think that decision making system, you know, in an autonomous fashion is going to be really powerful. And it’s already started. I think we are going to see a lot more improvement on that, because the reliability of that is is one key thing.

Vineet Arora

The other the other pattern that I’m seeing very clearly is the need for Or explainability. What that means is, yes, you know, it’s powerful, you know, till the time you’re using Siri and, you know, Alexa and others, I don’t think a common person really asks, okay, how is this working? Right? They don’t need to know. They’re just using it in the business where there is requirements by either government or other agencies to comply to certain rules and regulations, there has to be some level of explainability on how the AI is being used in your system, right to the level that it’s possible, because, again, generative AI is certainly introducing a little bit of a gray area where how gpts work behind the scene is, you know, not always open to every solution, but still, where is your data coming from? How much of data processing has happened? What kind of security guardrails have you put in in that is going to be there a lot of requirement. Another big shift in And usage of AI that is happening is moving the AI processing for complex data set from these large hardware environments to also the edge. The devices that you and I use on a day to day basis, right. It has already been incorporated in the smartphones and the other devices that are across your house and even in your vehicles.

Vineet Arora

Um, but the ability to use the same technology that exists on the cloud today for AI and to be able to make much faster decision by processing data processing inputs on the device itself is very, very powerful. Now for that, there has been advancement that is happening, and it will continue to happen where these algorithms, these need for the processing power are going to reduce, rather than the hundreds and thousands of CPUs and GPUs that were required to process this huge amount of data. And you will see all the devices, in my opinion, that everybody uses already incorporating a lot of AI. But you will be able to have a quicker and faster response time to solve complex business challenges much faster. It’s an exciting time. The possibilities are a lot, and what I would say is that these are only some of the areas we will see how, you know, robots come into the picture. All these sci fi movies that you have seen, how much they become real and when do they become real? Yeah, companies are announcing them. I think that is probably a few months, if not few years down the line. But certainly, uh, looking at, uh, the current advancement really helping us solve some very, very crucial, uh, business problems.

Michelle Dawn Mooney

And I want to talk about that because you’re talking about robots. But a lot of people have questions about the humans in the mix here when it comes to AI. How do you see the role of human oversight in AI automation, particularly in areas such as data privacy and compliance?

Vineet Arora

Yeah, I think that is sometimes an very underrated topic that many organizations don’t openly talk about it. But I think when you are talking about an AI strategy, all the users that are involved in your AI solutions, the users, the actual developers and the business stakeholders, I think all of them should be considered on what role they will be playing. What I see is that it’s it’s shifting from a direct control of a solution to probably providing strategic supervision. Right. Where you look at AI does something. Right? Yeah. Tools do something. You still need a human in the loop, as it is called, to verify that based on our knowledge of what we have done in the past, this output is correct. And then, you know, in the typical, uh, chatbots that many of us use, we see thumbs up, thumbs down. We are seeing more advanced scenarios where there is a feedback loop from the humans that have to be provided for AI to become better, for those algorithms to make better outputs and better results. So it’s not only thumbs up and thumbs down, but you could be a little bit more specific about you were wrong, you know, out here. And that’s something that is fed back into the algorithm for it to learn and then improve the next time around. Um, when you look at data privacy, um, as I said, yes, there are tools that can remove personally identifiable information from your data. There is still probably a human involvement that is required that the sensitive information that if you have not tagged, if you have not told that I tool in your configuration, of course, right, that these are sensitive information, there is no way for I to know that, right? So humans have to do a better job of also configuring these systems to make sure that the tagging of of information is there.

Vineet Arora

So typical information like SSN, social security numbers and bank accounts and credit cards and even patient information and you know, addresses are fine. But sometimes you know the the there are informations which are again, let’s take in healthcare where you know, the race and the gender right may not be required or should not be exposed for a particular solution. Right. Humans have to be involved to say that this is also a sensitive information for making sure that it is. It is cleaned out by the AI tool that you are using. We see geographically Um, you know, geo specific scenarios. So Europe requires probably a different level of involvement of human due to the GDPR scenarios that they have. Um, right. There are there are need for people to review, uh, you know, even even if the output has happened and it’s a, it’s an after the fact, uh, results, um, like all the data that is gathered for any interaction. Right. Is there is there a need for doing some kind of an analysis to find patterns, uh, with the different AI tools and maybe, you know, provide some feedback so that oversight is very, very critical.

Vineet Arora

Um, I don’t see that going away. Different industries will have different levels of need for the oversight. Uh, there are roles that are getting defined and becoming critical. Uh, around AI governance and oversight that I think many organizations will need to start thinking about. Organizations have had different kinds of audit. Different kind of operating procedures. Right. And those have to be met. Similarly, in in the case of AI, you are going to have auditors that are going to make sure what AI is generating, what AI is doing is meeting certain standards and ethical guidelines. And that oversight I continue to see to be there for a long time. Some of them is going to be implicit, but a lot of it has to be explicitly defined. And I would again recommend organizations to think about the chief AI officer, that organization having these skills and these topics very, very critically built into their organization. On what roles do you need within your organizations and the guidance that you need to provide to other business groups for the kind of human oversight that will be required for their solutions to be adopted much more comfortably by their end users. Because accuracy or lack of trust, is one of the simplest reasons why somebody may not use an AI solution. And you need to ensure that by having some human in the loop.

Michelle Dawn Mooney

Yeah, absolutely. As we look at wrapping things up here, what are your recommendations for I.T leaders looking to leverage AI and gen AI for data driven decision making? And what are the first key steps that they should take?

Vineet Arora

Um, yes. I think a lot of organizations have taken those key steps. I will I will still sort of repeat that. In fact, a few months ago I did a webinar myself, and I wrote a blog around five key areas which organizations and leaders who are planning to implement generative AI and AI overall should be considering. And one of them, of course, is having a well-defined AI strategy. I referenced that earlier. Also having a strategy is, doesn’t mean that you are not going to execute for months and months, right? It’s a little bit of a balancing act, but having all key elements of a strategy that defines your investment required both in terms of the money and the time, but also the focus areas. So responsible AI, as it is called right, is a big area within an AI strategy. There are organizations that have published enough on responsible AI that organizations can. Other organizations can leverage and reuse, but again, spend time and investment in customizing it. Look at the investment that you need to make. There are organizations that have the desire and the money to probably reinvent the wheel. On trying to build an entire infrastructure on their own, for processing the amount of data, doing the kind of processing that is required to build and deliver AI Solutions. Many organizations in the world cannot do that.

Vineet Arora

And that’s where hyperscalers like Microsoft and Amazon and Google and many others are providing you the platform. So investing time in looking at the capabilities of those platforms, what is the ROI? If you already have one of those platforms or more than one of those platforms, where do you invest more time in what kind of a scenario? It’s a very crucial effort that needs to be spent. The next one is, is, is is important is as you are thinking about security and the infrastructure, right. What I call is planning for a secure and a scalable AI solution. That is an important aspect. You have to of course look at it. Where to invest time? Many organizations over the last year that we have been engaged with, and I’m sure many of the listeners of this podcast have been seeing have been doing pilots, POCs or what I call as proof of value engagements, right? Identify use cases where you can prove the value of implementing this technology. Right. This is also something that I think gets overlooked. Everything cannot and should not and may not be solvable by AI, right? There are simpler solutions to solve those business problems and don’t overengineer that. I have seen organizations, at least over the last few months, create a list of use cases and then go through a lot of meetings and discussions on which use cases will make sense.

Vineet Arora

We have a framework that we recommend to our customers on looking at ROI, which is very traditional, but also look at the right kind of technology that should be used. Maybe it’s a packaged AI solution that is available from a vendor. Just for example, Microsoft provides Copilot as one of their AI based generative AI based solutions integrated into the product rather than building a complete solution. Maybe that itself can solve certain use cases. So mapping your use cases to the right technical approach, right technology tools is also critical and important for you to know how fast you can deliver the value, at what cost and what time. Right. That is very important. Um, all the other traditional, um, scenarios, um, you know, remain true for any kind of a software development life cycle. So making sure your requirements are well-defined, but your team is well formed, getting the right skills done. Um, the newer things that you know has been evolving. Devops has been a process that has been very well entrenched into almost all organizations. Now we are talking about AIOps, right? What is required for that? And that’s another thing that organizations should be thinking Upfront that how do you make sure that as the platforms.

Vineet Arora

Right, these all these hyperscalers and the creators of the large language models are evolving their technologies. How is it going to impact the solution that you have built? I don’t think it can be a wait and watch game that oh, let them stabilize. It will continue to evolve over the next many months and years. How do you build a process within your organization that as those models become more powerful, as those models change and probably deprecate some of these capabilities? How do your systems adapt to it? So there is an entire, you know, deployment process for the AI solutions that has to be thought through. And there are tools and technologies coming in that are guidance coming in around AI ops that I think is very critical to look at it. Last but not the least that I’ll talk about is while we talked about thinking about responsible AI, I think we have to also look at, um, while you implement those controls, how do you invest time in training your end users, your, your actual customers in using the technology in the right manner? Um, I think probably everybody has chatted with some kind of a chat bot. But, you know, when you start sort of, you know, spending a lot more time, there are different types of chat bots with different levels of capabilities, right? Um, and the experience that the end user gets is very, very important.

Vineet Arora

Uh, frustration as a result of interacting with the chat bot has been talked about enough. Maybe a lot of time has not gone into designing that chat bot to understand the intent of the user, and probably find a different way to respond. Maybe hand it over to a human being, uh, agent to answer that rather than continuing the chat, because that could be certainly, uh, frustrating. Um, so those experiences are going to make sure that the time and money that you’re investing in building these solutions are also adopted and liked by the users. Uh, that is very, very critical and important. Otherwise, you can dream up anything. But if users are not using it, uh, you know, that’s that’s a lot of, uh, time wasted. Uh, you know, I think that that setting, that stage for successful AI adoption that drives a meaningful transformation in an organization, I believe is is super important, apart from all the other things that I talked about, that it should be paying attention, uh, in making sure their data, their platform, their security, um, you know, and everything else around the development process is taken care and user experience matters at the same time a lot more.

Michelle Dawn Mooney

And I think one of the biggest key lessons we covered a lot of of information here. But being proactive, as you said, this is not a game where you just want to watch and wait to see what happens. You really want to be on the cusp of of knowing what’s going down, maybe even before it comes and being prepared. So it’s hard to believe that’s going to do it for today’s episode of The Hitchhiker’s Guide to it. As I said at the beginning, I feel like we could be talking for days and still not get to all of the information that we could cover, but I want to give a big thank you to Aurora for sharing his valuable insights into how businesses are adopting generative AI and the evolving landscape of AI technologies. Thank you so much for your time. Engage in conversation. It’s going to be exciting, as you said, to see what’s around the corner, because it’s already exciting and I can’t even believe what we’re going to be talking about the next couple of months, especially the next couple of years. So I appreciate you being here today.

Vineet Arora

Absolutely. Thank you again, Michelle, and thanks for everyone who is listening in. I mean, the potential benefits of AI are immense, that I think almost everybody realizes that, but I think they can only be realized with some level of strategic and a well-planned approach. Um, you know, we at RenewAire, of course, help customers in those, and we’ll be happy to engage with anyone that is looking for those. Thank you again for the opportunity. Appreciate it.

Michelle Dawn Mooney

Thank you. And we also want to thank all of you for tuning in and listening to the podcast. We hope you gain some practical takeaways on aligning AI strategies with data governance and driving better business outcomes. Of course, we all want that. If you enjoyed this episode and would like to hear more engaging conversations like the one you heard today, be sure to subscribe to the podcast for more information on how Device42 can support your IT infrastructure and operations. Of course, you can visit their website. I’m your host, Michelle. Thanks again for joining us. We look forward to bringing you more conversations on the future of technology soon.