[00:00:00] Luke: From privacy concerns to limitless potential, AI is rapidly impacting our evolving society. In this new season of the Brave Technologist podcast, we’re demystifying artificial intelligence, challenging the status quo, and empowering everyday people to embrace the digital revolution. I’m your host, Luke Malks, VP of Business Operations at Brave Software, makers of the privacy respecting Brave browser and search engine, now powering AI with the Brave Search API.

[00:00:29] You’re listening to a new episode of The Brave Technologist. And this one features Vic Kumar. He’s the director of CX Innovations and Strategy at Oracle, with five awarded patents and 15 more filed in CRM automation, generative AI, and agenic workflows. Early in his career, he championed developer productivity and tools for faster enterprise solution delivery.

[00:00:47] He also played a key role in the early development and democratization of chatbot solutions for CRM at Oracle. Currently, his work focuses on applied generative AI and agentic workflows, exploring their potential for practical [00:01:00] enterprise adoption. In this episode, we discussedogenic workflows and how they’re evolving with the adoption of generative AI and the kinds of impact they have on automation and productivity, the limits and challenges of generative AI adoption within enterprise organizations, and working directly with developers and customers on all levels of the customer experience.

[00:01:17] And now for this week’s episode of the Brave Technologist. Vic, welcome to The Brave Technologist. How are you doing today?

[00:01:27] I’m doing very well, Luke. How are you?

[00:01:29] I’m doing well. I’m doing well. I’m really glad to have you on today. We don’t have very many folks from the consumer experience side of things, and looking forward to kind of deep diving on this.

[00:01:38] As the Director of Consumer Experience Innovation and Strategy at Oracle, what are you kind of focusing on primarily right now?

[00:01:46] Vivek: Right. So one of the biggest focus for me and my job is using the generative AI based solutions in the enterprise space right now. I’m working on a number of key solutions.

[00:01:59] So basically, [00:02:00] you know, these days, every company, every enterprise is jumping into the generative AI and how to, you know, build solutions where most of these companies are, you know, focusing on pretty basics, solutions such as You know, write an email, summarize this content for me or write this content for me.

[00:02:17] We are going way beyond those basic use cases of generative AI. We are kind of, you know, testing the limits of generative AI into the consumer enterprise space, into the CRM systems. So we are seeing like, you know, how generative AI can be used to, you know, we can leverage it. To solve much more complex problems about market segmentation, how to solve these problems around salesforce automation, how can we make the life of sales reps easier, how sales managers can make sense of the vast amount of data and of the pipelines.

[00:02:50] That’s our focus around right now.

[00:02:51] Luke: Awesome. No, it’s just super helpful. How is Oracle kind of approaching that integration of AI into the customer experience solutions? I know you gave a [00:03:00] couple of examples of projects, but it is one of those things now where everybody’s just talking about, Oh, it’s going to be everywhere.

[00:03:05] Right. Product design and experience is hard and, and, you know, and at Oracle, I would imagine being Oracle and all, you know, you all have like a pretty robust R and D and a lot of experience in the field. So I’d really love to hear a little bit on how Oracle is approaching us.

[00:03:20] Vivek: Right. So I would say, you know, AI is in the DNA of Oracle.

[00:03:24] It’s not something we picked like a couple of years ago and just trying to ride on the wave. We have been working into the AI space for more than a decade now. We have been trying to empower enterprise applications from a very long time. The only difference that has come is When we started, it was pretty much get the data, train the model, you know, try to deploy it, test it, understand, do the feedback, rinse, repeat, and keep, keep doing that until, you know, it is satisfactory to meet some customer requirements and all.

[00:03:54] Things have changed with the generative AI a lot. And, uh, the approach to integration at this time [00:04:00] at Oracle is that We are doing it at every layer, so, you know, starting from the platform to then building those products and even for internal development, tooling, developer productivity at every layer, we are leveraging AI in today’s world, you know, so every single solution we look at, like how AI can improve the solutions, how AI can eliminate some of those manual labor or can help us enforce compliance, all those things.

[00:04:27] So, so yeah, AI is pretty much every layer at heart right now.

[00:04:31] Luke: Are there like a lot of surprises that you’re seeing where you think that something might give you like a big productivity win or some efficiency where it actually like doesn’t necessarily pan out that way? Is there a lot of experimentation that type of thing going on?

[00:04:44] Vivek: Right, exactly. So that’s what I was saying in the beginning that we are beyond those basic use cases of generating AI or AI in general. We are trying to see in a real world when, you know, that rubber hits the road kind of situation, exactly how practical is AI, how much really [00:05:00] we can leverage to solve some of the real world problems, right?

[00:05:03] So for example, a lot of investment goes into writing the code, building applications at traditionally we used to, you know, build every single page, literally writing the code for it. But now we are leveraging AI more and more and to automate a lot of those things, we don’t have to kind of. Code for every single page, every single component kind of thing.

[00:05:23] We are able to leverage AI as a code assist, as a, you know, co pilot kind of situation where we, you know, developers are assisted a lot with a lot of boilerplate code and we can do a lot of dynamic page generation, write applications much faster. The same thing is going towards the testing side of it.

[00:05:41] Gone are the days where we had to write thousands of thousands of test cases. And those running for days and days to make sure the software is at the right quality. Now the AI is able to help with speeding up all those tasks much more.

[00:05:55] Luke: Yeah, that’s fantastic. Now, are there any like trends or strategies to that [00:06:00] that are jumping out is like the hype’s been around for a little while now, right?

[00:06:04] And like you mentioned, you are hitting it at that deeper level across the stack. Are there any trends that you’re seeing that are, people aren’t necessarily aware of this, but this is going to be what people are going to be using.

[00:06:14] Vivek: Right. So I think one of the interesting trend that’s coming out of all this generative AI and LLM is this agentic workflows and agentic models.

[00:06:22] So until now, people were like, okay, there is a use case where you use something like a chat GPT, you know, you throw in the problem or throw in the statement and chat GPT will bring out something, you know, that was the like. Fundamental or any elemental use cases of generative AI. But now the trend is even at Oracle, what we are doing is we are building this agentic workflows, which can leverage, not just like a single use case, but a army of agents.

[00:06:51] Who can make decisions dynamically based on what is the requirement they can invoke more than one agent at a time collaboratively [00:07:00] based on those agent outputs, they can, you know, make those dynamic decisions and kind of automate the entire workflow around that for really complex business problems such as, you know, sales forecasting, a lot of, you know, sales force automation around deal progression and all those things.

[00:07:18] Thanks. So, yes, agentic workflows to highlight is something that’s really going to trend and really changing the way we have been doing business so far.

[00:07:29] Luke: So, just for folks that might not be quite so familiar on that type of workflow, it almost sounds like how, you know, people talk about like a cell, like in a cellular level, but then there’s like elements of helpers within the cell, right?

[00:07:40] All doing different functions, Is that kind of how it works? Or maybe you can help unpack that a little bit more for people.

[00:07:45] Vivek: Sure. So agentic workflows, like there are two words in this agentic and the workflow. Right? So the word agentic is pretty much like something like an agent, not not like a fully blown agent who is doing everything on its own.

[00:07:58] But there is an [00:08:00] agent, something like agent and then workflows. So workflow is, uh, You know, a piece of orchestration that is made to solve business problems, right? So what happens is these workflows basically make use of these agents family more than 1 to bring the business decisions outcomes. So, for example.

[00:08:20] When end user throws a question or a problem to it, the workflow picks the most appropriate or the right agent who is capable of handling that part of the problem so that that agent is basically, you know, takes that that business problem tries to solve it and it results into, you know, the output along with some conclusions or some helper text that helps the workflow again to make a decision like, okay, okay.

[00:08:48] Is this the right outcome that was required, or do I need to take help of another agent within the system to pass this information and to get the next level of solution or next level of [00:09:00] condition? Right? So the whole workflow, the agentic workflow works like a pretty much like a. Autonomous system where the workflow is capable of deriving those agents to bring the right business outcome.

[00:09:15] So it’s, pretty fantastic. You know, the way it’s

[00:09:18] Luke: super dynamic. Looking at this from like the perspective of people working on software, right. Are you going to have like specialists that specialized in kind of tuning these different agents in these workflows? Is that kind of how it’s shaping up or are they just kind of doing their own thing?

[00:09:33] Vivek: Yeah. So the, we, we, It works is like I said, where the workflow plays the role of a orchestrator in the whole whole system, the agents are like specialized pieces. These specialized pieces knows how to do one part of problem solving, right? So, for example, right? There could be an agent that knows how to calculate a forecast or knows how to.

[00:09:55] Make decisions around the sales stages a CRM [00:10:00] system, right? Or how to do the data validation, right? Like, for example, if customer places an order, that one agent could just be knowing how to validate the customer. That’s it. You know, that could be a super specialized agent. Another agent could be just making decisions about the, be able to, Handle the order history of that customer to see if it’s a new customer, repeat customer, what he has been ordering like that.

[00:10:24] So basically these agents are very specialized pieces that knows to do one thing very well. And this workflow then basically is capable of In working these agents based on what they are good at passing that information, bringing the output back and making the next decision dynamically based on what they got back.

[00:10:44] Luke: That’s fascinating. It’s awesome. I mean, it’s like so much more to it than just, you know, talking to a prompt, right? For the

[00:10:49] Vivek: listeners, right? In the traditional ways, a programmer has to code a lot of if and else like if this happens, then do this else this is and that was never ending job. Basically. You keep [00:11:00] finding those conditions, keep writing the software manually, doing this whole manual plumbing.

[00:11:05] Now, this whole agentic workforce takes that whole guesswork and this manual plumbing out of the system. Systems are truly scalable and, dynamic here.

[00:11:14] Luke: And even for like somebody like an entrepreneur that is always time scarce, is always resource deprived, right? Like there’s something like this type of a workflow.

[00:11:22] It totally like helped them, you know, have that much better of a chance of success. I would imagine. How far away are we from that being a reality from your point of view?

[00:11:31] Vivek: Oh, we are doing it now. Actually, as we speak, Oracle already released agentic workflows and their service infrastructure or for the, for the, you know, service systems.

[00:11:41] We already have the framework ready. It’s already rolled out to the customers where they are able to use this agentic workflows to solve real world problems into the customer service space, same sales side and all those areas. So they are already there. It’s not like something maturing. [00:12:00] I mean, sure. It will continue to mature and getting more and more advanced, but it’s already to a state where it’s already pretty much practically usable.

[00:12:08] Luke: Wow. That’s fantastic. That’s awesome. I’m sure people would love to hear that. Switching gears a little bit. I mean, you know, you’ve been awarded patents yourself with a five or I think with what 15 more filed too, I would imagine. Can you go a little bit more into detail about the patents, whatever ones you feel like expanding on?

[00:12:23] Vivek: I definitely have been doing a lot of work into the research area and filing patents where I see, you know, I, have stumbled upon something probably people might not have. I can talk about a couple of patents, which probably you just will find very interesting. So one of the patents was around unifying the customer insights.

[00:12:42] so basically today, if you know, if you go to the amazon. com and look for a product, you will find probably not, not a hundred, but even thousands of reviews for the same product. Where people are giving those reviews ranging from star one to star five with feedback as a human, we don’t have sanity to go over those [00:13:00] thousand reviews to really understand why something is a one star versus five star for the same exact product.

[00:13:05] Right? So my patent work was actually involved in using all this data sets to put into a machine learning model and to figure out. Exactly why a review like what is the true value of that review? What is the exact review about it? For example, right? Like there’s a pretty good product and it gets one star review because it arrived late to a customer.

[00:13:27] Right? And now the delay is something Because of that, customer gave a one star review, but that may not be something that really matters to you because you are either not in a hurry or not, right? So, a product should still be like five star for you, right? So, one of my patents was all about figuring out the relevance of those reviews and to get a true feedback score, rather a extreme ends of one and five star kind of thing.

[00:13:50] So, that was one of the patents that I filed sometime back.

[00:13:53] Luke: That’s awesome. I mean, because then you could also, I’m sure there’s product quality, but then also, like, I’m sure Amazon from a [00:14:00] logistical support side, right? Like, we could see something like that and be like, okay, well, we really need to get on top of our shipping process or whatever, you know, you’ve got all these, like, you know, shipping cases that are hurting our reviews for products and getting in the way there.

[00:14:11] It’s super interesting.

[00:14:12] Vivek: Exactly. And we, Oracle has Oracle commerce product and we had the exact same thing. So, so basically again, I use Amazon because it’s a more commonly known, known system, but my work was probably more towards the Oracle Commerce platform where we have a, you know, advanced B2B and B2C platform and exact same thing.

[00:14:32] Our customers run e commerce stores and they get the similar things, like they get a lot of reviews and it’s very hard for customers to get the true value of those reviews. Totally.

[00:14:42] Luke: Totally. Any other patents that

[00:14:44] Vivek: you feel like sharing a little bit about? Yes, definitely. One of my, my earlier work was, uh, towards the area of chat boards or digital assistants.

[00:14:53] So I would say a couple of years ago, just that they were the biggest thing into the AI or the IT where [00:15:00] everybody was jumping towards building a chat board or using a chat board. And one of the biggest problem. With the chatbots, which I addressed through one of my patent was that in the large enterprises, almost every department even starts to build a chatbot for their specific operations.

[00:15:16] Every product within a company like Oracle, you know, have its own chatbot system. And then there is a philosophy in the market where we say, you know, Hey, sell where the customer is. So, you know, today customer could be on WhatsApp, customer is on Slack, customer is like, you know, Facebook everywhere, right?

[00:15:32] So now with every company building these hundreds of thousands of chatbots within the system and then trying to build them for every possible channel. It’s just insane. So what I did was what my parent was about figuring out and automatically, you know, making these chatbots available on you. All the possible channels, you know, so instead of letting every team to build a boat and they chant all the possible channels [00:16:00] for it, my patent basically addresses a way where these boats can sell, discover all the possible channels that have been already built by other teams.

[00:16:09] And then can automatically be available on those channels. So basically it reduces a lot of development, investment time and resources around that.

[00:16:17] Luke: Yeah, totally. I mean, like that, that kind of integration time is just normally like such a barrier for somebody, you know, development cases like, uh, that, that we run into, I can imagine.

[00:16:27] Yeah, this is fantastic. I mean, early in your career, you know, you’re, you were an evangelist for the developer productivity. I mean, how did that experience shape your approach to innovation with AI?

[00:16:39] Vivek: Right. Good question. So when I joined IT like anybody else, I used, I was assigned to write code and build, build some of the products.

[00:16:47] And somehow I found that, you know, uh, that’s not the most interesting part. Uh, what about trying to find ways to, you know, make this thing faster, make it easier for people who are building like me, [00:17:00] somehow cut their time, make them more productive. So from the very early on, I became big on, uh, finding ways to improve developer productivity, how to, you know, bring some innovation in, in how we do day to day job.

[00:17:13] And obviously when, when you do that kind of thing. You always need to, you know, be a little ahead of the curve. You have to explore what, what are the new or innovative ways to solve those problems? Because if they are very well known that everybody will do it, right? But they’re not, not evident sometimes.

[00:17:28] So my job in, even in the early days was to keep exploring those new areas, new ways. And while I did that, I think, That journey led me to go more and more towards newer advancements in AI and then now in generative AI. So my all that experience around helping developers to do better. That passion became, you know, translated that passion into helping the customers to.

[00:17:52] You know, products and do the digital transformations even faster, you know, so I continued the same path and I saw [00:18:00] a crossing my parts in the journey early on and I became big on AI because like I was saying earlier, too. One of the biggest power of AI is it takes you away from manually making those if else kind of decisions.

[00:18:13] It helps you to understand the data patterns and make dynamic decisions on the top of it. You just cannot scale that way by, you know, manually writing any kind of code and all. So yeah, my early work towards developer productivity and all, I was kept me, uh, you know, awake at nights to figure out what’s new happening with AI and all, and I continued on that journey, you know, and, and still, still doing that.

[00:18:36] Luke: How involved are the customers like in the process is something where you all are working with developers and then they’re giving you feedback or you’re working with support or both, or how are you able to kind of make sure you’re not going too far into a rabbit hole on a solution to a problem that might not be as big of a problem once you start talking to the market, you know, how, how does that balance out for you?

[00:18:56] Vivek: All right. It’s an interesting question. I would say there’s no black and white [00:19:00] answer to this. A lot of times it could be too early to involve a customer or a partner to, you know, evaluate a solution. You kind of have to trail the blaze, you know, kind of a trailblazing apart initially to certain level where you say, okay, you have some sort of, uh, you know, MVP or a minimal viable product, right?

[00:19:18] So you really, really have to reach to certain stage where you can eliminate at least some of the absolute blockers. And once you see, you know, you can play with the technology to some extent, you have some confidence in terms of what you’re trying to achieve. Usually, that is the time when we start involving customers as, uh, or even partners at something called design partners, where, you know, we select a couple of customers where we see this is a major pain point for them, and we kind of, you know, sign those NDAs and then work with them closely to say, you know, Hey.

[00:19:51] Considering this is your domain, this is your business problem, and this is how we are trying to approach it. What do you think? What do you get? We take [00:20:00] those feedbacks, you know, build that feedback loop and continue to iterate our products like that.

[00:20:05] Luke: That’s awesome.

[00:20:05] Vivek: Like you, you rightly said, right? You don’t want to keep building forever without really knowing if it’s really worth solving that problem with that much of investment and all.

[00:20:14] So yes, it balances on this.

[00:20:17] Luke: Yeah, well, and it’s, it’s one of the things where it’s easy to kind of get too far into it in some cases. I mean, because especially I would imagine with the workflows being that dynamic, right? Like, it’s just kind of like, where do I go? Where, how did I get here? You know? And then you’re like, wait a minute, the customer’s over there, you know, or way, way off in space.

[00:20:33] You

[00:20:33] Vivek: need to kind of, you know, we say it like this way, you know, if you board on the wrong train, It’s better to get out of it as early as you realize that, you know, you don’t want to keep keep on the train forever, right? So yeah, you go harder. It will be to come back, you know, so it’s it’s it’s always the case.

[00:20:49] So yeah, yeah, well,

[00:20:51] Luke: you’ve got a real, real rounded, you know, experience set, right? Like, you know, you’ve done developer advocacy all the way to now where you’re working with, you know, on the [00:21:00] customer experience side and innovating on that. Like, what’s your favorite part of your job?

[00:21:05] Vivek: One of the things, like I have been saying, I have been big on productivity and innovation aspects of my job.

[00:21:10] So the beauty of my side of work is I have been doing things that either people think it’s not possible or it’s too far out in the future. So, Making those things work and be able to, you know, show it to the leadership, to show it to my management and, and getting their views, seeing that spark in their eyes, Oh, wow, I didn’t know this could work like this, or, or wow, we are so close to something like this, that’s kind of, you know, my paycheck, I would say, you know, that, that’s my reward for doing those things.

[00:21:40] So, yeah, always, you know, finding new ways of doing things, exploring new, new approaches to the technology to solve the same problems. That always fascinates me.

[00:21:50] Luke: That’s awesome. And what are some of the big challenges that you’re seeing with just adoption around consumer AI? We can touch on scaling and stuff later, but first off [00:22:00] with the adoption side, like what are the challenges you’re seeing?

[00:22:02] Vivek: You’re right. So in the enterprise space, adoption, there is really a big challenge around adoption, technology adoption. There are multiple reasons for that. At its lowest level, right, customer always wants to see what is the investment, you know, like in terms of cost, in terms of effort. What would they look like when they want to, you know, adopt a new feature, like they want to use generative AI or something.

[00:22:24] Then it goes to the third level, which is to the upper management. What is the ROI on that effort, right? And both in short term and in long term, right? How does this effort adoption and all this looks like in short term? What are we looking at in terms of gains, right? Because at the end, it’s, it’s, it’s business for everybody.

[00:22:40] And same thing, then they look, okay, what about fast forwarding to five years from now? How does this pan out? So ROI is a big thing. And I think the, another biggest challenge to, uh, other than realizing like how you do really measure that ROI in the, in the solutions, the other big problem or, or challenge I would say is the [00:23:00] people who are using those systems, these are sometimes, you know, like man of habit kind of thing, you know, where they have been doing the way they have been doing for decades.

[00:23:07] Sometimes they don’t really like changes, even, even it’s for the betterment. Uh, they are very, very resistant to those changes. So it’s a big challenge to make technology, the overall solution in a way that it’s easier to adopt. It’s, uh, feels like seamless or not really a big step forward towards making those changes into the way they do day to day job.

[00:23:30] That are the biggest challenge. So, I mean, don’t really care how advanced is the solution or how complex it is behind the scene or whatever the technology marvel it is for them at the end of the day. It has to be easier to use or should be pretty much the way it was, you know, the way they have been seeing.

[00:23:47] It should just work the same way.

[00:23:49] Luke: Is some of those sensitivities kind of feedback from some of those kind of used to help improve your process at all with with how you all are like kind of innovating and integrating that into the [00:24:00] experience?

[00:24:01] Vivek: So Oracle has a big team of user experience design and development and these teams constantly work with customers, partners, users to Ensure that what are their expectations, how the design has to be seamless, how customers should be able to adopt those new changes or new developments seamlessly,

[00:24:20] So, yes, that’s always a big part. I mean, technology solution or a technology product is never successful without a great user experience. This is a part of the process, part of the design.

[00:24:31] Luke: What are the kind of concerns that come to mind? I mean, like, you know, so much of people’s perception of where the technology is at is so it’s like Terminator or Dave from, uh, you know, Space Odyssey, right?

[00:24:43] You’re in the mix and you’re also on the enterprise side too. So you’re seeing how customers that are, are in the business world are integrating and working with these things. Are there any concerns that kind of you’re seeing repeat patterns with where you’re like, Oh, wow. People just. Are not understanding [00:25:00] this or that, or this is kind of leaving them wide open for something, anything like that?

[00:25:04] Or is it pretty much what you’d expect?

[00:25:06] Vivek: I would say one of the areas, especially with the AI is the privacy and the ethics concerns. So almost every customer, you know, when they use solutions like this, they always have a lot of questioning about how ethically the AI has been trained, how private is the customer data, you know, there’s a common perception sometimes, you know, hey, oh, okay, I need some sort of training and when you do the training, you need some data, where are the data getting sourced from, you know, how good is that data?

[00:25:35] How, how real data is that? How is the privacy taken care while? You know, using that data, that kind of concerns is always a big thing, especially delivering AI solutions, and we really have very, very strict and stringent processes around data privacy and ethics part of AI at like almost every layer. So, yeah, I would say, you know, for the customer side, when using AI kind of solutions.

[00:25:59] That always comes [00:26:00] up.

[00:26:00] Luke: Yeah. I mean, some of the, so much of this too, I think is going to kind of fall in that similar bucket of like operational security, like, uh, don’t click every link in the email kind of, you know, types of things. Right. Like, and I’m sure some of this will come with time as people use the, use the software solutions more and more.

[00:26:16] Where do you see this going? You see these agentic workflows becoming the norm looking out three to five years from now, like how different is the work experience for somebody then than it is now from what you’re seeing?

[00:26:30] Vivek: Well, here’s the thing. Even with the agentic workflows, a lot of people go and hype about saying, Oh, they will eliminate like human workers in the, in the system, everything would be completely automated.

[00:26:40] That’s not really true. AI is here to help you or argument. Workflows people are doing in day to day life. It’s basically to aid people rather replace them. That’s what I’m seeing. These all agentic workflows and all, they will become more and more autonomous. They will become more and more advanced, be able to do a lot of [00:27:00] things that you have been doing, you know, manually day to day.

[00:27:03] There’s one area that AI can never replace. That is like the empathy or the human emotion part of it, right? So, You cannot completely start doing a whole lead prospecting where you start sending emails automatically, responding them automatically without involving a human at all. Those machined or, you know, machine looking languages or, or those kind of correspondence, humans are ultra smart sensing that kind of things, you know, when they see, you know, hey, on the other side, it’s not really a human I’m talking to, but it’s a machine.

[00:27:36] Uh, nothing turns them off more than that. So in other words, yeah, these systems will be much and much, much more smarter over a period of time in next three, five years. A lot of things will be done with that, but at the same time, yeah, they cannot take that human touch out of it. So yeah, they will help, but not really replace anything like that.

[00:27:57] Luke: No, it makes a lot of sense. I mean, you know, there’s still, still got to [00:28:00] push the thing forward, right? Like, or push the button, right. At least, you know, and kind of steer it toward a direction. What advice would you give to professionals? I mean, we’ve got a lot of people that listen to this that might be either, you know, developing software or entrepreneurs or just enthusiasts, right?

[00:28:13] Like, um, they want to get more involved in the rise of AI and learn more about it. Any advice or resources you’d recommend they take a look at?

[00:28:21] Vivek: Oh, yeah, definitely. So I would say, you know, no matter at what stage you are in your life, you are a student or you are early in early levels of your career or even a seasoned professional.

[00:28:34] If you have been, you know, living under the rock around the AI AI, I would say come out of it and, uh, pick any course, even, even go on YouTube and look for generative AI. Just Google about it. There are plenty of material. Almost every big enterprise, including Oracle, they have been offering, you know, free content over, you know, what AI is, what generative AI is.

[00:28:56] There’s plenty to learn, so I would say, you know, there’s no [00:29:00] getaway without AI. You have to learn AI. You have to learn how it works. Not everybody has to be a data engineer or a data scientist, but you really need to understand how to leverage AI in your day to day job. Find your local group. There must, there are a lot of local networking groups and meetups and all where people talk about AI like almost every day or weekly basis.

[00:29:21] Join those groups, listen to them, see how you can. Apply those principles, apply those concepts in the problems you are dealing with day to day life, you know, and definitely you will see once you understand the application of air in your space. Then you will also understand how to solve those problems using AI, you know.

[00:29:41] That’s how I would say, you know, get on the AI board as soon as possible. That’s fantastic.

[00:29:46] Luke: No, that’s fantastic. It’s great advice. You know, and I know, Vic, you’ve been really gracious with your time, and we really appreciate you coming on. Is there anything we didn’t cover that you want our audience to know about?

[00:29:57] Vivek: We are in a very exciting era of AI and generative [00:30:00] AI right now. A lot of things that were not imaginable even like a year ago are now possible with, you know, this whole generative AI. So I would say this is one of the best time to be living as a human and get on this. That’s all I would say.

[00:30:18] Luke: Can’t argue with that.

[00:30:18] That’s, uh, I can’t think of a better, better note to end on. Uh, well, finally, um, where, where can people find you online? Are you out there on LinkedIn or X or any of these platforms?

[00:30:27] Vivek: I’m mostly only on LinkedIn and, uh, you can definitely find me as Vic Kumar, uh, on LinkedIn. And if you will see us, Oracle, You should be able to find me pretty easily with Vick Kumar.

[00:30:39] Luke: Fantastic. We’ll, we’ll link to it too in the, in the show notes. Again, Vick, I really appreciate you coming on. This is a great conversation today. I learned a lot. I think our audience learned a lot and you know, we’d love to have you come back to and check back in on things, you know, as things kind of keep playing out.

[00:30:53] Definitely. Pleasure

[00:30:54] Vivek: is all mine. So.

[00:30:55] Luke: Thank you. All right. Well, thank you very much, Vic. Have a good one. You too. Bye bye. [00:31:00] Thanks for listening to the Brave Technologist podcast. To never miss an episode, make sure you hit follow in your podcast app. If you haven’t already made the switch to the Brave browser, you can download it for free today at brave.

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