Enterprise AI Leadership: Bridging Strategy, Execution & Innovation | AI Accelerator Podcast

AI is transforming every industry, but according to Shay Harel, the organizations that succeed won't be the ones with the most AI tools. They'll be the ones that can connect strategy, execution, and innovation.

In this episode of the AI Accelerator Podcast, host Matt Zembruski sits down with Shay Harel, SVP of Engineering at Advisor360, to explore what it takes to lead enterprise organizations through the AI revolution while continuing to deliver business results today.

Drawing from a career that spans manufacturing, customer support, QA, hardware engineering, software development, cybersecurity, fintech, product leadership, and executive management, Shay shares how leaders can balance long-term transformation with short-term execution.

Having led global teams across the United States, Israel, India, China, Australia, Canada, and the United Kingdom, Shay explains how AI is reshaping leadership, decision-making, collaboration, and innovation inside modern organizations.

At the heart of Shay's leadership philosophy is one powerful idea:
"Strategic thinking plus a Get-Stuff-Done attitude is where the magic happens."

In this episode, Shay reveals:
◼️ Why successful AI adoption requires both strategy and execution
◼️ How leaders can bridge the gap between business goals and technical innovation
◼️ The balance between AI automation and human judgment
◼️ What enterprise organizations are really doing with AI today
◼️ Microsoft Copilot, Anthropic Claude, and the evolving AI landscape
◼️ Why non-technical leaders need to start learning AI immediately
◼️ How AI can uncover insights that traditional reporting misses
◼️ The future of AI-powered decision making inside organizations
◼️ Why culture is often the biggest barrier to AI adoption
◼️ How global teams accelerate innovation and problem solving
◼️ The importance of aligning engineering, product, support, sales, and customer success
◼️ Lessons learned building zero-to-one innovation projects inside large organizations
◼️ How to protect existing revenue while investing in future growth
◼️ Why leaders must experiment with AI before asking teams to adopt it
◼️ The mindset shift every executive needs to make today

Chapters
00:00 Intro
01:00 Guest Introduction
03:00 Background & Career Journey
07:00 Enterprise AI Adoption
11:00 Microsoft vs Anthropic vs OpenAI
16:00 Advice for Non-Technical Leaders
20:00 AI + Human Decision Making
24:00 Organizational Change & Leadership
27:00 Final Advice
29:00 Where to Connect with Shay

Key Learnings
✔ AI adoption is a leadership challenge as much as a technology challenge
✔ Strategic thinking and execution must work together for successful transformation
✔ AI should enhance human decision-making, not eliminate it
✔ Non-technical leaders must develop AI literacy now
✔ Enterprise AI success requires alignment across departments
✔ Culture and organizational readiness often matter more than technology selection
✔ Global teams bring diverse perspectives that accelerate innovation
✔ Leaders should personally experiment with AI before driving adoption
✔ Organizations that delay AI adoption risk losing competitive advantage
✔ The future belongs to companies that combine human expertise with AI capabilities

Shay's Most Powerful Quotes
"Strategic thinking plus a Get-Stuff-Done attitude is where the magic happens."
"I've been lucky enough to experience every layer of technology, from customer support to executive leadership."
"The line between what AI handles and what humans handle is shifting every week."
"If your company hasn't started, you're already behind."
"Great organizations align engineering, product, support, customer success, and sales."
"AI gives us insights that traditional reporting could never uncover."
"Innovation is about building the future while protecting the business that funds it."

Follow Shay Harel
LinkedIn: https://www.linkedin.com/in/shay-harel-6170352/

Follow Matt Zembruski
Website: https://leadingaiagility.com
LinkedIn: https://www.linkedin.com/in/mattzembruski/
Email: matt@leadingaiagility.com
Phone / Text / WhatsApp: +1 978-618-5778

Transcript

SPEAKER_00 0:00

Welcome to the AI Accelerator Podcast, where we explore how companies are accelerating AI adoption and how people together with AI are creating superhuman workforces today. I'm your host, Matt Zembrowski, founder and CEO of AI Agility. And today's conversation sits at one of the most interesting intersections in AI adoption today, a heavily regulated conservative industry, wealth management, meeting the most disruptive technology of our generation. Very excited to introduce you to our guest today. Shy Harrell is a senior vice president of engineering at Advisor 360. I had the great opportunity to meet him earlier prior, so there's a lot of good things to go into. He's uh the Advisor 360 is a wealth management technology platform behind thousands of financial advisors. Shy brings over 20 years of technology leadership and a front row seat to AI adoption on two different fronts. Number one, inside his own engineering teams, and number two, inside one of the most regulated industries out there. A lot of questions here that come up all the time to my company, and I'm excited to share them with you today with our esteemed guests. Shy, welcome to the show.

SPEAKER_01 1:14

Thank you, Matt. Thank you for having me.

SPEAKER_00 1:16

And Shy, why don't you um just start by telling our audience a little bit about yourself, Advisor360, what you do there, and um what you're most excited about?

SPEAKER_01 1:26

All right. So quickly about me, I've been in technology all of my life. Um, I was lucky enough to start in the trenches. I've been in customer support, yielded by customers, so it builds character. I've been on hardware, QA, software, all the stacks, all the way from kernel, all the way to application development, um, the classic engineering leadership or manager to executive. So um, you know, I've I've been around. I was blessed to be in multiple industries, um, many of them around enterprise, so dealing with really big, heavy, demanding, and often, as you say, heavily regulated customers. Um, I was lucky to experience storage and cybersecurity and marketing and sales and government and now um wealth tech or fintech. So again, been there done that.

SPEAKER_00 2:23

And and now, and now at Advisor 360, I think it's very interesting. I didn't know a lot about the company before you and I had met earlier. And uh it, you know, just can you explain more about the mission there? I mean, there's just so much going on. I have some experience in financial services, but just share from um your leadership position in Advisor 360 what is the company really offering into the financial services market and sort of what's the unique value proposition there?

SPEAKER_01 2:51

Okay, so again, at the very top, our mission is to be able to enable more financial advice to more people. The pool of financial advisor is not growing, it's actually shrinking. So, how do you get more people, more family, more households to enjoy financial advice so they can prosper? So, um, one of the things that financial advisors today uh grapple with is that they have a lot of technology. They have tools for CRM and they have emails and they have tools for trading and they work directly with the custodians. And there is, we call it the swivel chair effect. You have to log in and you have to jump and you have to do it's it's overwhelming, and again, it makes you inefficient. So Advisor 360 is a category on its own. We're what we call all in one. We have native applications that have everything that you need, or we can integrate with your existing application. And the most important thing, once you have everything in one place, everything is unified. All right, you can see the data about your insurance and your trading and everything from one place and all of your household, and you don't have to jump back and you know back and forth. This makes you very, very efficient. Now, uh, when you talk about AI, how you add AI to the mix, this is just a force multiplier. Not now I'm not only efficient, but AI helps me both be even more efficient and do more things, even on my behalf. So I just hit approve. Now, going back to the initial mission is how do you add more people? Now that I'm more efficient, I can bring more people into the company, into the firm, which means it's a win-win for the financial advisor, more AUM for the company, and for more people to get financial advice. That's basically it.

SPEAKER_00 4:29

Yeah, that's fantastic. I love the way you describe that because a lot of what you're doing for the financial advisors is what um I'm always thinking of every single day. How do we help make more humans in the knowledge worker economy become superhuman in the workforce by leveraging the power of AI? And you're doing that in in a very um in a in a in a very um um smart and powerful vertical with the financial services professionals. So you're allowing them to serve more clients per per financial advisor, which then allows you to have more exposure so more people can get the benefit of that. Um that's uh that's that's amazing. How did that so let's let's get back to um you mentioned briefly just like AI came in now, now they can be more efficient, and now it just it takes everything Advisor 360 was doing and makes it even more powerful and more um more of a force multiplier, as you said. Let's talk about how AI first came on the scene um there or how was it received there? Like when you started to when when did the AI um opportunities for AI integration into your product and your platform start to um start to really affect and uh really notice that it's a force multiplier? Did it was it well received internally by the developers and everybody? You know, uh tell us a little bit about that story.

SPEAKER_01 5:47

So so it's a sort of love-hate relationship in the beginning, and we all went through that again. So the big bang for everyone was basically Chat GPT. It's like, you know, AI was always there. I was using AI back when I was in super in uh cybersecurity, but then it was this very geeky thing that is not available for everyone. Chat GPT made it available to everyone, and it was, you know, um a leap into the capabilities. So, of course, as um engineering leaders, we told the people start use it and make it, you know, let's make let's be more effective because there was a lot of hype about it. And then you have a lot of people saying, I tried it, it doesn't really work, it's all hype, and I'm going to do what I always did because the person that I'm a smart developer, I can do it. Everything changed around the end of last year when um Anthropic uh started to introduce uh the latest um models, and that's we can see a real pickup. Okay, so when we ask the engineers to go back and re-evaluate it, then you get the light bulb really going, and then they say, Oh my god, this thing is amazing. So that's basically the transformation from when it started till today.

SPEAKER_00 6:58

Yeah, very it's fascinating because I've seen that across multiple industries, uh Shai, where um last Thanksgiving time, November time, when they released the latest uh of their of their top models um out to the out to the world, uh that's when it really made a big shift. It sort of became that's when AI sort of took a took a step change, step change forward. Um, so you mentioned uh we mentioned anthropic, right? So claude, cloud code, you have a lot of technology professionals in your organization. Um I'm just curious, what about what other models um are used, if any, uh throughout the company um for your, you know, maybe the non-technical professionals, maybe, maybe admin, maybe operations, maybe uh, you know, sales marketing, et cetera. Uh are there other models being used or how is the AI adoption going there?

SPEAKER_01 7:47

So I think most of them at the end of the day, they don't really care about the actual model. They consume it through some sort of a high-level UI. We use a lot of the engineers use augment. Okay, that's a tool that lets you integrate the AI into your uh workflow. Um, as far as I know, in our other departments at the non-technical, they also use Cloud. And at the end of the day, for a person to say, well, I want to use Sonnet versus Opus 4.6 or 4.7, uh, this is not what makes the huge deal. I think usually the the most demanding uh computational and design stuff is for the latest model, but um again, most of the people, at least in our organization, are starting to move to cloud.

SPEAKER_00 8:32

Very good, very good. That's excellent. Um and uh again, I ask these specific questions because they come up all the time with with our with our clients, and everybody's very curious about it. Um, for your clients now in the financial services who have different, um, probably similar levels of scrutiny with legal regulatory compliance that you would have to abide by because you're serving that industry. Um, but they're um either the broker dealers or they're they're they're being um uh supported by a broker dealer if they're an independent financial services practice. What have you seen there for uh AI adoption? Are they are they also choosing anthropic? Are they um staying with the Microsoft Copilot incumbent? Uh, you know, what what have you seen there?

SPEAKER_01 9:14

I'm just curious what uh your knowledge of so on that area again, internally for what they use, again, they'll be they tend to be a little bit less what I call sophisticated internally, or again, because again, they're not in the business of developing software. What they need is to do financial advice and naturally uh they're non-technical, and and that's fine. Their job is not to be technical. Um, so they consume the AI mostly through using the applications that are saying, I'm gonna give you an AI-enabled solution. So, from their perspective, they don't really care how the sausage is done. Let's put it this way. They're looking for an AI solution that will solve a business problem. And for whatever it's underneath the hood, they don't really care. It's up to the provider like us to say we use this LLM versus this LLM.

SPEAKER_00 10:05

Very good. Yeah, because I know that would that would go along with what you're using. Like if they're if they're the uh when they're using your platform, it's integrated and they're using your platform. When they're not using your platform, they would choose uh whatever model makes sense. And I I've seen varied adoption levels, that's why I wanted to ask you from uh from a financial services industry perspective. I've seen a lot of the um uh incumbents or the Microsoft Copilot, um, which now they're integrating with their own with their own uh models with with Anthropic and things like that. Um but they're very, very good. Excellent. So there's there's I'm just curious about the the software engineers internally, because I I come from an IT background, I know you do as well. And um what I've seen in in um across industries is that when AI first came uh to the scene, wait 2022, there was a lot of confusion, people didn't really know what to do. And uh what I've seen is that like I the IT part of the organization, the development organization, sort of becomes the early adopter there. And have you found the same thing with your organization where uh, and as you mentioned earlier, sometimes the engineers were, yeah, it works, or yeah, it doesn't really do much. I'm just gonna keep going myself. So it took a little while, but then late 2024, it sort of did the upswing. Have you noticed that IT is leading the AI adoption in your organization as well?

SPEAKER_01 11:26

And that's sort of what you see out there, yes, and and it's very natural because again, most people say, I can use Chat GPT to do basic stuff. And the engineers are usually the the geeks who can go and figure out how to connect it to something or how to ask it the right question or really comprehend what this thing can do for me. But at least internally for our um company, I'm amazed by the level of adoption of um you know Gen AI in all departments from sales, marketing. I have program managers and project managers who actually traditionally are the one that just you know herding the cats. They are creating dashboards and applications and deploying them. So I'm I'm very, very happy to see to see that.

SPEAKER_00 12:13

That's really exciting. Uh that's really exciting. Let's let's double click into the engineering side a little bit because you shared something that was uh interesting when we first met prior to the podcast. You were talking about how uh you had expected the software engineers to be the first adopters, like, oh my goodness, they're gonna be able to dive in and get all this benefit from it. But it was actually the test and the QA people who jumped in first and really started to get this force multiplier effect that you mentioned. Why do you think that happened? What does it tell other leaders out there about their hidden early adopters and how they can how they can look for those in the organization?

SPEAKER_01 12:50

So I think the the main reason for that, if we take a step back, this is the strength of Gen AI and LLMs. A developer will need to work a lot harder to come up from a clean slate, have to talk with the thing and product management and their peer and go back and forth and design it and try it and talk to it until eventually you have code that is generated and something is built. For the QA people, for the people who test the code, they don't have to start from a clean slate. They have something to tell the AI, go look at that and build me a test. So this is much faster. So they realize that I have I already have something that I can work with. Let me just ask the AI. So the level of effort is much um lower, and then the uh the barrier for adoption is much lower. So they're the first one because they could take advantage of it much faster. And then again, we we teach the developers multiple techniques of how you can be efficient, but it was very surprising to see that the QA people, the guy the guys who need to generate the automation, the verification, and so on, there actually some of them jumped on it before.

SPEAKER_00 13:58

Yeah, that's fascinating because my my experience in the technology world, um, oftentimes the QA and the uh the the QA um experts and leads are often uh they become the bottleneck because there's so much development happening, and then it even if they're doing agile, a lot of times the QA uh leaders get get pushed so much work toward the end of the sprint. It's always like that. Whereas now you bring AI in, and now it sort of gives them a little breathing room, like okay, great, if they can be twice as effective, uh they don't get as they don't they don't become the bottleneck as often. So it can be help the team really move a lot more effectively.

SPEAKER_01 14:33

Exactly. Not only that, that they can also create more uh tests with more coverage, that tests that are more efficient, and so on.

SPEAKER_00 14:42

Exactly, yes. And I I've I've also seen the uh the engineers, the developers um um if they're leading and the QA person doesn't know as much about the AI, the engineers are able to um that my exposure and experience that they've been able to say, okay, why don't we work with AI to create some test plans and test harnesses, etc. And then and then they sort of like lead the adoption there. But then the QA be because the uh learning curve is a lot faster with AI, um, even if the QA individuals, test individuals don't understand the full complexity of the software, AI can understand some of the things that they don't.

SPEAKER_01 15:22

Yep, absolutely.

SPEAKER_00 15:23

Yes, very, very good. So, question for you about uh non-technical leaders. There's a lot of non-technical leaders who uh you know tune into the podcast. Um, and uh what I've seen and I've observed and interacted with a number of uh uh people is that uh a lot of non-technical leaders think, oh, well, AI is a technology. Historically, throughout my experience, I'm not an early adopter of technology. I don't really understand a lot of it. Um, so I'll wait for it to become a little bit more mainstream. You know, you know, what do you what are your what are your thoughts on that? I guess what would be your advice to the non-technical uh leaders and professionals out there um so we can sort of start to bridge the gap between AI as a more easier adopting technology than previous technologies have been.

SPEAKER_01 16:13

So it depends. There's probably a spectrum of um situations. Let's talk about one. You're a non-technical leader, but you know that your engineering teams are already tinkering with it, but you don't know if what it does, how it works, and so on. In this case, you're lucky. All you have to do is to grab one of your tech people and tell them, I'm gonna free my Friday. You sit with me, you show me how to start, connect it to my systems, and you know, let me, you know, give me a one-on-one. I think that a lot of non-technical leaders, the aha moment they'll get is um again, if they're lucky, they have technical people who can just uh tell them what to do because executives they are in the business of tracking, understanding, looking at trends, seeing um, you know, what's the they look at the bigger picture. Okay, so uh one thing I would recommend them is learn how to use this thing to help you see the big picture. You can unleash it on your Jira and your BI tools and your reporting and tell me, give me the insight of what's going on. Show me is this thing working or not looking. Look at Slack and based on the engineer's conversation, tell me how's the sentiment? It's stuff that you can't even think about or we cannot do just by trying to go and run a basic report that's done today. All right. Um, so if you're lucky, I'll recommend do that. Otherwise, uh, if your company hasn't even started, you have no one there. First of all, it's a it's bad because it means you're behind. All right. Pay $20, download cloud at your own home, and again, go through the same thing that I said. Try to run it and converse it and say, don't try to build a website. You don't get it, it's not to you, but try to solve little things that um you you usually won't be able to do because of the scale. Okay, ask it to do small things for you, and then you say, aha, now I get it. All right. And in today's world, there is YouTube, there is a lot of resources for you to get started weekly.

SPEAKER_00 18:11

Yeah, interesting. You're getting into the concept of um what I often communicate as like using working with AI as a thought partner, like a strategic thought partner, right? And that's where you mentioned anthropic with the releases late late last year, really sort of took it to another level. And um I've been bringing up the topic lately about cognitive load. And I know that you and I talked about this earlier, you know, and you have a you have a a very good, a very good viewpoint here that I want to share with the audience. So if AI doesn't just make your work easy easier, but it also changes the nature of your work and the way you're working and reduces your cognitive load and the process, like you just shared, you can you can cover a lot more things without as much effort or without having to have the deep understanding uh of things. So it shifts, uh you had talked about earlier, it shifts bottlenecks, right? From you know, once once you've brought AI in and you've integrated in with uh with um some people, some process, now you've shifted a bottleneck away from one part of the organization uh to another part of the organization. You know, what is the data and and obviously it keeps iterating, right? Then you're doing it there, and you're you're so there's a lot of this adaptation that's happening, you know. So being a senior leader on the inside of one of these organizations, what does it feel like um day to day, like for yourself and for your leaders and your organization there um to move into this um uh different rhythm where there's more um frequency and consistency of adoption, like there's a lot more iterations, there's a lot more adoption. Um adaptation, I guess, is a better word than adoption. Yeah.

SPEAKER_01 19:50

So we can talk about it, but one comment, it's interesting comment regarding the um the cognitive workload. What we find, and again, as you say, people expect that now I'll have to think less. Now, if you really use AI at the agents at the scale to really be a force multiplier, you found out that you're still tired at the end of the day. And why? Because now you have three or four agents doing stuff for you and you have to jump between them, supervise them. All right. So you still need to think, but it's a different way of thinking. It's like I don't need to think how to solve this coding problem. I just need to coordinate to make sure they all of them are actually doing the right thing. So that's that's one thing interesting. And and you're right, you mentioned bottlenecks. So um, again, a lot of uh executives think, oh, I'm just gonna tell my engineers to use AI, and within two months, we're gonna double, triple, quadruple everything, and then they disappoint. What's going on? Now, what you don't pay attention is you can have the most brilliant engineers generating swaths of code, but if the rest of your process or pipelines are not adapted to that, then you're gonna hit the wall at some point, and actually deployment to production is not gonna happen because, whatever, because your other side of the house is not um able to absorb that. All right. Or you see that again, a lot of people generating and doing stuff, but at the end of the day, you say, but what am I getting at the end? And this is the biggest frustration point. So, one of the things that we're we we realized pretty soon, we said, um, what we've done until now, it's probably not going to last. So we're now in an experimental mode. So, one of the things I tried, I challenged a small team of the engineers and I said, I want to create a production-ready feature within a week. Completely crazy, undoable. It's even with AI. But we did that just to push the boundaries and to say what's going to prevent us from trying to do it in a week. And now you start to see and to identify the bottlenecks of um, you know, where where things are not working. And by the way, what you realize is that now you have to go and do a lot more thinking ahead of time. Because in the world of agile, uh, thinking ahead of time was frowned upon. Oh, we just do a little bit of theorizing, we're gonna code and then we try and go and deploy. Now you move back to again, I don't want to say the dreaded waterfall, but you need to think ahead of time. Because if you simply product management, if you define what it is, if you think about the end result and you put it in what we call markdown file, then you can leash the AI and let it run and do its thing. So that's at least one way of changing how we do things. I'm gonna give you one more example of how we do things, is we realize that um a lot of people are doing what we're doing, they're adding skills for the AI to do its work. Now it turns out that Joe is adding skills and Mary next to him is adding the same exact skills, wasting their time, and now we have all these 10 different of the variation of the same skills. So, what we're trying right now is to shape the organization to say, let's have a skills repository. So everyone can use that. Same thing is especially for legacy applications, when you have to really build a lot of chunk of context before the AI can really do its work because it has to understand all the complexity of the system. You do it, you unleash the AI, you're done, you're throwing it away, and then the person next to you have to build it again. So we said it's a waste of time, it's a waste of token. Let's try to build this in a way that everyone can consume it, and that's how again you prevent the bottleneck, the you, the uh, the the abuse of tokens, and so on. It's it's again, everything is changing, and we're experimenting with a lot of things.

SPEAKER_00 23:30

Yeah, that's that's fan, that's fascinating. Thank you for sharing those uh particular examples too. And I want to I want to I want to shift over to a concept that you shared earlier, but the whole concept of human in the loop. I know you're a strong believer of keeping the human in the loop. Um, this is a big uh a big deal because uh on on the surface in the media they say, well, AI is replacing people and that we don't need the people because it's all automation. AI just comes in and just takes over everything's robots. But the reality is you're a big believer of keeping the human in the loop. It's not a temporary training wheel, it's a permanent design principle. Like you said, their roles are changing. Now they're becoming more of the strategist. How do you orchestrate across uh large um numbers of uh agents and agentic uh workflows and things? Um where did how did you get to that conclusion? Like, where do you draw the line between uh what AI handles and what humans handle? Because I know that's that line is shifting week by week.

SPEAKER_01 24:28

Okay, so one comment for anyone who says that all the humans can do replace. I always tell them, yes, so show me what you how do you get something enterprise ready to production and support it. Okay, if you're going to sleep, do you think the agent is gonna wake up and do all the crazy stuff that is needed to have a financial institute uh being taken care of? All right, so humans won't be replaced anytime soon. But here's here's one of the um it's uh AI is a double-edged sword because it's non-deterministic. And when you have something that is non-deterministic, you need either to control it with there's a bunch of ways to control it, the technical level, but at the end of the day, you still need the human to make sure that it doesn't hallucinate because it can easily hallucinate and start to build the thing that you didn't want. You need to make good judgment, all right? Is that the business case that I really wanted to solve or not? And um AI can't replace the people management. So at the end of the day, you still need to have people that operate the AI, and you still the people still need to be motivated, they want to grow, they want to do something. So AI is still needed there. But on the application side, especially in financial services, they tend to be very conservative, and everything has to be accurate down to the cent. AI will uh it will run the numbers sometime because it's easier. And the what once you lose the trust of a financial advisor because the number was wrong, that's it. So you always need to make sure that they're they feel that they're using AI to be a force multiplier, but not the replacement. And again, so at the end of the day, especially because of the non-deterministic nature, because of hallucination, because of just AI taking the wrong road, you still need the people there for a long time from my perspective.

SPEAKER_00 26:21

Yeah, very good. That that that I fully agree with everything you shared. And plus being in the financial services space that you're in, uh, the the the compliance and accountability is down to the human, right? We don't, you know, AI is not responsible for mistakes made. It's it's the human, right? It's your it's it's your uh your organization and the leadership of your organization that represents uh your platform and uh and the service you provide. Yeah, very good. Let's look at as we're wrapping up, uh Jai, let's look ahead for the next for the for the leaders listening to this uh now, let's look ahead for the next one to two to three years, like into the future a little bit, as far as we can take our best guess. You know, for the C-suite leader listening who knows that AI matters, but they haven't maybe made a big move enough yet uh in their organization, what's one thing you tell them to do next? Like what should they do? How should they approach it?

SPEAKER_01 27:14

I I tell them you have to embrace it. This is not yet another technology of the day that you can say, yeah, it's like it's a it's a generational leap. All right. And if you want to use that, then your competition will use it to outcompete you, and and you know, the eventually you get out of uh business. What you need to make sure is that you always ask the technical team to show you how, when they're using it, what how they can multiply the output. Otherwise, you have a lot of people excited building cool stuff, but at the end of the day, not moving the needle. What you also need to make sure is as again, as um senior executive leader, is to say, this thing is cool, but it's also costly. I need to make sure that we can control the cost, otherwise, we'll be faced with a bill that will just be detrimental. Okay, so that's that's another thing. So the thing is jump into it, you have to use it, make sure that you have the right people in the organization who know how to take how to really use it efficiently and not just play with it. Make sure that you control the cost, make sure that you can actually show when I use that, here's what I get better. And another thing is don't just focus on the technology people. Um, you should be able to use it across the organization, all departments from legal to marketing to operations. Everyone can and should be using it.

SPEAKER_00 28:42

That's fantastic advice, Shai. Thank you so much for sharing that. And as we're as we're wrapping up, what's the best way for uh someone listening to contact you or Advisor 360 or learn more about your company, your products, yourself?

SPEAKER_01 28:56

Yeah, so you can always find me on LinkedIn. Um, the company has a great website. We have a great engineering website that is linked from the company. You can read amazing articles. We have very smart people over there. You can contact us through there. So there's there's a bunch of a bunch of ways to contact us.

SPEAKER_00 29:14

Excellent. We'll include all those in our show notes with our team. We put this together. So if those listening, you can uh when you're not driving uh or or uh or walking and you're sitting down, you can look at the show notes for this and uh and reach out to uh to Shy and Advisor360 and learn more about what they're doing, especially if you're in the financial services industry. Uh I'd highly recommend you take a look at their uh their offerings there. And for everyone listening, the AI transformation is happening now, it's happening under our feet, and it's happening all around us faster than we can possibly comprehend. And the companies that are investing in their people alongside their technology are the ones who are really going to win. And thanks for joining us today on the AI Accelerator. Until next time, keep accelerating.