Ep. 131: AI Is a Tool, Not a Search Engine

Speaker A: Welcome to the Shift Happens podcast, where we explore the latest trends and insights in the digital workplace. From the role of AI in the workplace to the future of remote work,

Speaker B: we cover it all.

Speaker A: Tune in as we chat with industry leaders and experts. Whether you're a seasoned pro or just getting started in the digital landscape, we've got you covered. Subscribe to Shift Happens wherever you listen to podcasts and stay ahead of the curve.

Speaker C: A new tool does not guarantee transformation. It can just create a same old situation with shinier software. On this Shift Happens episode, Marco Galimberti, CEO of APAC at Venki, joins us to talk about what it takes to

Speaker B: move beyond using AI like a search

Speaker C: engine and start applying it across the enterprise in a meaningful way. We explore how AI can drive real impact across manufacturing and retail, from process automation and forecasting to customer experience, personalization, and faster experimentation in marketing and product development. Marco also shares why leaders have to model the behavior, build data integrity, and help teams embrace change without falling into doom and gloom thinking, let's dive in.

Speaker D: Shift Happens Podcast.

Speaker B: Hey, everybody. Welcome back to another episode of Shift Happens Podcast. And today I'm, I'm so excited, and I'm sure you'll be excited too, to talk to Marco from Venki. If you don't know Venky, you just have to go to their, uh, stores and you'll never forget them. Right, Marco?

Speaker D: First, first step, making a visit.

Speaker B: That's right. That's right. Well, welcome, Marco. Thank you, thank you for joining us today. And, uh, we're going to have a great conversation. Now, to get started, I always ask our guests this question. Which song best described change or transformation to you?

Speaker C: And why?

Speaker D: Um, I think I would use the Motley Crue, uh, same old situation. Because in the end, I always feel that when we're at a crossroad and we are sort of forced to take a step towards a change and that there is sort of, uh, I would say really a shift happening. Um, then you have really, you know, team A and Team B. Right. So team A is the excited team, the one that tried to look at the half, uh, cup full. And then you have the team B, that half cup empty. And, uh, the team B tends to be the doom and gloom. That is what we see today, really. The doom and gloom, uh, team. And um, I like to think that in the end, if we were everybody to embrace it and to move on from our fear, uh, of change, uh, they're probably will be better for everyone. Uh, although, you know, we still have to you know, hear and respect uh, the doom and gloom views on uh, on the change that is happening today.

Speaker B: You know, I love that choice by the way. It's a great song from a uh, great band, Motley Crue. And, and I think what you describe, right, because it's not just about the transformation. I mean we, we look at the folks with a half glass full, they think about oh it's transformation, things will get better. But then it's also a song about patterns. And you talked about the doom and gloom or even other companies how they're adopting AI. It's easy to introduce AI no doubt. You see it on the news, everybody wants AI but sometimes even though I think there's a group also that they're going to adopt it, but they keep doing the same things underneath. So new tools, same habit, same workflows, same decisions. Right. And while it's okay, maybe you do things faster, I think the true value of AI uh is beyond that. It's really the transformation. So maybe let me start here. Right? Where do you see organizations uh, falling into the same old situation with AI thinking they're transforming but when they're reality they're just maybe using a shinier new tool.

Speaker D: Yeah, uh, that's I think the risk overall because the problem is that I think we were all given global access. I mean globally we were for the first time uh, given access like everyone indistinctively uh, from all over the world to the same tool. Right. And in the end is a new tool. Um, and then I think the problem is that uh, the use uh, uh of the laser tool, uh, so the leisure motivational push uh, of everyone was uh, stronger uh than the enterprise application. So everybody started using AI as a new sort of search engine and um, question and answer. Right. Uh, and in the end I mean now really you can see some you know, uh, AI providers that are really going full on, on enterprise. Uh, and the problem is that I think most of the people that had hope, um, they didn't do the second step which is understanding how I can improve my work because they really stop that. Okay. It's a new super powerful search engine optimization Search uh, engine. Right. So I ask a question and he will give me an answer. Uh, so thank God I had the fortune of uh, being um, involved in some sort of AI classes from really not, you know, people that were really going beyond and they were really going into the enterprise, uh, and the use of uh, the tool, uh, from an enterprise perspective. And these really changed how now uh, we approach to the topic and how we are rolling out, uh, across the organization. Uh, but again it's a simple situation. So you have a new tool, uh, then everybody starts making use of it and they get accustomed to the certain use and then you know, really going and taking each one of them and saying, hey, look, if you use the tool uh, in a different way, probably the output of your work or in general, uh, can be different.

Speaker B: Right?

Speaker D: So can be totally different. And this has to, unfortunately this is the most expensive thing because it requires time, passion and commitment from the top down to the bottom in letting them understand that this is not a search engine. I mean this is a tool that they will need to uh, sort of start mastering and that they will be coexisting and co living for the next 30 years. So at least 20 years for CME, uh, that's what's going to happen.

Speaker B: And the thing is, right, so we talk about transformation, you hit the nail on the head. The transformation has to start from the individual, right? We expect, oh, AI will transform companies, no doubt. But the first step to transformation is the transformation of the mind of the individual. Where it's not just a search engine, it's not just somebody who will record your meetings or write your emails. Right. And you being one of the top leaders in the company, uh, you know, taking that charge is critical. So Venky is both a manufacturer and retailer. Certainly, you know, I love the store, I go there, I buy your chocolates and gelato, but at the same time the back end you manufacture. So how do you think about AI differently now in these two contexts beyond AI being a search engine on both ends?

Speaker D: I think it will have a huge impact but a very different uh, extent. One is going to be mostly on uh, sort of. We're going to have a tool that is going to basically replace process automation and coding, uh, in a way that will allow us probably to uh, automate workflows and improve the flows and optimize flows and together with lean engineering, I think will make the job of the lean manager probably less it, uh, dependent and less IT driven and could probably bring benefits to people working uh, in the manufacturing facility or in the back office, uh, overall, uh, better outputs. And as I always say to my team, I don't think there is anybody in the world that is really happy to do data entry and a repetitive job on Excel for the rest of his life. So if we can make his life uh, better, um, and in a way his work, uh, more meaningful, uh, faster, more precise, um, with relatively less effort so we probably have also the chance to upskill the person to do something else. Um, but for what concern, front end. Then it's a different story I think. And it's probably the relationship of the customer with the brand. Uh, probably we need to expect that customers will be way even more than now. They will be more acknowledged about what we do and who we are. Probably uh, we also need to understand that we uh, can use AI uh in a way that we can predict uh, what customer might want, customer might react. Uh, we can get sort uh, of feedback on uh, what customer actually like or not like. We can get feedback on uh, for example when we do our price calculation on price elasticity, I mean we can do things that we were taking as a load of time and probably even some external consulting and so on and do it on our own. On the creativity part I think it's going to have a huge impact and it's going to allow us to uh, try fast and fail fast and try again. Um, I would say with less efforts than before. So it actually allow us to go faster. And probably what I always find very interesting, uh, AI is a great connector and a great uh, pattern finder. So meaning that when you have a big set of data, uh, uh, and you have multiple elements that you want to put together is really good uh, at finding patterns that on your own would take hours and hours and would take probably uh, work of multiple people. And that's um, I think the most interesting thing um, that could affect also again our relationship with the customers and the relationship of the customers with us.

Speaker B: As you share some of these example, are there any examples or use cases that you've started working on today leveraging agentic AI and uh, how are you applying these agents?

Speaker A: Hello Shift Happens podcast listeners. I have an exciting offer for you. Join us for our in person Shift happens conference October 10th, 11th in Washington DC. Registration is free. That's right, it's free. And you walk away with actionable strategies from industry leaders and peers to, to make Shift happen in your digital workplace. Visit Shift Happens to to register today.

Speaker B: We'll see you there.

Speaker D: Well now agents is um, everybody wants to make his own agent, right? So I can tell you that uh, we have done an agent that is helping us again on the analysis of price elasticity across the world. Uh, we have an agent that is helping us on uh, sales, uh, analysis and um, profitability analysis on all our stores all over the world. We are now working on an agent that is optimizing, was trying to optimize we are still working on optimizing shifts, uh, in stores all over the world. Um, and uh, so that's the sort of uh, cost, uh, optimization, um, implementation that we're having at the moment, which is in a way the easiest. Now the interesting part will be how to drive extra top line by implementing A.I. um, uh, well. And I think the marketing team is making good use of it.

Speaker B: So this is really cool to see how you're applying across the business. And I want to go back to your first point when we talked about the song. Right. Like the doom and gloom glass, uh, half empty folks. Have you seen any changes with those individuals or groups of people now realizing, wow, this actually is better for me, I'm more productive. Or I could do, you, uh, know,

Speaker C: help our business do better?

Speaker D: Um, I haven't. I mean in, in the organization, we recently hosted this large workshop for everyone and everyone came out very excited. So nobody was like, oh, my job is um, uh, is going to be over. Right. Right. I um, think in the end the doom and gloom of uh, the mass unemployment that AI is going to create, I think it's maybe sometimes overstated, but I know that there are contrary opinions on it and I respect it in the sense that uh, I look back, uh, 30, 40 years ago, I was not born, but I was told that there was a tool called AutoCAD that would have brought the end to the architect job and engineering. And actually they're still there. Right. And I heard about Excel and the calculator in the 1960s saying oh, you know, uh, you know, general ledger and accountants, they're going to be out of, there's going to be a collapse. I remember, I think there was an uh, the New York Times or, or there was a front page of a newspaper in the US when the calculator was introduced, which I don't remember the year to be honest. I think it was long, if it's the 60s or the 70s, and said there was a massive preoccupation over uh, how this will bring effect to the labor market. And fast forward, uh, they said the same with Internet. They said the same with Excel. They said the same with a lot of tools. And in the end I think people just got too used to learn to use the tool. And again, if some process, they have been completely wiped out and some jobs, they've been completely wiped out in the last 40 years. Yeah, a new wave of job will be wiped out and people and the human being. I always have the faith and the confidence that the human being in the End will adapt to a new environment. Right. And we will not be uh, starving. There are many jobs that are going to be completely unaffected by the uh, AI and especially relational jobs. Uh, they are going to unlikely to be affected as anthropic made this beautiful chart, um, you know, giving the chances of uh, jobs being disappearing uh, or not from the map and so be it. So that's how I see it and

Speaker B: I really like your analogy. Right, because you're right. Before the calculator there was the abacus. Right. Or maybe manually counting. We don't do that as much anymore but it doesn't mean we don't count. We still count. I think what technologies and tools like this can bring us is more innovation. I'm pretty sure there's things we don't even think of that will exist because we have these new tools that will help us. Um, you talk even about the manufacturing for example. There's a lot of innovation in manufacturing. Like I was watching the news around how fast EVs are created because of robotics and AI. Now a lot of EV companies, um, in your world how has manufacturing been impacted where AI is uh, delivering value to you or what are some of the things you guys are looking at?

Speaker D: Uh, but I think mostly will be probably process automation. I think there will be better accuracy, better forecasting. Uh, there will be probably optimization on supply chain optimization on I think you know again finding patterns on suppliers or what can we do better with each supplier m where we can, you know, how we can rethink probably the supply of certain products of certain other things. And you know again, uh, it's going to be on the manufacturing. It's going to be probably on uh,

Speaker B: um,

Speaker D: process automation I would say which was already happening before that it was just that I think was a little bit less democratic, still very IT driven. Now it's relatively easier to build a software on your own or a process or a flow that you want to make it repetitive. Um, and before that, I mean before all the softwares that were, you know, automation software were a little bit more cranky in a way, uh, and they required a little bit more coding and probably they required the use of a consultant and so on. So in the end it's a democratization of uh, processes. Right. So now you can really, I mean you can build your software on Claude, um, doing pretty much anything, uh, just by doing a nice prompting and working on it. Now it's uh, incredible.

Speaker A: Yeah.

Speaker B: In all these tools, right? Like lovable, for example, people are building multi million billion dollar Business off of these tools.

Speaker D: Exactly. So that's going to be a source of new value creation and uh, uh, that's it. And probably, uh, again it's going to probably also make uh, the setup of you know, a manufacturing facility or the setup of uh, uh, whatever is related to um, new product innovation. The launch of a new product or the uh, engineering behind a new product is going to make it probably easier, um, and faster. So be it again, that's it. Like let's go, let's, let's go with the flow. Um, the only thing I'm worried about is uh, uh, the problem. We need to realize faster that also our, the education, the whole education system that we have that is actually, you know, training students from, from the day they are kids and you know, from five years old up to you know, the things K12, uh, that has to change. Right? Because probably, you know, again, even at university teaching how to use Excel or teaching how to use certain tools is going to be probably completely redundant. But it's actually very important that people get even stronger fundamentals of mathematics, physics, chemistry and having a great solid knowledge on then what to do, um, with AI by leveraging on a uh, basic

Speaker B: knowledge in Marco, that's the risk, right? In fact, sometimes I think about my children. My son's in college, my daughter's in high school. And I encourage them to use AI, but my wife, she keeps me at bay. She would always say, well then your kids will be lazy. Right? And I realized because of exactly what you mentioned, like you and I, we had the basics. I had basic math, basic engineering. While my son is studying engineering. Sometimes I notice even with mental math, right, he needs a calculator. So there's this risk of, I hate to say it but some people may become lazy, stop thinking and become lazy. Oh, there's a tool, there's Google. I don't need to go to library. There's AI. I don't really need to validate or check. And we see that today, it's in the news, right? Uh, even organizations like police forces made wrong arrests because they relied on AI without checking. So that I think will be high risk and high challenge and caution to a lot of leaders in the organization relying on this. But at the same time you cannot remove the human in the loop.

Speaker D: Yeah. So as exactly you said so that's why I see for the moment AI probably better, uh, in the hands of the seniors rather than the two juniors. Uh, in the sense that in the end we went from you know, writing papers and doing Calculation, uh, you know, at school, probably even. I mean in my school when I was at uh, high school, calculator was forbidden. So uh, back in time so we could not use that in class. Calculators. Okay. So I started using calculator, uh, towards the end of high school. Like allowed. Right? Allowed. Yeah, yeah. So we were doing really like calculation like by uh, hand and really thinking. Yeah. So yeah, probably we need to realize fast that education has to adapt and change. But I see already, you know, these ban on smartphones and trying to dedigitalize the kids, uh, which in the end I find it positive in a way. Uh, because in the end it will be. They will have access to all the incredible digital tools that they want and they will do incredible things. But it's really important, at least on those years they really focus on uh, the basics. Maybe I'm too antiquate on saying this, let's see when my son will go to school. But um, uh, it's interesting, but I agree with you also because AI is still hallucinating.

Speaker B: Right.

Speaker D: And can make mistakes. So if I ask um, copilot to make an analysis on, on our numbers on um, you know, make a sales analysis on our performances in Japan and I see the numbers not add up. I am aware of the numbers. I can spot it. But maybe the accountant that joined three months ago that is less familiar with the numbers, doesn't have the numbers in their head. Then they might hallucinate and you know, they might show numbers that are not uh, that they lack integrity in a way. So it's extremely important to educate everyone on really getting uh, the basics.

Speaker B: Right? That's right. That's right. Well, speaking of the basics, you mentioned a couple of times around the benefit of using AI for pattern detection or data analysis.

Speaker C: Right.

Speaker B: So would you say, I guess especially in your business, data is your asset. Um, can you speak more about the value of data, at least in your line of work, and uh, how do you ensure that there's good data quality, good data structure and availability, frankly to make AI useful?

Speaker D: Yeah, that's. I think, um, I was just discussing with friends that probably, uh, a topic in general that we tend to um, uh, underestimate, which is integrity of the data. Uh, so we've been doing a lot in uh, really improving our integrity of the data, uh, across the organization. And actually we are an extremely transparent organization within the organization. So uh, everybody has sort of um, uh, pretty much visibility on what's going on. And we encourage people to be very data driven. I Actually have to say culturally, uh, uh, we are a company that, I mean really take decision less with guts. We don't trust our gut and we trust data, um, much more. So the problem is that now there is a proliferation of data, um, because of course, uh, you can track everything in our life. You can track everything in the stores, you have an overabundancy of data and at one point funneling all the data, uh, and giving meaningful outputs in the end is complicated at the moment. I think AI is going to help us on that. But the problem is that when we operate a vertical organization with manufacturing, with retail, with consumers, with traffic, we have data on the weather forecast, we have data on uh, uh, the traffic in front of our stores, we have data on our returning customers, we have data on the spending of the customers. Then we have data related to our production output, we have data on uh, uh, our client reordering, we have data on our supply lead time, we have data on everything. It's incredible the amount of things that you can track at the moment. The problem is uh, how much you, I mean we always want to have the feeling of being able to track everything. But then how much use we make of what we track, uh, exactly. You know, that's the issue that I think all the organization are actually facing. Look at the data that on my own I can track. I have a Garmin and a Whoop and I can track, you know, how much intake I have of water every day, how many hours I slept and um, you know, how much airport I did in the last six months, uh, how much sport I did, which sport I did and what was my heartbeat, uh, during uh, all those sports sessions. What's my average heartbeat when I go running, uh, on the Sunday instead of the Wednesdays, because I, I used to run on Wednesday and Sunday. So in the end it's the use that you make of it and the meaning that you give to uh, to the data. Overabundancy, I don't think it's uh, necessarily good. And sometimes it distract the organization right from the meaningful things. So it's always, you know, we went from zero data to all data all in. Uh. So I really hope that AI in the end is going to help us in connecting the dots, as somebody used to say, somebody famous.

Speaker B: So this is very, very good point. Right? Like you, I track everything. I have a similar thing, you know, Garmin and Hume, my hrv, resting heart rate. But, but when I started really tracking it, there was a point where it freaked me out because I, you know, Every hour I looked at it, I'm like, oh, maybe I should do this. So, so there's that balance that's needed. And then I put, I actually I built an agent, I think I showed it to you. I built an agent that helps me with this now to regulate it, to, to capture what's important um, to me for my health. And I think you're right. I think that's similar to organizations, they have a lot of data but what are the key things? Right? So in your business you're looking at your growth, your profitability, your efficiency from manufacturing. That's where you see the value in ROI as you continue to scale and grow your business.

Speaker D: Totally. So again, yeah, so we went, I mean for the happiness of the tech companies, we are very well equipped with any kind of tool to track anything, literally anything. Uh now, you know, it's really, you know, interesting at one point how we're going to cope with that and decode all that data amount that we have into uh, something meaningful.

Speaker B: So a lot of leaders I talk to are in a similar um, juncture where really measuring the ROI and the business impact. So in your organization, if you can share, how do you think about business impact, especially when some of these values is top line and harder to attribute.

Speaker D: I don't think we are there yet on the ROI of our use of uh, AI, if I am completely honest, uh, in the sense that we are still in a phase where we are now rolling out. Uh, the ROI that we see immediately is definitely when we talk about process automation, uh, workflows and so on is definitely working hours saved and we are talking about thousands and thousands of working hours that we could potentially save. Which doesn't mean again that we are you know, making redundancies. But this means that we can do more stuff with the same amount of stuff. So I mean we can do more things with the same amount of people. So uh, and that's, and that's already a gain, right? That's already um, extra productivity that you gain immediately. Then on the top line, um, I would say excluding graphics, I mean excluding the simple things, right? Translation, uh, cost, um, uh, communication, agencies and creativity and all that work that probably, yeah, we'll have a saving. I think it's going to be, uh, on a longer term perspective, definitely the ROI will be how well we will be able to leverage on AI to create fantastic products, improve our stores and improving our communication, improving the way we talk to consumers, being really uh, down to the heart of the consumer and telling them what they want. To hear and really touching, um, really being able to leverage AI and touching their heart and really getting into really what they want and not what we want to sell is really what they want. Um, which is the ultimate, the ultimate thing, right? Which that's how I see, as I was saying, the way the relationship, how the consumers are going to change now choosing on E commerce and choosing in a store, uh, it's a very static uh, act, very little dynamic, right? So you choose, you browse and you see what you like the most. The other thing is, um, okay, I know what you bought before and this very classic CRM and the system is going to give you suggestion and, and the staff is going to give you suggestions and they are able to read it and they're able to suggest it's something that you might be interested in. Because the most difficult thing is really you know, educating our staff and uh, also you know, driving the E commerce in a way that it becomes as close as driving the choices in a way or your variety of choices in a way that we get as close as possible to what you want. Another thing is now I have the tool. Yeah, exactly. Now I have the tool that allow me to really get as ah, really close as possible of what you want. Not offering you a uh, variety of products that one of them might be what you want and understanding what you like. I worked on the floor for uh, a long time and the embarrassing like do you want to try these? Like do you want to try that? Do you like this? Like that. Um, and the worst thing possible when you are a salesperson is trying to break the ice and giving a hint that is exactly opposite of what the person is looking for. That's where the conversation starts. Very complicated. So I wish one day really to have direct conversation with the people selling me stuff as a client, uh, and having them suggesting and knowing me and giving me uh, what I'm looking for rather than pushing me products uh, randomly. Um, because also as a consumer we are overwhelmed at the moment in how many newsletter, how many newsletters are we receiving that are completely useless? Uh, things that products that were not interesting. How many WhatsApp messages. Now we receive from vendors that push us things and make us upset and really the feeling of being overwhelmed. Right. I mean at one point I hope that we will not be able to be overwhelming to clients and really going spot on uh, to what they like. So that's the word they dream about.

Speaker B: You hit the nail on the head because it's really about trust and customer loyalty.

Speaker D: Right?

Speaker B: So that's why I don't even think twice now today. Like Amazon, you know, when I started using Amazon, uh, I still thought is this really the best deal or best price? Now I don't even think about it because it recommends, it understands a pattern. Same with like Netflix. It knows the shows that I like, so that builds that loyalty and trust. I trust that while I know it's a technology that's doing it, but I trust that it knows what I want, it knows that I, you know, uh, if I have allergy or not. So if I go to your chocolate store, it won't, it won't present me with things that has nuts, for example. I think that's the key now. It's a hyper personalization and uh, making it convenient and easy frankly as a customer.

Speaker D: Yeah. So what I see as an improvement, uh, but I don't know, I mean we talk about Genai of course now, but artificial intelligence have been a thing for decades now. So this was just in the background and we didn't see. But I mean the Netflix, uh, you know, proposal of new series, you know, to us that we, I don't know, I watch uh, the Formula one, I watch Kobe Bryant, I watch this and then they push me something sport. Well I mean that's in the end that's an artificial intelligence. That is an automation but driven by artificial intelligence. Now the Genai, um, is adding basically the conversational pattern, right, uh, on how things are going, uh, with consumers. But one thing that I see, Uber has uh, been improving a lot in his experience, I have to say, um, uh, you know Kong, we have Uber. So I'm using Uber. And you know, now when I land at the Hong Kong airport and I reach on the phone and he sees me at the Hong Kong airport when I'm still in the Runway, he sent me a notification saying, I see you're at the airport. Do you need a ride home?

Speaker B: That's awesome.

Speaker D: And when I book a ride early, I mean I book in advance for the morning, maybe at ah, 6am, 6.30am to go to the airport. He's asking me when are you coming back? Do you need a ride for when you land back? And I can even choose now I think it's a uh, tool also available in the U.S. probably I can choose the airline and I can choose the flight number and uh, it will ensure that I will have a taxi, uh, when I land at the time when I land and I just need to tap and say, okay, you're landing, tap the taxi. So that's I think a huge improvement from a customer experience. And probably they are making good use of Gen AI to improve. And in the end, ultimately, I think it's driving top line for Uber. Right. Because I could take the train sometimes or I could take a normal taxi, and as soon as I land, they push me sort, um, of to book an Uber. Right. So that's a good top line, uh, sort of, uh, generation for. Yeah, proactive.

Speaker C: Right.

Speaker B: Without waiting for you. Yeah, yeah, yeah.

Speaker D: Without being overwhelming though, because, yeah, I landed and, yeah, I need a transport back home. There you go. Yeah, that's, I think, an interesting, uh, small application that I see that has been improving in the last 12 months. And I think that probably is happening also thanks to, um, Gen AI.

Speaker A: Probably.

Speaker B: Well, Marco, this has been a really, really awesome conversation. Uh, I want to be respectful for your time, but before we wrap up, one last question for our listeners. So, for our listeners, a lot of leaders really, uh, thinking about, or they're already in the thick of rolling AI, what are some of the key lessons and takeaways you can share to avoid falling behind AI uh, especially they're in the business of manufacturing or retail.

Speaker D: For leaders, I would say we have to be the one carrying, uh, the burden of giving good examples to the team and the organization on how we use ourselves. AI every day, what we did with AI and uh, pushing them to say, hey, did you check, I mean, you send me a PowerPoint or you send me a presentation or you send me a budget. Did you run it on AI before that to adjust the tone or to see if somebody was, uh, you know, not precise or some numbers were not adding on and so on. So we have to do it on our own as leaders. And we need to educate the team that actually it's okay if they use it. It's okay if they come to, uh, sort of a budget meeting with a presentation that has been corrected, uh, and vetted by run on copilot. And, um, it's absolutely okay. So, I mean, we cannot let, um, the organization think that now, you know, making use of AI for certain things is just not right and there should always be the human doing certain, uh, things. Absolutely. The budget is done. Now, I give an example of budgeting. Budget is done by humans, right. And is done by people and, uh, sales as achieved through the work of, uh, our colleagues. Um, but again, if you want to, you know, make sure that, or if you want to find an interesting pattern through the data and you want to be vetted by AI before the meeting with the leadership or whatever. Just go ahead and don't be sort of shy to say that you did this with AI. I'm actually sometimes, you know, I'm so excited because I start reasoning and I ask what AI thinks about an idea that I had and it comes up with some very interesting points. And then M immediately shared on teams with, uh, my colleagues and said, this is what Copilot said about it. Right. So we are evaluating a new menu, for example, and, uh, post a picture and you ask copilot, what do you think about this? Like, you know, there is something that's. And you know, it's absolutely okay. It's. We need to, we need to get used to it. And sometimes I still think people is embarrassed to say, uh, yeah, that they did something using AI. No, actually it's good, like, go ahead because it's your, it's your co pilot. Right. So it's your sparring partner. So if you use it to have, uh, to confirming your hypothesis or whatever, it's absolutely fine. And um, in any case, it's a second opinion that you have. Um, it's a second opinion. Right. It's not. It's better than your own single opinion. And maybe it's validating your hypothesis or maybe is giving you some, um, aspects or is opening up some point of views that you were not thinking about. So it's great. But it has to start from us. Absolutely. Uh, this time we are really the change managers.

Speaker B: It can't just be a lip service. Right? It can't just be, hey, let's use AI and then, you know, it's status quo. You know, same old situation.

Speaker D: Exactly. Same old situation.

Speaker B: Yeah. All right, my friend. Thank you so much. Thank you so much, Lex.

Speaker D: Thank you.

Speaker B: I'm a big fan of Enki. I go to your store a lot in Washington.

Speaker D: Thank you.

Speaker B: And continue. Hopefully all the best in your business.

Speaker D: Thank you so much. Likewise. Hope to see you soon again.

Speaker B: See you.

Speaker C: Big thanks to Maro for joining us today. The key takeaway is that AI value comes when leaders treat it as a long term capability and not a novelty. Use it to remove repetitive work, connect patterns across complex data, and accelerate testing and learning across the business. But do not skip the fundamentals. Data integrity, clarity on what matters, and the discipline to keep humans in the loop. If you want adoption to stick, leaders have to go first and make it normal to use AI as a sparing partner for better decisions. If you want more episodes like this, make sure to subscribe wherever you get your podcast until the next time Shift

Speaker A: Happens Podcast Shift Happens Podcast is a production of AvePoint, Inc. Produced and edited by the App Point Brand team. Stay up to date on the latest trends in digital workplace transformation by visiting apppoint.

Speaker B: Com.