Why Your Retail AI Strategy Is Probably Wrong (And How to Fix It) | Spotlight Series

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Hello, everyone.

I am Chris Walton, one of the founders of omnitalk.

And today I am bringing you the experts, the consultative experts, that is.

And they're here to help us understand how retailers should approach AI engineering and and digital product development.

So please join us in welcoming our two guests from Experian Technologies.

First, it's global head of Data and AI Suraj Ali Mohammed.

And also Mahesh Vijay Raghavan, Experience VP of Retail and consumer goods.

Gentlemen, welcome to both of you and thank you for joining us.

And hopefully I didn't get those names.

I got those names.

Hopefully I got those names as correct as I could get them.

I think.

You guys tell me, Siraj, how are you this morning?

I'm doing really good.

10 on.

10 on that.

Chris.

Great.

Awesome.

Awesome.

And Mahesh, how are you?

Excited.

Very excited.

Chris, you know, you got my last name perfect.

And, you know, we are so excited to be here and kind of a little exposed as well.

You know, the thing about your podcast is that, you know, you don't let people hide behind buzzwords.

So I'm actually a little nervous as well.

Are you?

All right?

All right.

Well, that's awesome.

No one's ever admitted that to me on camera or on air before, so because you said that, maybe I'll take it a little easier on you this week this time.

But let's start with you.

So tell the audience a little bit about your background.

Tell us about Experian Technologies too, and all that it does as well.

So that is one of my favorite parts as well, you know, describing about experion.

So the simplest way to describe experion is this.

We help our customers turn complexity into clarity.

We are actually a product engineering and digital experience partner that spend nearly two decades deep in retail, manufacturing, automobile and consumer ecosystems, working across stores, supply chains, digital platforms, data platforms, and digital channels.

Our strength actually is in product engineering.

We discover, design, build and deploy Products that solve real business problems.

From modernizing legacy systems to building customer facing applications, to developing AI powered platforms.

We have actually worked with over 300 different clients globally across 35 countries with 5 offices in 5 different geographic locations like Japan, Australia, USA, UK and India.

So what is different about us is that we don't lead with technology, we lead with business outcomes.

Our teams sit at the intersection of product, commerce, data and AI, helping our customers move from kind of a reactive operations mode to predictive decision making.

Siraj can vouch on this.

Over the last few years, our big focus of us have been on operationalizing data and AI, not as an innovation lab, but directly embedding into areas like demand planning, pricing, customer engagement, workflows and all that.

So that is on a high level what experion is all about.

Great.

So Siraj, I'm curious too.

So how would you sum up what it's all about too?

And I also want to know, I also want to know what does a global head of data and AI do on a regular basis?

What does an average day look like for you in that field?

I have no idea, no conceptualization of that.

So yeah, what does my average day look like?

I have data for breakfast, lunch and dinner probably.

As global head of data, I'm helping customers kind of try and figure, figure out their challenges with data.

How do they manage better, how do they put it to best use in terms of analytics or even reporting.

And more often these days is all about how can they use AI to improve their, their business functions.

Right.

So, and Mahesh touched on that point around business outcomes.

As a firm, we put a lot of focus into that.

We don't build data and AI solutions just because you need to report back to the board that you've done something around data and AI.

We want to make sure that whatever you, we help you put in there, either be it from a design perspective or even a roadmap or even implementation, we want to make sure that you're doing it because it's going to give you an outcome, a value add to the business as you do it.

Right.

So that's what gets me excited every morning.

I'm, I'm, I'm so gutted to say this, but I'm sure my kids will kick me for it as well.

But I probably do love data quite a bit.

Do you love digesting data in any particular way?

Like do you love a spreadsheet?

Do you love like a table?

Like what, what, what's, how should we think about that?

Siraj?

I Think.

I think variety keeps me excited.

So bring it all on.

Right.

So these days, I think for retail, you're gonna talk a lot more on streaming data and consuming data wherever you get it from.

Right.

So.

And more and more as we get into this sort of unstructured world of data, meaning, you know, you're no longer looking at it as tables, you're actually looking at as images, as audio files, as interactions that comes through.

And you want to consume it as you get it.

Right.

So you don't want it to be, want to be very prescriptive on how that data comes along.

So variety, that's all, when you put.

It that way, Siraj, I guess I'm a data junkie too.

The amount of content I'm consuming and creating all the time too.

When you put it that way, that's really interesting.

I want to get.

I definitely want to press you on the point too that you said about being outcome focused too.

But we'll do that in a few minutes.

But before I do that, I want to ask you guys, to both of you and Mahesh, we'll go to you first on this one again.

But you know, I've been asking this question a lot, this next question, a lot, particularly coming out of nrf.

And that is I want to know how each of you would sum up the state of AI across the retail industry.

And I, and I purposely said the state of AI as well to see how you guys respond to that.

But would you say that it's overhyped, under hyped, or properly hyped?

I can't.

I gotta put you your feet to the fire on this one too.

So, Mahesh, let's start with you and then Siraj, to have you answer next.

Yeah, I love this question, Chris.

So that's why, you know, conversations like Omnitalk resonates with us very well.

You know, where it's not about shiny technology, but, you know, it's more about what actually works on the floor.

So, yeah, on the AI part, I think in AI in retail, it's kind of overhyped and under implemented is what my feeling is.

It's overhyped at the highest level, where every retailer sounds like AI powered, but if you walk the stores or talk to planners, not much has actually changed day to day, to be honest.

And it's undervalued where it, where it really matters, like, you know, inside forecasting or inventory decisions and shrink reduction pricing, et cetera.

So the hype isn't the problem, actually.

But, you know, the execution gap is a problem, is what my opinion is.

And if AI is still in the dashboard, you know, it's overhyped.

But if it is driving a decision at 6am in the morning before the store opens, then it is kind of underappreciated.

That's what my take is.

That's really interesting too.

And the reason I left it broad too is, I mean, there's, you know, AI comes in very many shapes and sizes.

There's the traditional forecasting type AI, there's the genitive AI, then there's the agentic AI too.

And so, yeah, I think I probably agree with you, Mahesh too, in terms of saying like feels overhyped, particularly because of those last two and how much focus they're getting.

But then when you look at actual implementation, it's.

There's a little bit of a gap there.

But Siraj, would you agree with that or is there any color you'd add to this conversation?

Yeah, you bring consultants on a, onto a call, they're going to say that it's, it's.

There is a gap.

Right.

But actually very aligned to Mahesh's thinking.

Except that I define it slightly differently when you ask me, is it hyped?

I would say it's probably properly hyped.

Right.

So, because retail is always pushing the frontier of what technology delivers back to the business.

Right.

So I find that, you know, you always look for the best experiences coming off retail.

You talked about nrf, right.

Who doesn't get excited coming from an nrf, Right.

So.

And you're always being challenged with what technology can deliver.

And today I think we are starting to see how technology can deliver to some of those asks.

Right.

You no longer need a team of PhDs to try and help you with, well, what is the biggest real time analytics that you can get out of the sales that's happening at the moment?

Or what is a trend that's going up?

Can you try and stock up ahead of the predicted demand that's going to come up?

You don't need a massive data science team to do that.

You can do it with a smaller team.

So that sort of accessibility to analytics, data and AI, that barrier has actually gone really low.

Right.

So where I think there is a gap is that sort of implementation that Mahesh was talking about.

So the hype is there and I think that is properly there because the technology is asking for it, but we feel it as a hype because the services are not capable of catching up to that.

Right.

Or there Is a gap in that?

Maybe the question is like, it's irrelevant if there's a gap in some ways.

Like if, you know, is there, is it really a gap or is it the intention of the, of the retailers in terms of how they're implementing it?

Because the other thing that I heard at NRF is there's just so many pilots going on.

So if you think about that, in terms of the number of pilots going on, you say, well, there's no gap, but yet maybe those pilots aren't as fruitful as we want them to be.

Which leads me to think like, Mahesh, you know, is onto something.

But then at the same time, you also are on to something.

Siraj.

So, so how do you think about it in terms of all the pilots that are ongoing?

Is that the right approach?

Or, you know, are we missing something in our intentions?

I think the two things there.

So you talked about the gap.

Yes, there is a gap.

And I think it's down to everything, right?

So everything from data readiness, we find a lot of that people don't have the right kind of data.

There are things like operating model readiness is, are the people and the human capital of the business, are they ready to kind of deal with that and where actually sits within your business?

That's, that's another gap that we need to figure out.

To your point, on pilots, I think I'm not against pilots in general.

You need to do a pilot just so that you know where you're at and you want to learn things, right?

As an organization, you, you wouldn't go ahead and sign up to something that you have no clue what it's all about.

Especially if you're going to sign a multimillion, multi year, multi million contract, potentially, right?

You want to make sure that you've got all your, you know, you've done all your homework before you jump into it.

But at the same time, the problem with AI in general is that pilot fatigue of some sense, as in a lot of businesses are doing a lot of pilots.

And yes, some of these will come back with value, some of them won't come back with value.

And I think what we really need to think about is, and we do this as a lab from Experian, for example, which is all about getting a platform ready.

Let's get your platform ready to be able to address some of these AI initiatives as it happens, and let's bring those use cases back onto the forefront.

Let's work with the businesses to try and figure out what are the high priorities and by the way, this is not just the retail sector that's actually facing it.

I think it's right across because again, as a head of data for Experian, I get pulled into financial services conversations or insurance conversations, which are having very similar trends as well.

Right.

So bringing a platform front in the forefront and then trying to think about it as well.

I've enabled the capability to try and test out AI and bring all the use cases, do the prioritization, try and learn.

And as you see some of those things that are worth pursuing further, you then have the platform to, then that allows you to scale up.

And if it's an idea that didn't have legs, then you don't want to take it.

You've learned a lesson, you move on to the next one.

Right.

So I think you have to move away from pilots into platform.

And I think that's where I think we're probably going around the circles a bit more.

Right, got it.

So kind of the go slow to go fast approach is what you just.

Basically what I take from what you just said is like, you've got to get your piping, you've got to get your piping done correctly, your frameworks done correctly, your platform correctly.

So one of the other things that I wanted to ask you that you made me think about is I was at FMI recently and I was talking to a grocer out there, a CTO of a major U.S. grocer, top 100 U.S. groceries or top 100 U.S. retailer that is a grocer, so big grocer.

And she was saying to me that, you know, I think she, it sounded like she's taking the approach that you're, you're espousing here, which is that she's talked, she Talked about the LLMs particularly being eventually becoming commodities and therefore they wanted to invest in creating, creating their own LLM platform, so to speak, for their business.

What do you think about that approach?

Is that in right in line with what you were saying or what is the nuance there?

So, yes, it is getting to a state where it's actually commodities, especially because you've got a lot of options available that you can kind of latch onto fairly quickly.

So, Claude Llama, GPT4, whatever those are, you can actually pretty much pick it off the shelf and start to use it and implement it into your pipelines.

And so really I think there are only two ways you can do with this.

I mean, advanced organizations, people who have a data science team and have done a lot of this research, much Ahead of time probably will get into this, the area of, okay, I'm going to build something that's actually much more fine tuned to our business and we can try and mimic a lot of that.

But, but I'd argue the, the opportunity that we really haven't explored is in terms of how you bring the, and I call this intelligence by the way.

Right.

So how you bring that intelligence better to use along the models that you have as, that you call as commodities, for example, that are currently available straight off the shelf for anything that you would really not worry about like, you know, your privacy or your cost efficiency side of things.

Let's just get some of those off the shelf models and let's just work on it and try and be at the forefront because you can solve a lot of these complex problems with new cutting edge models that the big organizations out there are actually putting it out for you.

Right.

But if you are worried about things like oh, but I don't want my data to be out there or I don't want to be doing this because it's going to just cost me quite a lot just to run tokens wise and everything else just to run it using some of those commodities that you talked about, then you probably want to take consider how you do it on an open source that is hosted internally and things like that.

So again, from a solution perspective, you probably want to go a bit hybrid in terms of trying to figure out which is the right one for your business.

So I'm going to say it depends.

So in essence, like what you're telling me is like you could actually, if you want to go slow, to go fast, you could actually potentially end up going too slow if you're not careful in.

That's interesting.

I've never thought about that.

Okay, so that brings up my next question really, which is.

I've heard two schools of thought on this next question too, which is it's around the AI.

It's kind of in the realm of what we're talking about, Siraj, in terms of how do you actually implement AI effectively in the organization.

So there's some people that say, you know, you should just use AI to automate all the repetitive tasks, that you can just get the efficiency from automating the repetition and automating it right out of the business.

Or you can use AI to automate repetitive tasks, but that you should first check to see if the process is even needed to begin with.

So Siraj, first you and then to Mahesh, where do you each come down on on the best way to implement AI.

And where's the nuance in that too?

Because I'm guessing as consultants you're going to tell me.

It depends, but I want to get your opinion.

I think you know the answer to this one.

The first one, I'm clearly in Camp 2 by the way.

Right.

So that's interesting.

I don't know that I am, but keep going.

Yeah, because the first one I think is, you know, let's, let's bring AI or automation in anything that is repetitive.

I totally agree with it.

Which should be your motto.

If everything that you do as repetitive within your business is definitely needed and, and is something that you want to carry on doing, right.

What happens with most organizations out there is actually there is at least 30 to 40% of fat that you can actually cut off.

And you're just doing it because the guy before you did that and the person before that did that, etc.

Etc.

And you're just carrying on doing a lot of this.

So you're actually just gonna do automate.

You're just gonna have an automation chaos in some sense because it's just gonna go a lot of failures straight away, very quickly.

But I think, and I advise this to everyone or anyone who asks me about AI, stop, pause and think because you wanna try and improve the process that you're trying to do, right?

And think about how AI can enable you to do that much better and in a much more creative way than you did before.

If you needed five people to do that before, it doesn need five agents to do that again, you can probably make sure that the outcome is what you're focusing on and you can look at, well, how can AI get me there quicker or much more efficiently?

I think, I think I fundamentally agree with you.

I think the part where I, where I struggle with it is, you know, I think about retail like when I think about retail at the 30,000 foot level, it's like, you know, the job is to get products to, let's just take the stores as example.

The job is to get products to shelves for people to buy them.

So I think you're right.

The outcomes, you're not going to transform the outcomes.

Right?

And that's what I think.

People get lost in the conversation about AI.

They think of it as such a transformative technology.

But at the end of the day, like the jobs you have to get done, the outcomes you have to do are not changing.

So let's figure out how to automate those or take as much repetition out of them.

But to your point about like, yeah, there's probably a lot of stuff that just doesn't need to be done that's been built up through the years through legacy debt is probably also very true.

So it's a little bit of like you got to do both at the same time.

You'd be surprised.

And to be honest, I mean, I think you'd be.

If you stuck to the processes that you had before, chances are that you're going to re engineer or backward engineer your AI to mimic what your team of humans used to do.

And probably they did it much better and will do much better than your AI can do because there won't be a logic to it.

But that's how they have just done it over time.

Right.

And, and you will restrict yourself in that creativity element, I think, which is, which is why I think you have got to really pause, stop and think what do you want to achieve and how do you achieve that?

And think about all the good ways that you can do it and probably make a lot of benefits or, you know, efficiency gains in the process as well as you do it.

Right.

Right.

Wow.

So Mahesh, like, I'm curious, so same question you.

But I'm also, I want to take, I want to extend it a, a little bit further as well.

So like what are you actually, you know, being that you focus on retail and cpg, what are you actually hearing your clients, what are they asking for, like in regards to where they want to implement AI?

So, you know, I was talking to a Canadian retailer last week, you know, where, you know, they have several brands under them.

So I was talking to an AI product leader, you know, that Canadian retailer.

So couple of things you know, he highlighted is around personalization, obviously agentic AI and how to bring in efficiency to their processes.

So those were on a broad level, the three areas that he touched upon.

So their ask is simple.

They are not asking us to bring AI, but they are asking for better decisions, faster execution and less wastage of time, effort and energy and all that.

So on the personalization front, I think the ask has shifted a little bit.

They aren't chasing hyper personalization for the sake of going for it anymore.

They are asking questions like how do I personalize without blowing up my margins?

Or how do I personalize across brands and how do I do it in real time?

Those are the kind of questions they are asking right now.

So I think personalization is moving from the, from the marketing experiments to kind of, you know, an operational personalization, you know, on, on offers on assortments, on content and so on.

Right.

So the, the second big ask is around agentic capabilities.

Right?

Agentic.

Okay, agentic capabilities.

Yeah.

So clients actually don't want AI that just recommends, you know, they wanted AI that acts within guardrails.

So what that means is that, you know, agents that can monitor inventory and trigger replenishment, you know, agents can flag an anomaly before humans can notice it and all that kind of things.

And third thing is, you know, obviously the efficiency, wherein I think, you know, that is the loudest task right now.

You know, bringing in efficiency into what processes they are, you know, handling.

So I know we all know that retailers are under really tight margin pressure and they are asking questions like how do I run a leaner program without degrading the customer experience?

Or where can I bring in automation that can remove friction and not the people.

So those kind of questions are what we are hearing from our customers on the efficiency side.

So that is showing up in areas like forecast accuracy, shrink reduction, labor optimizations and so on.

So that's about efficiency today.

It's not about cost cutting.

It is about the speed of making the decisions or decision velocity.

I would say the one downside to how you answered that question, it was very high level.

They said they're focus on personalization, they're focused on implementing agentic, they're focused on getting efficiency.

And so yeah, and in a way I can understand that.

But at the same time I'm like, those are almost really high level buckets.

So my, my obvious question for me is like, do they actually know where to start?

Or you know, are they kind of just throwing those buckets out there to you?

Because that's the buckets they hear and expect and that's where they think they should be.

Yeah, most of them actually know what they want, but they are not actually sure where to begin.

That's a stage where they are, they feel the urgency, but the starting line is a little fussy because everything looks like a priority.

What we see work is actually starting where the decision is frequent or the pain is miserable.

And also, you know, the data already exists.

So that is where, you know, I would say that, you know, the retailers should start their journey.

Chris, I was just going to add just onto that, remember the lab functionality that we talked about that I mentioned earlier?

We do a lot of this prioritization with businesses because that high list of use cases, some of those organizations who are really into it might come back to you with, well, I've got hundred of use cases that I can actually use AI for.

And now I don't know where to start.

Some people might come back and say, well, I know I need to use Agent tki, but I have no idea where to start.

Right.

So.

And you get these spectrum of people on both sides quite a bit.

And for those with hundreds of use cases, what I'd say is there are ways you can get around it.

As a consultant.

Well, who's a consultant who doesn't have a grid, right.

A quadrant that we can't put all of these use cases into?

We'd go, Mahesh, touched upon it, looked at it, look at it from a business value perspective, look at it from data availability perspective, look at it from a complexity of the solution perspective, do a weightage and that will actually get you to some sort of a prioritization that you can figure out what's the right approach for those who do not yet know where to start.

I think that's where you'd want to sort of lean in on experts or, you know, consultants or whoever to try and get some ideas around what are the values that this can actually bring back to the business.

I was actually surprised, Mahesh, when you talked about your Canadian retailer conversation, all the discussions there were actually business outcomes, right?

So we were talking about shrinkage.

You're talking about, these are all business outcomes.

So then the, the point would be how do you do some of those out of stock, you know, in time orders, and how do you make sure that your supply chain is actually ready to cater to what your customers need?

Then we can help you with that.

Or some of the, your technical teams can help you with that, your IT teams can help you with that.

So there is a conversation that you need to have, which we often see as sort of workshops or things where you bring some of those business challenges and then try to figure out, well, how do you do that with it?

Yeah, an outcome approach when it's aligned to the P and L, I think GAS make a lot of sense.

The other question it brings up to me, Siraj, is like, as I go to all these conferences and when I read the headlines every day too, it seems like everything is focused on the consumer facing side of AI and its implementations versus the internal focus side of it too.

I mean, is, is my impression correct and is there a right or wrong way that you would advise people to go about this first?

Because like, from my perspective, I'd be looking at the internal applications of AI well in advance before I'd be looking at the consumer facing applications of AI to get my organization a culture to what AI is and how it can work or at least I'd be, you know, I'd be like portioning my bets in that direction, so to speak.

Siraj?

Yeah, I think so.

This is one where I blow hot and cold, right Is when I kind of go well consumer faced and there are days when I go well internal definitely because you've got lots of opportunity to improve or efficiency that you can sort of gain with it.

My so my recommendation today, don't quote me this, I might change again tomorrow is actually just go for where you find your biggest, where you think you're going to find your biggest gain.

Right.

So and again some of that might be considered consumer face.

It is likely that it's going to become consumer face because there's a lot of potential that new ways.

Again going back to my point about there were so many different ways that retailers wanted to engage with their customers.

We talked about hyper personalizations, we talked about recommendations, you know, basket analysis, a lot of different things that you can do with data and do to help your customers get better experience.

I think you should look at that and see how you can gain some of those efficiencies.

The point that I'd also make at that juncture is actually if you're going to make some savings, don't take it away and you know, take that straight into your P and L and say oh profits and just put that in your pocket straight away, reinvest that because you're going to find ways of putting that back into the business in some other way.

Right.

So again for internal tasks there are so many things that are repetitive and you're actually just wasting time and energy because you have to sift through paper, do copy, pasting between different files, but bring in automation or look at the processes, re engineer it so that you can actually get through some of those much more quicker.

But now whatever savings you've made from that, reinvest that back into your business and get more out from the consumer.

So it's likely that you'll find a lot of that benefits from your internal processes first.

But, but your biggest brand benefits to the brand or you know, the biggest marketing efforts would probably be the from the consumer face.

So again that's, this is why I blow hot and cold on that topic quite often.

That's a really interesting way to think about it though.

I like that because you're basically saying like you know, look internally to find the efficiency gains and then instead of just like grabbing them and putting them to the bottom line, think about reinvesting them into the consumer facing side of the organization to then continue to, to make the ultimate, you know, retailer or organization that much more better in the long, better off in the long run.

I think it's a good way to kind of, at least from a leadership perspective to be like, to at least monitor like what is, what are the flows and the gains and the puts and takes I'm getting with my AI implementation here because at the end of the day, the other thing for me is I go internally versus externally because I think of like, you know, how much personalization can I actually do?

Right?

You know, personalization has been talked about forever and how much is that going to move the needle versus there's probably a lot of things internally where I can find a lot of profit really quickly and then to your point, I can reinvest it.

So.

All right, Mahesh, well, let's get you out of here on this.

One last question before I let you go.

You know, if, if AI is in a sense becoming table stakes or the use of it is going to become table stakes for every retailer day in and day out operates, how should they think about their digital experience as a key lever for differentiation, for trust, for user retention?

How would you answer that?

So I think as AI becomes stable stakes, experience definitely becomes a differentiator, but not in the way most people think.

Actually, digital experience isn't about the prettier screens or more features anymore.

It's about how intelligent, how predict and how respectful the interactions feels for the end users.

Right.

So in my sense, the retailers that will stand out are not the ones shouting AI powered, but they are the ones who know or who know how to make the experience feel smart.

Smart in the way that it anticipates intent, it removes the friction from the user and it adapts without being creepy.

It shows up in those moments, not in marketing.

So the differentiation shows up in those moments where the customer really experience it, not in the marketing collaterals.

That's what on differentiation.

But when we talk about trust, trust isn't built.

It's not built through one AI moment or one magical moment.

It is built through consistent, explainable, predictable interactions with the users.

Especially when things go wrong, both customers and employees can ask, why did this happen?

If something goes wrong, they can ask, why did this go wrong?

Can I control it?

Will it behave the same way tomorrow?

Also, those are the obvious questions that we hear from both of them, customers as well as employees.

If AI breaks trust once experience is going to suffer for a long time.

You know, it takes a long time for the people to come back and, you know, trust them again.

So that is where it is.

And when it comes to retention, it comes down to how easily you can make some, how easy you can make someone's life, right?

So for customers, it's, it's more of, you know, fewer decisions and fewer surprises when they are shopping for employees.

It's like, you know, less, less work, fewer overrides that they have to do during a transaction or, you know, more confidence in the system that way.

So, you know, I think the best digital experiences reduce the mental stress or I would say the cognitive load of the users and customers.

They don't actually demand attention, they earn it actually the digital experience.

So that is what I think on that perspective.

Chris?

Well, this always happens to me.

I, I don't know why but like at the end of the show I always get some big like epiphany or aha that I hadn't thought about until someone just says something and like, yeah, I mean the crazy thing to me and what you just said is how well your digital experience performs for your end consumer.

Is 100 going to become a differentiator, you know, relatively speaking, because it's gonna make, it's gonna make our understanding of the end consumer that much easier, that much more fluid, that much more, more organic.

And so the companies that aren't investing in their digital experience are going to lose out in the long run.

So like, you know, and that's always the debate, right?

In retail, like, do I invest in the stores, Do I invest in the digital experience?

But yeah, ultimately we saw E Commerce kind of go through this too where like, you know, people were kind of debating whether they should invest in E Commerce and now, you know, they're debating on, you know, how much they want to go into this side of things from their overall digital experience.

And yeah, it's going to become a bigger differentiator than I think probably a lot of us are really realizing.

Siraj, last word here.

What do you think?

Close us out?

I mean, exactly the same.

You will see, I think you'll see this becoming human centered more than ever, right.

I think the best implementations of AI for retailers will be where they don't talk about AI, where it's actually just embedded into the systems, smoothly flows between your design, your application, your experience.

You log into an app or you log in, you walk into a store and you just find the best experiences that Mahesh just talked about, right?

And everything from A customer perspective, they're happy, they're.

They're very confident, they trust what, what's been taken off you.

I mean, you and I are actually giving away a lot of our data back to these organizations in terms of our buying patterns, our scrolling patterns and things like that.

They're listening to us in various forms that we probably have no track about.

And if that data has actually been put to good use, that at the end of the day I am able to get what I want and more than what I want and probably what I could want in the future, then I think that's a win.

Win.

Right.

So the best retailers would be that have implemented AI would be the ones that will never talk about it, but actually have got that really totally embedded.

Yeah, Mahesh, go ahead.

Just wanted to add one simple thing.

AI isn't a project.

It's not a project.

It's like a muscle.

You build it over time.

Yeah.

And that's the big leadership question too, is how do all these leaders that have basically no muscle mass and have been trying to create muscle mass for the last two or three years since it came on, how do they now lead their teams through that?

Which is why we bring folks like both of yourselves onto this program to help us and help them through that exploration.

So.

So thank you both.

That was, that was really great.

Mahesh, if people want to get in touch with you, reach out to Experian Technologies in any way, shape or form.

Let, let our audience know what's the best way for them to do that.

Oh, they can email me or they can call me.

My email is Mahesh Vijay.

That is M A H E S H. V I J A Y@experion global.com and my phone number is 636-628-9591.

So always, always available on both.

All right, the phone number drop, we haven't had that in a while.

Wow, that's great.

That's.

Love that.

I love that because it's so organic.

So.

All right, well, thank you both again.

This was a really great conversation.

Made me, made me think a lot.

And I always love it when I can get the big aha at the end too.

So thank you for doing that because that always means a lot to me too.

So today's podcast was of course produced by the great AS Siriwort on behalf of all of us here at Omnitalk Retail.

As always, be careful out there.