Most Retailers Are Not AI-Ready!

Everyone’s talking about AI, but few retail leaders are asking the deeper question:
Is your company truly AI-ready—or just overwhelmed with data?

In this episode, I sit down with, Abhijit Patharkar, the co-founder of Nexlytix to unpack the unspoken barriers to effective AI in retail: siloed systems, misaligned storytelling, broken decision-making, and the myth that more dashboards mean more clarity.

We cover:

• What AI readiness really looks like
• Why storytelling must change based on who's reading the data
• The danger of building AI on broken foundations
• How leaders can shift from chasing tech to shaping strategy

If you're leading in retail and trying to make sense of AI’s role in your future—you need this conversation.

Listen now and rethink how your business makes decisions.

Transcript

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SPEAKER_01 0:50

In the retail space today, you cannot go to a conference, listen to a podcast, or even read something on LinkedIn without the mention of AI. But here's the question. Here's the question. Is your company ready for AI? Or are you AI ready? We're going to answer that question today. Today is a very special podcast episode edition dedicated to our sponsors, our sponsors who have focused in and made the investment on our study and our podcasts and every aspect of Worthy Retail Global. So we are going to be talking to Next Lytics today. Their focus is on helping you answer that question. Are you AI ready? So, hey, get ready for this episode of Retail Leadership with Steve Worthy.

SPEAKER_03 1:39

Welcome to Retail Leadership with Steve Worthy, where we go well beyond the corporate playbook to unpack major opportunities, hidden challenges, and critical issues that senior retail leaders face every day. So let's get started right now.

SPEAKER_01 1:59

Well, I I gotta tell you, I'm excited to have this conversation with you today. Um, could you could for those who don't know you, for those who don't know you, could you introduce yourself and uh and your company and then we're gonna get into the conversation um about AI. I said it very quietly because I wanted to I wanted to be some mystery around it.

unknown 2:20

AI.

SPEAKER_01 2:21

Go ahead and introduce yourself.

SPEAKER_00 2:24

It indeed is, right? And thanks, thanks for having me uh on your podcast, um Steve. So uh just a quick introduction about myself, right? I'm a Bijit. Uh I am uh one of the co-founders at Nextletics. Typically, it's an AI firm or an AI native firm for retail and CPG companies. And I'm a Columbia Alum as well, and uh looking forward to uh build a great presence around AI, as you mentioned, right? In the retail and CPG space uh through this company called Next Linux.

SPEAKER_01 2:59

Excellent, excellent. So before we get into Next Linux, let's talk a little bit about you, right? So Columbia Grab, but before before Next Linux, what what what were you doing and what and what space were you in?

SPEAKER_00 3:09

Right. So uh I mean entrepreneurship is not new to me. So this is not my first stint as a founder, right? Or uh or as a co-founder. This has been my second stint. So uh my first stint was back in 2015, uh from 2015 till 2021. So it was around six years, and uh that's when uh we did that exit in 2021 and I joined VMware uh in their office of the CTO, uh, which was more of a I would say research, uh research-oriented job role that I had. Uh I did that for a couple of years, uh, and then I decided probably it's time to jump back into entrepreneurship. And this time actually I thought maybe a bit of a formal training is needed. Uh so that's uh that's how Columbia happened.

SPEAKER_01 4:02

Yeah.

SPEAKER_00 4:02

And uh after Columbia, then here we are.

SPEAKER_01 4:06

This okay, this is this is so interesting because right, um, I am actually doing like, you know, this vlog thing where I'm I'm sort of I'm sort of documenting, you know, the day in the life of being an entrepreneur myself. And I honestly I just I literally just had this conversation early this morning. I get up super early and and I I tend to get emails, you know, with job offers to to kind of go back into the retail space. And I like and I and I and I look at them like, ooh, hmm. And and then I then I I then I literally thought to myself this morning, I said, you know what? I said, I would, I every day if I if I was to take that opportunity or something like that, I would probab I would be miserable every single day that I would have to like go to work, right? But the fact of what I we do as entrepreneurs, my gosh, I don't think anybody really gets it or understands it, right? So what made you want to go into this, into this space? Like what was something something that the impetus, you know, behind like, okay, you know what, I could probably take my knowledge and do a nine to five, but this is what I want to do.

SPEAKER_00 5:22

Uh well, I mean, uh let me first uh start about the fact uh that you get those job offers and no one better than me can relate to that because I too get those, right? The moment you s the moment you post something on LinkedIn, right? Uh and the algorithm works in a way wherein you start getting those uh notifications. Yeah. And uh it's like every day you get it at around six o'clock, say, for example, and you start thinking, okay, what am I doing here? Um I have this job of a year, giving like say 200, 300, 400, whatever. And uh what am I doing here, right? Uh uh is it worth it? Is it not? And uh I believe uh, and this is the same conversation I have with my co-founder as well, right? And uh he he just says just one sentence. Uh an entrepreneur is someone who does a root canal by will.

SPEAKER_01 6:19

Oh my gosh. That's that's so powerful. Why why did my face, why did like this part of my face start like I started to feel the pain when you said that? Oh my god. That's so good. That is so good.

SPEAKER_00 6:37

That's a powerful, like a heart-hitting statement, right? And you if you hear something like that, it kind of gives you the motivation to even go and work more harder because then I have to beat that pain. Oh my gosh.

SPEAKER_01 6:49

Yeah, that's your you know it's uh that's 100% correct, right? We've all we we volunteer for this, but so when you when you did your first foray into um into into entrepreneurship, what was what were some of the driving thought processes and what were some of the skills that you you that you that you had that you felt sure about? But what were some of the things that you know, you know what? I I I really don't know how to do X, Y, and Z, but guess what? I'm still gonna take this leap.

SPEAKER_00 7:17

Right. Uh that's a good question, actually. It's coming to that, right? Uh so I come from a family uh of entrepreneurs. So my dad was an entrepreneur. Uh a lot of my wife's family was also uh entrepreneurs, so it kind of gives you that uh extra push that is needed. Sometimes a lot of people don't get. Um, and it is needed, I'll be very honest with you. It is needed after all, right? Because as an entrepreneur, you have to do a lot of sacrifices, at least in the initial few years of your company until and unless your company gets that uh or your startup gets that plank or the runway to uh to take off. Um times are tough, right? And you have to make those adjustments. And it's not just you who the who makes those adjustments, right? It's your family, it's your partner, uh, even your kids for that matter, right? Uh make those adjustments. So so getting that uh I would say uh mental push from the family, uh, and the family knowing that nothing all will be not hanky dory. Uh there will be initial pressures, there will be initial hurdles. I was just talking with my dad this morning and I heard this and I got the same like I shouldn't use the word lecture, but same talk from him. Saying that, yeah, right. I mean, uh, what do we expect? You have chosen this path, so you have to live with it.

SPEAKER_01 8:50

Yeah. Yeah. Your dad sounds like my mom. Like that's something my mom would say. You're right. That's exactly that sound like something exactly that she would say. If I called her and start talking about what I'm going through, she's like, dude, you you you you you made this bid, so you need to lie in it, right? You'd like, that's your you chose this profession, so what are you gonna do? Right? That's interesting.

SPEAKER_00 9:11

Exactly. If you had to think about it, probably would have thought before doing this. Now you can't.

SPEAKER_01 9:15

Yeah, yeah, yeah. Oh my gosh. Well, let's let's let's move let's move and talk to uh talk talk about Next Lytics, right? Um I I'm I'm interested to to hear the and not just myself, but I think everybody who's gonna be listening to this and watching this, uh um the the origin story, right, of of of of Next Lytics. And we're gonna dive deep into you know um for the retail space and um you know data stacks and all that stuff. But give us a little bit of the origin story.

SPEAKER_00 9:45

Right. Uh so uh again, right, uh when it comes to the origin story, probably I'll have to go back to 2008, 2009 when I met my uh understand. Um and uh we actually clicked uh to be honest, right? Uh at that time I was just uh a fresher, just passed out of my undergrad. Uh my co-founder was in a phase of starting his own startup at that point of time. And um uh I joined him um as a tech lead in his startup, and I was there until around 2014, 2015, and that's when I started my own startup. I I moved out of that company of that startup and started my own. And uh just uh last year when I was at Columbia and I was doing my grad course, uh I kind of uh uh met my co-founder here in New York, and uh I kind of pitched him an idea. I I wouldn't say I pitched him an idea, it wasn't an idea. Uh I uh so we kind of uh uh got to talking and I said that I'd probably trying to do something in the data analytics space because both of our startups, right? My co-founder startup and my startup earlier was into the product development space. Uh wasn't pertaining specifically pertaining to data or or AI for that matter. And uh this time around, I was pretty sure that I did not want to get into product development. Uh and uh retail was a unanimous choice because as I said, my family comes from uh entrepreneurship in the retail space. My co-founder uh still has a startup which he runs uh product development for retail and strategy for that matter. So so retail came in pretty natural to us, and um uh so that's how we started discussing this uh probably a year, year and a half back, and uh we decided and and uh and we decided probably this time we can do it as co-founders, which wasn't the case last time. Yeah, and that's how that's how it started with data and retail, and then then uh then the more difficult part of uh trying to find the right message, trying to find the right I would say still still we are not there, and I believe no company is there because it keeps on evolving, message keeps on evolving, uh as in when you talk with more people, as in when you do more work, as in when the industry is evolving, right? Every day this uh I believe at this point of time uh you don't know what is coming in next week, and that's how fast-paced uh the industry has been when it comes to AI for that matter. Uh so to keep on evolving the messaging or the ideation or the pitch that you're giving to your customers on a I wouldn't say a daily, but at least a weekly basis.

SPEAKER_01 14:14

So what's the so at so at its core, at its core, what's the what's the problem that you are solving? Right? You and I have had we've we've had several conversations before, so I I I get it, and and of course, being a long-term person in retail, um uh uh understand it, but I I I want you to explain to everybody, like at its core, what's the problem that that Nextalytics is solving?

SPEAKER_04 14:38

You're listening to retail leadership with Steve Worthy.

SPEAKER_00 14:44

Right. Uh I would say uh at its core, at the more fundamental level, there are two problems that we're trying to solve. The first problem is what we were discussing about just now, right, is storytelling. Um, what does that mean, right? Um so what we have noticed is a lot of companies, retail companies in general, this uh this problem is not specific to retail, but a lot of retail companies, right? There is a bit of a disconnect between uh what the business wants when it comes to Theta and what their data teams or what their uh IT teams are giving to them. In general, like being from being from IT, right? Uh spending my whole life in IT. Uh, I kind of get a sense that there is always a race to deliver more and more. Uh but when it comes to data, it's quite the opposite, actually. Um you don't want an executive uh to have a 200-page report given to them uh and saying that okay, just read this through. That's not how it works. Uh and coming back to the aspect of what is the story behind the data. The the exec whoever is looking at the data, right? The numbers need to know the story behind the numbers. Need to know if your KPI has a certain value, why that KPI has that certain value. Yeah. What uh what story drove that uh KPI up or what story drove that push that KPI down for that matter, right? Um so the story behind that number is kind of more important than the number itself. I'm not discounting the fact that the numbers are not important, they are important. We are a data analytics company after all, right? Uh so the numbers are important, but the numbers can convey uh to a very limited extent. Yeah. Uh beyond that, you need to know the story of why um that number is what it is. Yeah. So that's an under uh that's a fundamental, that's one of the fundamental problems that we try to solve. And getting the story right from that number is a job done by AI. Uh you have AI models, you have built, or we have built those AI models, which essentially are curated for retail and CPG, or I would say consumer markets, which uh generates a uh a curated and a crafted story that say for uh say, for example, a category manager or a store manager or or a CXO for that matter um intra tries to understand. Because the story uh for a given KPI is looked at different perspectives from a different role. So, for example, a KPI is looked by a category manager or a store manager versus a CXO, everyone needs a different perspective, and that's where story coming, storytelling comes into picture because the story that you're presenting to a CXO will be very different than a story that you're presenting uh to a category manager or a store manager who's working on ground. Uh their stories are much more tactical versus the CXO stories will be much more strategic, and and that's what AI model uh AI modeling is uh now redefining the whole data analytics space. It's these minute distinctions uh in who the report is being viewed, in what context it is being viewed, um, is making it much and more possible and easy uh for these numbers to tell the story.

SPEAKER_01 18:26

That's okay.

SPEAKER_00 18:27

So that's that's the first fundamental problem. And then uh and then the second fundament uh fundamental problem is yes, you you have the story, right? But everything, the story is driven by the data behind it. And especially in retail, right? Retail is an industry which generates tons and tons of petabytes and petabytes of data, probably on a weekly, uh weekly basis. And it's it's a good thing and a bad thing. It's a good thing in a way that you have that data now to back your AI models against or to back your storytelling against. But the bad thing is that you are now drowning into it. Yeah, uh you have now more and more uh data redundancy problems, you have more and more data silo problems, you have dataization issues because now um every department in the company is holding a piece of information uh which essentially is not integrated with each other. And now in this heavily siloed data world, uh if you're trying to build a story on top of it, uh you're getting a dysfunctional or a distunct story, uh, which essentially is not uh anyone is not what anyone wants. And that's the other fundamental problem where what we call the Next Let's Blueprint is, where now we have those connectors in your various data sources which tries to connect it to your source and uh then take it further to build your dashboards or build your reports or even take it further further and build your story.

SPEAKER_01 20:07

Yeah. There's so many, there's so many aspects of the value chain that you that you that you've mentioned that a lot of people are I think they're taking they're taking for granted. Um I it's interesting, you know, years and years ago, I did a I did a short stint outside of retail as a business developer, right? And um part of my job was to go into companies. It was a project management company, and we were my job was to go into different business disciplines and talk about our ability to provide project management um uh capabilities. Uh whether it was this is I'm this is so old, I'm dating myself. These SharePoints, SharePoint sites, um, right? Like these are those are old school, that's an old school term. And um, but one of the things I I learned very, very quickly was very similar to what you're talking about, was around storytelling, was that the CEO needs to be told a different story around their their problem. The mid-level manager needs to be told a different story. The the um the frontline individual who's actually going to be executing on upon a lot of these different things are being are being told, uh need to be told a different story. And it's interesting, you know, but when this when this comes out, our our study is going to be released. And what we found is when it comes to AI, we'll talk a little bit more in depth about AI, is that there's this, there's this tendency um within retail. We have what we call the you know, crawl and walk, you know, and run, right models, right? We all uh everybody understands that that sort of analogy, where the people who are actually sort of um executing on these things or need to execute on them, they're still in the walk and sort of crawl phase, right? The front line are individuals, but the companies are in this massive, massive run phase. And I think they're running, but I I don't they they're running, but they're they're running without with the ability to tell the correct story. And I often feel, to your point around data, I often feel sometimes companies will hide behind the data sort of as a justification to go in a in a certain direction. Now, I I and I I'm not gonna let you talk bad about the industry. That's my job. So you're gonna I'll need to say, yeah, so what Steve said, what Steve said, it seems like I, you know, seems like everybody's like hiding behind the data. But what you're talking about is like, hey, you we I need you as a company to come from behind the data and actually be able to take this data to and tell a story appropriately, more so, not just for, you know, the the people on the outside looking in, but from an internal standpoint, I think that's the other issue that that I'm you know, sort of problem that you're solving is that it's not so much what the external people are saying from the outside looking in. It's really from an internal standpoint, the story that you need to tell and then allow the data to to to to direct you. Does that make does that make sense?

SPEAKER_00 24:26

That's absolutely true, actually, right? And and one thing I always feel, and this typically happens in retail, is when uh leadership thinks about integrating any sort of AI into retail, they always think from an external perspective that um, okay, how my end consumer is gonna interact with AI, or is it only for the end consumer? It is, right? I mean, after all, that's eventually you're trying to make their life much more easier and simpler uh to make that particular purchase. Uh so it is that. But often what uh a lot of people re uh miss out is the fact that there is a lot of internal processes, internal data that also can be uh, I would say, um streamlined uh with with your AI models. And this is not only pertaining to data, but also pertaining to the processes that happen uh in retail, they're quite complex, actually, right? Uh uh that can also be solved through a lot of these uh AI automations in place. Yeah.

SPEAKER_01 25:31

So before before before the actual input though, in of AI, right, is it I almost feel like companies miss a step. And and they they miss a step in the categorization of the of the ads.

SPEAKER_00 25:44

actual data um and then they just they they just sort of push it into the AI and then whatever the output is the output um are you are you helping solve that problem right because I believe that's one of the the fundamental problems around even before we even get to AI it's the categorization of what you currently have in a in a very um uh digestible uh um highly highly uh functional you know manner of data uh that's that's true exactly right and i'll I give you an example of one of the customers that we are talking to and uh they're trying to integrate um uh a customer bot an AI-driven customer bot and what they were facing an issue is that when they're trying when they were trying to get data points for a particular customer they had to go through 17 different data sources to get that and then feed into the AI uh bot or the model that uh they are working on imagine imagine this scenario right this is one company 17 uh you might have another company dealing with 25 different uh data sources and these will increase it's not like and and this is a problem that is not uh I I don't think so this is a problem that is solvable uh these will keep increasing because you will have more and more uh tools coming out for retail you will have more and more systems that will be generated for retail retail is heavily driven on a lot of legacy systems that are going there that that are going on for years uh we are not seeing an end for those systems anytime soon so the data sources will be increasing the data silos will be increasing the redundancy uh will be increasing and that's why the need for having a common source of truth or I would say uh a common cataloged source of truth which essentially will now act as your uh go-to data source uh for all your AI models to work on uh and that's where uh when I talked about the connectors earlier that Nextletic or the blueprint that Nextletics has is essentially the fundamental layer of that blueprint is to connect to these data sources and come up with a common catalog source of data and now take that common catalog source of data to the next level whether it is reporting whether it is insights whether it is storytelling or or even for your for that matter uh whether it is for your AI modeling. That's so good. That's so good. I was just looking over my notes and you may have you may have answered this one um but you and I we had a conversation a while back and and you you mentioned the idea around AI readiness right for a company right and um right that could be a slippery slope right that could be that can mean a lot of differ a lot of different things but when you can you define it um from a from an XLIC standpoint right so uh so AI readiness right and this is something that I was talking about right uh uh from a leadership level at least in retail uh and CPG um all all the execs and the leaders are trying to look at integrating AI into the systems whether it is external or internal that we spoke about um the problem is um is that company even ready to be integrating these AI models and when I say is that company even ready it involves a lot of factors right one factor is do you have a single source of truth because now you have bought an AI bot uh and then you're feeding in 16 different data sources yes AI is capable to handle those data sources but uh is it optimal in doing that no uh because of the fact that you don't have it catalogued in a proper way so that your AI model understands it uh so your math models understand it so that's one the second factor is also which is quite important is uh having proper access controls um and uh just uh I'll I'll give you one example right uh say a Microsoft co-pilot and you uh as a company you you you got a license for co-pilot and now you're trying to include multiple data sources to it so that uh your exec or even your staff for that matter can go ahead and access data at the fingertips. Uh now this is where access controls or proper set of access controls in place becomes more and more critical. Data at your fingertips is again a double edged sword. Yeah at one yeah on one side you have literally data that you can query based on an English sentence that you can ask right uh and on the other side you don't want that data getting exposed to uh to a certain set of people that you don't intend to get so uh so that's one the third one is um do you have proper profiling in place do you have proper uh data cleanup strategies in place uh just having a single source of data does not solve your problem uh what if you have redundancy in the data what if there are a lot of anomalies in data which AI itself can solve to uh to be honest it's not something of a rocket science uh nowadays to kind of find anomalies in the in the data set and try to filter it out or try to try to clean them uh AI models are built in a way or there are a lot of AI models which are built to kind of figure figure out those AI anomalies and then uh then clean up the data so have you do you have these set practices in place uh to make sure that you are actually ai ready even before you start purchasing these uh AI driven tools ai models to start integrating into into your systems whether it is external or internal yeah and that's where actually Next Lytics has come up with uh with an AI readiness calculator which essentially asks you sort of 10 to 15 different questions and then based on that it comes up to comes out with a score that okay you are 90% AI ready or 80% or or even your 50% AI ready for that matter.

SPEAKER_01 32:25

Excellent excellent and those who are listening or even watching this so um there there there's going to be a link for you to go and um you know try out that calculator so some so make sure you you do this right after you finish uh listening to this episode. You know I love what you said around the right person or excuse me the wrong person looking at the at the data. Right?

SPEAKER_00 32:48

I and it you know I it's it was it was it was it was you know you you you put it out there but I was like that's so good that I think we we we tend to even in in organizations we tend to have too many people looking at people too many people who don't need to look at uh uh uh the the the data looking at the data having an influence on the data and and br and and and interpreting the data in a completely wrong way based off of their lens right um how do you how can you help an organization make sure that's they sort of safeguard that process because it I I've I've I've seen it happen you know so many times you just you have the wrong people at the wrong table and they have a different perspective and you know the company wants to go you know this direction and then these are someone else comes in and they have a a different perspective but how how do how do you how how are you helping companies sort of you know uh solve for that you're listening to retail leadership with Steve Worthy in my opinion right uh data democratization needs to happen uh I feel it's a good thing um I I have noticed a lot of companies when when they talk about silos right it's it's uh it's a multifaceted problem one is the technical side of it which essentially I would say uh for the lack of better word can't be solved because those silos will be there uh um and it will go on increasing as we uh move ahead um and the other problem of or the other facet of uh data silos is uh the fact that you have those organizational uh data silos that exist where uh a lot of uh depart I wouldn't say departments but a lot of teams try to silo the data and not expose it externally for multiple reasons I wouldn't say for political reasons but for multiple reasons and that's where I believe uh when you have the right set of policies and when I say policies I mean control policies in place and the right set of integrations in place uh or the right set of APIs in place so that access to data is easier but also controlled. Yeah uh so that uh given your uh token for that matter or given your access you only get a set of um uh piece of data that you should be able to see that's good that's one uh the second one is uh I would say from what perspective you're looking at the data and that I believe and this when it comes to storytelling right uh yes I mean Nextlytics is built on top of it right I mean uh it's one of the fundamental uh problems that we're trying to solve um when it comes to storytelling uh and in the context of who's looking at the dashboard storytelling becomes more important because now uh that that piece of a sentence that is shown to that person who's looking at the dashboard uh will drive that person to think in that direction. Yeah yeah uh so that essentially uh I believe is that additional nudge that you're giving to to that person to think into that direction that's good uh which uh essentially will avoid the problem that you're trying to uh convey that's so good that's uh that's that is that is so good um uh two two more questions I'm gonna get you out of here um if you had to describe your your your ICP next lyrics ICP um you know uh talk a little bit about that for for those that who are who are who are listening um and may want to get in contact with you who who's who's your your um your ICP right so uh our our ICP is uh again right starting with FreTail and CPG so that's where our core expertise lie and that's where uh our um top level industry uh focus has been for the past year um now beyond that if I want to uh talk about uh what are the kind of companies that we want to work with uh so we're trying to work with the mid-segment at this point of time um because we feel that in the whole AI adoption strategy um that particular segment is kind of a bit behind as opposed to uh the the large scale segment where they're fairly I would say far into the game as opposed to the mid-segment um and we certainly feel that they should not lose the race and that's where our focus should be.

SPEAKER_01 37:54

No I I I I like that I was um um chatting with a company out of out of out of Germany who's um you know doing some some some things uh about moving into the into the into the retail space and part of their their grandiose idea was to you know like work with the the targets in the Walmart to the world I said listen to me um I s I said that's not a that's that's not a good strategy um it's just it's just it's not it's it's a great one like down the line but there are there is a there's a level a category of of retail of within the retail organization or within the retail industry that's ideal for what you know what they're offering and also for you've already identified it for yourself right they they want to play big right this segment that you're talking about they want to play big but they may not have the capacity right or even some of the the you know the the the bandwidth to handle it but you know you can actually provide a solution for them that actually allows them to to play big within within such a um a a massive a massive industry that you have that we have here within the retail space.

SPEAKER_00 39:01

Let's talk roadmap a little bit no sorry go ahead no go ahead yeah so yeah just just to add a bit right and that's absolutely right and uh when it comes to uh solving it right you don't have to really boil the ocean all at once no it it it's not that way uh it always starts with the smaller things that you put into piece uh put into uh fit into the puzzle and then you go on and build on top of it so it's not like we come in and we say that okay revamp the whole thing. That's not how it works. Uh we take step by step you take a data source at a time build out an insight out of it or build out the story out of it and then we take it from there. Trying to boil the whole ocean all at once is probably a wrong path and most of the times 99 out of 100 times you'll probably fail.

SPEAKER_01 39:52

You know it's interesting I I wonder how many and you you know you'll probably just you know just think about this this from a from an anecdotal standpoint. I wonder how many companies that you you've you've worked with or you're going to be working with who will go into this with one story that they've already sort of created around who they are where they are where they want to go and all these grandiose ideas we just we just literally just spoke a little bit about this from an entrepreneurial standpoint, right? Where we think we want to go this way. And even these the larger size companies 500 million to 10 billion dollars right they go into this idea around who they think they are but then when we when you guys get in there and you start talking about the data and the data tells a completely different story. Like it's just it I I always feel like I always be a fly on the wall when they come to that realization of like, you know what, not so much that we've been doing things wrong, right? But we just may have to pause a little bit and maybe go in it maybe go in a different direction or change up the narrative you know that we need to tell ourselves because we've been telling ourselves we we are we're we're we're this but now the data is telling us that we're something just so just slightly different. You know what what what are your thoughts on that?

SPEAKER_00 41:08

And and I believe uh it comes uh full circle to the storytelling aspect that you were talking about right because those stories able to detect those undercurrents or the the undercurrent points in your data which you're not able to see uh or I would say which you're not ready to see for that matter. So um and it needs to be told right uh for us as well right if you talk to me uh if we spoke uh at NRF a few months back uh probably uh the blueprint idea was not there but now we have that blueprint idea because we have seen as in when we are speaking with this uh uh with our customers and with our prospect customers we have kind of felt that um probably starting small makes much more sense because a lot of these companies are still skeptical at integrating this so that's an undercurrent we saw in our data actually uh which uh which forced us to kind of pivot um and I'm pretty sure that happens with retail as well because there are a lot of undercurrents which uh are not seen in those dashboards.

SPEAKER_01 42:17

Yeah oh my gosh and you just said something else too you've been you've been you've been dropping some amazing nuggets here but um around around individuals or around companies not being ready right not being ready to to to to to to handle the story that is that is actually being told that's that's so good.

SPEAKER_00 42:38

Let's talk roadmap um what's on what what without giving away too much right and and um you know uh you know having to have to send me over an NDA like um you know before this goes out uh right you know um give us a little bit of of you know the next you know six it's so funny I was talking to someone um uh a while back it wasn't it wasn't my coach but was another guy I sort of considered a um sort of a mentor and we're talking about you remember so you remember when long term was five years right you know like you know you would you would be strategizing you're like for long your long term is like five years you know short term's like you know two years two and three years like no not so not so much nowadays so um talk a little bit about your roadmap and um what's what what what's other what can we expect I would say for got it I I would say from a roadmap perspective right again I can break it down into two fundamental aspects one is uh from a business point of view uh I would say we we would uh at least uh 2025 right nowadays long term is five months I'll be very honest with you uh not not just five years um so uh I would say for 2025 we would still like to stick with uh the mid-market segment uh from a business point of view because that's where we feel the need is there and uh that's where we feel we fit in perfectly well when it comes to uh integrating our blueprints into their system that's one from a from a technical point of view uh one thing we did not talk about much which also is one of the important aspects of data is uh prescriptive AI or or recommendations that uh for that matter um so you have the story you have the facts you have the story behind those facts now what should you do with it uh that nudge that you get from a story uh is that enough uh or you need a bigger nudge to uh take that decision uh so we are there and not there yet uh so our dashboards have started integrating a lot of prescriptive AI into uh into the blueprints but uh I would say um probably a couple of months down the line as well we would get uh more and more solid into having uh much more better prescriptions um that aid uh the executive looking at the dashboard to make that decision yeah that's so good when you think about that it's going to require a different leader though like a different leaders leader leadership mindset right because here you have somebody now who is who is not just going off the data they are they they are thinking strategically and they're they're they're sort of looking at a crystal ball based on the data and what the story's telling them and now they actually have to make a decision based off of that and so they really make a sort of a prediction around where they're around where they and where they go um a lot of leaders aren't aren't aren't aren't really equipped you know to to to do that because they they take the data and they just sort of go off of that and then sometimes they may blame the data if uh if if one direction doesn't go um the the way that they had anticipated if you if you had to provide this is this is I I I said I was going to be this was going to be a last question about a while ago but I I can't help it. If you had to give leaders like one one thing that they need to to think about when it comes to not just the prescriptive AI that you're talking about but like their leadership capabilities now and maybe into the next five five ten twelve months what will one what would that be so uh to your other point right about leaders not being ready to uh take this info or take these prescriptions see uh when it the ultimate decision has to be taken by a human there's no denying that uh if you're relying purely on an AI model and then blaming the AI model saying that okay the model told me to take this decision that won't work um so the ultimate decision has to be taken uh by a human what prescriptive AI will do is it will give you a complete holistic view so that you don't miss out any data points while you're making that decision. That's so good. That's that's one. And the second thing is right when it comes to prescriptive AI right it is not only from an executive or a leadership perspective that you're looking at. Think of a dashboard where say a store operations manager who's actually a frontline manager is looking at is looking at a dashboard and you're giving them some bit of prescriptions as to what they can do to make their store operations better based on the data and the story. That also is quite important in my opinion for the whole operations uh for the whole retail operations.

SPEAKER_01 48:03

So so it's it's multifaceted right you have those high level execs uh the prescriptions provided to them again contextual and then you have those uh tactical store operations or the frontline uh executives uh and the prescriptions given out to them quite different but uh uh strong uh in my opinion again underlying the fact that the eventual decision has to be taken by a human no matter what yeah the decision has to be taken by a human please listen to that make sure you listen to that don't blame the AI um don't blame the AI don't do it don't do it um I I want to thank you so much for for for being on the podcast and um uh thank you for your for your partnership with um with everything that we do and with worthy retail and uh our whole ecosystem and um the the being a sponsor for the report um our masterclasses and uh and the courses podcast and um looking forward to you know our our partnership not just now but but but but definitely in the future um and and excited for everything that you're doing I I I gotta tell you you you this this this episode could have probably gone a lot longer because there were so many different things that you were nuggets that you were dropping in here that um I'm gonna have a hard time uh getting some clips you know meaning that I I'm gonna probably this whole thing is a clip this whole podcast is a clip you did an amazing you did an amazing job and uh anyway I just want to I just want to thank you so much for being a part of this podcast uh thanks thanks for having me and it's great right that's what the link is for that you'll drop in the chat absolutely absolutely all right excellent all right we'll talk a little bit