This episode, Jay sits down with Prashant Agrawal, Founder & CEO at Impact Analytics, about the work he’s doing to transform retail forecasting with AI.
In this episode, Jay sits down with Prashant Agrawal, Founder & CEO at Impact Analytics, about the work he’s doing to transform retail forecasting with AI. Prashant shares about his career journey, including his time at McKinsey, his entrepreneurial ventures, and the founding of Impact Analytics. They discuss the role of AI and machine learning in retail, particularly in improving forecasting, pricing, and product allocation, the quick ROI retailers can expect from implementing AI-driven solutions, and insights into the evolving role of AI in business.
Prashant:
“We are always forecasting. As people, we're always trying to see what's going to happen tomorrow. And we use yesterday, right? That is the basis generally of what you do and what's the data out there. Now in retail before COVID, we were basically just using some kind of time series. Last year was X and we're going to go up a little or down a little. Now the challenge with that is that there is so many SKUs, so many stores potentially, you kind of just peanut butter spread it, right? What AI ML forecasting can do is give you a better first draft. So instead of actually having to manipulate this in Excel in any way, you can actually get a good first draft that is at a SKU level and then hone your intuition, your experience around that.”
Links & Resources:
Prashant Agrawal, Founder & CEO at Impact Analytics
Prashant Agrawal is the Founder & CEO at Impact Analytics. Impact Analytics has been recognized by Fortune as one of America’s Most Innovative Companies and included in the Inc. 5000’s fastest-growing companies list for seven consecutive years.
He has driven transformation in the retail, grocery, and CPG industries, with top brands like Coach, Dollar General, and Calvin Klein using Impact Analytics for data-driven supply chain and forecasting solutions.
Prashant was a finalist for the Ernst & Young Entrepreneur of the Year award in both 2023 and 2024. As a Columbia University alum, he teaches a course on AI and advanced analytics in retail, inspiring future leaders.
[00:00:00] Jay Topper: The world of commerce is undergoing a revolution. Today's consumer expects a buying experience that is nothing short of perfection. Your company's digital IQ has quickly become a new standard that drives growth and loyalty. Welcome to Chiefly Digital, the digital leader's guide to modern commerce. Hi, my name is Jay Topper.
Today I have the pleasure of speaking with Jenna Flatman Posner, a former retail executive, and in my eyes, an AI guru. Jenna, welcome. Thanks for joining.
[00:00:39] Jenna Flateman Posner: Thanks for having me. I'm really excited to be here today, Jay.
[00:00:42] Jay Topper: I'm excited to have you here and I'm excited to continue our conversations long after this is done. So first I want to, one thing about your career that fascinated me when I looked across it and we discussed it. You're a two time CDO. I'm a two time CDO. There aren't even many one time CDOs out there. That's something that's just starting to catch fire. So, Tell me what you think, you know, who were you CDO for? And just tell me what you think of the role and the general, you know, broad scope of what you were responsible for when you were filling that role.
[00:01:14] Jenna Flateman Posner: Yeah, absolutely. So, you know, the majority of my career, the first half of my career was really on the retail tech side. Um, and when I had an opportunity to come over to the retail side, I saw it as a really interesting opportunity to.
See how the other half lives, right? I think as, as retail tech executives, we make a lot of assumptions on how tech is prioritized and how it's bought and how it's budgeted for and, you know, what it really means to create channel stakeholdership and, and alignment with your executives and, and get all that buy in internally.
And, you know, I thought, well, you know what, instead of making all of these assumptions, why don't I go over and see what it's all about and see how it actually works. Right. So I took a big risk, uh, took on a VP of digital role with Snipes, which is a global sneaker and streetwear retailer. And I came on board there in early 2019.
I was there for four years, um, and, and learned a lot. You know, I got to go through a re platform. I got to buy a bunch of technology, uh, buy tech from all of my friends, which was really fun. And I was able to get elevated to CDO in that tenure, which was, um, super humbling and really amazing. So, uh, from there, I took a CDO role at Solo Brands, which is a public portfolio company that owns a number of outdoor lifestyle brands, namely Solo Stove, Chubbies, Aisle, Oru, a bunch of others.
But I will say that the, the role in both scenarios was very similar, obviously a little bit wider working for a portfolio versus a single brand, but, you know, the idea of having a vision on, on how to build a resilient technical infrastructure. Um, an infrastructure that is built expressly for your consumers, um, everything from commercial planning to outbound communication and marketing to asset creation, um, you know, from a CDO perspective, the benefit of owning all of those functions is that you get to leverage technology to create efficiencies.
And so, in this new world of AI, being able to look through the AI lens about, you know, Creating efficiencies, it's a very exciting time to be a CDO. So for those in the seat, it's going to be a lot of fun, and it was really the impetus behind starting this agency was. You know, we're, we're calling ourselves a chief digital agency, bringing some of those, you know, digital, that digital expertise, the ability to create efficiencies through the implementation of technology to really drive efficiencies and growth across the business. So, um, yeah, it's, it's a lot, but it's exciting.
[00:03:32] Jay Topper: Yeah, I think that's really cool. I found, uh, as I'm a career CIO and CTO, and when I went into the CDO role, what I found was number one is it disciplined me. And I always thought I understood the business merchandising, marketing, supply chain. Uh, but it really disciplined me to look out across the organization at the outcomes we were looking for as a business first and the, and the technology second.
And I always felt like I was that way, but you really have to understand everything in the CDO role. And I, I thought it was interesting that we were both two time CDOs. So congratulations on that. We've done more than most in that regard. I don't know if that's a good thing or a bad thing, but we've done it.
[00:04:12] Jenna Flateman Posner: Yeah. I mean, I think that's the beauty of retail, right? And it's funny, when I first took the role at Snipes, you know, my first gut reaction was, I don't know, the first thing about retail. And the response to my manager at the time was, she said, you can learn retail. I can teach you retail. And the beauty is that everything's really daisy chained together, right?
So everything is really dependent on each other. And so, As a technologist coming in, looking at those dependencies and identifying opportunities for growth, automation, efficiencies, um, it's, I, for me, it's just, it's a really fun practice.
[00:04:45] Jay Topper: Now, one of the things, uh, one of the areas that you focus in that, that fascinates me, and, and again, I'm sure we'll be talking about it, uh, long after this podcast airs, but how did you get so Tightly coupled with AI and have that as such a core part of your professional belief system. Where did that come from and where is it today?
[00:05:07] Jenna Flateman Posner: Yeah, that's a great question. I think every CDO in any organization, a big part of the responsibility is understanding what's coming. And the more forward looking and innovative you can be, the better. And so, a big part of my self prescribed responsibility was making sure that I stayed very close to.
Technology that can help drive value back to the business. And so, as we found, you know, an experience, the democratization of AI through leveraging chat GPT, I think we all kind of got to play with it and realized that there were some real efficiencies and value to generate. And so, my team actually, at Snipes, I mean, ages ago, years ago, I started playing around with OpenAI and leveraging copywriting tools like Jasper and Hypotenuse and you know, we had a very big culture of ask for forgiveness, not for permission, and which I loved.
And so we, we got in really, really early and so I, I started to see the power of what OpenAI was. What access to utilizing AI could really do for the business. And I just kind of became, I can't say obsessed, but very inquisitive and excited about what it could do for the business. And so I just kept asking questions.
I kept participating in panels and attending panels, communicating with my peers and pushing the boundaries on how we could leverage AI and measure it. And once we kind of caught wind and, and got in our stride, it, it just kind of. Became a part of who we, who we are. And so now it's all moving and changing so quickly.
And so a few retailers and I came together and we co founded the Retail AI Council. One of the biggest challenges as retail executives is really cutting through the noise and figuring out, you know, everyone's saying they have AI now. So okay, that's cool, but like, what does it really mean? How do I leverage it?
How do I, um, how do I measure it? And, and who's. Who's real and who's just kind of talking right now or maybe projecting their road map. You know, I've heard AI horror stories of AI solutions coming to market promising AI when really they've got 40 people in the back who are editing images and doing all of this work.
The whole spirit of the Retail AI Council is to validate this AI that is out there and help retailers find the right partners. That's why we founded the Council and it, that's why we're still cranking with it today.
[00:07:21] Jay Topper: Yeah, and I, I, I actually couldn't agree with you more, and even before AI, there's, but AI has, has heightened it in, in my experience that there's a lot of shiny objects out there that platforms and, and vendors and partners portray they have, and, and everyone to your point is talking about AI, they all have to have an AI roadmap, but you really have to dig under the covers, and I think that's where understanding technology comes into play.
Is in my experience, having data scientists and engineers really probe into the technology of what they're doing to validate it because you can get sucked into things that have a really good user interface, but don't have a lot, maybe necessarily under the covers. If you were in retail right now as a CIO, CTO, COO, CDO, and.
And you were exploring some innovative AI platforms and concepts, and we'll get into what they already have because we talked about that, but just if you were out there exploring, what do you think is critical as a foundation with inside of a company to be able to Exploit these new offerings that are coming out that make use of your data, your customer's data, you know, business processes, whatever. What type of foundation makes it easier to harness those technologies?
[00:08:42] Jenna Flateman Posner: Well, I think that trust is a big piece here, and, and one way that I found successfully instilling trust within the organization. Rather than going out and finding these shiny new objects, big spoiler alert here, 50, at least 50 percent of the technology that you currently have implemented either has AI use cases that are in market or has a very aggressive roadmap to implement AI into the technology.
And so, in reality, you've already licensed AI today. And so, the challenge with a lot of retail executives is that sometimes they don't go super deep, right? So, sometimes they're like, all right. I know we need a fraud provider, or I know we need an ESP, or I know we need an SMS platform. And so, you know, they go through RFPs and all the vetting and everything, everything very thoroughly, but sometimes they sign that contract and they walk away, and the message to the team is, okay, tell me when we're profitable, right?
When is this thing actually up and running and functioning and driving value back to the business? And so, one. One thing that I've seen work in my own experience is actually going a little bit deeper and understanding more technically how are your current technologies leveraging AI today and bringing those use cases back to your teams, back to your peers.
Right. Because then it's not really a question about should we invest in AI. It's more of an education and a trust building exercise for them to communicate how we're already leveraging AI and how AI is actually, um, being leveraged in the business. And so I think there's a huge opportunity there. I think that.
It's a really great time, especially in kind of like this down time, like we have a bit of a down cycle right now from a buying cycle perspective. It's a great time to get re pitched on your current technologies, like have your technology providers schedule time with you, show you the roadmaps, dig into the last deployment and really start playing with these new tools because AI is being infused every day.
And so, I, I think that's a great way to kind of set a foundation for a, a trusted application of AI that already exists in the organization. So it becomes more informing than kind of asking permission. Right. And then once you build that trust, then you can kind of just operate, which is really important.
So that's what I would recommend. Look at your existing tech, educate yourself on the AI that you're currently leveraging, um. And really do the work to sell that in internally and build trust.
[00:10:55] Jay Topper: Yeah, you know, I had a boss, my last CEO, actually, you know, who spent her whole career in retail, talk about retail is detail and you have to get down in the weeds and you have to.
Keep those partners close to you long after you sign them and stay involved in what they're doing and who they are. We are a bit in a down cycle right now. And, and you can feel it, you can feel it in retail. You can feel it, feel it therefore in the partners that are selling platforms and you can feel it in the SI ecosystem that are implementing those platforms.
So I have this gut feeling that there's growing pent up demand because there's a lot of legacy technologies out there. Legacy ways that people have organized and stored their data. And that at some point in the next, you know, six, 12, 18 months, that pent up demand is going to come out because it's going to have to, because people can't stay stagnant.
So I actually am somewhat optimistic about the, the horizons of this buy cycle. How, how do you feel about that? Is that, does that resonate? What do you think?
[00:11:57] Jenna Flateman Posner: Yeah, no, I don't disagree at all. I think, look, let's call out the election, right? There's a lot of weirdness happening with the economy right now. So we know that, like, you know, we've got November to get to, we've got the end of the year to get through, Q4 to get through.
I think you're right. I think, you know, 12, 18 months, we're going to see a bit of a shift. From a pent up demand perspective, when it comes to AI, you know, I do think a lot of us have, have validated the efficiencies with an AI. I think that we've, we've done the things that we can conceive, right? So we've, like, applied it to copy.
We've applied it to asset creation, we've applied it to customer service and chatbots and we've seen some value there and I think what we've been doing is really waiting. Waiting for technologists to build on top of the access to AI that's been exposed to the general public. Great. And it's taken some time for you guys to catch up.
Right, and for you guys to actually have these offerings, and I think they're coming, and I think they're here. It's why we're talking right now, right? Right. So, so I think that there's been a little bit of a wait and see to really understand, you know, we don't want to do this ourselves as retailers. We're not building tech.
You're building tech. Right. We want to implement your tech. And I think when you think about the structure, security, the needs of, you know, making sure that our data is safe and secure. If I can trust. Forder to manage all of my payment data through a decisioning engine on the fraud side. Why can't I trust the rest of my, my technology providers to keep my data safe, right?
And so as retail executives, we kind of have to hedge a little bit and invest in you and trust you that you are going to comply with all the security required in order to manage our data. I think that that's what retail execs are waiting for. And also I think the economy and the election and everything is also kind of giving us like a nice pause button for a second while we wait for that to happen.
So, Yeah, I think you're right. I think 12 to 18 months, I think halfway through, I think H2 of next year, we're going to see things, let me shine my crystal ball while we're here, um, yeah, I think we're going to see things loosen up, and I think we're going to see a lot more retailers diving into AI than we have in the past.
[00:13:52] Jay Topper: I think that's right, and maybe it's an opportunity for people to get their house in order with regards to data and infrastructure and, and, and other things, so they're prepared. Within AI, if you look in retail specifically and you look out a year or two, or three at the most, but really maybe even two years as a cap, what type of technologies out there and enablements within AI are you excited about that think could be game changers?
[00:14:22] Jenna Flateman Posner: I can't even project that far out, but I will say that the trend that's getting me most excited right now. is, is really this recent wave of agentic AI solutions. So, you know, and it's funny because I, I try to talk about this and I've been trying to talk about it more often recently and it takes a minute for people to really get it.
But the concept of agentic AI isn't like, it's not about your chatbot, right? It's not about like having a customer service agent. So try to like push that out of your brain. The idea here is to have a humanized AI agent, a worker, right, think human, right, that works 24 hours a day, 7 days a week and doesn't quit.
And as long as you can document a process start to finish, right, step by step, the agent can complete it.
[00:15:06] Jenna Flateman Posner: So as long as it doesn't require problem solving or creative positioning, right, it doesn't, it doesn't need the human brain in order to process or think. So an example would be. Uh, common customer service issue today.
I buy something, I don't know, at, at 11pm, my customer service reps are not available. And I realized, oh my God, I shipped it to my mom's house instead of my house. And so, I type in a customer service and I make a request to change the address to ship the item to my house. What happens most of the time today is that customer service representatives come into work, they get a queue, that queue is actually ranked oldest to youngest, right, so you're dealing with the oldest tickets first.
By 11 p. m. last night, it's already shipped out the door to my mom's house. Right? And so that's a problem on a number of levels. One, you haven't met the customer's expectations, you've frustrated me, now I gotta wait. You've also wasted company money by shipping this, you're gonna have to do a return to sender or have my mom ship it back, which is a big inconvenience, like lots of things are wrong with that whole scenario.
[00:16:04] Jay Topper: Yep.
[00:16:05] Jenna Flateman Posner: Now, in order to actually change the address, in most cases, to modify an order, a customer service rep, there's two, one of two things has to happen. Either a brand has to build a really heavy integration between their service console and their order management system. So that a customer service rep can actually trigger a order modification from service into order management. We're getting nerdy here, Jay.
[00:16:27] Jay Topper: Yeah, this is good. This is good.
[00:16:31] Jenna Flateman Posner: Um, or, right, so I have to do this, like, heavy integration to make sure that that interaction can happen. Or worse, this customer service rep has access to my order management system and has credentials and can go in and actually modify orders in this really coveted system of record, which likely isn't a best practice.
And so those two scenarios aren't really good for the brand. So fast forward to agentic AI, imagine a scenario where that ticket can get, can get actually picked up by an AI agent. And that AI agent knows that the ticket type is an order modification and it's an address change. Well, there is a documented list of steps that it takes in order to go into OMS, leverage credentials, go into OMS, look up the order with the order number, hit modify order, change address, put in the new address, right, and they literally function within the order management like a human.
The cursor is moving. They're typing. Right. It's, it's, it's as if a human is in there doing this, but they are, they are trained to only do this task. And so the scenario here is at 11 o'clock at night when that request was made is when the address change was actually made. And so what's happening now is that that customer service rep that comes in in the morning is actually coming in to a series of tickets that have been opened and closed.
And those customer service reps are actually being reserved. To do really important human things, like talk to humans, like show them empathy, like problem solve, like make accommodations, right? These are things that AI agents can't do. And so rather than having these humans being bogged down with these mundane tasks, they're actually freed up to actually add more value to the brand and more value to the customer experience and ultimately drive more loyalty.
So. The agentic AI, now that is just one scenario, that is one task, right, one problem solved. What I'm really interested in doing from a strategic standpoint is like infiltrate these brands and start talking about, okay, what are your process issues? What are your process challenges? And how can we leverage agentic AI to solve some of those problems?
Um, I, I think, I think agentic AI is going to change the way that we operate and it's going to make us incredibly more efficient. So, that's what I'm pumped about. I mean, Pixera is a great example of, of one of those technologies that I'm advising for right now that is doing this and doing it incredibly well.
Um, I am most focused on this application of AI right now.
[00:18:45] Jay Topper: Yeah, and I'm going to ask you in a minute about Pixera, but going back to just to, uh, double down on one comment. You know, let humans do what humans do best, show empathy, be creative, interact with customers. I think it also elevates, it doesn't replace the humans, it, it elevates their own personal experience in a lot of cases where not only are they bringing more value to the organization, but they feel that within themselves and then they feel more valuable.
I know I do. My rote tasks don't make me feel anywhere as good, uh, anywhere near as good as I do when I'm doing something where I can really feel that value. I'm bringing an organization where I'm using my gut or using my, Experience or something to, to create, creatively solve a problem. So I think it's good for, good for employees as well.
I don't think it's a replacement scenario, uh, necessarily as much as it is a chance to upgrade the services you're offering your customer. I always like to ask my guests of a, uh, a side question and you mentioned Pixera. So tell us a little bit about like, about that. So people can perhaps follow it and look into it and, and keep an eye on it.
[00:19:50] Jenna Flateman Posner: Yeah, the concept here is, is really interesting. The hard part of the technology is actually building the workers, building these AI agents. And they're, they're humanized agents. And so the way that these agents have been trained for Pixera is roughly six years of monitoring human behavior over public servers.
So seeing how do mouses move? How do people log in? How do they engage? Where do they click? And so all of those actions and movements have been. Recorded and used to train these, these bots, these agencies, workers on, on how to actually take direction, take these tasks and actually go execute them. So here's a scenario where, you know, today you'll go into, um, an AI tool that does background swaps, right?
And the AI is trained to do this background swap. Well, there's another scenario where you could actually train an AI agent to function within Adobe. Right? Right. And so, what you're basically telling the agent to do is, the way a human would go into Adobe Photoshop. And make an adjustment to an image, they can do the same exact thing.
So if there's any sort of repeatable task, right, I want to outline this image, I want to knock out the background, I want to replace it with a new image, I want to smooth out the edges, I want to change the colorway of X or Y or Z or swap a model, as long as there are directions on how to do this that the AI agent can consume, it can complete the task.
I think, I think there's another interesting value proposition within Pixar as well, which is. The onboarding process that I've, that I've seen is pretty fascinating. And so, what happens is the idea is that you also want to kind of be building what, non technical term, but you want to be building this essentially an AI brain, right?
A database of understanding of, of brand ethos. So this consumption happens on the front end of the relationship where assets, copy, reviews, customer service transcripts, like any content that can be consumed, videos, et cetera, images, All get consumed and essentially digested by the AI that's translated into a language that other AI solutions can communicate with.
And so imagine building a brand specific brain that any AI solution can tap into and learn brand voice from. Right. So that every time you're leveraging a piece of AI to complete a task, or if you're telling the AI agent to use AI technology to complete a task. It can query this brain, need a better term for that, to actually make sure that the output is a reflection of the brand.
Right. And the more I think about that, Jay, like the more I really think about it, the more I start, and I sit at roundtables and I go to cabs and I'm talking with clients of technology providers and I'm asking these questions like, are you thinking to the future where, you know, open AI isn't, you know, tens of dollars a month, but hundreds or thousands of dollars a month, where it's going to become very expensive to start querying these databases.
Yeah. And what does the future look like for brands and our ability to actually create our own LLMs, create our own AI databases, so we can start actually querying our own data instead of querying external data? And those questions are often met with some like, you know, blank stares or lots of, lots of scribbling on pads.
But I do think what's going to happen in the not too distant future, and we've been talking about this for years, is that data is closer and closer to becoming a currency. And so we've got to start thinking about leveraging technologies like the Pixar's of the world to start saving our pennies, right?
Start banking them away so that when it comes time to actually, you know, do something about We have to leverage our own data. We have it structured in cold storage, somewhere off the server, somewhere unaccessible from open AI, where we can actually start building up our own databases that we can leverage to inform some of these models.
It's super fun having these types of conversations with the Pixaras of the world, because they're just like, yes, like, okay, the industry's not ready for this yet, but why don't we be ready for it? So that, um, when it is, we kind of have a structure and a story and a process for. For doing that.
[00:23:47] Jay Topper: No, that's really, really cool. And the whole concept of brand voice, uh, brand promise, uh, I've always thought that in an omni channel world, you know, at every touch, regardless of where that is, you're, you're representing that brand promise to the consumer, that brand voice. And so why wouldn't AI be part of that? You know, if you have every other aspect of your, all the content you're creating, the messaging, the humans, the digital, And, and why wouldn't AI represent that brand voice as well?
One big question that I, uh, that we also talked about, so you're a VP and a retailer right now. Maybe you've been there for a long time and it's tough getting out of your, you know, your legacy. Maybe you're brand new, just starting somewhere in technology, merchandising, e commerce, data. What advice would you give people going into these retailers or that have already been there?
About getting more up to speed and figuring out how best to harness AI. What, what are your, your couple of tips and tricks for, for them to, to believe and to, to show advancement in this area?
[00:24:52] Jenna Flateman Posner: Yeah, it's funny. You always hear the like crawl, walk, run, uh, you know, um, concepts around bringing new tech to market.
I think we talked about a little bit earlier, right? The idea of digging into your current architecture to understand where are you already leveraging AI? Number one, right? How would you measure it today? Number two, right? All of that is really great. I think the next step, kind of the, the, the walk phase of this is how do you leverage AI to impact and adjust and create efficiencies around measurable tasks today, right?
So if you know that you have a copywriter who is pumping out X number of, you know, images, I mean, we're, I, I would hope that everybody is tracking how many new styles are enabling on a site each week, right? So that is your product enablement process. It is your path to cash from the time that the.
Product hits the dock to the time that it goes live, that is a process and we all track it, right? Sometimes it takes 12 hours, sometimes it takes a day, sometimes it takes 2, sometimes it takes 4 for some retailers, it really kind of all depends. But there's a whole process that you have to go through, everything from shooting it, you know, getting it, getting your asset, getting your image ready, you've gotta categorize it, you've gotta assign attributes, you gotta write your copy, you've gotta, you've gotta Uh, there's, there's so much that goes into it, that whole process can be supported and enabled with AI.
And so that's really where I leaned in. And I said, okay, this is something that I'm measuring each week. If I can say by utilizing AI, I can actually increase, I can increase my enablement by 20 percent each week. Well, that has real value, right? And so I think the next step, that kind of walk phase, is really figuring out what are the processes internally that are most important to your business, and how can you find ways to leverage AI to create efficiencies around that process?
Because there is time and money associated. Um, with those processes, and if you can do that, you're going to be a better leader. You're going to drive more value, more top line, more bottom line, all of the good things. And so figuring out, you know, in my case, product enablement is kind of where I leaned in.
Asset creation, copy creation, dynamic attribution and categorization is kind of where I leaned in. And then I started having deeper conversations with my tech providers. I said, hey, PIM, like our product information management tool, you know, I am writing copy You know, third party AI copywriting tool over here, and I've got to copy and paste this into PIM.
[00:27:15] Jay Topper: Right.
[00:27:16] Jenna Flateman Posner: Could you do an integration with a copywriting tool? And like, could I just be walking into PIM with suggested copy for these assets? And they say, oh, well, that's really interesting. So it's like, how do you then take it that next level to say, okay, I've proven the value of having AI write this copy for me.
How do I create more efficiencies around the process? So, document your processes, weave in AI, continue to measure and continue to refine. I mean, I think once you do that and you can show the business that you can harness AI and drive value, then you're off to the races, right? No one's going to challenge you anymore.
So it's just, again, it comes back to trust. Like how can you deploy and measure the utilization of AI in a way that can build trust with your peers so that you can continue to operate?
[00:27:59] Jay Topper: No, I like it. And I love the fact you've brought it up twice now is. You, you know, companies are spending all this money on their platforms and I don't care if it's, Hey, ERP, what are you doing?
Hey, PIM, what are you doing? Hey, order management platform system, what are you doing? You know, no matter what it is, there's this whole host of companies out there. Ones you use and one you might use that, that is, is just a little bit untapped. I think in some of the retail organizations, vendor management has become.
Much more of an art, uh, and really, really trying to learn from what they're doing because you can't, you just can't do it all yourself. All right. So tell me what you're going to be doing and how, why would somebody, or how would somebody reach out to you if they wanted to interact with you in your, in your current, uh, new, new adventure?
[00:28:48] Jenna Flateman Posner: Essentially what we do is we bridge the gap. between how tech is built and brought to market, and how tech is bought and implemented by retailers. There's really a need on both sides. And as an executive that has been both a retail tech executive and a practitioner, right, a retail executive, I know all too well the deficiencies that happen in those processes, right?
And so we see it all of the time, we experience it all the time. We get into a sales process, we're sold the future. Our retail executives come into these processes and have a beautiful vision for the tech and implementation. They sign a contract, it's handed to the team, an SI is brought in, and standard documentation is shared, right?
And so what we get is kind of the vanilla version of what we've been sold, and the relationship from day one is strained. And so what I really want to do is come in, be a resource to challenge the tech that's being developed to make sure expressly for retailers, but also to be a resource to help translate the request of the buyer.
And be a resource to provide oversight to make sure that what is being bought is actually being implemented. So today that is coming in all sorts of, uh, you know, different projects. It's everything from, you know, coming and doing like really deep and detailed discovery sessions to taking a look at restructuring architecture and documenting things.
It's on the tech side, you know, sitting in product development meetings. We are a consultancy in the, in the true form of the word and. Uh, helping to solve problems and make sure that retailers and technologists are closer aligned and delivering more faster.
[00:30:23] Jay Topper: If you can approach that intelligently as a retailer, not only are you going to save a lot of heartaches, time and money, but your outcomes are going to be improved.
So you catch it on both ends for sure. So Jenna Flateman Posner. Thank you for joining. It's really a pleasure. I love every single time we speak. I love chatting with you and thank you for taking time out of your busy day, especially starting a new company to join me for a little bit today. Thank you.
[00:30:47] Jenna Flateman Posner: Same here, Jay. Really appreciate it.
[00:30:52] Jay Topper: Well, Jenna was amazing. And when I think back on our conversation, the last 30 or so minutes, there's a few things that came into my head. First of all, I remember when I joined, uh, the business world from the Coast Guard in 1997 and the internet. Was relatively new and the applications of what people could do online weren't there yet.
But everybody knew they were coming and everybody was trying different things to figure out how to harness it. I feel like AI is of that magnitude, uh, of as the internet is in of itself, that AI is coming. The computing power. The problems in business that it's going to help solve the challenges in everyday life.
And sure, we need to be careful with it. And we need to be secure with our customers information. And we need to, you know, follow a certain level of process. But at the same time, the capabilities of AI, we're just scratching the surface of where that's coming. So, message one is If you're in retail, know it's coming and prepare yourself.
And some of that is making sure your technology is organized, that you have a nimble team that can pivot quickly, uh, and, you know, harness your vendors and understand what AI roadmaps they have in front of them. And above all else, and this is a common theme when you go to pick and try technologies. Uh, to solve some sort of business problem, really get in under the covers, retail is detail and get under those covers into the technology because there's a lot of tricks out there.
There's a lot of people that claim AI and you have to get into the detail to get super comfortable that it is in fact machine learning and artificial intelligence and computing power and not maybe humans behind the curtains that are helping these things along. So. Very exciting. Very intimidating. Uh, Jenna gets into some great detail there and it was a real pleasure having her on.
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