AI Is Finally Coming for One of Retail's Most Broken Jobs And It's About Time | Spotlight Series

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Hello everyone.

I am Chris Walton, your host for today's interview and interview in which we will explore the often under tech side of one area of retail that is near and dear to my heart.

And of course, if you know me well, you know I'm talking about merchandise planning and allocation.

For those of you that are familiar with this podcast, I spent the first four years of my retail career at the Gap back in the late 90s in merch planning and allocation and I remember the job like it was yesterday.

It was one of my favorite jobs I ever had.

But I also remember having to manage spreadsheets upon spreadsheets to order every step style of denim across 40 various size options.

And back in the 90s, the gap carried a lot of denim.

But that is a true story.

I absolutely hated coming to work every Monday.

You can ask my boss, Jen Meyerberg.

It was a disaster until I got through all the different spreadsheets I had to manage and could start my day fresh on Tuesday.

But sadly, sadly, friends, I'm not sure the process is all that different now at many companies, even almost 30 years later.

So joining me to discuss whether this hypothesis is right or Wrong are Intello AI's co CEO Jeff Fish and its head of Merchandise strategy, Noah Hirschman.

Jeff, welcome to omnitalk.

Thanks Chris.

Thanks for having me.

And Noah, it's great to sit down with you as well.

Yes, it's great to be here, Chris.

Thank you.

All right, well, let's get started.

So Jeff, as I said at the outset, merchandise planning is kind of quote unquote under tech.

And I use the air quotes on that one because it's kind of my my quotation of it.

Do you agree with that?

That that merchandise planning has been under tech?

And if so, why is that I do agree.

I think that, you know, there's been lots of systems that have been out there.

I think when you were at Gap, I'm sure you had a, a complex merchandising and planning system that, that you used.

And what you did was you downloaded everything out of that system and you, and you used your spreadsheets to, to, to get to the outcome that you were looking for.

That hasn't changed a whole lot in 30 years.

And whatever systems that are out there generally are not very much loved by merchandisers, planners, allocators, buyers.

So the end result is they download whatever they're doing out of whatever system they have and they leverage spreadsheets.

We saw that as a huge opportunity.

We saw that there was ripe for disruption in that space and that's why we created Intello.

So why is that though, Jeff?

Like dig into that for me.

Why have we seen so little technological innovation, know, to improve the day to day lives of the average merchandise planner and allocator?

What, what, what, what caused that?

Merchandising and planning is complicated.

It's damn right.

One part art, two part science or two parts art, one part science, depending on what, what area of, of the industry you're in.

And you know, being able to, to input a set of data and then get an output that, that's easy, there's lots of systems that do that.

But to take the creativity of a merchant that understands their business inside and out, or the creativity of a planner and knowing what the trends are gonna be for a certain store or a certain line, and applying that to an entire assortment or applying that to an allocation, that's a lot of art.

And I think that systems historically just haven't been able to do that very well.

Yeah, what, like, what's the example of the art that comes into that?

Because like, I think, you know, there are, when I think about merchandise planning, there are so many things that have to link up to get the product to go to the right place at the right time.

And so like what, when you say art, I'm curious what, what comes into your head?

Yeah.

So you know, let's just use where you used to work, right?

Yeah.

Understanding what the trends are going to be for the following season, understanding what the buy should be to, to really fill all of those stores.

You know, we work with a lot of luxury brands for luxury brands.

You know, knowing the, the trends around sizing, you know, we think about, you know, what's changed so much in the US around GLP1s or what the next Color should be, you know, leveraging legacy ML systems or certainly just legacy systems.

It's very hard to be able to do that.

It's very hard to be able to apply that gut instinct to a system of record.

Yeah.

The other part about this too, Jeff, that I've been thinking a lot about recently is like, you know, a lot of what we're talking about, you know, has been around for a long time and it's all pre2022.

It's pre the new generative AI age where you can just do more things.

Is that a factor too in the sense of like, you know, as much as we knew the issues that merchandise planners and allocators had as an industry, we just didn't really have the technology to help them in any way as well.

Yeah, AI has been around for years.

Right.

Noah and I come from Microsoft and Salesforce.

We were using AI for many, many years.

And merchandising and planning systems have had ML models in place for at least 10 to 15 years, maybe back to when you were a gap.

And they've gotten better over time, but historically they've really been a black box of AI where you input some data and you get an output and you may agree with that output or you're not.

And generally, historically, merchants and planners have not agreed.

So that's why they ended up downloading everything into a spreadsheet and working their magic on their own.

I think since ChatGPT launched and the entire world is now using Some sort of LLM, whether it's Gemini or ChatGPT or Claude or any of the other open models, there's a level of comfort now that if you have an interaction with a model, it will reason with you, it will tell you why it did what it did.

And that gives a level of trust.

And we saw an opportunity post 2022 that if you could build that level of trust in a very narrow and focused way for merchandisers and planners, you can really transform how they work.

Right.

And yeah, that's interesting too.

The dichotomy between pre and post2022 is really interesting.

Yeah.

Because you always had MO, but particularly on the forecasting side.

But when you think about the actual planning and then the execution of the day to day job, that's where I think the rubber is really starting to meet the road with, with the new tech.

Noah, what color would you add here?

I'm curious.

Right.

Well, I mean, I think that merchandising is a little bit of a black box for a lot of people who are not as involved in retail as let's say you are or we are.

And so I think a lot of people, especially from, you know, some of the big tech companies, they understand CRM from, for example, very easy, very quickly.

They understand supply chain and demand forecasting and planning, they understand store operations.

But when they talk about merchandising, they're like, what exactly is that?

And so that's one of the reasons why there hasn't really been a lot of good tools.

And the other one is of course this, and I'll probably talk about this a lot, the difference between the left brain and the right brain, why I love retail and I've been a retail merchant for many, many, many years.

Right.

Is that kind of balance between the art part of it, which is what Jeff was saying earlier, you know, what's going to be the hot color, what's going to be the hot trend, you know, how do I use my creativity to be able to understand what's going to sell.

And the whole quantification part, which is very hard.

And the truth is, is that there is a lot of efficiency, you know, not having to use spreadsheets and being able to use the these kind of prompt driven LLM interfaces.

But even more importantly is how precise these are compared to humans.

Right?

And I think people have sort of realized that in terms of math and higher math and statistics and things like that AI is just better than humans are.

And they just, you know, and we're talking about, you know, these kind of very thin margin, very high velocity business models that retailers have small mistakes.

This inventory should be in this store, but it's not, it's in that store.

It can be something like, I think the last statistic I read is between 6 and 12% of total revenue can be impacted by just dumb merchandising and planning mistakes.

Right?

So it's very, very high stakes actually.

And that's why I think it's important to be able to say, look, this, you know, this AI high math, super statistical stuff that humans either don't want to do or don't know how to do or don't know how to do that.

Well, just let them do that and then allow the merchants to really shine what they're, what they're good at, which is really, you know, being able to predict the trends and do the art part, it's more fun.

And also I don't see that AI is going to take that over anytime soon, the real creative part.

But they could take sort of the day to day operations and just do it faster, better, more efficiently than humans can.

Yeah, there's a couple things that come to mind for me there when you said that, Noah.

I mean like the, the, the, those numbers actually, given my history at the Gap and then running large scale merchandise planning teams of like 50 plus people at Target, like, I actually, I actually think, I actually think those numbers are probably low in terms of the impact that they're having in terms of the potential mistakes that are out there.

We just, we, we have no idea as an industry in terms of the impact here.

And then the other thing too is like, we give these jobs typically to people that are fresh out of college, that are learning the retail ropes for the first time.

So they're, you know, they're having to learn things.

And the lack of a system, systematized approach actually makes the job more difficult for them too.

Because the other thing I was thinking about is when you mentioned CRM, the other thing I would say about the merchandise planning process, it's not linear.

Like it never works the same way for every item, the exact same way every single time.

Which makes the, which requires human brains to step in and solve problems that they haven't seen before.

Am I, do you agree with that?

No, I saw you shaking your head like that, to me, is the fundamental thing that is now unlocked because of what you can do with the new, the new, you know, revolution with AI, so to speak.

I'll tell you, it's one of these wow moments, you know, using, I mean, our tool.

Obviously we're going to be biased about intello AIs, you know, great interfaces, etc.

But the truth is, is that, you know, most people, as Jeff said earlier, are used to using an LLM interface.

They know how to create a prompt.

Right.

But so to be able to ask a relatively obtuse question, you know, so for example, if we're doing merchandise financial planning and we can be able to say, okay, you know, give me my financial plan for, you know, based on these parameters for the next six months and then you can say, oh, and by the way, can you just change them?

I need to get more margin.

So can you just like change the margin from 32% to 32.5% and then it'll go back and do all of the back end calculations, you know, just to be able to do that one kind of scenario analysis prompt where if I had to do that back in the day, it would have been like, you know, the CFO would say, wait, wait, we need an extra point out of You.

And I'd go, oh, no.

So I'd have to go back and start from scratch and do that.

And this does it basically instantaneously.

And then it'll also tell you the reasoning.

How did it get there?

What are the things that it had to change?

What are the assumptions that it had to make?

I mean, this is the real.

Wow.

Stuff about identic AI and certainly our, you know, from Intello AI school.

And it's really amazing to be able to do.

Yeah, right.

Yeah.

Yeah.

And you hope and you hope and pray that you didn't anchor the wrong cell in Excel when you're doing that analysis for the cfo too, is the other point of this too.

Right.

Then you have to redownload it again and it wipes out all the calculations you did before.

I mean, it's, you know, problematic.

Yeah, it's problematic for a ton of reasons.

All right, Jeff, we'll go deeper on that for me, if you will.

So, like, so what, what are maybe the big things, like the big bucket things that retailers can do now with their merchant planning systems that they couldn't do before 2022?

Yeah.

So, you know, retailers have been doing merchandising and planning for years before, before there were even systems.

And if we think about how we break up our.

Our agents, we think of them like people.

So there's a team that works on building the assortment plan.

There's a team that works on building the merchandising financial plan.

There's a group that works on in season optimization.

And in some organizations, large organizations like Target or Gap, they're all broken down very cleanly by teams.

And there's generally a big system that you use, and all these different teams are downloading things into spreadsheets, making their changes and uploading them back in and then collaborating within that one system.

Today with Intello, you can have multiple people across the organization, and it could be a large organization with, you know, hundreds of merchants and planners and allocators.

Could be a small organization with five or six and people are wearing multiple hats, and they can interact with the agents in real time and interact with each other.

So the agents are able to reason with them, they're able to talk to each other.

So our agents are set up on agent agent protocol.

So the A2A standard allows our agents to share data with each other and then interact with any individual that.

That has a login to Intello.

So if I'm working on my merchandise financial plan and Noah's working on in season optimization and Chris, you're busy building an assortment plan for next year.

All of that data is interacting in real time.

We're able to make assumptions and make changes in real time against each other's plans.

And the agents will then carry that data across the organization.

That's something that's never been able to be done before.

And the agents are learning over time.

So as compared to kind of a locked ML model where you, you deploy that ML, you build the demand plan and then kind of, you live with it, these agents are learning from the interactions that all of us are having.

They're learning from the data that maybe we're getting from your data warehouse.

And then they're also learning from the overall third party data that we're bringing in, which most systems have never even thought about.

So things like weather patterns, things like events that are happening and economic and geopolitical, certainly what's going on today with, with the price of oil, all of those things are factored in to the planning that, that are, that our customers are leveraging.

Yeah, that makes sense.

Yeah.

I mean that's where I see the value in this is like, you know, especially as you do this year over year over year too.

As the data, as you, as you unders, as the, as the systems understand what they executed and how well they executed against it, then you can say like, you know, in the planning meeting, like as a, as a theoretical exercise, right, Jeff, you can basically say where were our opportunities this past year and what did we not capitalize on?

What could we have capitalized more on?

And then start the analysis with that point of view.

From looking at all that exhaustive data,.

100%, You know, think about where a lot of our customers start.

It's with loss sales analysis.

The historically that's been a challenge and I'm sure you experienced it when you were in the retail space and you were trying to do lost sales against a certain cluster of stores or specific store or a product.

Historically that's been a problem.

Now that becomes a lot easier.

And so you start with your lost sales analysis.

You build your plan.

You trust the agents are doing the work over time.

And then the aha moment after, you know, doing five, six, seven runs or even a season's worth of runs, you start to go, well, you know what, I don't really need to do this anymore.

I can let the agent run fully autonomously.

I can always be in the loop.

We've got human in the loop actions across all of our agents.

But over time you build a level of trust with your agent.

The same way you build a level of trust with your team that they can go run on their own.

I'll check it every once in a while.

I'll even maybe do the final approval, but I'm going to let the agent do their thing.

I can go think strategically about what's hot for next season or where we should be thinking about it strategically within our organization.

These are changes in behavior and transformational moves that have really never happened before in merchandising and planning.

Yeah, that's, that's really interesting to me too.

Like, I think in apparel, like, you know, the styles and whatnot are changing pretty regularly too.

So like you've got all that benchmark history and then like all of a sudden like some type of denim is going to take up the floor plan out of nowhere.

At least you're thinking it is.

So it's like, okay, now here's what I think the plan is going to be for this new category of product.

What are the puts or takes with that that I have to think about in the total overall assortment, design and planning of, of, of everything I'm putting into my store.

So that helps in, in that situation too.

No.

I'm curious.

So Jeff, you, you kind of talked about the pre, the pre season planning side of this.

What are, what are concrete examples of what you can do now in terms of what you couldn't do before?

AI on more the in season management side of things.

Yeah.

So I mean, it really has to do with, you know, a lot of it has to do with inventory, rebalancing.

You know, remember that a lot of the, you know, certainly our customers are, you know, many of them are fashion and luxury and they really don't, you know, want to mark things down.

They would rather just have the right stuff in the right store and you get to buy, as you know, from being in this business and the passion side especially, basically by one time.

And then, you know, once you do that, you got to make sure that the inventory is constantly in a place where it's going to be the most productive.

And so this is a very proactive thing and it tells you ahead of time, sort of, you know, you need to move this, these few things here or there.

And you know, so one of our luxury customers who's been using our tool, you know, our systems for a while has already seen a 47% reduction in stock outs, which is, you know, in the millions of dollars of excess revenue.

Right.

And so that's like, you know, one of the important things Is that the other one is around understanding size curves.

You know, sizes are very tricky business as you know, and it takes a lot of the sort of statistical analysis to understand, you know, where the right sizes need to be and in what stores.

And so that's another thing that we can, we're able to do from a replenishment from the, from the, from the warehouse, the central warehouse, you know, after the stuff has been ordered and received, where the right sizes need to go, you know, where they're going to have higher probability of selling.

So those are, those are two, I think, really important things that are painful to do and also take a lot of you know, real heads down analysis and being able to predict where it's going that that's, you know, going to bear medium fruit.

Right.

And no, and, and Noah, go, go deeper into the rebalancing thing for me because when I first met you guys and you were talking to me about that, I was kind of like, you know, I was thinking more from my Target, even my Gap days where I was like, yeah, rebalancing, like how much is that really worth?

You know, like it's probably, is it worth the cost to even try to send it to a new store?

But then when you think about it from a luxury standpoint and the margins they're commanding on those products, that is a big deal.

And I had never thought about that until, until you brought it up.

So, so talk about why that is.

Well, I mean, so you have to look at the different types of retail and some retail, let's say like, you know, hard goods that have big and bulky items with low margin, you know, it's very expensive to, you know, brands in a store transfer those particular things.

Like television sets, for example.

But when you're looking at luxury items that are lightweight and small and easy to ship and that have a very high price point and high margin, it really does make a lot of sense to do continual in store transfers because the truth is, is that it's sitting somewhere and being dead is way, way more of a burden on the balance sheet than it is to spend a couple bucks to move it to another store where you know it's going to sell.

But the truth is, is that this is something that it's easy to get into trouble in arrears if you take your eye off the ball.

And you know this very well, you could all of a sudden have a buildup of something very, very quickly and be out of stock.

So think about the two things, right?

One of them is you've got capital being used right here that's, you know, not generating any value and then you're losing sales on the other side, which could be, you know, in the millions of dollars.

So you have to be ahead of the game and anticipate it.

And that's what our agents do is before a human would actually see something was wrong, it's already detecting that there's going to be a problem in the future and it's proactive about where to transfer it and when.

Yeah, having an, having an agent objectively do that work in the luxury industry has got to be low hanging fruit.

I have to imagine the way you just described that.

So.

All right, Jeff, so we kind of talked, we kind of started at this high level in terms of what's the history of merchandise planning and technology and now we've got into a little bit.

But I want you go deeper for me because I've seen, I, maybe I, I, I told you guys when I first met you, I've seen a lot of failed attempts to enhance merchandise planning with technology over my almost 30 years in, in this business.

So I'm curious Jeff, where does what you're trying to do at Intello AI actually begin and end?

Like what, what is it all about?

Yeah.

So we have three distinct ways of approaching this.

There's one way that you're a Gap or any other retailer and you've got a system in place that has probably been there for five, 10 years and you're currently downloading spreadsheets to get your work done and maybe you need to do lost sales analysis that your system's not doing really well or you need to do as Noah was talking about, size packs and size curves.

Or you need to do store clustering and you come to Intello and you go, you know, I've got this problem, I want to solve it and can you do it?

I've got X, Y or Z system.

Absolutely.

We can be the intelligence layer that sits on top of it.

So you know, you're a large organization ripping out those legacy systems can take years and it's a huge amount of change management.

But you need that intelligence layer and currently what you're doing is you're doing it in spreadsheets that's not scalable.

So you deploy our agents on top and we can instantly change how that team is working.

That's kind of one channel for us.

There's another channel for us that they don't have that legacy system or they have it and they're just not using it at all and they're living in spreadsheets.

Well that for us is, that's the real low hanging fruit, right?

That low hanging fruit.

In 30 days we can change a team.

We can say, you know what, upload your spreadsheets, give us your data and, and we'll show you how to interact with that data in ways that you never could before.

And Your team of 10 or 12 or 14 people, 15 people, whatever it is, now just added five or six PhDs that know your business inside and out.

Cause they have access to your data and they're gonna help you.

Right?

That's kind of the second channel.

The third channel is where our long term vision is.

Our long term vision is that every retailer is going through a transformation.

Some are more along the lines than others.

Some started five years ago, some are just getting started now.

But ultimately we think that every retailer is going to move to a lakehouse architecture.

Pick your lakehouse, doesn't matter.

Could be snowflake, could be databricks, bigquery.

That doesn't matter to us.

But we think once you get all your data in a place where it's centrally located, we can deploy teams of agents or armies of agents on top of that and we can interact with that data.

Just like you went and hired an entire new workforce that's working 24 7, that's thinking strategically, that's optimizing for you while the team that you already have are leveling up to do much more strategic thinking.

So we, as we break up that art and science, right, the science and math, the pattern recognition, the ability to input and increase massive amounts of data across data sets and then make decisions with that, that's where our agents thrive.

And our agents can thrive across any sort of data warehouse that you've got working with a team.

So our long term vision is that these organizations are going to go through this transformation.

They're going to move their data to a place that's easily accessible, where we can have zero copy access to it in real time.

And that sets of teams of agents work alongside merchants and planning teams that already exist today and just makes them much more successful.

So Noah talked about a 47% statistic.

Our, our data points are dependent on the team that we're deploying.

So in some cases it's about, you know, increasing sell through significantly.

Five, six, seven times that equals millions of dollars in increased revenue.

In other cases it's about being, just being more efficient, right.

In other cases it's about being flexible, right.

So the ability to create store clusters on the fly, which you can never do before at scale, and then be able to actually measure that and, and look back and look forward.

These are things that our agents can do.

We think that the long term play for us is that we go through that transformation cycle.

It might take a year or two for a customer or three, but we'll get many of our customers that are already on board and then future customers into that space.

Got it.

Makes sense.

Let me add on to the store clustering thing, by the way, if you don't mind.

So store clustering is something that for some reasons, I think so a lot of stores don't really do.

They have an A store and a B store and a C store.

Right.

And the A store is the big.

And they do it by, by, by square feet, essentially.

Right.

A is the big store gets the A assortment.

B is the second biggest one gets the B assortment.

Right.

But store clustering is actually a lot more nuanced than that.

And it has to do with a lot of the demographics and things that are supporting different stores.

And so a store may have one assortment and, and a B store may have a B assortment.

But the truth is, is that there's a lot of stuff in that B store that it could be selling and a lot of stuff in that B store that shouldn't be selling.

And it's really based on all of this data that we're able to bring in and be able to create these things.

And so that alone is very difficult to do.

It requires a lot of math and statistics and a lot of access to internal and external data.

But that bears a lot of fruit.

Because the truth is, is that just modifying assortments slightly based on that versus based on a kind of gut feel or a traditional planogram can be millions and millions of dollars and either lost sales or, you know, more productive inventory.

Okay, Noah, since you, since you said it, I was going to ask you, before you brought up cluster, I was going to, I was going to ask you, I was going to have you put your CEO's money where his mouth is.

But now I'm going to have you do it for both of you.

So what?

So how do you know, how do you know this works?

Where are you guys having success out of the gate?

So, I mean, as I said before, you know, we have a luxury customer that's already seen a 47% reduction in stock stock outs, but we also have specialty customer that's seen 42% reduction in broken sizes and improved sell through.

And so we've Seen, you know, the guys that we've done this for already, which are both in the luxury and also in the specialty retail, we've seen immediate increases in sell through and immediate decreases in stockouts.

So this is really what we're measuring in so far.

I mean, obviously in longer term we're going to be able to measure not having to mark stuff down because it's in the right place at the right time versus it gets to the end of the life cycle and all of a sudden it's like, oh my God, I got all this inventory here and now I got to get rid of it because the new model is coming or the new, new, new style is coming and I've got to mark it down.

We haven't had a long enough run with the customers to see that.

But I can guarantee that there's going to be a margin increase as well.

So it's, you know, obviously lost sales, use of, of capital and inventory and then you know, reduced mark markdown which increases the, the margin are the kind of the three ways that we're looking at it that we're seeing immediate results.

Jeff, you want to add anything to that?

Yeah, I'd add to that.

Every agent team that we deploy has a preset level of KPIs that we measure against.

And every customer deployment that we've had, we start with a baseline.

That baseline could be what their current sell through is and how do we increase that sell through?

Their current baseline could be what their margin is and how do we increase that margin?

And another baseline could be what's the level of efficiency?

So if you're, you know, you were doing rebalancing once a week and it was really hard and you had two people spending three days on it and now you're doing it twice a week and you've got no one working on it.

What's that value to you?

So there's different levels of KPIs based on the agent and the agent teams that we deploy for each one of our customers.

So Jeff, how long does it take to deploy like a typical deployment that you're doing with say this luxury retailer and the specialty apparel retail that you guys referenced?

How long does it take?

We can be alive in as little as three weeks.

Three weeks, wow.

As little as three weeks.

Is that typical or what's yet.

What's the average?

Average is six to eight weeks.

Average is six to eight weeks.

But we've got, we've gotten it done in less than 30 days before.

Geez, that, that's that's, that's ridiculously fast.

My God.

All right, so let's get, let's close this up here.

So I want to get you out of here on this.

So, like, as I mentioned before, you know, I've been a planner for, I've had planning stints both at the lowest level and at the highest level in my career.

And so if your vision is right, in terms of what we're talking about, the last question that it brings up for me is how will the actual job of merchandise planning change over the next, you know, say three to five years, which is a medium, medium term horizon.

Like, how's the, how's the average person's job going to change?

Jeff, why don't you start and then, Noah, we'll have you close us out.

Sure.

Yeah.

So we talk to chief merchants and merchandising, planning and allocation teams literally every single day.

And when, when we talk to these teams, there's no one that ever told me that I went to school to be a merchant or a planner.

And I love retail and I really went to school to spend all of my time in spreadsheets.

I really wanted to be a data analyst in spreadsheets.

And realistically, the majority of them, that's what they do.

The majority of what the team members do today doesn't matter how big the organization is.

They're doing data analysis and the transformation that we see with teams three to five years out, and I wouldn't even say three to five years out, I'd say, you know, customers that are using Intello 6 to 12 months out, that they get out of their head downs in their spreadsheets and they really start to think strategically about their business and they're able to think about not just, you know, next season, but long range planning and working with their CFO and their CEO and their design team.

Because the design team is always going to be the art part.

Right.

But they can, they can provide insights to that design team that they never could before by, by, by leveraging our agents.

And I think that's going to be the real transformation.

They're going to get out of the busy work, the mundane work of, you know, living, living in data analysis and looking at, and really spending their time and strategy.

Yeah, and what I hear from that too is from, for the average employee, like the, the benefits of that are, you know, one, you get to be more strategic because you can answer the questions you could answer before.

Before.

I mean, you can now answer the questions you couldn't answer before.

And then you also get to be more strategic, which just makes the job inherently more interesting and you have more confidence that you're making the right decisions.

Which as a new person fresh out of colleges, is kind of, kind of a challenge, you know, for the most part, like, at least it was for me back in the day, when I remember that.

But the other part you mentioned too, which I don't think most people thought about, is actually managing agents, right?

They're going to have to manage agents.

Nobody was.

I mean, maybe they're teaching that now in college, but I have no idea.

I don't, I don't know how to manage an agent.

So.

So, Noah, what, what do you think on that?

Yeah, I mean, it's a little creepy to be able to have, you know, your AI body, right?

I guess.

But people are going to be used to that trip.

I mean, look, at the risk of, of, of being direct about this, right?

Yeah.

There is an efficiency component which people are scared about, right?

They're thinking, oh my God, this guy's going to replace the job.

But if you just do the math, the amount of money you spend on somebody's payroll versus the amount of money that you're going to save from 12% lost sales improvement, and you know, the kind of margin that you're going to make isn't even in the same ballpark.

The truth is that this is going to allow people to be super powered superheroes at their jobs.

Okay.

It's going to have this AI sort of incredible Iron man suit.

I don't know what the analogy is.

To be able to do a lot of these things that we've been talking about that they either didn't want to do, didn't know how to do, weren't very good at, and all of a sudden you've got this tool that's not only going to do it for you better and learn it over time, it's also going to give you all the reasons how it, of how it did it, right?

And it's going to make you to be a better merchandiser.

So whether you're doing strategic things or whether you're just doing the similar function, but way better than you used to be able to do it before.

It's going to be a tremendous value for you to be able to use this tool.

It's not going to replace people.

It's just going to make them much, much better at their jobs.

That.

This is why I love doing this job.

I love this job because I, when I, whenever I, whenever I do a podcast, I Always tell a good podcast when I have like a sudden epiphany that I had never thought about before having the conversation and no, you just hit it on the head.

Like, for me, like, if I'm a retail executive and I'm thinking about, okay, how am I going to leverage AI and what am I going to do with AI in terms of my workforce?

The first step is using it as the steroid to get the most profit and revenue you can out of your organization by deploying it in a way like you guys have been espousing, right?

Like find the just the low hanging fruit on the ground and make it work.

That should be your first step.

You shouldn't be looking at it as a tool for headcount reduction and at all.

You got to get it that right first.

Because if you don't get that right first and you go the other direction, then you're going to be in a world of hurt potentially as well, because you haven't actually figured out the processes you need to run your organization.

So.

So that is, that is an awesome nugget that just hit me in the head and I'm going to probably be talking about that a lot and you can bet your bottom dollar omnitog listeners that you're going to be hearing me espouse that going forward.

So.

All right, well, thank you both.

That was great.

It was wonderful, Jeff.

If people want to get in touch with either one of you, what's the best way for them to do that?

Noah@intello.AI JeffTello AI sales at Intello AI find us on LinkedIn.

Lots of ways to find us.

We're easily accessible.

Awesome.

Awesome.

Well, thank you both for your time today.

I love that discussion.

It's the merchandise planning, as you can tell, has always been near and dear to my heart.

So that wraps us up.

Today's podcast was produced, of course, with the help and support of Ella Sirjord and on behalf of all of us at Omnitalk.

As always, be careful out there.