AI, Data & The Future of Merchandising | Tech Titans on Retail Planning

So first, I would just like to start with Bob and each individually have 1 minute just to say who you are, what you're about, and who you work for. And then after that, we will then go into our key questions that we have for each of the panelists. So over to you, Bob. Thank you.

Okay, thank you. Welcome everybody. My name is Bob Debicki. I'm the GM of Consumer at Anaplan, a planning company in a— basically a global planning company. I spend my time with retailers making sure that what we bring we bring to market from a technology standpoint addresses their very specific needs and try and help them move their businesses forward from a revenue, a profit, and a, you know, productivity standpoint.

Yeah, hi everyone, uh, Brian Lindauer, CEO of VibeIQ. Um, real high level, we help merchandising teams, design teams, and other cross-functional groups inside of your companies understand your assortment as it moves through its entire lifecycle so you can make it as best as possible when you put it in the market.

I'm Janisa Visram and I work for Optimum. Optimum Retailing is a platform that is a store engagement tool as well as a localized planogram tool that allows you to take your strategy of product and end up being localized at the store level.

Kyle Kachel with Centrix Software. Centrix Software is a retail platform planning product lifecycle management and market intelligence. And so yeah, looking forward to the discussion.

I'm Jeff Godowski. I'm the Chief Revenue Officer at Impact. I actually like what Bob said, so can I just borrow that? No. At Impact, we have an end-to-end retail planning platform. So been here about 4 years. Prior to that, I ran the retail team for North America at Blue Yonder. But we really focus on foundational planning, some merchandise financial planning, assortment planning, And then what I call execution around inventory and lifecycle pricing. Thank you.

Okay, amazing. So a very esteemed panel with a hell of a lot of experience. So we look forward to the questions and I will kick off with question 1 that we already have preplanned. This question is for Kyle at Centric. How are you helping merchandising teams move beyond spreadsheets and legacy systems to work faster, smarter, and more collaboratively?

Yeah, good question. Thank you. So, you know, I'm surprised just to start with the problem. Like, I'm more and more surprised at how many retailers are still on spreadsheets and not just the volume, but the size of some of these retailers, right? I mean, we're seeing retailers with thousands of stores that are still on spreadsheets. And, you know, the challenge with that is that you end up with— in a big company, you end up with different departments, right? You got, you know, merchandising and planning and finance, etc. And they each end up with their own silo after a while, right? Their own data, their own processes. So when you're trying to make decisions, you come together and everyone has a different set of data and a different set of processes. And so it's almost impossible to get to a decision. And in this world we're in, like, life's moving fast. I mean, you got tariffs, we had COVID, what what's next, right? You can't pivot unless you have a platform and you can make quick decisions, right? So, you know, that's really what Centric does— Centric Software. Um, you know, we, we bring all that together. Single source of truth allows you to connect your strategy to execution, and it allows you to pivot if you need to, right? A lot of companies, and during COVID had to change their entire business model. I mean, there's statistics on it. It's incredible. And if you can't— if you don't have a platform that allows you that agility, you end up in a really bad situation at the end. So, you know, with our platform, we've seen 2x increases in margins. We've cut stock levels in half. And, you know, as a result, you're seeing, you know, good customer satisfaction, right? You mean it's all about getting inventory to where it should be so that your customers are happy and so they come back and don't go somewhere else, right? So that's really what we're focused on. Yeah, and I hope that, I hope that answers the question.

So yeah, yeah, great. Thank you. It really does. So we'll go to Bob for the second question. What are the key capabilities you believe merchandising teams need most from technology today, and are traditional planning tools still delivering them?

All right, thank you. Well, I don't have all the answers, but I do want to focus on 4 key areas today. First one's around data. Anybody have any data challenges out there? Show of hands, anybody? No? Okay, I'll move on. No, so data. So let's start with the basics. Let's talk about structured data. So things like the ability to have a good ingestion system that can bring data in from different sources, internal, external, first, second, third party, whatever that needs to be. And then with the ability to do that in real time, so no longer do we want to do batch processes every week or every night. We need that to be dynamic so that as you're making planning decisions, things are updating to everybody else who's in the same circle as you. This notion of hierarchies, the ability to basically mirror your business from an attribute standpoint so that you can plan by any level within your product hierarchy, in your geography hierarchy, just the ability to have that capability. And I know spreadsheets really, you kind of choke at a certain level, right? You can't go much further than, you know, a very summary level. And then just the ability to have that rigor and that ability to mirror your master data capabilities if you have any. Many folks I've talked to are kind of somewhere in the beginning phases of that. And that's on the structured side. On the unstructured side, it gets a little interesting because you start getting into things like imaging. You start getting into things like, you know, content that maybe doesn't map cleanly to a geo or to a product or any of those types of things. And so you need a way of not only storing that information, but grabbing the attributes from that information and making sense of that in your planning process. You know, we'll talk a bit in my second piece about large language models, but essentially data's really critical if you can get that piece right and you have the tools, the technologies, the processes, the rigor around that, you're gonna be in a much better place. The second one is around, let's call it data science, right? Everybody's thinking about AI and ML, everybody's wondering what does that really mean? How do I do it? I don't know what to do. Somebody show me. Come on, Prashant, give me the answer. No, I'm just kidding. And it really does come down to the ability to not only bring that information into your environment, but to make sense of it and allow that information to grow and to learn such that it can be relevant for your business. The ultimate idea is that over time, you want to create some automation of lower-level tasks and some insights as to what your categories are doing and what your brands are doing, that's a really challenging thing to do, and you need to be able to do that going forward. And we're kind of right on the edge of that. I think as an industry, we're just figuring that out right now. Um, sorry, I won't— I'll finish up quickly here, but the next two areas are, um, collaboration. So, not only do you need to be able to do your job or the roles that are part of your department, let's say, you need to be able to expose them to people who in other departments such that they can see what you're doing is and how it impacts what they're trying to do. The classic example there would be my financial targets relative to my merchandising buys and that collaboration that takes place. We too often see people in rooms together trying to figure out whose numbers are right and what's going on before they even get a chance to do anything of actionable, um, nature with the data. And you really need to kind of take that off the table and automate that process altogether. Collaboration will help you do that. And to be able to do that in the cloud with the security that you need and the traceability to understand who made what changes, when, why, and how, and what they did, is really important to be able to manage. Then the last thing is to be able to give the planners, the merchandising teams, the ability to do their own sandbox of information to really take what's working and figure out new areas that are applicable to the business. And if they're successful, to be able to push them up so everybody could take advantage of them in a safe and secure way. That doesn't break anything in the organization. So those are the 4 areas I wanted to cover. I would say as an industry, we're pretty good in some of them, but we need to continue to grow on the data side, the AI/ML side, and the other 2 areas of collaboration and user control.

Great.

Sorry, I'm hogging the time.

We have loads of time. Don't worry. All good. Thank you, Bob. So I'll leave the next question to Brian. This is, what practical steps can companies take to improve data quality and product attribution so that planning tools and AI actually deliver on their promises?

Yeah. Okay.

I'll start by asking you guys a question. How's everyone doing?

Great.

Raise your hand if you've used Excel. Come on. Have you— who's used Miro? Anyone know what Miro is? All right, who's got kids in high school? Raise your hand. No one's participating. This is horrible. So you said practical, practical steps to get AI and planning working. You know, I think a good point here was made. Data is key. If you want to do anything with AI or planning or really take advantage of data, you need to create data your company can use. And the biggest problem that you all know is that all you have so much data wrapped up in places that you're your company cannot use, right? So I don't know if it's a practical step, but just highlighting that challenge, we all have to appreciate that challenge. And then just maybe just a question or food for thought, right? What are the biggest reasons why people use those tools, including the new stuff that's out there? Anyone, anyone heard of Airtable, Smartsheet, monday.com, Google Docs, right? These are like super, they're the benchmark for user experience. They're the benchmark for, uh, like low training necessary, like my kids can use it. They're coming out of Google Classrooms into the workforce. These are the tools they know how to use. They're going to use them. So food for thought is simply what's the role that user experience plays in driving our employees to a place where data can be captured, right? We have to pay attention to user experience. And you mentioned collaboration, where you collaborate, where you work. There's a huge distance today between where teams do their job and work and make decisions and communicate, and then the systems that manage your enterprise data, there's a huge gap there, right? So I just say to answer the question, if we want to do more with AI and more with planning and more with automation, we need to do some things to, to address that gap. Where we work, where people think and capture ideas, needs to be a lot closer to the enterprise systems that manage structured business data. Leave it at that.

Great. Thanks, Brian. So the next question is for Jennifer. So what practical steps can companies take to improve data quality and product attribution so that planning— oh, I'll do that again. How can companies create a single source of truth that connects planning, product, and merchandising teams for better end-to-end execution?

Yeah, I think this is an age-old problem that many of us have faced regardless of the size of the retailer. So whether it was at Macy's or TJX or Cotton or even now at OR, we're seeing that one of the biggest challenges is that tools aren't speaking to each other, right? There's siloed work happening and I think fractured systems in are creating fractured results out. And I think there's a value prop for every tool that each of the silos are using. So it makes sense that Anaplan is being used for your merchandising and planning. It makes sense that your design teams are using Vibe to make sure that all of what they need is being transferred out to ultimately create the best results. But the challenge really is, is there's no connective tissue today to bring all of these systems together. So the shorter answer is I don't think there actually is one tool that every system should be using. But rather how do we start to think about this from a connectivity standpoint where all of these tools can start to partner and integrate so that OR, for example, can take the allocation and give you exactly what you need from a visual standpoint localized to each of your stores. And secondly, I would say, is an active feedback loop. So we talked a lot yesterday and we heard a lot yesterday about how the customer is interacting with our stores team. As the frontline communication platform or people that really get to know our customers the best. But where does that feedback go? You know, oftentimes it's anecdotal, it's untimely, and the way that it goes back up is not really triaged to the right place. So I think once we close the feedback loop and figure out how to connectively work together with the tools that are best optimized for each outcome, I think that's really where we'll start to see impactful results. And see moving from strategy to execution to results.

Great. Thanks for that. Brian, this is your next question. So what does agility look like in modern merchandise planning? Connects planning and how can tech support real-time or in-cycle decision-making rather than just seasonal planning?

Season versus seasonless. It's like this is a topic we're running into and are seeing a lot, not just time at VibeIQ, but working in PLM for a really long time. It's that aspiration that we're all— companies want to get to, right? Seasons are so entrenched in so much technology and process in the current state. Maybe it's different for some of your companies, but as a rule, just an observation or idea. There's so much technology out there that's built on top of the past, right? And a lot of A lot of seasonal development is based on development. But I think there's an opportunity for merchandising as teams form to bring their own tech to bear and to bring their own data structures. Like a split between development and merchandising is almost necessary if you want to unlock seasonless planning but have it married to a season-bound development schedule. It's just a topic that keeps coming up, I think, in every company we go to. I don't think anyone's really figured it out. There's some ideas floating out there, but I think there's something there around merchandising and planning, having one way of representing the world and the data and what their market plan is, and making sure that you have allowance for development to move at a different pace, and then finding a way to bridge them together. As a pattern for consideration.

Great. And then the last question we have here for us is for Jeff, and then we'll get back to the Slido where there's 3 questions that have already come in. So Jeff, how would you approach scaling technology to support new categories, channels, or markets without increasing operational complexity?

I think around new categories, I know we've— I've heard the word attribution been talked about a lot. Attribution is going to become just more and more important within your data. It will, especially for those— a lot of people here are fashion retailers. So as I look at new items from this fall versus last fall, what are those attributes that can give me a good starting point and give my models a good starting point for learning what that forecast should look like? I think channels, quite honestly, I think channels, all of us have figured out to some extent. I mean, Omni was 20-some years ago, and we kind of figured out if we couldn't handle wholesale, e-com, and our own retail with our customers, that we probably weren't going to be in this game very long. So I think the biggest one is markets. So when we look at markets, and, and I think it's important to just truly listen to how brands move from North America to EMEA to APAC to other markets. We're working in Dubai now, we're working in New Zealand now with customers. And the nuances that exist in those markets. So you try to find commonalities that you can leverage your solution, your process, and your workflows. And you also have to look at what are the nuances for like when we take Tapestry from North America to EMEA to APAC, I would like to say 85% of it's the same, but then there's gonna be nuances within those markets. Victoria's Secret, same thing, global implementation for inventory. So North America and EMEA are very different. and they have a franchise model. So how do you account for that? Um, Levi's is the same. We're working with them on a global process. So the market nuances I think is more of a challenge from a standpoint of how you're successfully delivering value across markets for a global brand. Um, and we continue to learn each time we do it, we get a little bit better and it's not the same. It's not the one size fits all. It's different for all companies, um, depending on what the relationships in those markets are. But I think, I think that's really, really a challenge for a lot of people as they start taking on global implementations with global brands.

Yeah, I think that's a really good point. I think in my experience, most of our regions work in silos. They have their own system, their own process. And when there is that global or IT or tech alignment, then you see all the cracks in the infrastructure because nothing talks to each other. There's no, kind of consistency. And I think, as I said before, if you're an SME company, I think you have a great opportunity to utilize a lot of these resources to get there. If you're a massive company that's worked in one way for 30 years, it's quite hard to retrospectively work back to align everything, to clean everything up. So yeah, I think if you're a brand starting tomorrow, you can really get everything in order quite nicely with everything that's out there compared to, let's say, 30 years ago.

I'd say in some respects it's easier for up-and-coming brands than the more established brands, right?

Absolutely.

Because you have a little bit more of a blank canvas to kind of paint on and you have less to undo. I think it's a little bit harder for brands that have been established in global markets for a long time and have their own nuances within those global markets. And it's the only way they know how to do it. So that's kind of our experience.

Yeah, cool. So we have 3 questions on the Slido. Um, I will read those out and then— now we have 4, great. So I'll quickly get on to them before some disappear. So the first one is from Annie Wong: Who is most important to convince when introducing new tech, leadership or end users? I think it's a really good question, and to be honest, I would say both, which is a bad reply. But honestly, I feel you have to convince leadership because if the budget's not there and signed off, why This is the business case. This is the money I need because this is the outcome. It's only going to come from leadership for approval. And that's why buy-in is important. But as I think we've all heard over the last two days, end users is also critical because you can have sign-off by leadership, you can have the systems in place. But I know companies that have multi-millions of euros spent on systems that are not used. Literally, they are just dismissed by the end users. And they will continue in Excel. So not the best reply, but I truly believe it's both. Over to you.

I would say, um, I would say leadership. They're both for sure, but I would say leadership for a different reason.

Yeah.

Because leadership, not about budgets and approval and they see the value. Leadership is to help the users knock down walls when they have to and to help overcome the challenges they're gonna face in a project. I don't care who out of any of us it is. But you're going to need internal alignment and the support of leadership within that company to help knock down the walls and break down the barriers that you're going to face just inherently within a project.

Yeah.

Kyle?

Yeah, I would also say if leadership doesn't force the accountability and also define the process for the end users and the end users don't adopt that process and it's not configured right, you lose the end user. But if leadership isn't involved and have accountability, then, you know, so they're both equally important, but you need both, otherwise it's not, it's going to fail anyways, in my opinion. So.

Oh yeah, I'm going to take the easy way out and say the magic is when leadership agrees that there's something to be fixed and they create a budget and approve it. And then the passion of the end users kicks in and they say, we really believe in this solution. Then you're off to the races. If you have leadership, to your point, making decisions and mandating usage, that doesn't always work. And if you have end users who are passionate about something with no authority to bring it in, in any great scale, that's more of a science project. So I'm gonna take the easy way and say the combination of the two.

I think it's super important because the end user would not be signing off a budget. The end user will not be responsible for a failed KPI, or why did you lose 10 million? Like, so that's why I think we've all been talking about it, right? Collaboration. Everyone should be brought along the journey. And I think it doesn't matter what level you are, you're impacted in some capacity. And if there's general buy-in overall, I think the journey— if the journey is a car crash or not, you're all together on that journey. And I think that's what's kind of really important when it comes to spending a lot of money on something that will make things more efficient. So we'll go to the next question from Annie Wong again, which is great. What are the main factors that lead to failure in adopting new technology? I'll give that question to Brian.

Yeah, sure. Maybe first one that comes to mind is, like, this idea that freedom is really important. And I think when you roll out new technology, I think there's often a mentality that one size can fit all. And if you're in a small, if you're a small company, that might be fine, right? But most of your companies are not small and there's all sorts of different processes and personalities and product categories. And quite often I've seen tech come in and just try to put one footprint and one process on top of everyone and that can reject, they can reject the system, right? So I think maybe as you're thinking about a project, you need to find a balance between, consistency for the purposes of the company and the per— for the purposes of data and scale, but then an allowance for individuality, freedom, variability across different groups. If you miss that, then you might not satisfy anybody.

Can I just add to that as well? I think the other thing too is that making sure that the technology actually solves the problem that the customer wanted. I think sometimes we have the wrong decision makers in the room that aren't going to be the end users. And so the technology fails because it doesn't end up being used by the people or in the manner in which the problem actually needed to be solved. So I, I agree to what you were saying, but I also think there's a, a component of ensuring that we're actually solving the problem for them rather than creating new ones.

Yeah.

Okay. So we'll go to the next question. How are you incorporating agentic AI into your platforms? How does your platform architecture allow brands to leverage data across their tech stacks for AI use? Who would like to take that one?

I can take it.

Yeah.

Yeah. So I mean, we had our user conference last week and I said, you know, every time you go to NRF in January, there's a theme, right? The last 2 years has been AI and then GenAI, and people walk around going, what does this mean to me and why should I care? Um, this next year I can tell you it's gonna be agentic. It will be agentic AI and people will still be walking around saying, why should I care? Um, we've started looking at specific use cases across our platform and which Agentic makes sense, i.e., you're out of stocks, pricing decisions, things like this where Agentic AI and the agents can actually serve up to a user and you don't have to go look for the data, but this is what I think you should look at, or I think you should do, right? It's not across our entire platform right now because it doesn't make sense across our entire platform. The second part of this question is truly one of the things that I think is really important is when you look at technology, anything, Prashant, our CEO, says anything built 10 over 10 years ago or coded 10 years ago is tech debt. And so you have to look at a platform, i.e., Azure, AWS, we're actually a Google, we're a Google house. And if you're native SaaS, like from the ground up, you have the ability to ingest technologies as they come through. Through very quickly. So we have a whole team right now of engineers and data scientists that are just really working with our customers. It's not what we think. We're working with our customers on like, hey, where would you like to see agents in your workday? Where would you think agents make sense in your workflow? And not just doing it for the sake of doing it and hope that we sell it. So we're actually trying to work with our customers on early adoption cases on where agents would make sense within our platform. And the people are using those tools.

Great. This question is for Kyle. For Impact Analytics, how can you trust a forecast?

For Impact Analytics? Well, is that for the company Impact Analytics? Oh yeah, that's not for me.

I want to hear Kyle's answer.

I don't know.

Come on, Kyle.

Are you sure you want me to answer that?

No, no, no, no, no.

I'll pronounce it right.

Can someone give me a Man United score right now?

Hey, you're welcome.

When I was reading it, I was like, oh, the impact of analytics.

I was reading it and then I said, this one's for Kyle. And I'm like, he forgot my name already. Now listen, I mean, I think it's different and I think it's, you know, whoever in impact analytics is in this room, thanks for the softball. I'm not gonna belabor it too much. I mean, I think we all look at our machine learning models and we all think we have the best models. I think across the platform, here's what I will say is we have a, our platform, we have a single forecast. We have a forecast that serves for assortment that's at a style level that's 9, 10 months out as you have to buy. We have the same forecast rolled up in a hierarchy for MFP as rolling 12 months. Then we have a weekly SKU location forecast we're making pricing decisions and allocation decisions on. But it's the same forecast. It's just at what level of time horizon I'm looking at it and what level of hierarchy. What we do within our forecast is, you know, the biggest thing I think around user acceptance is trusting the forecast. So we actually expose main drivers of the forecast. So we can say seasonality is 30% weighted in this forecast, price is 20% weighted in this forecast for this item or this style. So we just, we find out that from around user adoption, as one of the earlier questions, that as we get people comfortable with the forecast and expose the forecast, also we keep a scorecard. If someone overrides the forecast, we'll come back a week later and say, was it a good override or was it a bad override? but it also allows the models to continue to update and learn. So we try to be very interactive early on with our UAT, our go-live, our post-go-live, what we call hypercare around really getting trust within the forecast because it drives a great deal of our platform.

Yeah, just one of the things we, we're not Impact Analytics.

Yeah, but we, but we inspire— you connect that one. Yeah.

One of the things we do is we provide explainability. So anytime the further you get into leveraging AI for an effective forecast model, the more you're gonna be questioning how did they get that number? How did that come up with that result? So there's a whole series of explainability things that you can go into as a planner and say, oh, I get it, this is what they did. Eh, maybe you don't trust that. Okay, whatever, let's tweak it, whatever. So yeah, explainability's really been helpful as we get deeper into AI.

I think one of the things you said too yesterday, Steve, was that we have to remove the emotion out of things. And I think that trusting a forecast, even if it is done by a human, is still trusting a human versus trusting the system. So why do we trust the human but we don't trust the system?

Yeah, it's a super good point. I think equally, if you actually— if you're in your planning teams or in merchandising teams, look at your accuracy versus sell-in. Like if you're in a wholesale position, what did the planner or in our case, what did the planner or the merchant— what did the planner or the merchandiser do versus the market close or sell-in? And you'll see that the accuracy is not amazing. Of course, if you're a smaller brand with less options or you have consistent carryover or NOS, you're going to be hopefully a bit more accurate. But when it comes to a lot of new styles every season, yeah, we see the accuracy is very challenging. So I think it's a super valid point that we build our whole business and buys and everything on a forecast. That they themselves say, well, I roughly guessed it because I don't know. And this is how sometimes the conversations go more on a product merchandising side rather than maybe planners looking at sell-through assumptions, etc. So that's why I always say like start small, grow slowly, let the trust come, show the business case, show the results. And now I'm in a position where it's like, People are trusting it. People now want to grow. Other people like Footwear is like, oh God, can we get on board? And that would never have happened like 2 years ago. So it's a long journey, but the, the structure's there. So in theory, in the future, if someone said, well, AI wasn't very accurate, but it'd be like, well, it wasn't any worse than you, and we automated it and we placed the buy and you saved a week of your workload. So you can't really question it, right? But of course, we are more accurate and we are saving money. So I think it's a very, very, very, very valid point that we both were talking about yesterday.

And I think also any adoption of technology is scary in the beginning, right? If we think about Uber for today, can you imagine 15, 20 years ago, we would never fathom getting into a stranger's car and having them take us home. But today it's just part of your rhetoric. It's literally what you do. So to me, it's the adoption will always be slow in the beginning, but it's those early adopters that see the benefits of that faster.

Kyle, did you have a point?

No, you know, I was just thinking, I mean, look, I, as AI came out, you know, having sold planning software for, you know, too long, it was, it felt made for forecasting replenishment. It felt made for planning, right? I mean, when you think about trusting a forecast, It's the— at the end of the day, isn't that what AI is for? You put your inputs in and ultimately it should learn from there. And, you know, that's why we have a layer of AI within all of our applications. I mean, it just, you know, it was made for planning, forecasting, right? So anyways, that's it.

Great. So we have 2 more questions. We still have 7 minutes. So if there's any other questions, fire away. So the next question is, and I really like this question, and I think we will all have something to say. Why do end users dictate the tools they use? Why isn't leadership making it a requirement to use the invested tools? It's a great question. I think if we were in certain countries, it probably would happen that you would have no choice and that would be your life for the rest of it.

But this is why we exist.

Yeah, exactly. I think I think all I can talk about personally is that, yeah, companies pay multi-millions to bring in a platform that's meant to make your life easier. And people are like, "Well, I haven't got time for that," or, "I'm not going to do that." And honestly, it's that clean-cut at the moment where they just don't use the system. And then when people are like, "Well, why is that late?" or, "Why is that not done?" or, "Why is that wrong?" And it's like, "Oh, I didn't have time," or, you know all the stories. It's like, well, just use the system that was there then and it will be okay. And this is that whole thing about, like allowing freedom, allowing entrepreneurial spirit, allowing creativity in the business. But of course, yeah, I, I personally— I'm gonna go off tangent— I truly believe it's all about time and calendar. And I've been saying this, right, a few times. I met with an SVP on Tommy, and he wanted my insights on how he's going to construct the product world and how he wants to be able to look at new processes. And he wants my sparring on it. So, I was saying my view. Everything sounded lovely, but then I am like, if you spend 3 weeks longer than the System Freeze deadline locking down that collection, everything we have just discussed all day means nothing. You all know reasons why, because everything else runs late. I think that is the whole thing. There is so much creativity and talk, and, oh, we need to do that sketch again. I do not know why. Clearly they have— they don't know why. It's just they decided to do it. So again, there's another sketch review. Maybe we need a third proto on the new outerwear because they don't like the down. Like all these conversations then delay the rest of the process. So I always feel like 9 months of the calendar is creative and the other 3 is everything else that needs to be done to secure your global margin for your business, which will therefore never happen. So that's why I always say about calendar, If you stick to deadlines, then systems and processes and man time is allowed because you plan your calendar for it. So what I'm trying to say is, with creativity taking over so much, and it should be a creative business, do not get me wrong, but just do it in the timeline because then people have time to use the systems. They're not complaining, oh, I've only got a day to update PLM. No, you actually have 3 weeks on the calendar. But everyone was obsessing over that shawl collar. And truthfully, they are the conversations we have. So I think That's how I see the perspective of why it's not mandated. There's, there's a much bigger conversation about what's happening in the operating model for, for them to mandate you to use that system. If anyone else has— Brian?

Yeah, I mean, I was just gonna maybe state the obvious, but, um, you know, I, I think it's just really important to appreciate when you put a system in place, how much of that decision and solution is for the company's benefit. Versus for the user's benefit. And you should know that equation at all times. And I think just implementing PLM systems for the past 20 years is a great example of a system that is for the enterprise, but really doesn't benefit any one user. Like there's no one user that says, yep, gimme that, right? The company's like, you're doing that. And that's, you know, that's why people sign up for Airtable and sign up for, smartsheet and start creating, 20 years ago it was Access databases, I was just talking about that with someone else. 'Cause that's easy for them. So just as we make decisions about software, whatever it is, know that equation. Where's the benefit to the user, where's the benefit to the company, and make sure you're ahead of that. And if you have no answer for the user, then they're probably gonna go use a spreadsheet.

Or Mural board. I think it's interesting too because the planning space in general is younger. People are coming outta school and they're used to using technology more so than people who may be a bit my age that are still in planning. God help 'em. Um, but they're younger and they're, they're, they're, they're more familiar and they're not as scared as technology in general. Okay. Make no mistake, we all compete against the same monster in this room. Called Excel, all of us do, right? That is our number one competitor, all of us up on stage. So I'm seeing a little bit more of a shift over the last maybe 7 or 8 years as I've seen planning personnel, directors of planning, planning managers coming outta school and not being as scared of technology and being able to accept technology quicker.

Cool.

So we just have over a minute left and we have 3 questions, so I really want to get these 3. So I'm just going to pick one of the panelists and just take 20 seconds or something just to make sure we get a great answer out there. So next question is— okay, that one just went from the screen. So the next one is, how are companies justifying the energy needed for AI? Bob?

So far they're not. That's the simple answer. Will they? I believe they will. When? I don't know. I think people are still trying to figure out Exactly what does AI mean for my company? They don't care what it takes to get AI into your company just yet.

Okay, great. Next question. Someone mentioned explainability of AI. Can you elaborate on that?

Oh yeah, that was me. Yeah. So when you're, you're doing forecasting there, you know, our system, it, it gives you different options based off different algorithms inside the system. And every time it does that, and there's a series of reports that show what was chosen, what constraints were there, what variances, what kind of business rules it might have used, all those kinds of things. So that you can walk through the tree and go, oh, okay, see how they came up with that, that particular answer. It's that simple.

Great. 3 seconds, last question. With the rise of Agentic, what is the future of fashion enterprise software? Do you think your platforms will be obsolete with the proliferation of agents? Who would like to take that?

I'll take it.

And we, and we'll take it.

I think, I think agents will enhance our platforms. They'll enhance usability and user experience and give people decisions quicker. And I really do believe that, but I don't think anyone is in a position right now to say, what does this truly, truly mean for planning a year from now, 2 years from now, or 3 years from now? I think it's as use cases develop. And as people see value in agents, you're going to see them across all of our platforms.

Great. A big round of applause for the panel. Thank you so much.

Thank you.