We are here at eTail Palm Springs and seeing and hearing the latest and greatest in e-commerce and retail.
AI and automation are transforming retail operations, from customer experience to supply chain efficiency. But as we move into 2025, retailers face increasing tariffs, labor shortages, and cost pressures. How can AI not just improve margins but actually reshape the way retailers operate to stay competitive in a rapidly changing landscape?
Joining me today is Nick Stuart, US Consumer Products Senior Analyst at RSM, where he focuses on AI and automation’s impact on the retail industry. Nick brings deep expertise in customer experience, supply chain optimization, and workforce efficiency, helping businesses leverage AI-driven solutions to scale and improve margins.
Resources
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Transcript
Greg Kihlstrom:
We are here at eTel Palm Springs and seeing and hearing the latest and greatest in e-commerce and retail. AI and automation are transforming retail operations from customer experience to supply chain efficiency. But as we move into 2025, retailers face increasing tariffs, labor shortages, and cost pressures. How can AI not just improve margins, but actually reshape the way retailers operate to stay competitive in a rapidly changing landscape? Joining me today is Nick Stuart, U.S. Consumer Products Retail Consulting Leader at RSM, where he focuses on AI and automation’s impact on the retail industry. Nick, welcome to the show. Thank you so much, Greg. Happy to be here. Yeah, looking forward to talking about this with you. Before we dive in, though, why don’t you give a little background on yourself and your role at RSM?
Nick Stuart: Yeah, thank you so much. So yeah, I’m the retail consulting leader for RSM. RSM is a leading middle market provider for audit, tax, and consulting services. And I really focus on our industry trends, analyzing what’s going on in the marketplace, focused on innovation, both bringing that back to our internal teams, as well as providing thought leaderships to our clients.
Greg Kihlstrom: Great, great. So, yeah, we’re going to talk about a few things here today relating to AI and retail. I want to start with the customer experience. And so AI, we talk about a lot on the show, of course, as you imagine. I know you were just on the AI summit here at ETEL. AI, it’s becoming a critical tool in doing a lot of things, including improving customer interaction. So how are you seeing AI transforming customer service and retail today?
Nick Stuart: Yeah, I mean, I think that one thing that’s interesting in the retail space is that AI is not new, right? We’ve seen chatbots, we’ve seen these machine learning technologies be put into place for customer experience for quite a long time. A lot of people have become really frustrated with them, but I think the LLMs have really changed the game. They’ve become more humanized. They are able to take on tasks that kind of more mimic humans. But I think the real opportunity is actually accelerating and improving the ability for customer service agents to work with AI to be able to improve the customer experience. So one of the challenges that we’ve seen is there’s so many different data sets that a company has. Serving a customer, you really have to have access to all those data points, your orders, your payments, ability to be able to do refunds, all of the things that really sit in different systems. One of the biggest challenges customer service agents have had is the ability to consolidate that information and provide quick service, right? So it’s both slow, which is not a good customer experience, and the customer service agent kind of fumbles through, really making it like a not a great employee experience.
Greg Kihlstrom: Yeah, and I mean, those two issues, I mean, not only the customer service reps kind of fumbling for the right answer, but also AI just needs, it works best with just a lot of data and a lot of good data and breadth of data. Sounds like the two kind of solve similar problems, right?
Nick Stuart: Yeah, humans with technology, right? I think that that’s the one big theme when I hear people talking about what the opportunities are, at least initially. I don’t see it as much complete replacement, but augmentation to be able to really improve the output and capability of people to do their jobs.
Greg Kihlstrom: So, you know, talking with a lot of retailers and working with them, what are some of the key areas that you would advise like focusing on when implementing AI and customer service?
Nick Stuart: Yeah, I think going back to the employee workflow, what is the current employee experience starting there? What are the opportunities to augment key things that require a lot of data that you have that you can leverage? So training is a really good one, right? Training has always been a problem because one, things change really fast. It’s really expensive to document. And quite frankly, as an employee, it’s not a good experience to go through a 100-page manual and try to understand what it actually means on a day-to-day basis. So LLMs are great at being able to train people on data sets. I think that’s a really good one. I think looking at ways that a customer service agent can, with a natural language model, be able to query all of the data and get a quick answer and quick access to information about the customer that then they can use to service the customer is really key. So looking for those opportunities really of ways that they can make that employee experience more effective and more productive.
Greg Kihlstrom: Yeah. And so you mentioned early on about some of the initial frustrations with things like chatbots and I’m sure we’ve all been on the phone tree doom loop kind of chatbots, you know, the dumber chatbots kind of suffer from the same issues. But, you know, as you mentioned, with LLM and some other more advanced technologies, they’re getting a lot better. But still, you know, how how would you recommend that a company look at balancing those? You know, the balance is definitely needed. But how should they kind of look at when to use AI versus human and how to make that right balance? Yeah, it’s a good question.
Nick Stuart: I think you need to build in workflows and processes that leverage what a customer is looking for, which is quick access, ability to self-serve, right? Always going back to customer, like you hear that throughout this show, always going back to what your customer is looking for. So if your customer is looking for quick access to be able to ask a question without the delay, of being able to get to a customer service agent, that’s a great use of an AI tool. But you have to give them the ability to jump over that with what they prefer as a human element. So you’ve got to offer both solutions to your customer and let your customer dictate what experience that they want.
Greg Kihlstrom: So let’s, another big topic, not only here, but just in general is supply chain. And we’ve been hearing a lot about a lot of challenges that retailers and other companies have been hit hard in recent years with issues. How is AI helping predict and potentially prevent some supply chain disruptions?
Nick Stuart: Yeah, I mean, the one thing that’s certain is that there are going to be supply chain disruptions, right? So we don’t necessarily know what they’re going to be in six days, let alone six months. So I think you need to build an infrastructure that is nimble, that allows your supply chain folks, your procurement teams, your logistics operators, even your warehouses, to be able to make decisions with data quickly. Right. That’s been the biggest challenge with supply chain over the years is one, it’s very hard and very data intensive to be able to compile and build data driven decisions. So, you know, AI is very good at pulling that data forward, compiling it from multiple systems so you can normalize it. and then using the LLMs in the natural language model to be able to ask questions. Hey, should I pull this order forward to make sure that I meet all of my customer demands and orders? That’s a question that I’ve seen done in a live environment. That is extremely powerful. That would take days, maybe even weeks for a data analyst or a procurement person to look through and figure out. If you can do that in minutes, you can make decisions really quickly that you can actually impact, you know, your customers and ability to deliver to your customers.
Greg Kihlstrom: Yeah, because I mean, that’s, you know, on both ends of that, you know, you’ve got either excess inventory or you’ve got like stock outs or something. So being able to do that, I mean, yeah, in a in a chat like interface, right? It sounds sounds amazing. Yeah.
Nick Stuart: Yeah, and with current cost of capital, you know, the CFOs are really kind of making sure that they’re looking hard at how much inventory that they’re sitting on, right? More than there has been in the past. So there’s more demands, not only from a supply chain impact and disruption, but also just the current cost of carrying capital like that is extremely expensive. So there’s more need to be able to keep inventory levels to where they’re necessary without kind of diminishing the amount the ability to deliver. Yeah.
Greg Kihlstrom: And I mean, I think the other part here is, and you touched on this, is to ask that question previously, it would take a request to some data team to the, you know, I’ve seen that firsthand, you know, it can take days, weeks, you know, to get those, the democratization of this, right? In analytics, whether it’s, you know, predicting something, you know, a potential challenge or predicting an opportunity or just getting quicker insights. How are you seeing adoption of AI-driven analytics in retail?
Nick Stuart: Yeah, it’s a really good question. Analytics is always tough because I think of historic Power BI, Tableau, whatever the platform is that you build, you’ve got to first look at what data do I need for this? Then you’re building dashboards that’s perceiving that you’re going to have the right information for people to make decisions. If it’s not, then you’ve got to go back to those data teams and those data visualization team members to build new dashboards, which is a common problem. So the biggest problem is without a ability to be able to ask a question and get the data you need for that specific request. You’re always guessing what to data visualize. So I think that the real opportunity becomes that natural questionability. And I think the adoption is going to be really high because it doesn’t require people to have anything more than a question.
Greg Kihlstrom: Yeah, yeah. And so how does that change? That dynamic, again, probably a lot of enterprise people listening to this, that dynamic of data engineer is still very valuable, but how do the roles change in the organization from that perspective?
Nick Stuart: Yeah, I think it becomes proactive and not reactive. So I think you’ve got data folks that are working on data cleanliness, understanding any data biases, testing and working with the front end subject matter experts to ensure that the solutions are working the way that they intend. Instead of receiving a request that I need this data to answer this question, right? So it’s it’s really moving up and making those people more solution focused instead of you know Firefighting I mean and that to be honest that sounds like a win-win for everybody, right?
Greg Kihlstrom: I mean the you know, the marketers the the e-commerce folks are able to ask questions and get answers quickly without having to, again, send something in a queue for two weeks to get an answer. And the data folks are doing probably more valuable work, or seemingly valuable, because they’re not just order takers, right?
Nick Stuart: Yeah, and it comes down to actual value, right? So one of the things is if you’ve got the same team members with the same output, yeah, the employee experience is better. Maybe they make better decisions in a marginal way. But as you scale, right, as you get more people doing those supply chain jobs, if it’s all reactive, you have to have more data folks, right? Because the output of questions is just higher. With this strategy, you’re really able to scale better without having that kind of labor increased need at the same pace.
Greg Kihlstrom: And how does this factor into, so there’s more people with more access to data, which sounds great. How does this… factor into people making better decisions? Maybe some of that goes to data literacy even, or how do you look at that component of it?
Nick Stuart: Yeah, I mean, that’s going to be a challenge, right? One of the risks with all of this is the access to data is so much higher, you become more reliant on it. You’ve got to make sure that people understand how the models work, understand what data they’re querying with those requests, so that if they see bias in the data or something that doesn’t make sense, they’re thinking through that and not making blind decisions. That’s why there’s a human in the workflow. If there was no need for that human element to be in there and make that decision, you’d skip it. I don’t think we’re there yet. I think we’ve got to have that human decision-maker in the loop. I think that’s a key role for those people and there’s going to be some upskilling to get there.
Greg Kihlstrom: Yeah, yeah. But I mean, that said, having more agency, for lack of a better term, with the data, being able to ask those questions, being able to get things quicker, what impact does this have on things like employee satisfaction and even retention?
Nick Stuart: Yeah, I mean, I think about myself. I don’t like rudimentary tasks. I don’t like searching for materials when I think that they should be accessible to me without having to search for those. I like training to be very specific for the need that I have. So all of those things can really improve the employee experience and their output. Yeah. Yeah.
Greg Kihlstrom: And so, you know, I think you’ve kind of touched on this already, but, you know, there certainly there’s a lot of fear, you know, sometimes misunderstanding about, you know, how AI will either replace jobs or fundamentally change. I mean, you mentioned upskilling as well, not necessarily a replacement, but, you know, there’s a lot of uncertainty here. What’s your take on, you know, how should retailers be thinking about this as they’re, you know, the train has left the station. So, you know, it’s not going back. But how should they support this in a way that is also supporting employees?
Nick Stuart: Yeah, I think they need to be transparent with their employees. I think there’s a lot of fear out there. So being transparent about what the goals are. Having employees understand that it’s actually going to improve their day-to-day is a huge hurdle, but one that I think will be really successful if communicated correctly. The reality is that if you’re sitting at the executive level, Labor’s really tight. There is not enough labor right now to be able to scale businesses. It’s been a challenge. We’re seeing productivity as US GDP entirely actually increase, right? You’re seeing increased productivity at the kind of macro level. And I really believe that part of that, not all of it, but part of that is some of this automation that’s being put into daily jobs, and they are increasing output per head. And I think that communication from the executive level down, we’re trying to achieve more productivity with the same hours that you guys put in today, right? We’re going to invest in technology to be able to increase your output, which is going to allow us to scale with less overhead as a company.
Greg Kihlstrom: Yeah, and it’s, I mean, it’s the good kind of productivity too, right? It’s like, it’s the meaningful, strategic, you could even call it creative output, right? That humans, I mean, let humans do what humans do well and machines do what machines do well, right?
Nick Stuart: Right.
Greg Kihlstrom: Yeah, no, exactly.
Nick Stuart: I mean, you know, if you tell employees that we’re going to increase productivity by you working 50% more, that’s not not going to be a message that’s received well, right? But if you tell somebody that they’re going to be able to do their job more effectively and be able to focus on the tasks that actually achieve a greater productivity with better tools, I don’t think anybody’s going to be mad about it.
Greg Kihlstrom: Yeah, totally. So looking ahead months, a couple of years even, we talked about a lot of the challenges that retailers are facing. What’s on your radar as far as, you know, whether it’s AI, whether it’s other things to help navigate some of these challenges?
Nick Stuart: Yeah, I mean, I think the pace at which the technology is moving is breakneck speed. So, you know, right now we kind of know what the capabilities are of these LLMs. We kind of know what’s on the roadmap a bit for automation. You know, AGI is something thrown out there like it’s going to happen. I heard as of this morning, it’s supposed to happen in 12 months. So just And I won’t be surprised if it is. It’s amazing how fast it’s moving. But I think, you know, as far as from an industry perspective, I think, again, you need to focus on what you can control. I think you need to create an experimental culture within your organization. Because at the pace that this is moving, you need not only kind of the top level people thinking about innovation, you need everybody in the organization thinking about innovation. So in order to prepare ourselves for what is inevitably unknown at this point, you’ve got to have a culture of experimentation so that when those new tools are released, there’s a culture to be able to really innovate quickly.
Greg Kihlstrom: Yeah, yeah, love that. Well, before we wrap up here, one last question I like to ask everybody. What do you do to stay agile in your role and how do you find a way to do it consistently?
Nick Stuart: Yeah, I mean, if I’m not agile, I can’t do my job. So I am very focused on making sure I look at priorities, what things that are most important to our customers, and making sure I focus not only my time, but our service line times, our internal folks, making sure we’re adjusting to the current market trends that we see in the marketplace, and then making plans and prioritizing those things based on where we see the highest output.
