#655: AI and Automation in the supply chain with Nick Stuart, RSM

Summary

The Agile Brand with Greg Kihlström® – Episode #655:

AI and Automation in the Supply Chain with Nick Stuart, RSM

Release Date: March 26, 2025

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Episode Overview

In this episode, host Greg Kihlström sits down with Nick Stuart, US Consumer Products Retail Consulting Leader at RSM, for an in-depth discussion on how AI and automation are transforming the retail sector—with a particular focus on customer experience and supply chain operations. Against a backdrop of increasing tariffs, labor shortages, and cost pressures, the conversation explores real-world applications of large language models (LLMs), the shifting dynamics of data access, and what it takes for brands to stay agile and competitive in 2025 and beyond.

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Key Discussion Points & Insights

1. AI’s Impact on Customer Experience

[03:00 – 07:58]

• Evolution of AI in Retail:
  AI in retail is not new—chatbots and early machine learning tools have been around for years, often causing customer frustration due to their limitations. However, large language models (LLMs) have significantly improved the quality and ‘human-ness’ of these interactions.

• Augmenting, Not Replacing, Customer Service:
  Stuart emphasizes that AI currently serves best as an augmentation for human agents, not a replacement. The key opportunity is accelerating access to relevant information—orders, payments, refunds—across disparate systems, so customer service is both faster and less frustrating for both customers and agents.

• Focus Areas for Implementation:

  • Start with optimizing employee workflows.
  • Utilize LLMs for targeted, efficient training of service agents.
  • Leverage natural language interfaces for agents to query all customer data easily.

• Balancing Human and AI Touchpoints:
  The best customer experiences give end users a choice between AI-driven speed and human empathy. Nick advises mapping workflows so customers can self-serve when desired while always retaining easy access to human support.

Quote:
“I don't see it as much complete replacement, but augmentation to really improve the output and capability of people to do their jobs.”
—Nick Stuart [05:05]

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2. AI & Automation in the Supply Chain

[07:58 – 13:30]

• Proactive Disruption Management:
  AI helps supply chain and logistics operators respond rapidly to disruption by aggregating and normalizing data from multiple systems—giving staff the power to ask questions like, “Should I pull this order forward to meet demand?” and get answers in minutes rather than days or weeks.

• Inventory and Cost Optimization:
  Immediate, AI-driven insights help manage inventory more efficiently in an era where the cost of excess stock is higher than ever and CFO scrutiny is intense.

• Democratization of Data:
  Natural language analytics enable more employees to self-serve their data needs without relying on slow, overburdened data teams. This reduces wait times and shifts data staff from order-takers to strategic partners focused on data quality, bias prevention, and system optimization.

Quote:
“With this strategy, you're able to scale better without having that kind of labor increase need at the same pace.”
—Nick Stuart [12:58]

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3. Data Literacy, Upskilling, and Employee Experience

[13:30 – 15:43]

• Opportunities and Risks of Expanded Access:
  As more employees gain access to powerful analytics, upskilling and data literacy become critical. There is risk of over-reliance on AI outputs, so human judgment remains indispensable.

• Enhancing Work Satisfaction and Retention:
  Automating routine searches and providing specific, personalized training can enhance job satisfaction and reduce turnover.

Quote:
“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.”
—Nick Stuart [13:49]

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4. Change Management: Communicating and Implementing AI

[15:43 – 17:27]

• Transparency is Crucial:
  Leadership must be forthright about AI’s purpose—improving employee workflow and productivity, not merely reducing headcount.

• Addressing Fear and Resistance:
  Nick notes the importance of clear executive communication—AI helps maximize productivity per employee, especially in a tight labor market, and allows companies to grow without proportional increases in staff.

Quote:
“We're trying to achieve more productivity with the same hours that you guys put in today... invest in technology to be able to increase your output, which is going to allow us to scale with less overhead as a company.”
—Nick Stuart [16:49]

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5. Future Trends and The Need for an Experimental Culture

[17:27 – 18:54]

• Rapid Technological Advancements:
  The pace of AI development is “breakneck,” making it essential for organizations to build a culture where every employee is encouraged to experiment with new tools and approaches.

• Preparation for Uncertainty:
  Brands must stay nimble and foster innovative mindsets throughout their organizations to adapt swiftly to unknown future disruptions.

Quote:
“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.”
—Nick Stuart [18:44]

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6. Staying Agile: Nick Stuart’s Personal Approach

[18:54 – 19:21]

• Prioritization and Responsiveness:
  Nick attributes his effectiveness to constant reevaluation of client needs and prioritizing the most impactful trends, ensuring both his time and his team’s efforts are aligned with core business priorities.

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Notable Quotes & Memorable Moments

• On Human-AI Collaboration:
  “Humans with technology. Right. I think that's the one big theme... I don't see it as much complete replacement, but augmentation.” —Nick Stuart [05:05]

• On Democratizing Data:
  “The real opportunity becomes that natural question ability and I think the adoption is going to be really high because it doesn't require people to have anything more than a question.” —Nick Stuart [10:58]

• On Experimental Culture:
  “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.” —Nick Stuart [18:44]

• Greg on Human-Machine Division:
  “Let humans do what humans do well and machines do what machines do well.” —Greg Kihlström [17:04]

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Key Timestamps

• AI in customer service and agent augmentation: [03:00]
• Balancing AI and human support in customer journeys: [07:15]
• AI’s transformative role in supply chain disruption response: [08:20]
• Inventory, cost management, and the CFO’s new focus: [09:54]
• Democratizing analytics and shifting data team roles: [10:25]
• Upskilling, data literacy, and job satisfaction: [13:30]
• AI, transparency, and employee communication: [15:43]
• Fostering experimental, innovative organizational culture: [17:43]
• Nick’s personal approach to staying agile: [18:54]

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Conclusion

This episode offers rich, practical insight into the blending of AI, automation, and human judgment in retail and supply chain contexts. Greg and Nick explore not just the technological opportunities, but the cultural and organizational shifts required to harness them—highlighting that transparency, upskilling, and fostering a spirit of experimentation are as crucial as investing in the right tools.

For more industry expert insights, listen to the full episode or follow The Agile Brand with Greg Kihlström® at theagilebrand.com.

Transcript

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[Greg Kilstrom]

Welcome to Season seven of the Agile Brand where we discuss the trends and topics marketing leaders need to know. Stay curious, stay agile and join the top enterprise brands and Martech platforms as we explore marketing technology, AI, e commerce and whatever's next for the omnichannel customer experience. Together we'll discover what it takes to create an agile brand built for today and tomorrow and built for customers, employees and continued business growth. I'm your host Greg Kilstrom, advising Fortune 1000 brands on martech, AI and marketing operations. The Agile Brand podcast is brought to you by Tech Systems, an industry leader in full stack technology services, talent services and real world application. For more information go to teksystems.com to make sure you always get the latest episodes, please hit subscribe on the app you listen to podcasts on and leave us a rating so others can find us as well. And now onto the show.

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 Stewart, US 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.

[Nick Stewart]

Thank you so much Greg. Happy to be here.

[Greg Kilstrom]

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 Stewart]

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 leadership to our clients.

[Greg Kilstrom]

Great, great. So, yeah, we're going to talk about a few things here today relating to AI and retail. 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, 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 Stewart]

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, that you know, a company has serving a customer. You really have to have access to all those data points, your orders, you know, your payments, ability to be able to do refunds, all of the things that really kind of 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 not a great employee experience.

[Greg Kilstrom]

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, it works best with just a lot of data and a lot of good data and breadth of data. So sounds like the two kind of solve similar problems, right?

[Nick Stewart]

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, but augmentation to be able to really improve the output and capability of people to do their jobs.

[Greg Kilstrom]

So, you know, talking with a lot of retailers and, 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 Stewart]

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 hundred page manual and try to understand what 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 Kilstrom]

Yeah. And so you mentioned early on about some of the initial frustrations with things like chat bots and I'm sure we've all been on the phone tree doom loop kind of chatbots, 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 you know, how should they kind of look at when to use AI versus human and how to make that right balance?

[Nick Stewart]

Yeah, it's a good question. 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 what they prefer as a human element. So you know, you've got to offer both solutions to your customer and let your customer dictate what experience that they want.

[Greg Kilstrom]

So let's. Another big topic, not only here but just in general is supply chain. And you know, we've been hearing a lot about a lot of challenges that retailers and other companies have been hit hard in recent years with with issues. How is AI helping predict and potentially prevent some supply chain disruptions?

[Nick Stewart]

Yeah, I mean the one thing that's, 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 kind of 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 my customer demands and orders? That's a question that I've seen done in kind of 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 Kilstrom]

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. Sounds amazing. Yeah, yeah.

[Nick Stewart]

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.

[Greg Kilstrom]

Right.

[Nick Stewart]

More than, 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 ability to deliver.

[Greg Kilstrom]

Yeah. 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. I've seen that firsthand. It can take days, weeks to get the democratization of this in analytics, whether it's predicting something, a potential challenge, or predicting an opportunity, or just getting quicker insights. How are you seeing adoption of AI driven analytics and retailers?

[Nick Stewart]

Yeah, it's a really good question. I mean, 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. Right. 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 Kilstrom]

Yeah, yeah. And so how does that change that dynamic? Again, probably a lot of enterprise people listening to this, like that dynamic of data engineer is still very valuable, but how do the roles change in the organization from that perspective?

[Nick Stewart]

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 that 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.

[Greg Kilstrom]

I mean, and that to be honest, that sounds like a win win for everybody.

[Nick Stewart]

Right.

[Greg Kilstrom]

I mean the, you know, the marketers, 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 more, probably more valuable work or seemingly valuable because they're not just order takers. Right?

[Nick Stewart]

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 increase need at the same pace.

[Greg Kilstrom]

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, you know, how, how do you look at, at that component of it?

[Nick Stewart]

Yeah, I mean, that's, that's, that's going to be a challenge. Right. You know, one of the risks with all of this is, you know, 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. Right. And not making blind decisions. That's why there's a human in the workflow. Like, 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 and I think that's a key role for those people and there's going to be some upskilling to get there.

[Greg Kilstrom]

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, you know, what impact does this have on things like employee satisfaction and even retention?

[Nick Stewart]

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.

[Greg Kilstrom]

Yeah, yeah. 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, 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 Stewart]

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.

[Greg Kilstrom]

Yeah.

[Nick Stewart]

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. You know, the reality is, is that if you're sitting at the executive level, labor is 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, at the kind of macro level. And I really believe that part of that, not all of it, a part of that is some of this automation that's being put into on 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 Kilstrom]

Yeah. And it's, I mean, it's, 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.

[Nick Stewart]

Right, right. Yeah, no, exactly. I mean, you know, if you tell employees that we're going to increase productivity BY you working 50% more, that's 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 Kilstrom]

Yeah, totally. So looking ahead, you know, months, a couple years even, you know, 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 Stewart]

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.

[Greg Kilstrom]

I heard as of this morning it's supposed to happen in 12 months. So just.

[Nick Stewart]

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 a 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 Kilstrom]

Yeah. Yeah, I 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 Stewart]

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.

[Greg Kilstrom]

Great. Great. Well, again, I'd like to thank Nick Stewart, Retail Consulting Leader at rsm. To learn more about Nick and rsm, you can follow the links in the show notes and stay tuned for more of my interviews from here at ETEL Palm Springs.

Thanks again for listening to the Agile Brand brought to you by Tech Systems. If you enjoyed the show, please take a minute to subscribe and leave us a rating so that others can find the show as well. You can access more episodes of the [e-mail] that's the agile brand.com and contact me if you're interested in consulting or advisory services or are looking for a speaker for your next event, go to www.gregkilstrom.com that's G R E G K I H L S t r o m.com the Agile brand is produced by Missing Link, a Latina owned, strategy driven, creatively fueled production co op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. Until next time, stay curious and stay agile.

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