Practical Use Cases: Personalization & Cataloging

Live from the Morgan Stanley Global Consumer & Retail Conference, our analysts discuss how AI is reshaping the future of shopping in the U.S.

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Michelle Weaver: Welcome to Thoughts on the Market. We're coming to you live from Morgan Stanley's Global Consumer and Retail Conference in New York City, where we have more than 120 leading companies in attendance. 

Today's episode is the second part of our live discussion of the U.S. consumer and how AI is changing consumer companies. With me on stage, we have Arunima Sinha from the Global and U.S. Economics team, Simeon Guttman, our U.S. Hardlines, Broad Lines, and Food Retail Analyst, and Megan Clap, U.S. Food Producers and Leisure Analyst. 

It's Friday, December 5th at 10am in New York. 

So, Simeon, I want to start with you. You recently put out a piece assessing the AI race. Can you take us through how you're assessing current AI implementation? And can you give us some real-world examples of what it looks like when a company significantly integrates AI into their business? 

Simeon Gutman: Sure. So, the Consumer Discretionary and Staples teams went to each of their covered companies, and we started searching for what those companies have disclosed and communicated regarding their AI. In some cases, we used AI to do this search. But we created a search and created this universe of factors and different ways AI is being implemented. We didn't have a framework until we had the entire universe of all of these AI use cases. 

Once we did, then we were able to compartmentalize them. And the different groups; we came up with six groups that we were able to cluster. First, personalization and refined search; second, customer acquisition; third product innovation; fourth, labor productivity; fifth, supply chain and logistics. And lastly, inventory management. And using that framework, we were able to rank companies on a 1 to 10 scale. 

Across – that was the implementation part – across three different dimensions: breadth, how widely the AI is deployed across those categories; the depth, the quality, which we did our best to be able to interpret. And then the last one was proprietary initiatives. So, that's partnerships, could be with leading AI firms. 

So that helped us differentiate the leaders with others, not necessarily laggards, but those who were ahead of in the race. In some cases, companies that have communicated more would naturally scream more, so there is some potential bias in that. But otherwise, the fact pattern was objective. 

Walmart has full scale AI deployment. They're integrated across their business. They've introduced GenAI tools. That's like their Sparky shopping assistant. As well as integrated to in-store features. They talked about it. It's been driving a 25 percent increase in average shopper spend. They've recently partnered with OpenAI to enable ChatGPT powered Search and Checkout, positioning where the company, where the customer is shopping. 

They're also layering on augmented reality for holiday shopping, computer vision for shelf monitoring. LLMs for inventory replenishment. Autonomous lifts, the list goes on and on. But it covers all the functional categories in our framework. 

Michelle Weaver: And how about a couple examples of the ways companies are using these? Any interesting real world use cases you've seen so far? 

Simeon Gutman: So, one of them was in marketing personalization, as well as in product cataloging. That was one of the more sided themes at this conference. So, it was good timing. So, the idea is when product is staged on a company's website; I don't think we all appreciate how much time and many hours and people and resources it takes to get the correct information, to get the right pictures and to show all the assortment – those type of functions AI is helping enable. 

And it sounds like we're on the cusp of a step change in personalization. It sounds like AI, machine learning or algorithm driven suggestions to consumers. We didn't get practical use cases, but a lot of companies talked about the deployment of this into 2026, which sounds like it's something to look forward to. 

Michelle Weaver: And Megan, how would you describe AI adoption in your space in terms of innings and what kind of criteria are you using to assess the future for AI opportunity and potential? 

Megan Clapp: Yeah, I would say; I'd characterize adoption in the Food and broader Staples space today is still relatively early innings. I think most companies are still standing up the data infrastructure, experimenting with various tools. We're seeing companies pilot early use cases and start to talk about them, and that was evident in the work we did with the note that Simeon just talked about. 

And so, the opportunity, I think, going ahead, lies in kind of what we see in terms of scaling those pilots to become more impactful. And for Staples broadly, and Food, you know, ties into this. I think, these companies start with an advantage and that they sit on a tremendous amount of high frequency consumption data. So, the data availability is quite large. The question now is, you know, can these large organizations move with speed and translate that data into action? And that's something that we're focused on when we think about feasibility. 

I think we think about the opportunity for Food and Staples broadly as we'd put it into kind of two areas. One is what can they do on the top line? Marketing, innovation, R&D, kind of the lifeblood of CPG companies, and that's where we're seeing a lot of the early use cases. I think ultimately that will be the most important driver – driving top line, you know, tends to be the most important thing in most consumer companies. 

But then on the other side, there are a lot of cost efforts, supply chain savings, labor productivity. Those are honestly a bit easier to quantify. And we're seeing real tangible things come out of that. But overall I think the way we think about it is the large companies with scale and the ability to go after the opportunity because they have the scale and the balance sheet to do so – will be winners here, as well as the smaller, more nimble companies that, you know, can move a little bit faster. And so that's how we're thinking about the opportunity. 

Michelle Weaver: Can you give us also just a couple examples of AI adoption that's been successful that you've seen so far? 

Megan Clapp: Yeah, so on the top line side, like I said, kind of marketing innovation, R&D. One quick example on the Food side. Hershey, for example, they're using algorithms to reallocate advertising spend by zip code, based on the real time sell through. So, they can just be much more targeted and more efficient, honestly, with that advertising spend. I think from an innovation perspective too, these companies are able to identify on trend things faster and incorporate that and take the idea to shelf time down significantly. 

And then on the cost side, you know, General Mills is a company is actually relatively, far ahead, I'd say, in the AI adoption curve in Staples broadly. And what they've done is deployed what they call digital twins across their network, and it has improved forecast accuracy. They've taken their historical productivity savings from 4 percent annually to 5 percent. That's something that's structural. So, seeing real tangible benefits that are showing up in the PNL. And so, I think broadly the theme is these companies are using AI to make faster, and more precise decisions. 

And then I thought, I'd just mention on the leisure side, something that I felt was interesting that we learned from Shark Ninja yesterday at the conference is – when asked about the role of Agentic AI in future commerce, thinks it'll be huge was how he described; the CEO described it. And what they're doing actively right now is optimizing their D2C website for LLMs like ChatGPT and Gemini. And his point was that what drives conversion on D2C today may not ultimately be what ranks on AI driven search. 

But he said the expectation is that by Christmas of next year, commerce via these AI platforms will be meaningful; mentioned that OpenAI is already experimenting with curated product transactions. So, they're really focused on optimizing their portfolio. He thinks brands will win; but you have got to get ahead of it as well. 

Michelle Weaver: And that's great that you just brought up Agentic commerce. We've heard about it quite a bit over the past couple of days, Simeon. And I know you recently put out a big piece on this theme. 

Agentic commerce introduces a lot of possibility for incremental sales, but it also introduces the possibility for cannibalization. Where do you see this shaking out in your space? Are you really concerned about that cannibalization possibility? 

Simeon Gutman: Yeah, so the larger debate is a little bit of sales cannibalization and a potential bit of retail media cannibalization. So, your first point is Agentic theoretically opens up a bigger e-commerce penetration and just more commerce. And once you go to more e-commerce, that could be beneficial for some of these companies. 

We can also put the counter argument of when e-commerce came, direct-to-consumer type of selling could disintermediate the captive retailer sales again. Maybe, maybe not. Part of this answer is we created a framework to think about what retailers can protect themselves most from this. Two of them; two of the five I’s are infrastructure and inventory. So, the more that your inventory is forward position, the more infrastructure you have; the AI and the agent will still prioritize that retailer within that network. That business will likely not go elsewhere. And that's our premise. 

Now, retail media is a different can of worms. We don't know what models are going to look like. 

How this interaction will take place? We don't know who controls the data. The transactions part of this conference is we were hearing, ‘Well, the retailers are going to control some of the data and the transaction.’ Will consumers feel comfortable giving personal information, credit card to agents? I'm sure at some point we'll feel comfortable, but there are these inertia points and these are models that are getting worked out today. 

There's incentives for the hyperscalers to be part of this. There's incentive for the retailers to be part of it. But we ultimately don't know. What we do know is though forward position inventory is still going to win that agent's business if you need to get merchandise quickly, efficiently. And if it's a lot of merchandise at once. Think about the largest platforms that have been investing in long tail of product and speed to getting it to that consumer. 

Michelle Weaver: And Arunima, I want to bring this back to the macro as well. As AI adoption starts to ramp the labor market then starts to get called into question. Is this going to be automation or is it going to be augmentation as you see a ramp in AI adoption? 

So how are your expectations for AI being factored into your forecast and what are you expecting there? 

Arunima Sinha: There are two ways that we think about just sort of AI spending mattering for our growth forecasts. One part is literally the spend, the investment in the data centers and the chips and so on. And then the other is just the rise in productivity. So, does the labor or does the human capital become more productive? 

And if we sum both of those things together, we think that over 2026 – [20]27, they add anywhere between 40-45 basis points to growth. And just to put things in perspective, our GDP growth estimate for the end of this year in 2026 is 1.8 percent. For 2027, it's 2.0 percent. So, it's an important part of that process. 

In terms of the labor market itself, the work that you have led, as well as the work that we've been doing – which is this question about adoption at the macro level, that's still fairly low. We look at the census data that tracks larger companies or mid-size companies on a monthly basis to say, ‘How much did you use AI tools in the last couple of weeks.’ And that's been slowly increasing, but it's still sort of in the mid-teens in terms of how many companies have been using as a percentage. 

And so, we think that adoption should continue to increase. And as that does, for now, we think it is going to be a compliment to labor. Although there are some cohorts within sort of demographic cohorts in terms of ages that are probably going to be disproportionately impacted, but we don't think that that's a sort of near term 2026 story. 

Michelle Weaver:  Well, thank you all for joining us and please follow Thoughts on the Market wherever you listen to podcasts. 

Thank you to our panel participants for this engaging discussion and to our live and podcast audiences. Thanks for listening. If you enjoy Thoughts on the Market, please leave us a review wherever you listen and share the podcast with a friend or colleague today.