2025 started with an expectation of slower economic growth and stubborn inflation.
While growth did cool, the real surprise was the disconnect between the economy and financial markets.
Unemployment ran higher than projected, yet markets showed resilience, powered largely by an AI-driven capital spending boom.
Looking ahead to 2026, the backdrop is brighter.
Global growth should accelerate modestly, inflation should ease in the second half of the year and real incomes look poised to improve.
We expect the U.S. to lead the charge and remain most constructive on the U.S. market.
Thank you for listening throughout 2025 as we've navigated these issues and events that shape financial markets and society.
We hope that you'll join us next year as we continue to bring you the most up to date information on the financial world.
This week.
Please enjoy some encores of episodes over the last few months and we'll be back with all new episodes in January.
From all of us at Thoughts on the Market, happy holidays and a very happy new year.
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 US consumer and how AI is changing consumer companies.
With me on stage we have Arunima Sinha from the global and US economics team, Simeon Gutman, our US hardlines broadlines and food retail analyst.
And Megan Clapp, US food producers and leisure analyst.
It's Friday, December 5th at 10 a.m. 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?
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 AIs.
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 one 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.
It 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 screen more.
So there is some potential bias in that.
But otherwise, the fact pattern was objective.
Walmart has full-scale AI deployment.
They are integrated across their business.
They've introduced Gen AI tools.
That's like their Sparky shopping assistant, as well as integrated to in-store features.
It's been driving a 25% increase in average shopper spend.
They've recently partnered with OpenAI to enable chat, GBT powered search and checkout positioning 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.
And how about a couple examples of the ways companies are using these?
Any interesting real-world use cases you've seen so far?
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.
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?
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 become more impactful.
And for Staples broadly, and food ties into this.
I think these companies start with an advantage in that they sit on a tremendous amount of high-frequency consumption data.
So the data availability is is quite large.
The question now is 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 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 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 can move a little bit faster.
And so that's how we're thinking about the opportunity.
Can you give us also just a couple examples of AI adoption that's been successful, that you've seen so far?
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 kind of incorporate that and take the idea to shelf time down significantly.
And then on the cost side, you know, general mills is a company who's actually relatively far ahead.
I'd say an AI adoption curve in staples broadly.
And what they've done is um deployed what they call digital twins across their network and it's improved forecast accuracy.
They've taken their historical productivity savings from 4 annually to 5.
That's something that's structural.
So seeing real tangible benefits that are showing up in the P&L.
And so I think broadly, the theme is these companies are using AI to make faster, more precise decisions.
And then I thought I'd just mentioned on the leisure side, something that I felt was interesting, that we learned from Shark Ninja yesterday at the conference is, you know, 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 DTC website for LLMs like like ChatGPT and Gemini.
And his point was that what drives conversion on DTC today may not ultimately be what ranks an 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 kind of optimizing their portfolio.
He thinks brands will win, but you've got to get ahead of it as well.
Yeah, 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, 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?
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 positioned, 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 gonna 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 more.
The retailers are gonna 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 incentives 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.
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, and 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?
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 2027, 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 18, for 2020.
2027, it's 2.0%.
So it's an important part of that process.
In terms of the labor market itself, the work that you've 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-sized 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 complement 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.
Well, thank you all for joining us.
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