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