Salesforce, Inc. (CRM) June 9, 2026 Conference Transcript

All right. We're going to get started with our next session. Very pleased to have Patrick Stokes with us for our next fireside chat. Patrick, of course, is president and CMO of Salesforce, has nearly 20 years of experience in product leadership roles. That includes the last 15 or so at Salesforce. Patrick, thanks so much for being here.

Thanks for having me. Really appreciate it. My pleasure.

So just by way of getting everything started, it'd be helpful if you could outline for us your primary responsibilities and focus areas at Salesforce.

Sure. Happy to. Well, I'm exactly who you all want to talk to. I'm the chief marketing officer at Salesforce. I've had a bit of an interesting journey. So I've been at the company for about 15 years, longer than most, not quite as long as some. Most of my career has been on the engineering and product side. I started as an engineer, moved into product, kind of 10 years and product at Salesforce. I ran our platform and then moved over to marketing about two years ago, two and a half years ago, right when all the AI stuff really kicked off. I didn't quite know it at the time. And then the whole world changed, which was difficult. And then about four months ago, moved into the CMO position.

Fantastic. So you're coming off of a solid Q1 in which Salesforce impressed with Agent Force disclosures, of which there were several, and you reiterated confidence in second-half revenue acceleration as well. But maybe just briefly recap for us what you personally viewed as most important or instrumental kind of out of the last quarter.

Yeah, well, certainly we're very excited about the continued growth in Agent Force. I think 1.2 billion now on the agent force side, up from, I believe, 800. So we're very, very excited about that. We're starting to see customers reach real scale on that with some pretty sophisticated use cases. I'm also equally excited about the AWU growth. We're certainly seeing our own token consumption from our customers. And that's agentic work units. Agentic work units, yep. And this is this idea of, you know, tokens in and out, that's effectively a measure of reasoning or a measure of intelligence, but it's not a measure of actual work getting And so we thought it was important for the market and really the technology sector as a whole to kind of have a way to measure actual work getting done by these pieces of intelligence. So we introduced that in Q4, and I was very excited to see that continuing to grow alongside token growth, of course. It's interesting watching the two of them kind of grow at, you know, each of them kind of picks up pace at certain moments in time. It's also really interesting looking at the AWUs kind of across different industries and different segments. You can start to see where different usage patterns are emerging. And then lastly, I would say I was very, very excited about our headless launch, which we did not that long ago in March, I think it was, at TDX, our developer conference. And I'm very excited about what that means for Salesforce and the reaction to it we're seeing from our customers. So it really opened up kind of a new way of thinking about our role in this agentic era.

All right. That's great. I definitely want to dive more into Headless in a few moments. But let's stick with Agent Forest, because I think it's important to understand what's underpinning the growth that you were just talking about. So there were a lot of announcements around Agent Forest, I would say, dating back to Green Forest last October. Many other improvements have since been unveiled as well. But how would you characterize the agenda capabilities of Agent Forest today? And what are the most important enhancements to both the tech and the ecosystem, right, over the last six to nine months that are really enabling your customers to unlock more value.

Yeah. I mean, I think the two biggest advancements in the last, let's call it six months or so on the agent force side or one, something that we call agent force script, which is, you know, if you start building an agent, you get very excited early on because you're like, all I have to do is write human instructions. And like, I've coded my agent, you know, they're effectively, if I'm being very reductive, they're effectively just prompts under the covers. And so anybody can write those. But what you realize when you're starting to try to get agents to do multi-step and complex workflows is you have a lot of like, do this, but unless this happens, then do this. And you start writing it like that. And the agent gets very, very, very confused very, very quickly. And the irony of programming languages is that's effectively what they are. if this, then that types of flows. And so we pioneered this way to kind of put little micro moments of scripting into the prompts so that you could eliminate some of the probabilistic problems of working with an LLM. You've all seen this. You ask it a question, and then you ask it the same question, and you get two different answers. So it's a probabilistic, non-deterministic system. When you're trying to execute workflows, you don't want that. You want deterministic. You want do it the way I've asked you to do it. every single time and so agent force helps you with that and that's been a big unlock for our customers that are trying to to either do this at scale or do it in regulated environments do it where there are certain policies that they need to make sure the the agent is following and then the second i would say is voice for sure voice is very very exciting um for our customers um and you know was a a pretty significant computer science project for for us um to get that get that working uh get that working well voice is a a tricky thing because there's humans and people have different ways of talking and there's interruptions and latency and all of these things that you kind of don't think of until you actually start trying to build it you're like oh this is this is actually pretty damn hard yeah and there are patrick you know several um ccast incumbents right that have had existing solutions for a while how do you think about where agent force voice stacks up at this uh at this stage yeah i mean i think we're i think we're in um i think we're in pretty good shape um if you had asked me three months ago i would say we probably have have work to do but i think um at the moment you know we're we're now running salesforce uh our 1-800 number on on agent force voice um you can call it call it now and and you'll talk to to agent force um and we have other customers doing doing the same um i i think you know over we'll probably

be sitting here with our q2 earnings after talking about the the big kind of scaled voice customers just like we we are the kind of chat customers with with agent force okay super interesting and then one other comment on agent force we've started to hear of some forward-thinking customers that i would say are deploying multi-cloud agent force use cases so they might extend from service cloud to sales cloud yeah or you know you're doing case resolution in service cloud and that triggers there's a campaign and marketing cloud. Is this something that you're really kind of seeing as well? And is your go-to-market aligned enough, frankly, to be able to sell more holistically in this way?

Yeah, good question. I mean, it's funny. The first part of your question is like, we saw that from the very beginning. There really isn't a usage of Salesforce that doesn't cut across. The platform is actually quite a bit more munched together than you think that it is from a pure usage perspective. From a buying perspective, it's maybe a little bit more discreet. You have sales service, et cetera, et cetera. As soon as you start using AgentForce, you immediately get into scenarios where you're like, okay I'm gonna use it to try to qualify a lead but then I want to put those leads into a marketing campaign you just you immediate you know or service I I'm gonna use it for for case resolution but I need to know if there's an open opportunity because I'm gonna handle my case resolution in a different way so there's there's tons of those types of use cases what we've tried to do is is make it easier to to buy that so this is what our kind of top end additions like uh we call it internally a4x um so agent force for sales and commerce uh service etc they they kind

of come with the entirety of the platform it's like here it all is go implement your agent um and uh so you're not kind of buying individual piece parts okay great uh and then um maybe uh just to sort of zoom out a bit I think what a lot of investors in this room and and elsewhere are wondering is like when will the rubber hit the road right when it comes to enterprises meaningfully deploying agent force in production and at scale certainly we know that some have gotten to that level right but it just comes down to more you know broad adoption in that capacity yeah I think I think your second part there kind of got it I think certainly we are seeing many

many, many hundreds of customers reach meaningful scale, either scale from a usage perspective, in some cases simple use cases, but that are being interacted with by hundreds of thousands or millions of individual consumers on the other side, customers like Southwest, who are now doing 20% to 30% of all of their inbound requests from customers with AgentForce. So it's like the curve of sophistication of the interaction and then volume. And so there may be kind of lower on sophistication, but very, very high on volume. And then you have other customers that are very, very high on sophistication and maybe a little bit lower on volume as they experiment. So we're seeing kind of all ends of that spectrum. But I think the real basis of your question is, OK, Patrick, but when is everybody going to be doing that? When is 187,000 of your customers, they're all going to be doing that?

Or at least a very significant percentage.

At least a very significant percentage. I think we're definitely nearing that. It's certainly part of how we think about H2 and accelerating the business. That's kind of all factored into that. So I definitely think you're going to start to see a rapid acceleration as the product gets easier to use, as the CIOs start to kind of trust what we've built. And that also means, in a way, I have to be careful how I say this, but in a way, given up on what they've tried to DIY, right? It's kind of like the cloud in the early days where everybody went out and tried to build their own cloud. And, you know, you could look at it and say, what are you doing? Well, what they're doing is they're creating intuition of what it takes to do it, and then that's putting us in a better position because now they come with that intuition and they go, oh, okay, you've actually solved the problems that I ran into and maybe didn't want to solve. And so we're definitely starting to see a lot more kind of openness to the platform from CIOs than maybe we were six, 12 months ago when everything was so new.

They were all just trying to figure it out. okay yeah super encouraging um and of course uh just maybe to sort of stay with this train of thought i mean the the big fear out there remains that ai and specifically the frontier labs right that they will um drive significant disruption and deceleration in the salesforce business you know why is that view incorrect well i mean there's there's the data side of it and then there's the subjective side i mean the data side shows that it's it's not correct at the moment Our seats are still growing.

Our businesses, our core businesses are still growing, and we're very happy with the growth. But also when you look into the AI labs, this is what's most fascinating to me. And many of these labs are run by, have people in the go-to-market organization that we know. They're all using Salesforce extensively, in fact, more than some of our biggest customers. And the reason that is, is because these AI labs, what they're doing is they're not using Salesforce the same way customers have for the last 20 years. They're not using it as a UI. They're not logging into it every day and logging a call and an opportunity. They're using it through their own agentic interface, through Claude Enterprise or through Codex. They're hooking it up with MCP. This is how Headless comes into play. And that's how they're using Salesforce. So what we're seeing is there's actually an expansion of usage, an expansion of consumption. That's separate from the whole seat conversation is kind of a separate one, which I'm sure you'll hit on. But just if you separate that for a moment, the existing seats, this new way of working with Salesforce, we're seeing usage spike up quite a bit. And so that's very, very encouraging for us.

Okay, terrific. And yes, that was a perfect segue to Headless. So, you know, I do think the Headless 360 announcement, Patrick, just seems like a really clever and interesting way to drive stronger connections, to your point, with the frontier AI models. Also, you know, opening up the Salesforce platform to external AI agents and coding tools via MCP, as you also highlighted. One of the things I would add is just also reducing friction, just making Salesforce easier for developers to use. so I guess is there anything, is that a fair characterization, anything else that you would sort of highlight as part of this?

Yeah, definitely a fair characterization, I mean the inspiration for Headless came from just watching people use Salesforce in a different way we have these partnerships with the AI labs and we're watching them use and we can see the API data, we can see that their consumption is through the roof, but like they're using it through Slack. For example, the AI labs are all big, big giant Slack users or they're using it through their own cloud interface. But we're also seeing it just in the, you know, this is a very, very hot kind of space right now. So you go to X or you go to Reddit and it's just like an army or you go to these random, you know, these discord channels where all these developers hang out and there's just these, you know constant uh uh kind of um sets of conversations of people trying to figure this out there's something that i think people can sense um where they're like there's a new way to work they they i think that that's what they're sensing that there's going to be a way to work that all of the friction of going to all of these discrete applications that have been purpose built for the function that I do, that's going to go away and it's going to be replaced by a new interface that interacts with kind of the underlying capability of those discrete applications. So the applications aren't going to go away, but the UI is going to be massively disrupted. And so we started to see that pattern emerging and that was the inspiration. It's just like, okay, well, if that's what customers want and many other companies kind of in our space saw it as well and they were like, we don't want that. We're terrified. We don't want to lose our users. We don't want to lose the UI. And we just took the exact opposite approach and said, no, we endorse it. Let's open this up. We're going to have to figure out how to monetize it, which is something that we're talking a lot about and I'm sure is very relevant for all of you. But the pattern itself is really a no-brainer. And that's why I think it's so exciting. I think that you saw a very positive sentiment, which we were all very excited about. It's been a couple months of the SaaSpocalypse, and so kind of getting back in front with a message of like, no, no, guys, we see this. We understand what it is you're trying to do, and we want to actually enable that and endorse that. We think that that is the future. I think it was a little bit of a reset moment for us.

Okay, and clearly it sounds like a mechanism that puts Salesforce more directly in the token path.

Absolutely. Well, I mean, when you look at these tokens, tokens represent intelligence, as I said earlier. And right now, by far, the single biggest use case for consuming tokens is coding, right? It's the killer app. One engineer can generate like $100,000 a month bill without much problem. Now you can debate whether that's highly efficient or not. But these labs are making a lot of money on these tokens. There will probably be some sort of normalization or reckoning of that. Right now, everybody is just like, everybody code, you know, and so people are, you hear these stories of people faking their, you know, blah, blah, blah. So there's going to be a normalization. But what the AI labs are, we think that they're looking for is, well, what's the next killer app? What's the next killer use case? And that's where we think Salesforce is perfectly positioned because we think it's knowledge work. We think it's people like you every day that are showing up and having meetings and analyzing things and making decisions. You have to access information, and you want to be able to access information in a way that's low friction, and then you want to be able to connect that to the intelligence of the AI, and that's effectively what we can provide.

Okay, terrific. Perfect. And then you alluded to this, Patrick. We know there has not been an official decision on Headless 360 pricing, but maybe you could just sort of speak to some of the early considerations and how that monetization might present itself for Salesforce.

Yeah, I mean, so there's kind of two arms to the puzzle. One, there's actually quite a few very important technology decisions that we need to make in terms of how do we ask people that are building agents outside of our platform to identify those agents to us in the same way that they identify a user to us. And that's important to solve because that provides the layer of kind of governance and licensing and permissioning that we need to put for the agent to exist and consume from Salesforce in the first place. So there's a number of technology decisions there, there, many of which will almost certainly result in some sort of agent user license showing up. So just like we have human licenses, we'll likely have agent licenses as well, where you have to self-identify the agents that you run on top of Salesforce. Now that sounds like if you're in Salesforce, at least, where we have this 25-year legacy of seat-based pricing, that sounds like the answer. okay great so we're going to charge for the for the agent licenses um that's possible um but we're trying to be as thoughtful as we can on this and make sure that our own uh kind of legacy bias on that doesn't doesn't come in too much we're trying to be very uh uh kind of forward in the way that we think about it but what really what we're doing is we're talking to our customers and our partners and we're going to them right now and we're saying look blank slate here's your contract imagine that you could just rewrite your contract right now and have whatever unlimited usage of headless that you want for the environment that you're trying to create. You tell us what that contract would look like. And those are the conversations we're having with our customers right now to make sure that we can do it thoughtfully. What we don't want to do, what you can feel in the room, I feel it in every room, and especially rooms like this, is you all want to know the pricing model so you can you can model our future growth and you know we want you to be able to do that as well but we want to make sure that we don't give you something that turns out to be wrong and then and then we we have the wrong model so that's that's why we're being careful here yeah it makes perfect sense and the other thing which um i don't know how much of this you guys have thought through right but if i think back to the early instantiations of agent force pricing right where you know and then you know this was a very different world right because everyone was trying figure it out like six months ago everything's warp speed so uh but you know initially it was

per conversation pricing and then it was per action and then we got to the point of having um you know sort of um like discrete you know subscriptions right then we had the agent force one edition and then you have alas right where you have you know agent force elas for customers that are willing to make very very big commits um so i guess the question here is uh you know is the bias from your perspective and i won't hold you to it i realize it's early stages but to sort of give customers some choice but maybe to not make it overly complicated uh which some might argue was um uh maybe an initial impediment to agent force adoption again in the very early innings before you kind of were able to work through all that i think that's a

very fair uh characterization or or criticism whatever you want to call it um yeah i think I think there's like a Goldilocks type of scenario that we haven't totally found yet. When we first started with Agent Force, it was something that was brand new, and customers didn't know what to expect, so they wanted a consumption model, and so we gave them a consumption model, but it turns out that it was a very complicated consumption model, and so it was very, very difficult to kind of predict. It was hard enough to predict their own usage, because they didn't really know how they were going to use it at the time. And then, even if they could predict their own usage, it was hard to turn that usage into to understand the commercials of it because our consumption side was so difficult. So we made it easier. We're like, okay, what if you just bought the Ayla and then you don't worry about it? Use as much as you want and don't worry about it. Customers like that as well because it's simple, but it's also very expensive. It's more expensive than the, so it's, you kind of have to pick your evil, but that's not what we want. We don't want the customer to have to pick their evil. We want to get them to something that they can really trust and believe in. And I think we're seeing other models emerge outside of Salesforce that are interesting. And, you know, I think what this comes down to is it's not really about what benefits It's we want to find the model that benefits our customer. And so we're going to experiment with as many models as we can. And the downside to that is it looks like we're confusing the market, which I get. It's like, how many pricing models do you have? And how do we measure this? But our approach is like, yeah, that's a moment in time. And we kind of just need you to trust us on this. We're going to figure it out. We're going to do it with our customers. And we're going to find the right thing to do.

Very helpful. Yeah, thanks, Patrick. And while we're on the topic, so last week, Salesforce announced the acquisition of Contentful, which has a CMS content management system. Can you expand on kind of what this IP will help Salesforce accomplish?

Yeah, well, first of all, so the interesting thing about Contentful is that it's effectively a headless first platform. So they didn't spend a great deal of time worrying about what the UI for content could look like. Because if you think about content, it's really just like a feature of a campaign, which is a feature of marketing. So, you know, the SaaS era has created just like this massive sprawl of these purpose-built applications, and that's especially true in marketing. If you look at the LumaScape for marketing, it's like there's so much. So they were like, we don't think that's where the world is going. We think the world is going into a world where there's some sort of intelligence that's orchestrating campaigns in real time and doing one-to-one personalization. And so what it would need to do that is it would need a headless CMS. It would need to be able to pull the content out when it needs it. The CMS would have to have enough metadata, enough context in it, so that that intelligence on the other end can go grab that when it needs it. So part of the attractiveness of the acquisition was certainly that, just bringing a little bit more headless DNA into our product organization. But also, this has been a little bit of a gap in our marketing cloud strategy for many years. And so it certainly had some attractiveness in terms of kind of shoring up our marketing cloud.

All right. Terrific. I'll ask one more question and then we'll pause for any of you folks that may have a question to ask Patrick. So I want to just talk about Slack for a moment. So a lot of investors, quite frankly, believe that Salesforce overpaid for Slack all those years ago. now my opinion is that view has some merit but I will also say that slack has quietly become a more integral technology in an agentic world and many people don't really seem for my investor conversations really seem to be aware of that to the extent that it's actually occurring and going forward it also seems that because of slack bot that this can really become more of a center of of gravity for Salesforce. That's the opportunity that we see, but it'll be helpful to hear your vision on where you think all this is heading.

Well, it's pretty similar to your characterization there. Slack for us, I think the market in general kind of sees Slack as a collaboration tool and they kind of put it in the same bar as Teams. And I think that's just a really kind of unsophisticated view of what Slack really is, which is you know where slack started was it was a platform for developers so developers needed a way to work together to build projects and so slack emerged as a solution for that and it wasn't just because you could create channels and invite your friends into the channels it was because of its uh its openness its connectivity to things like github and jira it built this ecosystem of connectivity. So it became very, very sticky for developers in the way that they work. We're seeing that same thing now happen to all sorts of different functions. Certainly this is true in Salesforce. I mean, we only work in Slack. It's unbelievable how much we get done in Slack, but we're seeing it with our customers as well. And especially the new logos, the small customers, and even the AI labs, which we say it's small, they're like trillion dollar companies, but they only have 2,000 people in them. So on the scale of employees, they look like they're small, but they run their whole business on Slack, and it's because of that connectivity. So that's one side of it. But the other side of it that's very exciting is that it is already a conversational interface so it's already really well prepared for an agentic future for when you have teams of humans working side by side with agents but it's even more than that because you could say well patrick we kind of already have that with with claude you know and with chat gpd that's a human working with an agent and it is but it's one human working with one agent it's a single player environment And that single player environment, it's useful, it's nice to be able to work like off on the side and just talk to Claude, but most of us work within teams. Slack is inherently a multiplayer environment. And once you start putting agents into a multiplayer environment, all sorts of interesting kind of usage patterns start to emerge. So one person can ask a question, and the agent answers, and then a second person can ask a question, and it becomes this very interactive multiplayer type of experience. Imagine using Cloud Code, which is also a single-player environment in a multiplayer experience. These are things that the labs are kind of very interested in doing with us at the moment. And it also adds an element of trust. How many of you have ever had your boss or your manager send you something that you can tell was just done in Claude? And they're like, you know, look, here's my analysis. And like you instantly know it was done in Claude just by, you know, the way it's written. We all have a Spidey sense for this now or OpenAI as well. But what you also are probably developing a Spidey sense for is that your manager is full of shit and whatever they prompted to get that answer was full of bias. And so, of course, it's very convenient that the analysis that the AI did matches, you know, what the person on the other side is asking for, because that's just human nature in the way we prompt it. Well, when you move that into a multiplayer environment, all of a sudden this kind of new paradigm of trust evolves, because now you can see the prompt, right? And you understand how it arrived at the answer. And in fact, multiple people can kind of prompt it at the same time. and so you're getting you're getting a much richer answer that's much more based in uh multiple viewpoints uh which is a much more trusted answer and that's something that i don't think anybody has really demonstrated yet but we're on the path to do i have to say that's a fascinating point i hadn't thought through that in that degree of depth uh that may be something again that really enables you to kind of drive some more separation right as slack bot continues to and slack more and broadly continues to get adopted. So that's, yeah, that's- And even Slackbot, amazing tool, incredible capability, but even Slackbot right now at this moment in time is single player, right? So you ask it a question, it answers for you. You can share the answer, but imagine instead of that, you're just in a channel and you have, you know, 15 members in the channel, but two of them are agents. And you just ask a question in the channel and the agent responds to them. So that user interaction model or paradigm hasn't been fully explored yet, but that's where we're going with Slack, and it's super exciting.

Okay, tremendous. So fascinating conversation, so that took quite a bit of time, but it was important, I think. Any questions for Patrick? Yes?

Well, I think, you know, what we have at Salesforce, whenever we're building product and trying to bring product out into the world, what we're trying to do is identify what's the thing that's scarce. Right now, the intelligence is not really scarce. I mean, there's limits in how much energy we can provide in the world to kind of deliver the inference through the GPUs. But the thing that Salesforce delivers that's scarce is context and trust. And so if you're building anything with AI right now, it's very, very easy to just start talking to an LLM and you get excited about how intelligent the answers sound, but then you realize it doesn't know anything about your business. And so then you start trying to figure out ways to dump information into it so it understands your business. But that runs into all sorts of complexity and limitations and how much you're putting into the context window. You can get about a million tokens into a context window right now. But if you put a million tokens in your context window, that's going to be an insanely expensive call. That one question is going to generate a ton of tokens. And the more you put in there, the more confused your LLM will get. So you're you have to engineer this way to get the exact context that it needs in the moment, pick the right model and then deliver an answer. And that's what Salesforce can kind of do behind the scenes in that moment and then what gets really interesting in that multiplayer environment and inside of slack is the one piece of context that nobody's really truly thinking about well there are some us and others that we're working with but the really interesting piece of context is the institutional knowledge of the people in your organization which is encoded as those conversations inside of slack so we bring that in as well it's not just your data and your metadata it's the conversational data as well which adds all that additional context and then we engineer a way to bring that all in at the moment that a question is asked

optimize the token spend and that's that's really where we think our value is going to be in the future hopefully that answers your question you're welcome uh 30 seconds if someone has a quick one for uh for patrick if not i'll just ask one more so i just wanted to bring up a recent podcast with with Mark Benioff, he mentioned that Salesforce is on track to spend $300 million on tokens from Anthropic this year, pretty massive amount. Tell us just briefly what that will do for Salesforce in terms of productivity and innovation, and maybe even inclusive of, you know, the cost implications.

Do you want to see our leaderboards? I can pull them up on Slack right now, we can go through team by team and see who's really using. I mean, it's just been, honestly, it's been, it's been insane in our engineering organization. Watching the pace of innovation right now is unbelievable, and there are pockets where it's even more unbelievable. Slack, for example, is just fascinating to watch that team now. I show up with ideas, and on Monday, the ideas are in production, which is really astounding to watch. But it's going much deeper than that as well, because a lot of that, most of that spend right now is coding, but not all of it. There is a very, this is what I don't think the market has totally caught up to yet. A very material amount of that spend is just knowledge workers. It's my team in marketing. It's Val's team. It's people in sales. It's people like Miguel that are using it to do their forecasting. They're not coding. They are hooking up the MCP servers and consuming tokens from Anthropic to do their knowledge work. And we think that that's where the kind of next big wave is going to come from.

Super insightful conversation. Patrick, thanks so much.