Thanks, everybody. I assume we'll have some folks filtering in after lunch finishes up, but, you know, super excited to have Parker Harris with us, co-founder and chief technology officer of Salesforce. Just a couple English majors talking about headless technology. It should be good. So, really excited to have you here. So much going on, you know, in the industry around agentic, AI. You've been through so many cycles, so it'll be a fun conversation.
I've never been through a cycle like this one, but I've seen cycles.
No, I don't think anyone has in terms of the pace and the loss and just sort of size of it. It's pretty amazing. So why don't we just jump into it? And if you have a question, raise your hand. We try to keep this as interactive as possible. But to your point on cycles, you've been through a bunch. And we've seen a lot from Salesforce the last month or so on headless. Why are you all as a company, you know, sort of so excited about that as part of the broader IAI strategy?
Yeah, I think we were surprised, you know, we didn't make it the headline of Dreamforce last year. It was kind of a more recent idea, and we launched it at one of our world tours. And the feedback was just phenomenal. Like everyone, you know, the press and on, you know, social and our customers were like, like, well, this is a brilliant strategy. And I think what we're most excited about is just meeting customers where they are. We've had APIs to our service forever, and but with the rise of... And it's also kind of related to Claude Code that really hit that tipping point in February, that the first place we thought is, Salesforce should just be easier to configure, to implement, to diagnose, you know, and why not vibe-code it?" So that was, like, the first step. Like, let's open everything up, headless, and you can hit it with that. But then, if you look at, and, you know, Salesforce always follows consumer trends. Like when we started the company, it was about Amazon Bookseller. You know, when we launched Chatter, it was looking at Facebook. And right now you look at the model companies and and commerce and There's UI coming into these products. It's a part of headless was also. Let's rethink our Experience layer the experience is actually in the headless layer because you define the user experience in metadata And we interpret it and we play it out in what we call lightning Now that can come to you, but you're not saying what I told you you should see You're just telling the AI, this is what I want, and so give me my, you know, top deals for the quarter. Tell me what trouble tickets or cases that Kirk might ask me about at Epicor, and it will paint in that response beautiful UI, not just a bunch of text. And so it's really the new experience layer. we're seeing customers use it from things like Claude Cowork, you know, with OpenAI ChatGPT, but also from Slack, which I've been spending a lot of time with the past couple of years being a great engagement layer for kind of everything headless. Not just Salesforce, but everything in the enterprise.
We'll definitely talk more about Slack. I guess when you think about the headless strategy, you know, what does success look Like, is it opening up the TAM again for you in terms of just these people that might not have come through Salesforce, you know, traditionally through, say, more of the app layer? Is it, you know, when you think about where you'd want to be in a year on this strategy, you know, what would you guys think about as success? I think, first and foremost, it's about adoption.
So, you know, users are moving and they're looking at these new services. services. Success would be massive adoption of headless. And I haven't seen the stats of MCPs on the agent board side, been more close to Slack. But the Slack business use MCP interface just spiked. You know, we just released it, you know, I don't know, a couple months ago. And it is just spiked. So, the number of people wanting access to that corpus of information is just like so so we're seeing the adoption and you know we're talking a lot internally about what are the monetization strategies for this because I think part of success is also there's you know our current monetization like let's just get more licensed revenue and you know an agent may be talking to Salesforce agentically through headless but it's talking at it as a named user because it needs to get the right data with the right security protocols, the right contacts for that genetic response, there's a lot of named users. But there's also opportunities for usage-based pricing. And we're talking to our customers and saying, well, where do they want us to go?
Yeah, that makes tons of sense. You mentioned Slack. Let's, you know, double-click on that a little bit. You're probably one of the products, when you think about it at Salesforce, that has, you know, perhaps the most network effect to it within your customer organizations. You know, how does that feed into sort of the broader headless strategy? You know, why is that such a great engagement layer for the agentic enterprise?
Talk to us about that a little bit. Well, take Slack where it was most successful when it started before we ever acquired it was with engineering. So, the engineers would take it and be like, great, I'm going to hook it to Git for source code control and Jira for, you know, my bug tracking and planning and connect it to my, you know, monitoring, you know. Give me all the tools, but don't make me leave what they call the flow of work and just work there in context and What is amazing about slack is that expanded from engineering groups to all knowledge workers Where they're working together and humans all humans at the time before AI Like great. I can work in slack and we and we're just getting more work done. It wasn't just about communications It was really about work. Now, it's about AI. It's about getting work done with AI, both as my assistant there, so Slackbot being a native one, but also third parties, you know, Claude Cowork in there or, you know, my linear agent if I'm coding. They're all in Slack because, and they all want to be in Slack. And it's basically where AI assisted or more and more AI autonomous work is getting done but it's where humans are working together with AI with each other and so Slack calls it multiplayer When I use like a codex or a cloud code that's single-player I'm just working myself with it and then if but if I wanted to work with other people Slack's really the best place for that. And so you'll see more things coming where we're opening up more services where when people want to work together, where they're coding or they're doing knowledge work, they're in Slack. And by the way, in both Anthropic and OpenAI, like, that's all they use is Slack. They have Slack, they have Salesforce. They don't really log into Salesforce because they're sitting in Slack using their models and stuff they've built, sometimes our stuff, and working with all of these headless APIs to get their work done.
Has AI given you an opportunity to go back into those customers that might have bought Sales Cloud 10 years ago and say, look, financial services is a good industry, as an example. It's never been a great Slack industry for whatever reason. Maybe people are in Bloomberg chats.
We're working on that. Would you like to buy some Slack?
Our CIOs here, you can pitch them, but I think the idea would be you should rethink this in concert with AI. Is that kind of the message your salespeople are trying to reintroduce it to, again, expand the surface area where you've been?
And I guess, have you seen early success on that, and maybe financial is a tough one, but other industries? Well, let's take sales, for example. So, we always try to, like, use everything ourselves first, call it dogfooding. And so, the sales manager agent, as an example, is this agent that is built on Agent Force that, you know, we have all these leads, we have a lead database, all these prospect leads, and there's a lot that we think aren't valuable, like, don't call them, because you're too expensive as an employee to call them. We call these because we think these are close. But we've taken all the leads we think are lower value, and we've put them on this sales manager agent, which agentically is having conversations with our customers over email, WhatsApp, you know, voices coming where they may call you. But it's not a one-way batch and blast like market automation, like, hey, you're interested in Salesforce automation, and, you know, see if they clicked, and then somebody calls So it's a multi-party conversation back and forth. So that's an example where we can go back in a customer and say, would you like to close more business without adding more humans? We can help you do that. Or the qualified, great acquisition, ex-Salesforce team came back in recently, come to the website site and just engaging with the customer on the website as an agent to get that prospect to the right place where maybe they'll even buy or they'll hand off to a human. And so there's huge opportunities that we have just to go back from the customer base and say, are you an agentic enterprise? Have you found more productivity with AI or not? And if you've not done any of that, we can help you get there.
All right. And you mentioned sort of monetization around, you know, this headless concept. I mean, you guys had the app exchange for a long time, right? API-based sort of revenue stream. I mean, should we sort of think about that, you know, in a similar vein, whereas, you know, you could still buy agents directly from Salesforce, you can build within this, you know, within Lightning, or look, you might be able to build agents on quad and, you know, hit, hit, you know, come in, you know, through the mcp server and get data that way is you know is that sort of the the way we should think about it and i i guess from you from your perspective you know you are again you're trying
to meet the customers where they are is that is that kind of the idea so we have an agent exchange and uh agents can be built on agent force the third parties they can uh if it's built an agent force it's not going through mcp it's just native or third parties can go through um to the mcp interfaces. Customers are building some themselves, which is totally cool. We're just trying to solve what is that use case they're trying to solve and, you know, is it more sales? Is it, you know, happier customers in the service department? Is it, you know, lead gen and market automation? Whatever it is. And we're going to do our best to provide services that, like, it's just... You can do it, but it's gonna be easier or better with us But it's been true ever since we started the company when we started the company We're called salesforce.com and we started the company like well, we're probably gonna do more than Salesforce automation Should we pick a different name? Didn't pick a different name and people told us like change your name But we had Salesforce automation and then you know you have customer service with Siebel or whatever like great We will integrate and so we always want to meet the customer where they are and whatever they're doing, but we'll still pitch, you know, in these customers, you know, the integrated platform and just all from us. It's going to be easier and probably cheaper for you long term and just cost to maintain and run. Okay. You know, Agent Force has been
out there now for maybe 18 months. And what have you all learned in terms of, you know, adoption, you know, sort of, you know, removing the friction? You know, what have companies that are seeing real success with it done correctly and, you know, how do you sort of expand that out to the rest of the…
How many times have people used the word forward-deployed engineer? Exactly.
Plenty.
You know, it's a new term and a number of other companies kind of coined that phrase. I think one of the things we've learned, which is kind of obvious, is agentic AI is non-deterministic, which we know. And so, but, you know, you don't want in your call center, like, it could do multiple things, you know, we can't tell exactly what it'll do, but we'd like it to do, that's not a great answer, if, you know, like, how's my portfolio doing, you're like, you want to give the right answer, you want it to have the right context. So, what we found is being in the customer and, you know, it's no longer about being in the customer in the sales process and saying, here's a demonstration of what we can do It's more like, why don't we build it with you? We have the genetic coding now. We can manipulate the entire platform really, really fast. And we want to show you how it's working. And then we want to work with you to make it successful. So that's one thing. Another thing is that determinism, non-determinism, in the harness of agent force, we started out just saying, well, the models are going to keep getting better. And so when I say, do these 10 things in this order, that's great, it will do that. It turns out, nine times out of 10, it does, but I wanted it 10 times out of 10. And so, and a lot of companies are doing this, we've pulled out some of what is really deterministic logic, which is, you know, workflow, basically, and we built AgentScript, which is essentially a way in an ICY to basically script out what do you want the agent to do, like coming into the website, ask them who they are, you know, or file a claim for insurance as a series of steps you need to do, and then each of those steps, some of those could be non-deterministic, you know, AI through an LLM, something that, you know, that kind of interaction, so mixing the two together. And that's been really successful. And it's actually faster and cheaper because you're not hitting tokens to do some of those things that you really don't need a model for. And so what we've found is these things are brilliant brains, but you don't use them for everything. And I think what we first did is like, well, great, let's just have it do everything.
it turns out they're not great at everything. Yeah. And you all have obviously invested in Anthropic and a number of these native AI companies. Yeah. Thank God. Exactly. That's good.
That was a good one. That was a good one. Yeah. Well, we like to tell John Samurjai you saw it, but not enough. You didn't see where it was going. Always too little after the fact. But,
you know, one of the questions that you bring up around this, you know, sort of harness and orchestration concept is, you know, is that where the value has to accrue longer term for companies that want to participate in this agentic world? Meaning, you know, to your point, the base level intelligence for models will continue to get better over time. You know, so when you think about how you differentiate, how you deliver value to customers, does it need to be your sort of ability to take that brain and then deliver sort of customer value on top of it? And is that, I guess the second part of the question would be, is that durable, meaning is that delta between one of intelligent models, you know, the intelligence again will keep getting better, is it durable? Is that sort of value-add at the orchestration level durable?
Well, I don't think it's just orchestration. It is like everything we're doing is the harness, because we're not building the models, we're We're using multiple models, you know, mixing them, you know, for the right use cases, some for performance, some for cost. And when we say harness, like AgentForce, the entire AgentForce, you can call it a harness because, you know, it's basically using these models to do customer service or do sales. We've got, you know, orchestration in there. We have telemetry for monitoring. We've got evals or testing, you know, the output of it can then get used to update the whole configuration, the prompts and everything. And so that's hugely defensible. You know, we've always been a CRM company. That's why our ticker CRM. We will stay in that lane and, you know, we're not trying to be, you know, a multipurpose like just like use us for any sort of AI. We're going to be CRM-enhanced with AI, autonomous, and I do think that's defensible. And we can also take 27 years of our customer base, the implementations, the business logic, the metadata, all of that's already out there. And they're asking us, you know, our massive sales force, they're like, hey, take us to the future, because we have those trusted relationships, so I think that's also a huge advantage we have, and then we're taking them there. And we keep using, you know, these better and better models, but the models don't have the context, and they don't have the context that, you know, is secure, like, you know, we don't put all the data in the model. It doesn't have, you know, the exact right context for the question, because if you put too much data to the model, it has a hard time, or you spend a ton of money, or both. And so, all of that, I think, is highly differentiated and defensible.
And you mentioned data, obviously, you bought Informatica, you had data cloud before that. How important has that been for you all to build a data platform in the back to complement Agent Force? I mean, we can call it context now because that's the cool word.
But, yeah, we built our data cloud, which is really two things. One is a data platform for collecting data, but also a data activation platform that connects to all the other data platforms out there. MuleSoft for API management. Informatica has been an incredible acquisition. It's exceeded our expectations in the first full quarter. So it's been great for the business. But I think we have too many brands right now. But you know people know these brands so it's fine, but you know, we were doing customer mastering That's very important. We weren't doing product mastering, you know So our customers or financial instrument would be a product or a car from for you know, whoever Informatica has an amazing MDM solution for things like product mastering And if you're an agent and you want to talk, you know to get the right context you want to get the right context on you know, I'm going to the car website and I want to buy a car. Which car? You want the context, all the context for that product. And so, mastering that is super important. And our vision is not that all the data is there, it's going to take forever to make that work. It's a logical, semantic, ontologic, maybe is a better word these days, layer that combines the metadata of the history of Salesforce with metadata from Informatica, of here's all these other data sources, with metadata from MuleSoft, of here's all these other API connected data sources, with Tableau, which is a semantic layer to understand what is the semantic meaning of all this data. All of that comes together and gives us that rich context layer that AI can then use. So, it's a huge event. I'm so happy we were able to get Informatica.
Yeah. Is connecting the data to the agent still the biggest challenge for a lot of your customers in terms of, you know, sort of the promise and the reality right now?
It's not connecting the data. It's the AI shows them where the data is not clean. It's not right. They haven't mastered it. We even found that when we, you know, you think we were perfect, but Cobbler's Children, when we stood up our help.salesforce.com AgentForce agent, And it started showing us where, in our data sources, it wasn't clean, it wasn't quite So, we had to go and fix that. And we had all the tools, obviously, with our products to do that. And so, you know, getting your data right is definitely that first step for any success with AI.
Any questions? I have a bunch more. I'll open it up. I'll keep going. I think the next one, I'm trying to think of the next one. Verticalization for you all and bringing sort of more, it seems to me like in an AI world, the ability to bring an agent that not only understands the sort of domain in terms of being a salesperson, but understands the context and then actual maybe even the nomenclature that goes into a different industry, something that might become more valuable over time i think through app exchange you all let some of your like avivo went out and you know sort of originally did that in pharma um you know how do you think about that going forward for you all because i could see having you know sales agents that are tuned for retail might be different than insurance that might be different from financial services so i know david's schmeier spent a lot of time on this topic and i've talked to him a lot about
this topic? I mean, shout out to Keith Block earlier, former co-CEO. He really started the motion to go industry vertical, which, you know, originally our sales engineers would just go and say, sure, I can, like, take Salesforce and I'll just configure it for banking, you know, retail banking or investment banking. But then we realized it's more than just the data model. And so, like, when you – everybody's talking about vibe coding your CRM. It's like, yes, you can create a data model but it's far more than that and so i think we have a huge advantage as you go deeper into our product line you look at our industry verticals we have a lot of industry vertical business processes built out we are building out industry vertical agent force agents and agent force you know skills and topics that you can use in your industry that understand, you know, an insurance claim, understand a know your customer motion in banking, understand, you know, like, I'm trying to think of other examples, but just understand all of those, and, you know, instead of handing you, you know, a horizontal, here you go, it's a toolkit, go at it, we can have it out of the box, and and it keeps getting better and we're exploring with our research group you know how do we how could we might we fine-tune some smaller models that are industry specific that really understand the business process of that industry to to make them even smarter yeah and that sort of please my
next question i think i know the answer will be but you know i expect you all believe that this is going to be a multi-model world where you're going to be using the right model for the right action in the right context. Is that happening already underneath AgentForce, meaning if someone asks a fairly simple question, you don't necessarily need a frontier model, you might just want an open-source model or, to your point, a small language model. Is that already going on and how, I guess, instantaneous is that when you put in a prompt, is AgentForce smart enough to know the context of the question so I can go to the right model to get the right answer or is that
I don't think it's exactly like that. It's more like the core reasoning loop. The large foundation models are really useful, like, to reason what you want. To then, you know, voice has its own models. To do, you know, checking on ethics or, you know, violations, that can be a simpler model. model, just understanding the question of what are they asking and parsing it out in the right way can be a smaller model. And so, the first step is not like a cost optimization, it's like, let's choose the right model for the use case, because often it's a performance thing. I don't need to run through a trillion-parameter model to do the simple use case, and by the way, it's going to be expensive, and it's going to take too long. And so, quality is the first step, but then performance and cost will be the next, next And so, we're mixing models all over. And we can do it at run time, we can mix and match. I think we will head in in the future, we will look at should we have fine-tuned models per customer for some of those use case that, you know, maybe we're dynamically updating the models from each customer. like we wouldn't mix the data so that's another idea and and finally like we're always looking at well what's the next frontier model that you know what can it give me and we'll you know in the next anthropic model or open AI two biggest ones but you know we also look at companies like Mistral that were invested in and and cohere and other other model companies to look at what do they have we've shied away from the Chinese models for various reasons a lot of which are we sell a lot to the US government right maybe you could help me with a question I
get a lot which is there's obviously going to be some workflows that that are deterministic meaning if you have a policy around CPQ you can't just have a model come up with sort of a guess it can't be how does that get integrated you mentioned you know maybe it's the agent script point you made earlier about, like, how do you start mixing in the benefits of both probabilistic models, but also within, you know, sort of the parameters of having deterministic outcomes to some degree. You can't have salespeople being like, all right, like, close enough on discount approval.
I think one of the best examples is a company called Regrello that we bought, which I think is called Agent Force Operations. Operations. Yeah. We never change the names of our products. you know that. But it, at design time, uses AI heavily. So, it's trying to understand the business process of a corporation that's not written down. It's like, well, oh, you want to, you know, you know, give a discount on professional services. So, this is actually an internal example where we wanted to, you know, for a customer, I want to give them, you know, highly discounted professional services in the deal for, you know, the implementation. Oh, well, to do that, you need approval from these three humans, you need to go in these four systems, blah, blah, blah. And it can look at all the data. You could draw a diagram, it could parse it, or it could look at some of the emails that are going around. And it's using AI to understand, well, what is the real human business process? But then it takes that and it turns it into workflow. Because at runtime, it doesn't need to be the AI running that process. It's like, first, I'm going to ask Kirk for approval via email, and then when he says yes, then I'm going to go to this person, then I'm going to make sure this system goes, and it's obvious what it is. Yep. But figuring that out required, you know, a genetic process. And so I think more and more you're going to see that, and so like companies like Dell are like, wow, we're saving a ton of money. You know, we used to call it supply chain, was the first term we used, because they use it in their supply chain area, but it was just simplifying their internal business processes significantly.
We obviously talk a lot about agentic, and I feel like we're sometimes in a little bit of a bubble when we talk about this in the industry. You know, when you go out and talk to CIOs or you're talking to some of your bigger partners, I mean, how early are we? I feel like, you know, everybody wants the agentic enterprise tomorrow, but when you go out and talk to customers... I think we're really early.
I mean, we're still super early. Right now, the hot area that's getting automated is customer service. That's where you see a lot of little startups. That's where we're playing. And then, in collaboration with Slack and Slackbot, and you see Claude Cowork as an example. Obviously, coding is a huge area. But yeah, those are the areas that we see right now.
And any industry you think that's farther ahead? The ones that are more regulated It seemed to be obvious. They'll take a little while longer in certain functions, but you know, I think it's more like the CIO
Okay, it's more of the leadership of the company. Are they leaning in or not? I was just in France I was meeting with the deco, which is a big recruiter And they're going all in that they they source temporary labor, you know contractors to Corporations all of all sizes and I went out to one of their recruiting offices because I wanted to see our software in use And so they were using Einstein for sales, so that's machine learning, so just help me understand, you know, score some leads and score this candidate, you know, is this a good candidate, match this candidate with the right thing, so that's machine learning. Then it was using agent force to, you know, that outbound to have an interaction with with the candidate, but it was because Pierre Matouchet, the CIO, is an amazing CIO and he's forward-leaning and he's going all in and he's figuring it out. And so, you know, I think it's – and then it's about, like, are you picking the right problem to solve? I think there's a lot of DIY out there that some has worked, a lot has failed. I mean, that's selfishly saying, let us help you. So that's another thing we're seeing, but it's still super early and I think with AI the demonstrations are so compelling we think Everyone's doing it and we all have this FOMO like well, I got to do it, you know And that's why a year ago every every CEO said everybody do AI You know and everybody, you know bought various tools and this stuff and now we're seeing more, you know Consolidation and more, you know use case by use case success
One thing I forgot to ask one more time about headless earlier in the conversation is it would seem to be that headless in a market that's moving this fast lets the customer understand that they have optionality with you, meaning you're not boxing them in. And I would imagine at a time where a lot of CIOs frankly aren't sure in which way they might want to go two or three years from now, that's actually a benefit, meaning I can count on you all to be flexible with me because I think every organization is going to have to be somewhat flexible in an AI world.
It's resounded incredibly well. And we just want to meet people where they are and where they are is moving. And we have built user experiences for 27 years and you can customize them, but we think this is the first version that makes the most sense for you for sales, service. And maybe the future is not that at all and how I interact with enterprise solutions is going to be personalized just for me, and maybe it's not me, it's my agent or agents. Look at how people are coding, they're managers of agents now, like go and rate the spec and test it and do it. I think every job function will move in that direction. And we want to meet the customer where they are, so what service do you want to do that What user experience do you want? If you want to vibe code a new UI for part of Salesforce go for it And you can use parts of what you've already configured you can build your own If that's valuable to you great if you want to use it in and have it surface in these other tools Great if you want to be multiplayer and have multiple people working together. We still think slacks the best If you want to use teams many people have teams, you know selfishly. I think slacks way better we will help you use Teams as a service. And so, and I think the world is moving so fast, we can't predict where it's going to be. We all have to be super flexible and super fast and move with the same pace. You've obviously been at Salesforce since the beginning.
Do you, are you pleased with the agility? I mean, it's a big company. So to move, you know, I think there's sometimes a view of like, you know, innovators dilemma or those kind of things with companies, but do you feel good about the level of velocity that's going on?
I feel really good. I credit Mark Benioff. I think he is an incredible entrepreneur, and he's like, he talks about the interrelated dilemmas, like, we have to go rethink how we're organized in sales. Should we, you know, when we think about four deployed engineers, you know, we have sales engineers. Well, what's the difference? If we're not building demos, How should we think about that? Should we deploy them more out into where the business is happening? You know and so You know headless. Yes, let's go all in on it and So I'm very pleased with the rate of change that we're driving We have an internal process called the V2 mom that helps us stay aligned and we just keep rewriting it Because it keeps changing, but that's how you know Tone from the top, change, try some things, don't be afraid to fail, and that's coming from Mark, and then use that V2MOM process to say, like, we're changing now, now everybody, we've rewritten it, here's how we need to align. But it's never perfect. You know, I just talked to some of the leadership in our technology and product organization. They're asking how do we take more chances and do more. So we've got to keep hammering on it.
Yeah. You're obviously very in the weeds on all the tech. And just out of curiosity, what's the, you know, sort of idiosyncratic thing that you guys have broken through on more recently that perhaps only you would find it, you know, interesting, but I'm kind of curious what you're spending time on, maybe that's, you know, in the bowels of the technology, whether it's data, governance, you know, model.
Well, a lot of it has been, for me personally, has been in the Slack business unit. And the breakthroughs I'm seeing is, how do you think about multi-player cloud code where multiple people are working together with AI to build something? And that could also be co-work, or it could be codex, or it could be linear. So, it's a breakthrough of thinking about, well, Slack is a channel-based experience where you're doing work together and it's all human-based and we're bringing agents What if that agent is building code or writing an S1 to go for it? Maybe Anthropics using Anthropics to write an S1, that'd be interesting. But how do we do that together? And so, what is that experience? What's the identity of the agent, you know, who like and and how do you bring it all in together? And so it's not some maybe it's not the sexiest answer of like, oh, we figured out this, you know, agentic loop or that But it's really more at the user experience and I think Change happens at the user experience, right? You know when Steve Jobs launched the iPhone is like both the world just changed because of the experience You know, the battery doesn't last a day, you know, the apps for it may be the best, but it was the experience. And so, I think as a company, we're really leaning in harder on, well, what is the experience of the future? And we're trying a lot of ideas. And in Slack, you know, what is the experience of, you know, many people working together with AI?
And is that pretty much the operating environment at Salesforce now? Is everybody in Slack?
A hundred percent. We're all in Slack. We're all using Slackbot. I mean, Slackbot as an agent agent, you know, helping people. The adoption rate is the fastest I've ever seen of any feature we've built. And it's helping everybody get their jobs done. And it's phenomenal. And so everybody's working. We now have lots of meetings. We just had a big meeting in Las Vegas, Los Angeles, with our top 500 people. And we launched internally Tableau analytics in Slack, but implemented for all of our regions and all of our sellers, so they can run their business, like, instead of going into Tableau or going into Salesforce or something they built themselves, you know, pulling data out and putting it in Excel, God forbid, they're just living in Slack, running their book of business of what's my pipe for the quarter, you know, what are my top deals, what have I closed so far, who has trouble. That's all happening in Slack with Tableau tied to all the data and it's a great use case to go sell because I could say like, hopefully they're not showing everything, but you know, Kirk, let me show you how I'm running my business at Salesforce and here it is and, you know, it's a really compelling way to sell. Yeah, is that does that was my next question actually which was it's a reference selling you know software's always a reference So anything in the enterprise is reference selling. I mean certainly selling sales first automation if you're a salesperson Yeah, yeah tools. It's very easy
And do you think that slack bot when you show it to customers kind of changes their perception perhaps about you know They're they're where you can go with your technology. Yeah
At a fast I think there's a view of a team shop where they're like, yeah, we don't need another chat tool and we're like, yeah, but can it do this? And they're like, oh, wow, and it's tied to Salesforce. And we have Salesforce channels and Slack where, oh, all the data's there and TableAnalyx, you know, all my work, everything's there. It changes how they think. But we have more work to do there.
Yeah. Anything in particular you think you have more work to do?
I think we have more work to do on both enablement with our teams of, like, telling that story and co-existent with Microsoft Teams. You know, like in Slack, you can now join Teams meetings. You can MCP to the Teams data. Slackbot could use that data, you know, if you're also working in Teams. But people, most customers I talk to, they're not working in Teams. They're using Teams for video and they're using Teams for direct messaging, which is a little bit of work. But they're not really working together deeply on something. And so, but we have more work to do on getting some IP out there.
Just because I think it gets lost a little bit, I mean, I think Mark talked about Slack getting to 10 billion at some point in time. What's the monetization sort of thought process behind Slack changed at all because of this?
So, the monetization is still very much license-based and we move you up in the additions. and so you want more access to more Slack bot, you move up in addition. So that's the typical motion, really successful, and it's one of our best executing business units. But we also see opportunities for some additional usage-based pricing to come out, which we haven't gotten out yet, but we're talking about working on it. Everyone wants their Slack corpus of data. All our partners want access to the APIs, they're dying to build products with it, because now with Agenda AI, the intelligence you can derive from all the unstructured data in Slack, and the messages, the files, all the collaboration, is a huge asset, and that's what you can see using Slackbot, but maybe you want to use some other tools, and so we can monetize that as well, the access to all that.
Yeah, pretty amazing. The amount of context from a business is in Slack for a lot of them. I got one more, unless anybody else has a question. Kind of an open-ended one, sort of maybe a softball to some degree. But yeah, if you guys win an agentic enterprise, what would you view as success over the next couple of years? Obviously adoption, you mentioned it earlier, you know, is it Slack becoming, you know, from a lot of your customers, becoming the operating system for them? Is that, you know, what are the, I don't know, KPIs to some degree that you're keeping an eye on to know that you're on the right track around? I'd like to see
Slack become the interface for getting work done, for sure, and I think we are well on our way to that I think success is also that we you can clearly see how the enterprise has changed with a combination of human workers and digital workers and that's reflected in you know our our solutions making that possible but it's also reflected in our numbers where you're seeing like okay that's amazing you know this company is so much more productive because a third or a half of the workforce is digital labor, and our investors can see, and that value is now seen in these mix of license and consumption-based revenue. And I think we're still on the evolution to consumption-based revenue. You see it. We've had it. Marketing Cloud has had it for years. Agent Force is doing really, really well. Data Cloud's doing it. you know, but I want to see that like really clear in the market and it's not just our pricing and our revenue there, but what has it done to the customer base, like how does that show up where, wow, you know, your call center is half the size and those people are now doing other higher level jobs and your customers are happy and, by the way, it's also a revenue-generating center because, you know, everything is blending now. It's like, you know, this was true before, but the agents, they don't care if, oh, I'm a service agent, but I could also tell you, like, you know, I could upsell you on something. So that's kind of the future I see.
Agentic work units, some of you keep an eye on?
Agentic work, yeah, definitely. I mean, we really were trying to move away from tokens. I mean, because tokens are not a great measurement of did something really get Not every token has value. It just tells you this is how much we've paid model providers and, you know, helps with their S1s. Like, you know, it's kind of like leaderboards for VibeCoding and what's your, are you token maxing and who's using the most tokens to write code. And if you read about that, people are saying, well, that's a terrible metric because people We're just going to try to use the most tokens, and it's not really a right metric for output. And so, AWUs is definitely the right metric. We're trying to tie Slack to that metric, as well as it drives a lot of agentic work units. And then from agentic work units to outcomes also.
Well, we're right at time. Parker, thanks very much for being with us.
Really appreciate it. Thanks, everybody.