Investing.com
Published Jun 10, 2025 08:01PM ET
On Tuesday, 10 June 2025, Salesforce Inc. (NYSE:CRM) participated in the BMO 2025 Virtual Software Conference, shedding light on its strategic focus on AI innovations and addressing customer adoption challenges. While the company highlighted advancements in AI and data integration, concerns about customer readiness and market competition were also discussed.
In conclusion, Salesforce's presentation at the BMO 2025 Virtual Software Conference highlighted its strategic focus on AI advancements and data integration, while acknowledging the challenges of customer adoption. For a deeper dive, readers are encouraged to refer to the full transcript.
Keith Bachman, BMO: Everybody. Good morning for some. It's Keith Bachman here. We're from BMO. We're sorry we're a touch late.
We've run over, I gather, in terms of our virtual conference. But for me, this is my last I'm thrilled to have Salesforce on with us. There's a a few from IR, but we're just gonna go to Susan. And a way to start this is we're gonna ask Susan to give her background before we launch into questions since, as Alex has told me, this is the Susan's one of our engagements with the investor community. So, Susan, why don't you tell us a little bit about yourself?
Susan, SVP on Salesforce's product organization, on the agent force product team, Salesforce: Yeah. Thanks, and nice to nice to meet you and happy to be here today. I'm an SVP on Salesforce's product in Salesforce's product organization, on the agent force product team. And I've been in Salesforce for about fourteen, fifteen years at this point. And along that pathway, I've enjoyed what I coin as the best job in Salesforce, which has been sitting at the edge of a lot of the innovation that we've been doing with AI and data.
Prior to this, I had a heavy hand in a lot of our Einstein and machine learning products. But for the last three or so years, been part of the foundational team with all things generative AI and agentic agent force technology.
Keith Bachman, BMO: Okay. Perfect. You know, I'm gonna start a little bit differently in that a lot of investors ask us what the difference how do we get here? And what I mean by that is how do we get to this thing called agent force? We used to talk all about Einstein.
How did we get here? How was the evolution? How has that unfolded?
Susan, SVP on Salesforce's product organization, on the agent force product team, Salesforce: It's a great question for me. Thanks for asking it. I mean, obviously, back in the the 2016 era, about a decade ago, there was a a convergence of of data and processing power that made sort of a big step change in machine learning possible. And in those days, as you, you know, commented on the Einstein brand, we had a lot of both out of the box capabilities for predictive things like lead scoring, opportunity scoring, classification, those type of more traditional machine learning things, which really defined, you know, our our time about a decade ago. Now, obviously, with, you know, moving to the current day and age, the the capacity of the machine learning models crashed on the world very, very aggressively about two and a half years ago in terms of not just the impact in the consumer marketplace for the ways we all enjoy it in our personal lives, but for everyone managing a large enterprise in terms of how does generative AI, you know, impact, not just their technical stacks and the user experiences they have for their employees and customers, but in, you know, business models as well.
And so the, you know, the original, working models with LLMs, in from that time frame was a lot around prompt engineering and leveraging generative technologies to summarize things and to generate content. And the agentic shift, takes us into a new category of things where we can, you know, permit and allow these applications to take on more autonomous experiences with the controls and the guardrails. And I would say all of the tooling that you need to, as an enterprise, which is much different from a consumer experience
Keith Bachman, BMO: Yes.
Susan, SVP on Salesforce's product organization, on the agent force product team, Salesforce: Bring to the foreground to put these things into place around workflow, around productivity super cycles of your employees, and in new new customer experiences, externally. So for us, the step function change was releasing AgentForce at Dreamforce last year, and a lot of that step function was brought by our builder tools itself, resonant in AgentForce.
Keith Bachman, BMO: How do you think and this is more of a market question, but you you've had an interesting seat, to observe this.
Susan, SVP on Salesforce's product organization, on the agent force product team, Salesforce: Yeah.
Keith Bachman, BMO: In my simple brain, there's causal AI and gen AI, and those might not be the the right nomenclature. But how do you think about those causal models and instill the the the effectiveness and necessity of those versus a probabilistic model, which I think about is is Gen AI? How do these two worlds
Susan, SVP on Salesforce's product organization, on the agent force product team, Salesforce: Yeah.
Keith Bachman, BMO: Cooperate, exist, compete? How does it play? And, again, this is not a Salesforce comment. This is more of a take a step back.
Susan, SVP on Salesforce's product organization, on the agent force product team, Salesforce: Come to the market. What what I would say, like, in the early days with generative AI when I mean early days, like, I'm I'm we're talking, like, two and a half years ago. Like, we live in doctors right now in this tech space. But, the initial, like and this is kinda winding time back for me a bit. Like, a lot of the questions were, like, can we solve everything with generative AI?
And people would start, like, spitballing and use case ideation, and you'd be, that is a great use case. You know what it is? It's predictive. Like, that's a regression model. And so the kind of the two, observations is, one, it was a whole new level of creativity and permission to really think about bringing technology in because it was such an important technology moment.
So that permission step, like, created all this ideation. But the way the two work together, like, a classic example would be a machine learning model or something that's doing a, you know, a predictive outcome will tell you the order of operation of what you need to focus on. The generative, like, use case will tell you how to do it and bring productivity to the foreground. I'll give a classic sorta Salesforce sales example. If I've got predictive signal about a customer that might buy or a customer that might churn, that's gonna move them to the top of a list of engagement.
But generative AI might bring additional capabilities in terms of, you know, creating customer briefs before a call or taking
Keith Bachman, BMO: serve those customers.
Susan, SVP on Salesforce's product organization, on the agent force product team, Salesforce: Yeah. Taking signal from data and from all the experience information that's collected and and crafting, like, using, you know, using autonomous processes is to to create that engagement in a more powerful way. So it's like, who do I call and why, and what do I say and I do when I get there? Sort of like a really nice peanut butter and chocolate example.
Keith Bachman, BMO: Yeah. And so both worlds will live on. Oh, yeah. Obviously, we're still super early in Genii, but the causal necessity and advances of causal AI will continue.
Susan, SVP on Salesforce's product organization, on the agent force product team, Salesforce: Yeah.
Keith Bachman, BMO: Okay. Let let's turn. Thank you for indulging me on on sort of the how did we get here. Yeah. Let's go on to the more poignant questions associated with Salesforce.
Everybody's got AI. Why is sales why is it important to Salesforce? And and, really, the nature of the question is
Susan, SVP on Salesforce's product organization, on the agent force product team, Salesforce: Yeah.
Keith Bachman, BMO: How is agent for diff how is agent force differentiated?
Susan, SVP on Salesforce's product organization, on the agent force product team, Salesforce: Yeah. I think it's a great question, and you're right in acknowledging it's such a big technology moment. You know, we recognize there are a lot of options, and we have what we think a tremendously exciting set of capabilities. And the way I would describe them, are a combination of things. Like, we're we're gifted with this deep, history with sales, service, marketing, commerce, analytics.
So we have this amazing suite of applications that all have humans and all have processes and all have automation attached to them. And so it is a really nice opportunity to take those applications and modernize them with the Gentec capabilities. So this exploiting the app layer is super nice. The thing I would say is that we've been, for a number of years, really accelerating our capabilities with data. Now, obviously, with Salesforce data, we always have a special relationship with it because it's in the platform.
It's permission aware. It's, like, workflowed and all that stuff. But the work we've been doing on the data cloud has really opened up the aperture and the technical ways that we can engage with data, which is so important for grounding, these agentic experiences. So data, the application, and then within the agent layer, we've been really busy doing two things. One, we are, let just write what I call it, like, the the joint opportunity and obligation of we have all this deep knowledge of the personas that sit in Salesforce products all day long.
Like, our researchers know that. Our PMs think about that morning, noon, and night. And so we can create all these accelerated agentic experiences that, one, take the think time out of wondering what to do, and, two, accelerate the time to value because you can configure the last mile versus starting with that clean sheet of paper. Now the thing we've done in addition to these out of the box applications is for the last three years, we've been building this deeply embedded enterprise grade agentic AI platform in Salesforce. Because as you know, Salesforce customers love our applications, but they also like making them their own, and that involves configuring and extending them.
The same is true with our AI in terms of all of this tooling that we have for people to customize it, all this observability. So when you move it into production, you have real time line of sight to what's going on in guardrails and all of this enterprise grade technology. So we would just call that, like, apps, you know, data, metadata, and agents, as sort of a framework. And but maybe taking a more specific look because I'm usually I'm usually discussing things at a at a deeper level. And what I would say, just kind of giving the pace of AI, the answer to this question will probably change for you in another six months just as it was different six months ago.
But the things that make it very unique for our marketplace right now is, sort of the following categories. And I'll call the one surface area. And surface area, meaning it could be meaning a Salesforce user experience, someone who's logged into CRM, someone who's logged into Slack, someone that's on our Experience Cloud, but you have a human in Salesforce that is gonna be super powered and supercharged with these agentic experiences. That is a huge advantage. And while it is from my perspective as, you know, one of the AI practitioners, a lot about AI, it also is about design and behavior.
And so it is a really unique opportunity to revisit all those experiences and really next level them in all sorts of ways. So that plus the fact that we've got this, like, super cool platform makes that great. I think the thing is, we have been very focused on some core principles around openness. And openness has come through our AI story in terms of openness to the ways we ground and work with data, openness in terms of selection of LLMs. You know, we've incorporated that, you know, in our product over the last couple of years.
And now with the latest open category of conversation, all these MCP and a two a frameworks. And so this openness, provides a future proofing state for our customers. And just given the rapid state of, progression in this space, honestly, what people often think is unique and game changing on day one, by by month three, could already fast becoming a commodity. And so this openness allows us to really bring, this future proofing mindset to architecture choices that people are making in the enterprise. Number three, it's AI, and so you have to have great AI.
And we have a number of things that I would say put us in that category. The trust models that we, like, really initiated in the marketplace in terms of things like, your data isn't stored with these foundational models. We'll mask all the sensitive data, like, all that sort of data safety. But trust is also about accuracy. And so the things that we've pulled forward in our product around, you know, including citation.
So you have, as a user, line of sight to what that source material that GenAI is using. The work that we've done in our reasoning engine and the work that we do in our Rag metadata pipelines, all of these things are around accuracy. So there's a whole bunch of things that make it very accurate and very trustful. And then, you know, when I look at sort of the big chapters across the last three years, 2023 was the year of, like, does this change my business model in the March of the consultants in the boardroom? 2024 was the year of, you know, POCs moving out of the lab and into production in in small bits.
It was also the year where, you know, Anthropic and Gemini and others caught up with OpenAI. And the year of 2025 is around full scale production, measurement, and observability. And so we've been bringing a lot of our, advanced research techniques into these observability models where not only are we using AI to generate the creation of these AI agents, We're using AI to create the test harnesses to evaluate them before they go into production. We're using AI to improve instructions because as we know, this is an emerging industry and and people need help in those learnings, so we bake our learnings into the product. And we're using AI eval models to understand if these agents are adhering to the policies and the instructions and the actions we're gifting them with.
So this kind of production mindset, is has been very, very powerful for deployment. And then finally, a long standing line I've had, like, since calling on financial institutions back in the eighties, is, everything is possible with time, money, and code. And that's always my job for folks. And so the skill set that we bring to the foreground is very unique in terms of leveraging this sort of a trailblazer mindset as well as having these command line interfaces for the community that enjoys that. All of this being a way to to go fast with products that people have already made investments in, you know, aka this huge Salesforce suite with some of the best AI and and and techniques around, you know, enterprise suitability.
Keith Bachman, BMO: Okay. Lot to chew. Sorry. Lot to chew lot to chew on there. We could we could there's a there's a lot to go on.
But let me you said one thing that sort of piqued my interest. You said it completely you know, the AI world could completely change in the next six months, so I'll say in the next year. But but what do you what do you feel like, a, you need to get right from where you are today from a technology platform perspective? And, b, what's the most what's the greatest source of friction on customers not adopting that are Salesforce customers right now?
Susan, SVP on Salesforce's product organization, on the agent force product team, Salesforce: Yeah. I think some of the things I just said around the pillars are the things we're working on right now. Like, observability is really important. These are generative capabilities, and many organizations are still feeling their way through trust with LLMs. And so you put them in front of your employees.
You put them in front of your customers. You you sort of want this. So we've been focusing on on that for quite some time. The part of your question, it was about you call it, like, friction or barriers. Is that how you phrased it?
Keith Bachman, BMO: Yeah. Why are you have a huge Yeah. Installed base of customers, and candidly, a fraction have adopted or generating ARR for you guys, and so most customers haven't. What's the you know, what do you find is a common source of friction about why folks aren't adopting?
Susan, SVP on Salesforce's product organization, on the agent force product team, Salesforce: Well, I would put, I'll just address it in terms of, like, where we see, I wouldn't call it friction, but ways we can accelerate people's understanding of it because we we, like, we are very, very busy servicing the needs of customers who, want to engage with this, whether it's things like use case ideation sessions and workshops to train people and the trails that we put on Salesforce to help educate people at scale about both the possibilities and the actual tools. So there's there's a ton of activity there, and and Alex can also reinforce some of the the actual traction we're seeing with with ARR and also repeat revenue. So there is massive momentum there. What I would say around, like, friction, and I think that's even too heavy a word. I'll give you two examples.
Like, when I think of categorizing use cases, I put it right now into buckets of the productivity super cycles for our employees. I would put the next category in terms of, experiences that we put in the pathway of our customers, like these external autonomous customer experiences. Now if we take those two categories, like, we'll start with the the customer facing use case. What we've seen at scale is and this is across, like, many different industries, retail, consumer goods, regulated industries, and financial services. So there's been sort of no holdout.
It's been very universal. It's very easy for people to conceive of use cases that face customers that do things like answer questions, deflect calls. If it's a call center that feels they can create a better user experience in a modern adaptive conversational way, like, that's both, you know, reduction in cost to serve, in terms of the technology to do that and a better customer channel. So, like, answering questions, and as a category, I would say reading from a database, meaning where's my order or where's my or where's my claim, or where's my shipment? Those sort of like, tell me what the status of this is without me waiting in a long queue and fighting with an IVR system.
Or the next category for that customer facing experience might be, I need to initiate a process. I wanna initiate a service request. I wanna initiate a claim. I wanna initiate a a beneficiary on my account. So, like, those things come really naturally and easy because they know the processes that they're already serving on their call centers at scale.
They're it's measured like crazy. So that is usually pretty, like, that can accelerate really quickly because the think time is is compressed because they know where the friction already is in their business by servicing it with measurement. On the sales side of things, it it it people need more help in terms of where do I start and why. I have all these processes in my organization that may or may not be completely understood by me, especially if I have a large, like, sales team. And so, helping people understand their business and their business processes and where AI automation and where the design of AI that is supportive of human can take friction out of the process might take some time for folks.
So we've been responding by, you know, just getting in the trenches with our customers and helping identify this stuff. But that's where I wouldn't call it friction, but it's an opportunity to think deeply, not just about jamming, like, some AI experience, but where do I have friction, and how are my humans compensating about it, and how can I inject AI there? So I won't call it friction, but I would just call it it's, it might take a little bit more time to get that road map of everything you wanna do and then put it in that two by two grid of high impact, like, you know, low risk kind of where do I start thing.
Keith Bachman, BMO: Right. Right. Right. Right. Right.
Susan, SVP on Salesforce's product organization, on the agent force product team, Salesforce: I don't know if that makes sense, but that's sort of, like It does.
Keith Bachman, BMO: It does. Let me ask about go back to something you said at the outset, and I'll use slightly different terms. But customers need to adopt the data cloud in order to be successful with your agents. And maybe help us a little bit with with the why that is the case, and I think, you know, data structures. But, also, as a technologist
Susan, SVP on Salesforce's product organization, on the agent force product team, Salesforce: Yeah.
Keith Bachman, BMO: A lot of customers already have Databricks or Snowflake, and you're sort of asking to stand up, for lack of a better word, another data lake, which is you know, nobody really wants to do that. And so I just wanted to hear a little bit about the data cloud and and how it's important to this process.
Susan, SVP on Salesforce's product organization, on the agent force product team, Salesforce: Sure. Yeah. I mean, there's I could talk for hours about this one too. So what I would say in a little bit tongue in cheek, I wish we had named the data cloud the activation data substrate. Like like, if I had invented sushi, I would call it, like, cold dead fish on white.
Dead fish. Like, really kind of pragmatic, name for it. So, of course, people have made these investments in Snowflake and Databricks and all these lakes, and the answer is thank you. Like, because what we're here to do is leverage and activate that data, not replicate it and rematerialize it. That's one of the Exactly.
Of of data cloud. And I think if there is friction, like, you know, to your question a moment ago, it might be in truly people understanding that, that you don't have to copy data into our environment. We leverage it in a very modern way in terms of just treating it as if it was a Salesforce table. So so there's so, like, that's sort of one thing I would say. It's it's it's it's not a separate data we call it data cloud.
It doesn't mean bring your data to our cloud. It means let us help you activate your data in all sorts of creative ways across Salesforce. Now as a product, exec at Salesforce, like, I see data cloud, in many ways. I see it as as as the original CDP in terms of a really modern Yes. To get all this you know, that's a category, and people buy it, and it's awesome, and it's leading.
I also see it as a way to bring additional data into the Salesforce ecosystem in a very modern way, like, not replicating it and all that stuff. And that's terrific because you've got humans and processes all through Salesforce that can be leveraged by this in in proactive and and reactive ways. So new data types that historically were not deeply resonant in Salesforce. And all of our product teams build on data cloud as if it is a platform because it is a platform. So with with with the agentic capabilities, with the agent force things, you know, with all of our Gen AI, like, you know, everyone has this little, you know, moniker of, like, you know, AI needs data.
Yes. It does. But not in the traditional sense of building models because we you know, most people are using the pretrained models. So we're not using it to build models, but we're using it to ground and inform. And when you're interacting with an LLM, the better instructions you can give it grounded with customer data, the more accurate it is.
So, like, I was talking to a bank the other day, and he's like, I really now finally get Data Cloud. And I have these amazing user experiences for my advisers because it's not just LLM giving me a summation of notes. It is an LLM that fully understands my customer because I have fax set data. I have transaction data. I have banker notes.
I have position information. I can't get that without that. And so grounding with this data is really important. And because it's, like, this awesome modern platform, we also put all our log files there. That's where our emails go for observability.
So, like, we're leveraging Data Cloud, like, in ways that it should be, but it's not, it's it's not to bring your data to our cloud. It's let us help you, really next level, the hard investments you've already made in building out those Snowflake and and Databricks environments.
Alex, Salesforce: And to add to Susan's point, because this goes back to your prior question, Keith, around the level of agent force adoption, what we're seeing from a customer momentum standpoint has been unprecedented in terms of interest in AgentForce, in terms of customers choosing Salesforce to start their agenda journey. Realistically, we know that takes time, and it goes back to your question around data and getting data in order. And so what's been encouraging for us is as we've seen customers choose AgentForce, and we mentioned 8,000 deals closed to date, they're realizing that it is a longer data journey. And that's why when you look at some of the stats we've given in the last earnings call around surpassing a billion in ARR for data cloud and AI, a lot of that's still coming from data cloud. And it's with the lens of how do they harmonize the data on our platform?
How do they bring in unstructured data to Susan's point where they previously weren't able to activate that data before, but now it becomes really key in the ejecta customer journey? And then how do they leverage tools like MuleSoft and eventually Informatica? So we are giving customers this unified data architecture. We're making it very simple for a customer to get all of their data in order with the lens that then you have your agents natively integrated on our platform, and you're easily able to tap into that data, get the value, and activate that data within your customer journey. So that's what we're really excited about, And it's important to us as we go through this agentic journey with our customers to continue giving all of you key milestones in terms of what we're seeing from adoption, but really giving you milestones into what we're seeing with customers when they eventually move from the POC experimentation phase that they're in now to a limited deployment to an eventual deployment at scale.
And once we get that flywheel turning, that's when we really think you see agent force become more material in FY twenty seven. Yeah.
Keith Bachman, BMO: Okay.
Susan, SVP on Salesforce's product organization, on the agent force product team, Salesforce: I would I would really echo all those momentum stats. And, you know, one of the things, like, if I'm at a conference and people ask for guidance, like, what do we do? I was like, you gotta start. Like, that's the thing. Like, start, commit, and go.
Because we are seeing, like with the use case, you have to figure out everything. You have to figure out your risk profile. You have to figure out how LLMs work. You have to figure out what data you have. You have to you have to figure out user experience.
You have to figure out all the boundaries. And then once you get that, you have this acceleration, like, platform. I'm working with so many customers right now that, you know, have launched, their one, two, 34 agents, and now they are creating what they call agent factories, because either they're going use case by use case and functional area by functional area, or they've got country one stood up, and now they're gonna go to countries like two through 56. So we are, you know, definitely, seeing this scale both in terms of variety of use case, acceleration of of regions, and things along those lines.
Keith Bachman, BMO: And so, Susan and Alex, when you think about I use the word friction, which did not go over well, but let's say discussion points. Alex would call me a source of friction. But when you think about your discussion with customers, is it understanding our economics? Right? And so are are customers still trying to understand how in the various scenarios underneath it or or and or how important are discussions surrounding I gotta pay more?
How is this gonna evolve?
Susan, SVP on Salesforce's product organization, on the agent force product team, Salesforce: Yeah. It's both. Like, it's a it's a it's understanding, like, what is this technology? What is your technology? How is your technology different?
Because I got a ton of choice. Like, it's all that. And it's where do I start and why? And is that thing gonna ROI, and how is it gonna impact my business? And, you know, I sort of, you know, categorize these these different, like, potential value points.
Like, it can be just categorically the productively super cycle. The thing that used to take, nine minutes takes four minutes. The things that used to take an army of people is a smaller number of people that, you know, people are redeployed to higher, like, higher margin and higher profile activities. Like so there's this whole productivity thing. For these customer facing experiences, you know, for many organizations, it's an opportunity to to understand the cost to serve, but more importantly, better channels and better customer experiences that lead to, increased loyalty, cross sell and upsell.
So they'll bake those not just into a cost to serve, you know, return, but how is this impacting the growth of my business? Now we're working with some organizations that sort of take that even to the next level. Like, how does this digital labor change operating models for me in terms of ways that I just hadn't anticipated? Like and the the classic example is, like and I've I'll repeat it. I didn't invent it.
You've probably heard it a million times. When you held your iPhone in 02/2006, did you imagine Uber? Like, so this whole thing is, we people are now starting to imagine these new digital labor scenarios that just weren't possible before. So and I I got some customer stories that that I can tell there. And then, you know, as people move from these productivity super cycles for employees to customer experiences that are just next level, like, the they're sort of the next sort of set of considerations is how do you have background agents doing the things that the humans are traditionally doing, which is sensing and responding to signal.
The customer called. This thing, you know, happened. They incurred usage. They didn't incur usage. They opened a new account.
They like, whatever all these data signals are, the AI automation can start the whole process and pull humans in the loop in new ways possible. So that is, you know you know, it you know, increased revenue, decreased cost, you know, and and new ways of working are are just, you know, categorically what we're we're seeing everywhere.
Keith Bachman, BMO: Okay. Well, unfortunately, we have to to move on a little bit, but we may come back to these. And the reason I say move on is we've had we had ServiceNow on yesterday, and they are talking a lot about moving into the front office. They refer to it as CRM. Part of it is their thesis is they have a horizontal layer for agents and agent orchestration.
They have a little slice of what I'll call applications within the front office. But how do you think about your differentiation if we take AI, AgentForce Data Cloud, versus some of your competitors? And specifically, if there's anything you'd like to call out from a, lack of a better word, a horizontal player like ServiceNow and how this you think it gives you provides you with differentiation in the front office.
Susan, SVP on Salesforce's product organization, on the agent force product team, Salesforce: Yeah. I I come back to some of the things I said before. Surface area where people live, context switching is terrible. Like, humans are finite in our ability to concentrate, and so context switching is sort of the the just not a great design technique. Right?
So having it in the work where you don't know you're using AI, like, you're just doing your job, and AI is supporting you at every way. The thing I would say is this, like, you know, in the the dawn of generative AI, it was like, write an email for me and summarize this text. It is way beyond that now. Right? And so Yeah.
Ability to have these things create an AI orchestrated plan, reason through what needs to do, be responsive to conditions changing, all of this, like, AI orchestration is just it sits on top of actions all day long. And Salesforce customers have deep investments in things like flow and workflow and actions. And then, of course, all their business processes that they've got armies of sales and service and marketing, both people and processes already there. So this kinda, like, surface area, actionability, the time and money and trouble takes to get there, the openness to data, the way we future proof, are all are are really, outstanding for for customers in terms of things to think about for us.
Keith Bachman, BMO: Okay. Let me take a quick pause to see if investors wanna jump in. I have a few more, or, Brad, if you wanna jump in from my team also. I'm just gonna take a a ten second pause. Okay.
We will continue on then. I I wanna maybe as we're heading down the home stretch, talk a little bit about how maybe not your directory, but MuleSoft Yeah. And Informatica, how this helps with because in my mind, Informatica is really an enabled nurture of the growth of data cloud. But why is let's take Informatica Why is it important to have Informatica to be part of Salesforce rather than just partner with them?
Susan, SVP on Salesforce's product organization, on the agent force product team, Salesforce: I'll I'll start, and then I'll pass to to Alex. Like, from the the AI side, I think, the excitement is palpable in terms of the ability to inject even more, customer information into the ways that we embed these experiences. Clearly, that is gonna unleash a whole lot of of value. Not everyone has stuff nicely packed away in a modern data lake. There is plenty of other applications that run organizations where Informatica is the big part of that mesh in that network.
So that will be terrific. And then there are, capabilities around lineage and governance that, you know, just kinda any good data stored or data pathway, whether it's just a straight data pathway or it's data pathways, you know, activated by AI will will be very powerful. So I see it from my AI practice in terms of next leveling what we can do by by grounding and and knowing the sources of these data. But, Alex, you probably see it from a larger m and a and Salesforce perspective as well.
Alex, Salesforce: Sure. So agree with all the points you hit. We also think the element of being able to bring rich metadata from a number of different sources, whether that's on prem, whether that's from the cloud at scale, is an ability that Informatica brings to the table. And we think, as Susan mentioned, with data cloud and with a lot of customers cemented on our core applications, you have rich metadata tied directly to the customer. But as you think about deeper complexity and the agentic experience, you probably wanna bring in metadata tied to product or tied to other assets.
And Informatica has a very extensive data catalog with that understanding of different types of datasets where we are now creating almost an asset three sixty, a product three sixty tied to a customer three sixty with the lens of you also bring in with Informatica very robust data governance policies. So agents have a permissioning set in place of they can read certain datasets, but they can write to other certain datasets. And so there's this element about data transparency, governance, and understanding that we think is critical for why Informatica needs to come to the fold of our platform. We did have a successful partnership, but for us to be able to build out this unified data architecture and offer to our customers a complete integration offering, we felt like buying the asset was the right move. Our lens is this is going to unlock significant synergies, whether that's from the go to market side, the g and a side, and the product side, where, ultimately, we wanna make it as simple as possible for customers to get their data in order on Data Cloud and on our platform.
And now we think with MuleSoft Informatica and Data Cloud, and then you have, of course, Tableau and Slack from the visualization conversational layer element. We have this architecture that allows customers to do that data work and then have agent force, which is already natively integrated on our platform to be able to action and activate, as Susan mentioned, all of that data.
Keith Bachman, BMO: Okay. And, Alex, just quickly, because I wanna ask one more of Susan. How much overlap is from a workflow perspective, not a customer perspective, is there between Informatica and Mule?
Alex, Salesforce: From a workflow perspective?
Keith Bachman, BMO: Like, the common use cases. Like, how how much is their common use case, or do you know that number?
Alex, Salesforce: I don't know off the top of my head. There's likely some overlap, but we do think that there's differences between when a customer would leverage MuleSoft, let's say, app to app, and when they're leveraging Informatica from an ETL, ELT standpoint and likely leveraging Informatica to bring in data at scale. So we do think that there are different use cases when you think about MuleSoft, when you think about zero copy, and then when you think about Informatica.
Keith Bachman, BMO: Okay. Susan, we're gonna end with you. And I wanna just hear a little bit of maybe about a customer example incorporating AgenTeq AI and Data Cloud that you think is representative about where this is heading over the next two to three years. Any customer because you talk to a lot of customers who clearly have a sense, particularly on the technology side of the market opportunities. Anything you wanna bring to life that should help investors understand where we're going?
Susan, SVP on Salesforce's product organization, on the agent force product team, Salesforce: Yeah. I'm always happy to bring some customer examples to the foreground. I started speaking about one a bit ago, a a wealth manager, where their journey with agent force definitely involves data cloud. And the as I mentioned earlier, what data cloud brings them in that is very specific customer information. Now when you're talking about GenAI and financial services specifically, like, especially when you get into areas of of wealth and health, it's like you have to be careful about recommendations.
Right? But you wanna show up, like, well informed, well prepared, and to drive gay great conversations. So in their example, you know, using the powers of of of our capabilities, they leverage all sorts of client data, whether it's generated by humans, whether it comes from their back office transaction systems or party data. And all of that together allows them with the click of a button to get a really rich household summary of everything about that customer and all the ideas about the products and services that meet the risk profile, the stated financial needs, and really sort of drives, like, the relationship in very positive ways. Now at the same time, they're also using the same sets of capabilities to help people do their jobs because there are all sorts of products and services to know.
There are all sorts of escalation, you know, policies and procedures to know and things like that. Now I'll kind of pivot from that organization. They started with a human in the loop with their employees. Now I'll focus on on another couple sets of banks where their agent was or their generative experience was they would like to expand the markets that they're serving, but they don't have the human capital to do it at the moment. They wanna go down market with white glove experience.
And so the thing they did was they set up a digital agent that explains everything about their commercial and their commercial banking products, I will just say. And then from there, all sorts of ways to schedule and connect with a banker. Now they they have this vision of being a sort of an AI bank in many ways. So at the same time, while they're standing up this user experience that faces a potential banking customer, they are also creating all these agents that take the friction out of the human population that are bank employees. Things like agents that do sweeps, things like agents that do loan prepays.
So they're, like, using their their human workforce to make sure they get everything right then that goes as part of the digital and AI bank. So we have this idea of of digital labor really at scale, not just call avoidance on a call center, but really activate a whole new category of work. Now also without using customer names, like, can give another example about, digital labor. This is an organization that, has I'll just call it a prescreening process. And, of that prescreening process, potentially millions of of people that might wanna touch.
So having a digital agent gives them the capacity to go at scale and truly operate twenty four seven, three sixty five and not really bound by nine to five and human labor. Now what they're finding with this is that it's funny because we tell people digital labor and, like, Mark's on a podcast and says digital labor, and then the customer says, like, oh my god. It's like digital labor. Like, you know, we were able to engage with customers, outside the hours of operations and in language we choose. Now that was very cool for them, but the next thing they observed was that the fidelity that the digital agents have towards completing the process and executing was very, very high, much higher than they, like, anticipated.
Like, that was pretty good. And when they get to the end state of that process, like, what their measurement of that process was is over over like, it used to be, like, four to one, like, four people on the top of the funnel, one at the bottom, a two to one ratio. So they've been, you know, receiving orders of magnitude improvement, and and that's kind of a digital labor conversation. So, you know, taking people through these discussions about, like, humans empowered by AI, digital labor, new operating models, digital twins of what they do, internally is just so exciting for organizations to imagine a future.
Keith Bachman, BMO: Okay. Perfect. I think we're going have to leave it there because I fear we've run over by a couple minutes, but we started a little bit late. Susan and team, thank you so much for joining us today. Super interesting.
We could go on for much longer. We appreciate your time, and we wish you all the best. Many thanks again.
Susan, SVP on Salesforce's product organization, on the agent force product team, Salesforce: Oh, thanks a lot, Keith. Thank you.
Keith Bachman, BMO: Cheers.
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