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. For me, this is my last one, and thrilled to have Salesforce on with us. There's a few from IR, but we're just going to go to Susan. A way to start this is we're going to ask Susan to give her background before we launch into questions, since as Alex has told me, this is one of our first engagements with the investor community. Susan, why don't you tell us a little bit about yourself? Yeah, thanks. Nice to meet you and happy to be here today. I'm an SVP in Salesforce's product organization on the Agentforce product team. I've been at Salesforce for about 14, 15 years at this point. 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. For the last three or so years, I've been part of the foundational team with all things generative AI and agentic Agentforce technology. OK, perfect. You know, I'm going to start a little bit differently in that a lot of investors ask us what the difference, how did we get here? And what I mean by that is, how do we get to this thing called Agentforce? We used to talk all about Einstein. How did we get here? How was the evolution? How has that unfolded? It's a great question for me. Thanks for asking it. I mean, obviously, back in the 2016 era, about a decade ago, there was a convergence of data and processing power that made sort of a big step change in machine learning possible. In those days, as you 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 types of more traditional machine learning things, which really defined our time about a decade ago. Now, obviously, with moving to the current day and age, 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 impact not just their technical stacks and the user experiences they have for their employees and customers, but business models as well. The original working models with LLMs from that time frame was a lot around prompt engineering and leveraging generative technologies to summarize things and to generate content. The agentic shift takes us into a new category of things where we can permit and allow these applications to take on more autonomous experiences with the controls and the guardrails. All of the tooling that you need to, as an enterprise, which is much different from a consumer experience, bring to the foreground to put these things into place around workflow, around productivity, super cycles of your employees, and in new customer experiences externally. For us, the step function change was releasing Agentforce at Dreamforce last year. A lot of that step function was brought by our builder tools itself, resonant in Agentforce. How do you think, and this is more of a market question, but you've had an interesting seat to observe this? Yeah. In my simple brain, there's causal AI and Gen AI, and those might not be the right nomenclature. How do you think about those causal models and still the effectiveness and necessity of those versus a probabilistic model, which I think about as Gen AI? How do these two worlds cooperate, exist, compete? How does it play? Again, this is not a Salesforce comment. This is more of a take a step back. I don't know how to market. What I would say, like in the early days with generative AI, when I mean early days, like we're talking like two and a half years ago, like we live in dog years right now in this tech space. The initial, and this is kind of winding time back for me a bit, like a lot of the questions were like, can we solve everything with generative AI? People would start like spitballing and use case ideation. You'd be, that is a great use case. You know what it is? It's predictive. Like that's a regression. 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. That permission step like created all this ideation. The way the two work together, like a classic example would be a machine learning model or something that's doing a predictive outcome will tell you the order of operation of what you need to focus on. The generative use case will tell you how to do it and bring productivity to the foreground. I'll give a classic sort of Salesforce sales example. If I've got predictive signal about a customer that might buy or a customer that might churn, that's going to move them to the top of a list of engagement. Generative AI might bring additional capabilities in terms of creating customer briefs before a call or. How to serve those customers. Yeah, taking signal from data and from all the experience information that's collected and crafting, like using autonomous processes to create that engagement in a more powerful way. It is 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. Yeah. And so both worlds will live on. Oh, yeah. Obviously, we're still super early in Gen AI, but the causal necessity and advances of causal AI will continue. Yeah. OK. Thank you for indulging me 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 it important to Salesforce? And really, the nature of the question is, how is Agentforce differentiated? Yeah. I think it's a great question. You're right in acknowledging it's such a big technology moment. We recognize there are a lot of options. We have, what we think, a tremendously exciting set of capabilities. The way I would describe them are a combination of things. Like we're gifted with this deep history with sales, service, marketing, commerce, analytics. We have this amazing suite of applications that all have humans and all have processes and all have automation attached to them. It is a really nice opportunity to take those applications and modernize them with the agentic capabilities. This exploiting the app layer is super nice. The second 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 workflowed and all that stuff. 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. Data, the application. Within the agent layer, we've been really busy doing two things. One, we are just ripe with, I call it like 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. 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. The second thing we have done, in addition to these out-of-the-box applications, is for the last three years, we have 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. 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. When you move it into production, you have real-time line of sight to what is going on in guardrails and all of this enterprise-grade technology. We would just call that like apps, data, metadata, and agents as sort of a framework. Maybe taking a more specific look because I'm usually discussing things at a deeper level. 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. The things that make it very unique for our marketplace right now are sort of the following categories. I'll call the first one surface area. Surface area, meaning it could be a Salesforce user experience, someone who's logged into CRM, someone who's logged into Slack, someone that's on our Experience Cloud. You have a human in Salesforce that is going to be super powered and supercharged with these agentic experiences. That is a huge advantage. While it is, from my perspective, it's one of the AI practitioners, a lot about AI, it also is about design and behavior. It is a really unique opportunity to revisit all those experiences and really next level them in all sorts of ways. That, plus the fact that we've got this super cool platform, makes that great. I think the second thing is we have been very focused on some core principles around openness. 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. We've incorporated that in our product over the last couple of years. Now with the latest open category of conversation, all these MCP and A2A frameworks. This openness provides a future-proofing state for our customers. Just given the rapid state of progression in this space, honestly, what people often think is unique and game-changing on day one, by month three, it could already fast be coming a commodity. 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. You have to have great AI. We have a number of things that I would say put us in that category. The trust models that we really initiated in the marketplace in terms of things like your data is not stored with these foundational models, we will mask all that sensitive data, like all that sort of data safety. Trust is also about accuracy. The things that we have pulled forward in our product around including citation. You have, as a user, line of sight to what that source material that Gen AI 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. There is a whole bunch of things that make it very accurate and very trustful. 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 POCs moving out of the lab and into production in small bits. It was also the year where Anthropic and Gemini and others caught up with OpenAI. The year of 2025 is around full-scale production, measurement, and observability. We have 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 are using AI to create the test harnesses to evaluate them before they go into production. We are using AI to improve instructions because, as we know, this is an emerging industry and people need help in those learnings. We bake our learnings into the product. We are using AI eval models to understand if these agents are adhering to the policies and the instructions and the actions we are gifting them with. This kind of production mindset has been very, very powerful for deployment. Finally, a long-standing line I have had since calling on financial institutions back in the 1980s is everything is possible with time, money, and code. It is always fine for folks. The skill set that we bring to the foreground is very unique in terms of leveraging this sort of trailblazer mindset as well as having these command line interfaces for the community that enjoys that, all of this being a way to go fast with products that people have already made investments in, a.k.a. this huge Salesforce suite with some of the best AI and techniques around enterprise suitability. OK, a lot to chew. Sorry. A lot to chew on there. There is a lot to go on. You said one thing that sort of piqued my interest. You said the AI world could completely change in the next six months. I will say in the next year. What do you feel like, A, you need to get right from where you are today from a technology platform perspective? B, what is the greatest source of friction on customers not adopting that are Salesforce customers right now? 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. You put them in front of your employees. You put them in front of your customers. You sort of want this. We've been focusing on that for quite some time. The second part of your question, it was about, did you call it like friction or barriers? Is that how you phrased it? Yeah. You have a huge installed base of customers. And candidly, a fraction have adopted or are generating ARR for you guys. And so most customers have it. What do you find is a common source of friction about why folks aren't adopting? I will just address it in terms of where we see, I would not call it friction, but ways we can accelerate people's understanding of it. Because we are very, very busy servicing the needs of customers who want to engage with this, whether it is 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. There is a ton of activity there. Alex can also reinforce some of the actual traction we are seeing with ARR and also repeat revenue. There is massive momentum there. What I would say around friction, and I think that is even too heavy a word, I will give you two examples. When I think of categorizing use cases, I put it right now into buckets of the productivity supercycles 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, we'll start with the customer-facing use case. What we've seen at scale is, and this is across many different industries, retail, consumer goods, regulated industries, and financial services. 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, that's both reduction in cost to serve in terms of the technology to do that and a better customer channel. Answering questions. As a category, I would say reading from a database, meaning where's my order? Or where's my line? 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. The next category for that customer-facing experience might be, I need to initiate a process. I want to initiate a service request. I want to initiate a claim. I want to initiate a beneficiary on my account. Those things come really naturally and easy because they know the processes that they're already serving on their call centers at scale. It's measured like crazy. That is usually pretty, that can accelerate really quickly because the think time is compressed because they know where the friction already is in their business by servicing it with measurement. On the sales side of things, 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 sales team. 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. We have been responding by just getting in the trenches with our customers and helping identify this stuff. That is where I would not call it friction, but it is 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? I will not call it friction, but I would just call it it might take a little bit more time to get that roadmap of everything you want to do and then put it in that two-by-two grid of high impact, low risk, kind of where do I start thing. Right. I don't know if that makes sense, but that's sort of like a struggle. It does. Yep. It does. Let me ask about, go back to something you said at the outset. I'll use slightly different terms, but customers need to adopt the Data Cloud in order to be successful with your agents. Maybe help us a little bit with why that is the case. I think common data structures, but also as a technologist, a lot of customers already have Databricks or Snowflake. You're sort of asking them to stand up, for lack of a better word, another data lake, which is, nobody really wants to do that. I just wanted to hear a little bit about the Data Cloud and how it's important to this process. Sure. Yeah. I mean, I could talk for hours about this one too. What I would first say, in a little bit tongue in cheek, I wish we had named the Data Cloud the activation data substrate. If I had invented sushi, I would call it cold dead fish on rice. Cold dead fish. Really kind of pragmatic name for it. Of course, people have made these investments in Snowflake and Databricks and all these lakes. The answer is thank you. What we are here to do is leverage and activate that data, not replicate it and rematerialize it. That is one of the questions of Data Cloud. I think if there is friction, like to your question a moment ago, it might be in truly people understanding that you do not 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. That is sort of one thing I would say. It is not a separate data. We call it Data Cloud. It does not 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, I see Data Cloud in many ways. I see it as the original CDP in terms of a really modern office to get all this. That is a category. And people buy it. And it is awesome. And it is 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. That is terrific because you have got humans and processes all through Salesforce that can be leveraged by this in proactive and reactive ways. New data types that historically were not deeply resonant in Salesforce. All of our product teams build on Data Cloud as if it is a platform because it is a platform. With the agentic capabilities, with the Agentforce things, with all of our GenAI, everyone has this little moniker of like, it needs data. Yes, it does, but not in the traditional sense of building models because most people are using the pre-trained models. We are not using it to build models, but we are using it to ground and inform. When you are interacting with an LLM, the better instructions you can give it, grounded with customer data, the more accurate it is. I was talking to a bank the other day. He is like, I really now finally get Data Cloud. I have these amazing user experiences for my advisors because it is not just LLM giving me a summarization of notes. It is an LLM that fully understands my customer because I have facts, that data. I have transaction data. I have banker notes. I have position information. I can't get that without that. Grounding with this data is really important. Because it's like this awesome modern platform, we also put all our log files there. That's where our email will go for observability. We're leveraging Data Cloud in ways that it should be. It's not the 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 Databricks environments. To add to Susan's point, because this goes back to your prior question, Keith, around the level of Agentforce 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 agentic journey. Realistically, we know that takes time. It goes back to your question around data and getting data in order. 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. That's why when you look at some of the stats we've given in the last earnings call around surpassing $1 billion in ARR for Data Cloud and AI, a lot of that's still coming from Data Cloud. It is 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 were not able to activate that data before, but now it becomes really key in the objective customer journey? How do they leverage tools like MuleSoft and eventually Informatica, so we are giving customers this unified data architecture? We are making it very simple for a customer to get all of their data in order with the lens of then you have your agents natively integrated on our platform. You are easily able to tap into that data, get the value, and activate that data within your customer journey. That is what we are really excited about. It is 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 are seeing from adoption, but really giving you milestones into what we are seeing with customers when they eventually move from the POC experimentation phase that they are in now to a limited deployment to an eventual deployment at scale. Once we get that flywheel turning, that is when we really think you see Agentforce become more material in fiscal year 2027. Yeah. I would really echo all those momentum stats. 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've got to start. That's the first thing. Start, commit, and go. We are seeing with the first 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 figure out user experience. You have to figure out all the boundaries. Once you get that, you have this acceleration platform. I'm working with so many customers right now that have launched their one, two, three, four first agents. They are creating what they call agent factories because either they are going use case by use case and functional area by functional area, or they have country one stood up. Now they are going to go to countries like two through 56. We are definitely seeing this scale both in terms of variety of use case, acceleration of regions, and things along those lines. 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. When you think about your discussion with customers, is it understanding or economics? Are customers still trying to understand how in the various scenarios underneath it and/or how important are the discussions surrounding, I got to pay more, how is this going to evolve? Yeah, it's both. It's understanding what is this technology? What is your technology? How is your technology different? Because I got a ton of choice. It's all that. It's where do I start and why? Is that thing going to ROI? How is it going to impact my business? I sort of categorize these different potential value points. It can be just categorically the productivity supercycle. 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. People are redeployed to higher margin and higher profile activities. There is this whole productivity thing. For these customer-facing experiences, for many organizations, it's an opportunity to understand the cost to serve. More importantly, better channels and better customer experiences that lead to increased loyalty, cross-sell, and upsell. They obey those not just into a cost to serve 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. How does this digital labor change operating models for me in terms of ways that I just hadn't anticipated? The classic example is, and I'll repeat it. I didn't invent it. You've probably heard it a million times. When you first held your first iPhone in 2006, did you imagine Uber? This whole thing is like people are now starting to imagine these new digital labor scenarios that just weren't possible before. I got some customer stories that I can tell there. As people move from these productivity supercycles for employees to customer experiences that are just next level, 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 The customer called. This thing happened. They incurred usage. They did not incur usage. They opened a new account. Whatever all these data signals are, the AI automation can start the whole process and pull humans in the loop in new ways possible. That is increased revenue, decreased cost, and new ways of working are just categorically what we are seeing everywhere. OK. Unfortunately, we have to move on a little bit. We may come back to these. The reason I say move on is we had ServiceNow on yesterday. 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. How do you think about your differentiation if we take AI, Agentforce, Data Cloud versus some of your competitors? Specifically, if there's anything you'd like to call out from, for lack of a better word, a horizontal player like ServiceNow and how you think it provides you with differentiation in the front office? Yeah. I come back to some of the things I said before. Surface area where people live. Context switching is terrible. Humans are finite in our ability to concentrate. Context switching is sort of just not a great design technique. Having it in the work where you do not know you are using AI, you are just doing your job, and AI is supporting you in every way. The second thing I would say is in the dawn of generative AI, it was like, write an email for me and summarize this text. It is way beyond that now. The ability to have these things create an AI orchestrated plan, reason through what needs to do, be responsive to conditions changing, all of this AI orchestration is just it sits on top of actions all day long. Salesforce customers have deep investments in things like flow and workflow and actions. Of course, all their business processes have armies of sales and service and marketing, both people and processes already there. This kind of surface area, actionability, the time and money and trouble it takes to get there, the openness to data, the way we future-proof are really outstanding for customers in terms of things to think about for us. OK. Let me take a quick pause to see if investors want to jump in. I have a few more. Or Brad, if you want to jump in from my team also. I'm just going to take a 10-second pause. OK. We will continue on then. I want to maybe, as we're heading down the home stretch, talk a little bit about how maybe not your direct area, but MuleSoft and Informatica, how this helps with because in my mind, Informatica is really an enabler, nurturer of the growth of Data Cloud. But let's take Informatica first. Why is it important to have Informatica to be part of Salesforce rather than just partner with them? I'll start. Then I'll pass to Alex. From 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 going to unleash a whole lot of value. Not everyone has stuff nicely packed away in a modern data lake. There are plenty of other applications that run organizations where Informatica is a big part of that mesh and that network. That will be terrific. There are capabilities around lineage and governance that just kind of any good data stored or data pathway, whether it's just a straight data pathway or it's data pathways activated by AI, will be very powerful. I see it from my AI practice in terms of next leveling what we can do by grounding and knowing the sources of these data. Alex, you probably see it from a larger M&A and Salesforce perspective as well. Sure. I 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. 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. As you think about deeper complexity in the agentic experience, you probably want to bring in metadata tied to product or tied to other assets. Informatica has a very extensive data catalog with that understanding of different types of data sets where we are now creating almost an asset 360, a product 360 tied to a customer 360 with the lens of you also bring in with Informatica very robust data governance policies. Agents have a permissioning set in place of they can read certain data sets, but they can write to other certain data sets. There is 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. 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&A side, and the product side, where ultimately we want to make it as simple as possible for customers to get their data in order on Data Cloud and on our platform. Now we think with MuleSoft, Informatica, and Data Cloud, and then you have, of course, Tableau and Slack from a visualization conversational layer element, we have this architecture that allows customers to do that data work and then have Agentforce, which is already natively integrated on our platform, to be able to action and activate, as Susan mentioned, all of that data. OK. Alex, just quickly because I want to ask one more of Susan. How much overlap is from a workflow perspective, not a customer perspective, is there between Informatica and MuleSoft? From a workflow perspective. The common use cases, how much is their common use case? Or do you know that number? I don't know off the top of my head. There's likely some overlap. 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. 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. OK. Susan, we're going to end with you. I want to just hear a little bit maybe about a customer example incorporating agentic AI and Data Cloud that you think is representative about where this is heading over the next two to three years. Because you talked to a lot of customers, clearly have a sense, particularly on the technology side of the market opportunities. Anything you want to bring to life that should help investors understand where we're going? Yeah. I'm always happy to bring some customer examples to the foreground. I started speaking about one a bit ago, a wealth manager, where their journey with Agentforce definitely involves Data Cloud. 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, especially when you get into areas of wealth and health, it's like you have to be careful about recommendations. You want to show up well-informed, well-prepared, and to drive great conversations. In their example, using the powers 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 third-party data. 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 the relationship in very positive ways. 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 policies and procedures to know and things like that. I'll kind of pivot from that organization. They started with human in the loop with their employees. Now, I'll focus on another couple of sets of banks where their first agent or their first 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 want to go down market with white glove experience. The first 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. From there, all sorts of ways to schedule and connect with a banker. Now, they have this vision of being sort of an AI-first bank in many ways. 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. They're using first their human workforce to make sure they get everything right because then that goes as part of the digital and AI-first bank. We have this idea of digital labor really at scale, not just call avoidance on a call center, but really activate a whole new category of work. Also, without using customer names, I can give another example about digital labor. This is an organization that has, I'll just call it a pre-screening process. Of that pre-screening process, potentially millions of people that might want to touch. Having a digital agent gives them the capacity to go at scale and truly operate 24/7, 365, and not really bound by 9:00 A.M. to 5:00 P.M. and human labor. What they're finding with this is that it's funny because we tell people digital labor and Marc's on a podcast and says, digital labor. Then the customer says, like, oh my god, it's like digital labor. We were able to engage with customers outside the hours of operations and in language we choose. That was very cool for them. 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 anticipated. That was pretty good. When they get to the end state of that process, what their measurement of that process was is over. 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. They have been receiving orders of magnitude improvement. That is kind of a digital labor conversation. Taking people through these discussions about 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. OK. Perfect. I think we're going to have to leave it there because I fear we've run over by a couple of 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. We wish you all the best. Many thanks again. Oh, thanks a lot, Keith. Thank you. Cheers.