The Exchanges

Q models. You know, I had very early, uh, in hindsight now forays into AI, things like Einstein and other things. And I know that's evolved into, you know, there's Austin Copilot and I send GPT and other things like that as well. Um, how much of the model development that you folks do now is internal versus using external sort of model sources, be they open source or closed source?

A We're taking really an open architecture approach because we have, we serve such a diverse set of customers. Some of our customers are large enterprises. They have their own models or they want to fine tune their own. Um, others are all the way down to SMBs who don't want to have anything to do with model selection and just want us to, to figure everything out for them. And so we're kind of taking the best of what's out there and we're, we're offering customers choice. And then there's a set of customers who have kind of asked us to take it on, right? They want us to figure out based on the data and the feedback that we're getting and given cost performance and latency objectives, they want us to choose the right model for the right task. So it's really a combination of using Whether it's, um, Cogen from, from our research team, which is the, which powers Apex, um, Cogen GPT that we have in, in our developer GPT, where you also fine tuning versions of that for domain specific models in customer service and for sales and for specific industries like healthcare and financial services. Uh, whether it's those in-house models, um, or it's working with our customers to allow them to very easily Spin up and fine tune their own models using the data that they have within Salesforce Data Cloud, or it's offering the choice of external third party models, be it Anthropic and Cohere, which…

AI assessment note: “it's really a combination of using Whether it's, um, Cogen”

Q gun for generative AI. And obviously you folks have been doing a lot in broader areas of AI before this. How much adoption do you see on the generative side so far? Is it large numbers of customers? Is it a handful? Is it mainly experiments? Is it pilots or people doing this in production? I'm sort of curious. About sort of the real traction that that's being seen today?

A Well, we have a lot of customers, so the answer is, is all of the above, right? We have, we have some customers who are, they've rolled out service GPT or sales GPT. It's operationalized across their contact center. It's already changing the, the day in the life of their contact center reps, which is pretty amazing, right? Just to, to talk to some of these individuals and hear them feel like they're doing the best work in their careers because a lot of the, the manual lookup and wrote tasks that Bogged them down before and made customers angry can now be largely automated or much accelerated with generative AI. So we have examples of customers that have done that. Um, of course, most customers are in the middle, right? They're still experimenting. They're realizing how important it is to get their data ducks in a row, and they're starting to do things like connect their Salesforce data cloud with their various data lakes. In their organization, and of course, the Fortune 500, every one of them has multiple different ones, and so one thing that's really exciting for us is we just announced and rolled out zero ETL data sharing partnership integrations with BigQuery, with Databricks, with Snowflake, et cetera, so that really customers can bring all of their structured and unstructured data into one place to really power these generative use cases.

AI assessment note: “the answer is, is all of the above, right?”

Q not really touch as much. But that other companies are focused on a lot of their future is sort of dependent on how rapidly enterprises adopt these things or how rapidly they ramp. And so I'm a little bit curious about your viewpoint in terms of, you know, are we in the first inning? Are we in the third inning? Like where are we relative to sort of enterprise adoption?

A It's hard to generalize because it's, there's a distribution, but if I, if I were to try to aggregate across everything, I mean, it's early, right? Probably the second or third inning. I'm not a baseball expert, but that's, like, probably roughly where it is. Like, there, there are enough companies now, though few, few and far between, but there are enough of them where it, it proves out the value. It proves out that you actually can, can transform business processes in a big way, but most companies, especially in the enterprises, you know, Their data is just like all over the place, and so that's kind of like step one, and we're seeing our data cloud grow as the fastest organically developed product in Salesforce's history, and a lot of that is driven by this, this need for data to power AI, whether it's for, for training and fine tuning or for reg.

AI assessment note: “Probably the second or third inning. I'm not a baseball expert”

Q of business oriented questions for you. Unlike much of the software developed over the last five, 10 years, that wasn't, I mean, much software is still very data processing heavy. So it's not free, but AI products in particular, there's real cogs, right? In terms of the compute. Um, how do you guys think about this at Salesforce in your product launches or in thinking about new SKUs and pricing?

A That is. Such a difficult question, and it's something that we talk about all the time. We've put pricing out there so far for our AI products. It's, um, it's really difficult. You know, I think that you have to cover your costs, but you also have to provide it in a way that customers can easily understand, and you don't need complicated calculators to try to predict token usage. So I think that's the balance that we're trying to strike right now. Overall, though, the key The key thing that we have to achieve is to show value, show ROI, right? Like in the case of, of Gucci and other retailers, are we reducing average handle time? Are we driving sales conversion uplift over the baseline? And so long as we're doing that, I think customers are willing to pay. It can't just be an added cost without a clear benefit.

AI assessment note: “you have to cover your costs, but you also have to provide it in a way”

Q to resolve or, or just, you know, issues as enterprises think about adoption here, not just do we have data of the quality and structure that's useful to retrieve or train in these AI models, but Actually, like who, who is going to be managing it and what happens to these models and ownership? Like, what have you learned from working with customers around some of their most sensitive data?

A There's so much. I mean, we, we don't have all the answers, but we've learned a lot so far. I mean, both how unstructured data gets treated and not all unstructured data is alike, right? There's unstructured data like a PRD or A service knowledge article where it's been written specifically with the intention of communicating a certain set of things, and you can probably assume everything in that unstructured document is important. Conversely, there's unstructured data that's in the form of transcripts, whether it's call transcripts, chat transcripts, or Slack channels, and it's the opposite. In there, there's, like, you can assume that most of what's in there was not intended for other people. There's, like, a lot of back and forth and clarification and just, you know.

AI assessment note: “how unstructured data gets treated and not all unstructured data is alike”

Q you know, or, or, or your phrasing of like, we would get your data ducks in a row and you have some internal and external use cases. If we just think about the externally facing experiences, I think it's much more intuitive for people to think about efficiency. In sort of like, for example, customer service versus like, what can you as an end user expect that will be better?

A Yeah. It is such an exciting area. Like AI is a new UI or maybe Slack is a new UI for AI. And, um, that's also been really amazing, right? It's just looking at first, like these acquisitions that Salesforce made that at the time, like admittedly they were not made in, in the name of AI, but whether it's Slack as a, as an interface, conversational interface, Or it's Tableau as visualizing data and pulling in more data sources. It's MuleSoft is having all of the plugins and extensions that you could possibly want in an enterprise. Like it's really played out nicely, but back to your question on on the user experience. I'll define it. I'll answer that in both. In both the literal like product UX, but also the day to day experience that we're hearing users of our Einstein GPT products share. So from a user experience standpoint, we have this pretty awesome prototype. It's called generative canvas where, you know, as you're conversing with, with the, with the Einstein co-pilot, it's kind of just popping up different components that you would need from From within what you're doing. So if you're asking about a particular sales opportunity, as you ask questions, it'll surface up and kind of drill down the visualization of that. And so that's an example of a previously what we would call a lightning web component page that you would have to hard code and hardwire, but through generativ…

AI assessment note: “back to your question on on the user experience. I'll define it.”