AI is the new enterprise UI with Clara Shih, CEO Salesforce AI

0:05Today I know Priors, we have an entrepreneur and executive Clara Shi. Clara is currently CEO of Salesforce AI and before that was the CEO of Salesforce service cloud service cloud service cloud service. She's co-founder of, as well as she was a board member at Starbucks. Clara currently leads artificial intelligence efforts across Salesforce, including AI co-pilot and agent platform, model development, go-to-market, growth, adoption, partnerships, ecosystems, and secure, responsible AI. So much stuff, I got tired just going through all of it. So she must be exhausted. Today on NoPriors, we talked with Clara about Salesforce's forays and degenerative AI and the future evolution of AI in the enterprise.

0:42So thank you so much for joining us today, Clara. Sure. Thanks for having me. I'm a big fan. So I was hoping to just start off with how you ended up taking on the CEO role for Salesforce AI. And before that, you're working on service cloud. And then we had sort of this big wave of innovation happen in terms of generative AI and Salesforce has been quite fast to adapt to it. So just hoping to learn a little bit more about how your role evolved and the kinds of areas that you focus on today. Yeah. I mean, if you go back to hearsay days, and you might know this, hearsay had and continues to have NLP to mine the messages that come through.

1:17And Hearsay mines it for both lead generation opportunities as well as to detect compliance infraction. So that was like really when, you know, just from an empirical standpoint, I got closer to AI and ML. And this is like all, you know, pre-large language models. And then when I joined Service Cloud, it's like almost three years ago, when you think about the customer service world, and there's a lot of AI, there's been chatbots for many years. And we were using very early pre-GPT types of transformer models to do that. And just as we started playing around with our own models and we saw open AI models and the ecosystems models get better and better, it just became obvious that this would be a core part of service cloud going forward.

1:59So I'd say probably a year and a half ago is when in the service cloud world, my engineering leader, Jayesh, and I, we really started to double down on these experiments, more prototypes. We were working with a couple customers, including Gucci, to develop very early prototypes of what now has become service GPT. And we were just learning and iterating and figuring things out as we went. Well, then, of course, fast forward to last year, ChatGPT is launched, and now every customer is super interested in AI. And across Salesforce, I think there was a sudden wake-up moment to say, how do we apply large language models to every cloud?

2:43And so I think we were in a position of saying, hey, here's what we've learned working with Gucci, working with these other prototype customers, and let's start to think about how this applies to sales and marketing and commerce and Slack. And by the way, instead of each of us building this separately, how do we create a common platform and shared services for everything from model fine tuning to prompt builder to the trust layer and the gateway so that we can all go really fast and also empower our ecosystem to do so. So that was formalized into a separate role in this new role that I took on about six or seven months ago.

3:19And I guess Salesforce for a long time now has been building a lot of its own models. I had very early, in hindsight now, forays into AI, things like Einstein and other things. And I know that's evolved into, there's Einstein Copilot and Einstein GPT and other things like that as well. 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? We're taking really an open architecture approach because we serve such a diverse set of customers. Some of our customers are large enterprises. is they have their own models or they want to fine tune their own.

3:53Others are all the way down to SMBs who don't want to have anything to do with model selection and just want us to figure everything out for them. And so we're kind of taking the best of what's out there and 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 CodeGen from our research team, which powers Apex CodeGen GPT that we have in our developer GPT.

4:31We're 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, whether it's those in-house models 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 are both Salesforce Ventures Investments, or OpenAI, which is the close partner, or Google Vertex, and offering people either the choice to buy those through us or to bring their own API keys.

5:10And then I know they also provide other things that integrated in the platform or taking a platform-based approach to things like co-pilots, agents, and an agent-based platform. Can you tell us more about what Salesforce is doing there and some of the directions that you're hoping to go in? And then also, I guess, related to that, how early do you think agents are and how do you think they evolve over time? Because it seems like we're kind of in the nascent phase of these things, but they're still very exciting. Yes. So the way that Salesforce is really, I mean, as you mentioned, the earliest foray into LLMs were models.

5:38We've had models for four or five years, large language models that we've developed, and we've open sourced many of these on Hugging Face, which is another Salesforce ventures investment. And then in March of this year, we announced our plan to introduce out-of-the-box AI features into every existing Salesforce cloud. So this is what I mean when you hear the words service GPT, sales GPT, marketing GPT. It's these prompt templates that Salesforce product managers have created based on where they see opportunities and operational bottlenecks for the jobs to be done for their buyer and user base.

6:14So a great example of this, the most popular one, is service reply recommendations for contact center agents. So customer sends an email in or they chat something in, and then we provide a suggested response grounded in the knowledge article and past similar cases for that particular customer. So that's what we have out in the market today. It's GA. We have customers using it, giving us feedback. So then in parallel, our platform team is building up the platform, as you mentioned, right? And the platform itself is Copilot, which is the natural language interface that will span across all of our clouds, as well as Slack.

6:53And then it's also Copilot Studio. And within Copilot Studio, there's three platform, like really big platform areas that we're building out. The first one is prompt builder. And as you can imagine, a lot of our customers, they want to take the prompt templates that the sales cloud product managers have created. They want to customize it. They want it to be in their brand tone. They want to point it to a different model. They want to make all kinds of tweaks. They want to ground it in different data that might be a custom field in their instance of Salesforce that doesn't exist in the out-of-the-box Salesforce, et cetera.

7:28So Prompt Builder, we actually just launched our pilot of that last week, and we're already getting customer feedback, which is incredible, just the speed at which this is all going. The second part of Copilot Studio is Action Builder. And that's where we start to give, empower the co-pilot with agent powers, right? With whether it's workflows or it's integrations. You think about our customers have spent decades building all of their customer workflows within Salesforce. They have all of their sharing rules and permissions. They have all of their integrations and the SLAs and the security guardrails for their integrations using MuleSoft.

8:06So any of those now with one click can be designated as an action for the co-pilot agent, which is pretty incredible. And then the third part of co-pilot studio is Einstein Studio, which is bring your own models. And this is a capability that customers have if they want to train or fine tune their own predictive or generative models using data that they have within Salesforce and or indexed by Salesforce in our data graph. So I think, again, Salesforce has done a flurry of really amazing work in a short period of time. One thing I always wonder about enterprise AI or the adoption of AI by enterprise is just the rate at which they're actually really using it.

8:43Because as far as I can tell, a lot of people woke up to the importance of this industry just a year ago, right? When ChatGPT launched, it was almost a starting 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's being seen today. Well, we have a lot of customers. So the answer is all of the above, right?

9:14We 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 day and the life of their contact center reps, which is pretty amazing, right? Just 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 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. Of course, most customers are in the middle, right?

9:48They'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.

10:28I guess if I were to think about it from a macro perspective and not a Salesforce specific question, but when do you think we'll really see large scale adoption of AI in big enterprises? Do you think that's, and it's always hard to predict these things, do you think that's a year away, two years away, three years away? Because part of what I always wonder relative to the ecosystem is, for example, you see all these tool companies, you know, around eval or around observability or other things that Salesforce may 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.

11:03And 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? It's hard to generalize because there's a distribution. But 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 proves out the value. It proves out that you actually can, can transform business processes in a big way.

11:38But 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 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 need for data to power AI, whether it's for training and fine tuning or for reg. That makes sense. You're basically saying step one is get your data in order. And then as far as I can tell, at least in my experience, step two has been either prototype something for external use, but it's still a prototype or start using it for internal tooling or internal efficiency gains.

12:14And then step three always seems to be, okay, now we're actually going to push it out into our own end products or to our own end users or customers. I would largely say that's true, but there's kind of like smaller pieces that you can bite off, right? I think the most common thing that we see, and probably because we're a CRM company, is in the customer service world. You don't have to have all of your enterprise data cleaned up, right? That might take a little bit longer. But can you have all of your knowledge articles across multiple knowledge silos? Can you bring that together using data cloud with our connectors and with vector search and embeddings to drive really good rag for any customer service question, whether it comes in to the self-service agent, formerly known as Einstein Bots, or whether it comes into a person?

12:59How did you think about just given the like breadth of the Salesforce product suite and your role to advance like AI across the organization? How did you think about educating the rest of the product management and engineering organization or teaching them like how these experiences can change with AI capabilities? I would say it's it's really it's it's not one way. Right. There's so much interest in all of this. And we have such an amazing team that everyone is just curious and wants to learn. And they they're coming up with ideas. I mean, so much of what we're building in the roadmap is coming from people from all across the organization.

13:38So I'd say it's been a very collaborative effort, but it is that is something kind of an ongoing effort. And especially as we think about, you know, as you're alluding to a lot, agents maturing and being able to do more. I think it's really going to dramatically transform how we approach software development, right? A lot of what was explicitly hard-coded as different branching and execution paths and painstakingly stucking out every screen in a user experience. Like a lot of that, maybe you could just like hand off to the agent to be able to do dynamically. Is there anything that you're excited about from like a change in end user experiences?

14:17If you project out like a year or two or three, right? Like if we follow a LOD's framework of, you know, or your phrasing of like, you 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? Yeah, it is such an exciting area. Like AI is a new UI or maybe Slack is a new UI for AI. And 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 the name of AI.

15:00But whether it's Slack as an interface, conversational interface, or it's Tableau as visualizing data and pulling in more data sources. what'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 the user experience, I'll define, 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 the Einstein co-pilot, it's kind of just popping up different components that you would need from within what you're doing.

15:47So 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 previously what we would call a lightning web component page that you would have to hard code and hardwire. But through generative Canvas, but we already have all the components there. It knows to call the right items and visualize the right things from Tableau, Salesforce reporting, be able to update records. So it's pretty exciting. And it's not ready for prime time. It's very raw and messy, but that's kind of how we're operating is just we're learning from showing that to different customers, testing it out ourselves, and then we're going to eventually roll out something that's pretty radically different.

16:28The other part of UX is working with Slack and thinking about how agents can be used, not just by one person, but by teams of people. And there's a lot of exciting UX work being done there. Now, in terms of experience, like the day-to-day experience of these users, even now, as I alluded to earlier, we're seeing customer service representatives, their day-to-day experience get completely transformed by generative AI. So Gucci is a great example. They hired a number of new service representatives during COVID. There was, you know, it was like high turnover in the early days of the pandemic. And the thing about being a Gucci service advisor is you really have to know your product, right?

17:15People are spending a lot of money. They have high expectations. And so it's been really great to basically use retrieval augmentation to basically help arm every Gucci service advisor with the right brand storytelling, the right troubleshooting that an expert would have. And what we've seen is that the average handle time on support issues has gone down. But instead of hanging up, the service advisor is able to have a deeper conversation. And because Salesforce has a 360-degree view of the customer, a service advisor can see that, you know, Sarah, you have, you know, we've solved your issue with your broken buckle and your purse, but we see that you have a belt and a pair of shoes in your Gucci cart, or you've been browsing on the website.

18:05And so now I'm also able to empower the service advisor to have a sales conversation and a marketing conversation with you to tell you about the heritage of these shoes and how, you know, Jackie Kennedy used to wear these shoes too. So it just is changing the job and it's really breaking out of these traditional department roles and functions into what does a customer really want and how do we empower that individual who's working there, even if they're a new hire, with all the knowledge that they need to know to be able to address the customer's wants and needs. That's very cool. One of the things that I feel like you would have a special view into is how enterprises are thinking about how their data interacts with all of these AI products, right?

18:48I think that has been one of the biggest concerns to resolve 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 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? There's so much. I mean, 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.

19:26There'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, 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, talking about my dog. Yeah.

20:02So then you have to like pre-process, you have to like mine that you have to do a step further before you use that for something like retrieval. And so that's something that we've learned and we're building in the capability to do that within that unstructured data. Of course, there's, there's data that should remain unstructured and can be vectorized and embeddings, but there's some data, there's actually a lot of structure data in there sometimes, right? In a phone transcript that a retailer might have with the customer, the customer might reveal that her favorite color is blue and that she has a teenage daughter that she also wants to shop for.

20:36Those should then populate the structure data fields in Salesforce, which is also something that we're doing. What do you think is the most unexpected things that you've seen emerge out of generative AI relative to the enterprise? or is there anything that really stands out? Everything. The fact that it works so well. And yeah, I mean, I feel like I'm surprised every week. Yeah, definitely predictions in terms of, you know, we look out two, three, four years from now, any major changes or overhauls in terms of how we think about enterprise software? Because you mentioned, for example, AI is the new UI, for example.

21:12I'm sort of curious, as we think ahead a couple of years, How does it substantiate in terms of software or business models or sort of changes in terms of how people interact with all this stuff? Yeah, I mean, I kind of liken it to I imagine that it was like this when cloud first became a thing. There were some applications where you're like, clearly this needs to move into the cloud. There's so much value to having it accessible on demand and on a mobile device and wherever you go. So and then there were some applications where you really wanted, I mean, even to this day, you want to keep it on premise.

21:46And it's probably going to be the same is true with with AI. Right. There's there's some workflows or some decisioning and branching that you want to be fully deterministic. You wanted to reproduce the exact same way every single time might be authentication. It could be a financial transaction. It could be a health care procedure and it'll stay the way that currently is. there's a lot of other ones where the job of the software engineer and product manager and designer is going to shift from prescribing the how to prescribing or describing the why and the what and the goal. And we leave it to the AI to figure out stochastically the how.

22:28I have two more sort 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. How do you guys think about this at Salesforce in your product launches or in thinking about new SKUs and pricing? 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 really difficult. 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.

23:19So I think that's the balance that we're trying to strike right now. Overall though, the key thing that we have to achieve is to show value, show ROI, right? Like in the case 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. Yeah, maybe two reasons I'm still pretty optimistic about this question is you certainly have a more complicated unit economics equation than like, it's a web app and like we're using database services that are like very efficient today.

24:00But in so many applications, you have net new capabilities or like massive productivity gains, right? So wherever you see orders of magnitude improvement in terms of value you can give to the customer. And then on the cog side, you know, being able to do the same task with these AI models decreases over time monotonically, right? As we improve at every level of the stack for AI. And so I definitely think it's a complicated question, as you said, in terms of presenting that answer, getting the answer like right to a really fair trade for customers. But I think there's a lot of opportunity. Yeah.

24:36I mean, just to give an extreme example of that, I met with Cristobal Valenzuela yesterday, who is the founder and CEO of Runway ML. And he was talking about their involvement in the movie Everything Everywhere All at Once, which is such a great movie. And I didn't know this, but they actually powered a lot of the special effects that you see in the movie to the order that of, you know, only requiring seven people on the video editing team versus traditionally a movie like that would have 700. And so, I mean, just thinking about, I mean, there's a lot of questions that that talks about from jobs and the Hollywood strikes and whatnot, but just from an ROI standpoint, right, I'm sure between the studio and runway, they're able to figure out a business model that works.

25:23Yeah, yeah, I think it's a great example. One last question for you, just because you have such a unique viewpoint as both an executive at scale and a founder, where do you think startups should focus their efforts, right? Right. You have Salesforce and companies of its scale that have all of its product and distribution and data advantages. What do you think is most interesting in generative AI putting on your entrepreneur hat? I mean, I see so many exciting startups out there. I think the startups that are, there's a foundational model, startups, if you can even call them that anymore, given how big they've become, or domain-specific startups that focus in an industry like legal or medicine.