No Priors Ep. 43 | With Clara Shih, CEO of Salesforce AI

1. 0:00 – 0:50

   Clara’s Background

   1. EGElad Gil0:00

      (instrumental music) . Today on No Priors, we have an entrepreneur and executive Clara Shih. Clara is currently CEO of Salesforce AI, and before that was the CEO of Salesforce Service Cloud and of Hearsay Social, a company 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 Copilot and agent platform, model development, go-to-market, growth, adoption, partnerships, ecosystems, and secure responsible AI. It's so much stuff, I got tired just t- going through all of it, so she must be exhausted. Um, today on No Priors, we talk with Clara about Salesforce's forays into generative AI and the future evolution of AI in the enterprise. So thank you so much for joining us today, Clara.

   2. CSClara Shih0:44

      Sure. Thanks for having me. I'm a big fan.

   3. EGElad Gil0:46

      So I was hoping to just start off with, um, how you ended up taking on

2. 0:50 – 3:25

   From cloud services to AI

   1. EGElad Gil0:50

      the CEO role for Salesforce AI. I know before that you were working o- on, um, Service Cloud, and then we had sort of this big wave of innovation happen in term of generative AI, and Salesforce has been quite fast to adapt to it. So I was just hoping to learn a little bit more about how your role evolved and the c- the kinds of areas that you focus on today.

   2. CSClara Shih1:07

      Yeah. I mean, if you go back to Hearsay days, and, uh, Elad, you'll, you might know this, Hearsay had and continues to have NLP to mine the messages that, that come through. And Hearsay mines it for both lead generation opportunities as well as t- to detect compliance infractions. So that was like really when, you know, just from an empirical standpoint, I got closer to, 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, you know, pre-GPT types of transformer models to do that. And just as we started playing around with, with, with our own models and we saw OpenAI 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. So I'd say probably, you know, a year and a half ago is when, you know, in the Service Cloud world, my engineering leader, Jayesh, and I, we really started to, to double down on these experiments, more prototypes. We were working with a c- a couple customers, um, including Juk- Gucci to develop very early prototypes of what now has become Service GPT, and we were just learning and, and iterating and figuring things out as we, as we went. Well, then, of course, fast-forward to last year, ChatGPT, um, is launched, and now every customer is super interested in AI. And, and across Salesforce, you know, I think there wa- there was a sudden, you know, wake-up moment to say, "How do we apply large language models to every cloud?" And so I think we were in a position of saying, "Hey, here's what we've learned working with Gucci, working with, uh, 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 and this new, new role that I, I took on about six or seven months ago.

   3. EGElad Gil3:19

      And I guess Salesforce for a long time now has been building a lot of its own models. You know, it had very early, uh,

3. 3:25 – 5:20

   Internal Model Development vs Open Source

   1. EGElad Gil3:25

      f- uh, in hindsight now, forays into AI, things like Einstein and other things, and I know that's evolved into, you know, there's Einstein Copilot and Einstein 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?

   2. CSClara Shih3:42

      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 wanna fine-tune their own. Um, others are, you know, 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, CodeGen from, from our research team, which is the, which powers Apex, um, CodeGen GPT that we have in, in our developer GPT. We're also fine-tuning versions of that for domain-specific models and 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 are both Salesforce Ventures investments, or OpenAI, which is a close partner, or Google Vertex, and offering people either the choice to, to, to buy those through us or to bring their own API keys.

   3. EGElad Gil5:10

      And then I know they also provide other things that are, um, integrated in the platform or taking a platform-based approach to things like copilots, agents in an agent-based platform.

4. 5:20 – 8:50

   The Co-Pilot Approach

   1. EGElad Gil5:20

      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? 'Cause it seems like we're kind of in the nascent phase of these things, but they're still very exciting.

   2. CSClara Shih5:32

      Yes. (laughs) Uh, so the way that Salesforce is rolled out, I mean, as you mentioned, the earliest foray into LLMs were models. We've had models for, you know, four or five years, uh, 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, 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, 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, and operational bottlenecks for the jobs to be done for their buyer and user base. So a great example of this, the most popular one is service reply recommendations for contact center a- 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, um, similar cases for that particular customer. So that... though 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, and the platform itself, um, is Copilot, which is the, the natural language interface that will span across all of our clouds, as well as Slack, and then it's also Copilot Studio. And within Copilot Studio, there's three platform, like, really big platform, um, areas that, that we're, we're building out. The first one is Prompt Builder, and, you know, 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. So Prompt Builder, we actually just launched our pilot of that, uh, last week, and we're having... we're already getting customer feedback, which is, 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, um, give... you know, empower the, the copilot in... 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. So any of those now with one click can be designated as an action for the Copilot agent, um, which is pretty incredible. And then the third part of Copilot Studio is, is, 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.

   3. EGElad Gil8:32

      So I, I think, again, uh, 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. Because 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

5. 8:50 – 10:54

   Enterprise AI Adoption

   1. EGElad Gil8:50

      is almost the 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? Are people doing this in production? I'm sort of curious about sort of the real traction that- that's being seen today.

   2. CSClara Shih9:10

      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 ServiceGPT or SalesGPT. 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 rote tasks that bogged them down before and made customers angry can now be largely automated or, or much accelerated with generative AI. So we, 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, um, partnership integrations with BigQuery, with, um, 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.

   3. EGElad Gil10:28

      I guess if, if, if I were to think about it from a macro perspective, um, 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 I know 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'm... 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

6. 10:54 – 13:23

   The future of Enterprise AI

   1. EGElad Gil10:54

      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, and so I'm a little bit curious about your viewpoint in terms of, you know, uh, are we in the first inning? Are we in the third inning? Like, wh- where are we relative to sort of enterprise adoption?

   2. CSClara Shih11:12

      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, I... it's early, right? Probably the second or third inning. I'm not-

   3. EGElad Gil11:23

      Mm-hmm.

   4. CSClara Shih11:23

      ... 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.

   5. EGElad Gil11:39

      Mm-hmm.

   6. CSClara Shih11:39

      But most companies, especially in the enterprise, as 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, um, data to power AI, whether it's for, for training and fine-tuning or for RAG.

   7. EGElad Gil12:00

      That makes sense. So 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, and then step three always seems to be, okay, now we're actually gonna push it out into our own end products or to our own end users or customers.

   8. CSClara Shih12:22

      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-

   9. EGElad Gil12:28

      Mm-hmm.

   10. CSClara Shih12:28

       ... thing that we see, a- and probably because we're a CRM company, is, is in the customer service world. You don't have to have all of your enterprise data cleaned up, right? That, 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, um, with, with vector search and embeddings to drive really good RAG for any customer service question, whether it comes into the self-service agent, formerly known as Einstein Bots, or whether it comes in to a person?

   11. SGSarah Guo13:00

       How did you think about, just given the, like, breadth of the Salesforce product suite and your role to, uh, advance, like, AI across the organization, how did you think about, um, educating the rest of the product management and engineering organization or teaching them, like, how these experiences can change with AI capabilities?

   12. CSClara Shih13:20

       I'd say it's, it's really, it's, um, it's not

7. 13:23 – 14:40

   Cross-team collaboration

   1. CSClara Shih13:23

      one way, right? There's so much interest in, 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. Um, so I'd say it's been a very collaborative effort. But it is, th- that is something kind of an ongoing effort, and especially as we think about, you know, as you were alluding to, Elade, agents maturing and being able to do more. I think it's really going to dramatically transform the, how we approach software development, right? A lot of what was explicitly hard-coded as different branching and execution paths and painstakingly, um, spec-ing out every screen in a, 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.

   2. SGSarah Guo14:13

      Is there anything that you're excited about from, like, a change in end user experiences if you project out, like, uh, a year or two or three, right? Like, if we follow El- Elade's framework of, um, you know, or, or, 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

8. 14:40 – 19:11

   AI is the new UI

   1. SGSarah Guo14:40

      service versus, like, what can you, as an end user, expect that will be better?

   2. CSClara Shih14:45

      Yeah. It, it is such an exciting area. Like, AI is the new UI, or maybe Slack is the new UI for AI. And, um, that's also been really amazing, right, is 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 plug-ins 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, I'll answer that in both, i- 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 w- 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, uh, previously what we would call a lightning web component page that you would have to hard-code and hardwire. But through Generative Canvas, where we already have all of the components there, it knows to call the right items and visualize the right things from Tableau, Salesforce Reporting, um, be able to update records. So that's, it's pretty exciting, and it's- it's not ready for primetime. It's, it's very, um, raw and messy, but that's kind of how we're, we're operating, is just we're learning from, from showing that to different customers, testing it out ourselves, and then we're gonna eventually roll out something that's, that's pretty radically different. Um, the other part of UX is, is working with Slack and thinking about how agents can be used not just by one person, but by teams of people. And, um, there's a lot of exciting UX work being done there. Um, now, in terms of experience, like the, 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. Um, 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, a Gucci service advisor is you really have to know your product, right? People are spending a lot of money. They have high expectations. And, um, and so it's been really great to basically use retrieval augmentation to basically help arm every Gucci service advisor with the, 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, and then but instead of hanging up, the service advisor is able to have a deeper conversation. And because they're, you know, Salesforce has a 360-degree view of the customer, the service advisor can see that, you know, Sarah, you have, you know, we've, we solved your issue with your broken buckle on 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. And 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, um, how, you know, Jackie Kennedy used to wear these shoes too. So it just is changing the job and, and it's really breaking out of these traditional department roles and, 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?

   3. SGSarah Guo18:36

      That's very cool. One, 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? I think that has been one of the biggest concerns 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,

9. 19:11 – 21:25

   Structuring the Dataset

   1. SGSarah Guo19:11

      what have you learned from working with customers, uh, around some of their most sensitive data?

   2. CSClara Shih19:15

      There's so much. I mean, we, we don't have all of 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 an- 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-

   3. SGSarah Guo20:00

      Talking about my dog.

   4. CSClara Shih20:02

      Yeah, so then you have to, like preprocess. You have to, like mine that. You have to do a step further before you use that for something like retrieval. And so, um, that's something that we've learned and, 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 in embeddings. But there's some data, there's actually a lot of structured data in there sometimes, right? In a phone transcript that a retailer might have with a 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. Those should then populate the structured data fields in Salesforce, which is, which is also something that we're doing.

   5. EGElad Gil20:43

      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?

   6. CSClara Shih20:51

      Everything. (laughs)

   7. EGElad Gil20:52

      (laughs)

   8. CSClara Shih20:52

      The fact that it works so well and, yeah, I mean, I- I feel like I- I'm surprised every week.

   9. EGElad Gil20:59

      Yeah. Do you have any 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, um, enterprise software? 'Cause you mentioned, for example, AI is the new UI, for example. I'm sort of curious, as we think ahead a couple years, how does this substantiate in terms of software or business models or sort of changes in terms of how, how people interact with all this stuff?

   10. CSClara Shih21:23

       Yeah, I mean, I, I kind

10. 21:25 – 23:18

    What’s next for generative AI in Enterprise

    1. CSClara Shih21:25

       of liken it to I imagine that it was like this when, 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." And then there were some applications where you really wanted... I mean, even to this day, you want to keep it on-premise. And it's probably gonna be the same true- 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 want it to reproduce the exact same way every single time. Might be, um, authentication, it could be a financial transaction, it could be a healthcare procedure, um, and it'll stay the way that it currently is. There's a lot of other ones where, um, the job of the software engineer and product manager and designer is gonna shift from prescribing the how to prescribing or de- describing the why and the what and the goal. And we leave it to the AI to figure out stochastically the how.

    2. SGSarah Guo22:28

       Uh, I have two more sort of business-oriented questions for you. Unlike much of the software developed over the last five, ten years, that wasn't... I- I mean, much software's 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?

    3. CSClara Shih22:55

       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, our AI- 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

11. 23:18 – 26:30

    Pricing challenges in AI

    1. CSClara Shih23:18

       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.

    2. SGSarah Guo23:29

       Mm-hmm.

    3. CSClara Shih23:29

       Right? Like in the, 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.

    4. SGSarah Guo23:46

       Mm-hmm. Yeah, maybe two reasons I'm still pretty optimistic about this question is you, you certainly have a more complicated unit economics equation than like... It's a web app and it's like we're using database services that are like very efficient today. Uh, but y- 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, it, you know, being able to do these same tasks with these AI models decreases over time monotonically, right? As, uh, 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'm, I'm- I think there's just a lot of opportunity.

    5. CSClara Shih24:36

       Yeah. I mean, just to give an extreme example of that, um, I- I met with, um, Cristóbal Valenzuela yesterday, who is the founder and CEO of RunwayML, and he was talking about their involvement in the movie Everything, Everywhere All at Once, which is such a great movie.

    6. SGSarah Guo24:49

       Yeah.

    7. CSClara Shih24:50

       And I didn't know this, but they actually powered a lot of the special effects that you see in the movie. To the- 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- and Runway, they're able to figure out a business model that works.

    8. SGSarah Guo25:23

       Yeah. Yeah, I think it's a great example. One- one last question for you, just because you have such a unique viewpoint as, um, both an executive at Scale and a founder. Where do you think startups should focus their efforts, right? You- you- you have Salesforce and companies of its scale that have all of its product and distribution and data advantages. Um, what do you think is most interesting in generative AI, putting on your entrepreneur hat?

    9. CSClara Shih25:48

       I mean, I- I see so many exciting startups out there. I think the startups that are... There's the foundational model startups, if you can even call them that anymore (laughs) , um, given how- how big they've become. Or domain specific startups that focus in an industry, like legal or medicine. I think that's super interesting. I think at the tooling layer, we talked a little bit about that earlier, there's a lot to be done there, um, to address different types of needs that- that different types of organizations have. And there's just so much... Like, we don't know what we don't know. And so it's a time to in- invent and test and, and see what's out there. I mean, Salesforce, like, we're doing a lot, but we can't do everything. And then on the applications layer, there's a lot of applications

12. 26:30 – 28:22

    Startups and AI

    1. CSClara Shih26:30

       to be built. Just like with our own internal teams at Salesforce, the way that those applications get built in the future will be very different than they're built today. I think that for startups that... To understand that and to maybe align themselves with- with data graphs that are out there, because that's so essential, um, for those applications to be relevant.

    2. SGSarah Guo26:48

       Thanks for doing this with us, Clara. It was a great conversation.

    3. EGElad Gil26:51

       Yeah, thanks for having us today.

    4. CSClara Shih26:53

       Thank you.

    5. SGSarah Guo26:56

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Episode duration: 27:14