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

Key Takeaways

today on no priers we have entrepreneur and executive Clara shei 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 a 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 um today on no prior we talked with CLA about sales forces for raise and a generative Ai and the future evolution of AI in the Enterprise so thank you so much for joining us today Clara sah thanks for having me I'm a big fan so I was hoping to just start off with um how you ended up taking on the CEO role for Salesforce Ai and know before that you're working on um 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 and I'll add you might know this hearsay had and continues to have NLP to mine the messages that that come through and hearsay Minds it for both lead generation opportunities as well as to detect compliance in fraction 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 large language models and then when I joined service Cloud it's like almost 3 years ago when you think about the customer service world and there's a lot of AI there's been chat Bots for many years and we were using very early you know preg GPT types of Transformer models to do that and just as we started playing around with with 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 so 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 jsh and I we really started to to double down on these experiments more prototypes we were working with a a couple customers um including GUI 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 chat jpt um is launched and now every customer is super interested in Ai and and across Salesforce you know I think there was there was a sudden you know wakeup 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 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 in 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 new role that I I took on about six or seven months ago and I guess Salesforce for a long time now has been building a lot of its own models you know I had very early uh in hindsight now forray into AI things like Einstein and other things and I know that's involved into you know there's Onin co-pilot 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 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 Coen from from our research team which is the which Powers Apex um codeen GPT that we have and 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 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 thirdparty models be it anthropic and coher which are both Salesforce Ventures Investments or open AI which is a close partner or Google vertex and offering people either the choice to to buy those through us or to bring their own API keys 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 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 recent phase of these things but they're still very exciting yes uh so the way the sales forces r i mean as you mentioned the earliest foray and llms were models we've had models for you know 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 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 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 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 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 co-pilot which is the the natural language interface that will span across all of our clouds as well as slack and then it's also co-pilot studio and within co-pilot Studio there's three platforms form 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 salescloud 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 Etc 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 it's the speed at which this is all going the second part of co-pilot studio is action Builder and that's where we start to um give you know empower the the co-pilot 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 guard rails for their Integrations using mu soft so any of those now with one click can be designated as an action for the co-pilot agent um which is pretty incredible and then the third part of co-pilot studio 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 sales for and or indexed by Salesforce in our data graph so I 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 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 chat jpt launched is 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 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 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 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 the manual lookup and wrote 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 big query with um data bricks with snowflake Etc so that really customers can bring all of their structured and unstructured data into one place to really power these generative use cases I guess if if I were to think about it from a macro perspective um and not a salesport 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 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 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 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 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 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 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 and 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 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 in to the self-service agent formerly known as Einstein Bots or whether it comes into to a person 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 I would say it's it's really it's um it's not 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 road map is coming from people from all across the organization um so I 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 the how we approach software development right a lot of what was explicitly hard coded as different branching and execution paths and painstakingly um specking 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 is there anything that you're excited about from like a change in end user experiences if you project out like a a year or two or three right like if we follow a a lad's framework of um you know or or or your phrasing of like you get your data ducts 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 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 is just looking at first like these Acquisitions that Salesforce made that at the time like admittedly they were not made 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 with moft is having all of the plugins and extensions that you could possibly want in an Enterprise like it's really played out nicely back to your question on 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 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 lightening web component page that you would have to hard code and hard wire but through generative canvas right we already have all 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 not ready for prime time 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 going to 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 that 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 of AI so Gucci is a great example um they hired a number of new service Representatives during Co there was you know was like high turnover in the early days of the pandemic and thing about being a a Gucci service adviser 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 adviser is able to have a deeper conversation and because our you know Salesforce has a 360 degree of view of the customer the service advisor can see that you know Sarah you have you know we we solved your issue with your broken buckle in 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 adviser 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 wants and needs 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 in ownership like what have you learned from working with customers around some of their most sensitive data 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 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 talking about my dog yeah so 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 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 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 those should then populate the structur data fields in Salesforce which is 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 I'm surprised every week 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 because 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 it substantiate in terms of software or business models or sort of changes in terms of how how people interact with all this stuff yeah I mean I I kind 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 going to be the same TR is true with with AI right there's there's some workf flows there some decisioning and branching that you want to be fully deterministic you wanted to reproduce the the exact same way every single time might be um authentication it could be a financial transaction it could be a healthare procedure um and it'll stay the way that currently is there's a lot of other ones where um the job of the software engineer and product manager and designer is going to shift from prescribing the how to prescribing or descri describing the why and the what and the goal and we leave it to the AI to figure out stochastically the how uh I have two more sort of business oriented questions for you unlike much of the software developed over the last 5 10 years that it 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 and your product launches or in thinking about new SKS 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 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 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 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 like we're using database services that are like very efficient today uh but 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 these same task 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 think there's a lot of opportunity yeah I mean just think of an extreme example of that I I met with um Christal Valenzuela yesterday who's 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 to the order that 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 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 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 I mean I 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 um given how how big they 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 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 thanks for doing this with us Clara it was a great conversation yeah thanks so much for Jo us today thank you find us on Twitter at no prior pod subscribe to our YouTube channel if you want to see our faces follow the show on Apple podcasts Spotify or wherever you listen that way you get a new episode every week and sign up for emails or find transcripts for every episode at no- pri.com