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

**Elad** (0:05)
Today at No Priors, we have 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 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 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.

**Clara Shih** (0:43)
Sir, thanks for having me. I'm a big fan.

**Elad** (0:46)
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.

**Clara Shih** (1:07)
Yeah, I mean, if you go back to Hearsay days, and Elad, you might know this, Hearsay had and continues to have NLP to mine the messages that come through. And Hearsay mines it for both lead generation opportunities as well as to detect compliance infractions. So that was like really when, just from an empirical standpoint, I got closer to AI and ML. And this is like all 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 chat bots 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. So I'd say probably a year and a half ago is when, in the Service Cloud world, my engineering leader, JS 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? 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 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.

**Elad** (3:19)
And 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?

**Clara Shih** (3:42)
We're taking really an open architecture approach because 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.
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 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 cogen from our research team, which powers Apex cogen GPT that we have in our developer GPT, we'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.