[VIDEO PLAYBACK] - Wow, what a year. We connected. We shared success. We raised the bar. Thank you. And in a year of tremendous change, one thing captured our attention above all else-- - AI, just the boom of interest. - The talk of the town is AI. - Artificial intelligence is poised to shape the world around us for decades. - Now, that's all good and fine, but let's not lose sight of the real bottom line-- trust. - We're bringing the human touch to every interaction with our customers. - When it comes to customers and trust, there can be nothing artificial about the experience. - It's all about CRM, AI, data, and trust, and the power that that brings. - You see, this isn't just a technological revolution. No, this is a trust revolution. - Salesforce is a huge trusted advisor when it comes to embracing AI. - AI has amazing potential, but you've got to have really good data. - Data Cloud allows us to integrate that innovation and deliver it at scale, securely, and reliably. - Working with Salesforce, we're creating these magical connections and personalized experiences in a way that nobody's ever seen before. - The Einstein 1 Platform, it's the trusted and secure way to put your data to work. Make no mistake. This is a once in a lifetime opportunity to lift human potential. - What the Einstein 1 Platform does is collaborate actually with the advisor. - It'll take their keywords and will automatically give me service replies over here on the right. It's like a helping hand. - It's improving the team as well. Everyone's performance is raised. - Yeah, it's all of us or bust. So no one gets left in the dust. - It's a turbocharger for our business. It's going to make us more efficient. Because AI is there helping us augment the team, we can transform how we do business and be more productive than ever before. - Because it always comes down to this timeless refrain-- business is the greatest platform for change. - We're in one of the greatest moments of our life with AI. - Yep, what a year. Trailblazers, trusted AI, working together, we are going to get it right. - We've been able to drive a 40% increase in productivity. - Three times the output that we have in the past. - We delivered the highest revenue ever, double digit growth. - Core values, true north. - Wow, shares in the green. - Incredible cash flow numbers. - A lot of margin expansion. - Salesforce is going to have a dynamite 2024. - Thank you for everything and all that's to come. [PLAYBACK ENDS] Please welcome co-founder Salesforce and CTO of Slack, Parker Harris. [APPLAUSE] All right. Well, welcome, everyone. Welcome to the New York World Tour. Welcome to the AI Enterprise, where customer data is the new gold. The world is going incredibly fast in technology. We all know that, and we're going to give a great show today and show you how Salesforce is going to bring all you forward into this new world. But as we always do, what do we do first in every one of our shows? You know what we do. We want to thank each and every one of you. Thank you to all of our MVPs-- [AUDIO FEEDBACK] --and Trailblazers. Sorry about that. Yep, and the golden hoodies over there. Thank you to our nonprofits. Do we have any nonprofits in the room? [CHEERING] Yeah, all right. Shout out to the nonprofits. Thank you to all of our employees, all of the SI partners, the ISV partners. All of you together have joined us over the past 25 years to create something very, very special. And we couldn't have done it without you, so thank you. We have an incredible world tour today here in New York City. We're going to go through a whole bunch of customer stories throughout the day today. In this keynote, we're going to really highlight an incredible story of a company called Turtle Bay. There are a ton of things to do here today, not just this keynote, 100 sessions, 20 demos, 70 sponsors. If you can't figure it out, go to that QR code and use the mobile app, the events app, to figure out, what are you going to do? Because you can't do it all. And what's most important to you? It's a unique experience for each and every one of you. So together, we're really proud over the past 25 years of where we have gone. Sorry about that. And we are at $38 billion projected revenue this year. We're very proud of that. But I think what we're more proud about is that we can do well. We can be $38 billion. I like that. That's good. But we can also do good. That is the model that we've always had as a company since I met Marc Benioff actually in 1998. And together with you, we have become one of the most innovative companies. Together with you, we have given back one of the top 100 companies that care. And together with you, we have been a very ethical company, world's most ethical companies. And really, why did that all happen? How did that happen? Well, it happened because of values. And I think the best companies in the world lead by their values. And Salesforce has always led by our values. Value of trust-- when we started the company, it was credit cards. Would you trust a credit card on the internet? Maybe. Would you trust your customer list on the internet? People were like, no way. I will never put my customer list on the internet. But it's also about data. And we're going to talk a lot about data in this show. And your data is not our product. That's also part of trust. But it's also the values of customer success. We want to make you successful before we do anything else. Before we talk about all the innovation today, we want to make you successful. It's about equality, and it's about sustainability. It's about those core set of values that guide us each and every day. And those values also came out of something that we created when we started the company. We created something we called the 111 model. The 111 model, when we were three people in an apartment in San Francisco, was very, very small. But look what we have done today. It's about 1% of our equity. We've given over $700 million out in 25 years in all-time giving. It's about 1% of time. Three people in an apartment, that's not much. 75,000 to 80,000-- I don't even know how many employees we have. A lot of employees around the world have given 8.7 million hours back. We give nine days a year. We tell our employees, go give back. Wherever you are, do good in the world. And we also give our product away to nonprofits. So all those nonprofits that raise their hands, 56,000 nonprofits are using our product to do fundraising to take care of others. It's incredible. And we also want many other companies to do the same thing. So we have this pledge1percent.org. Don't wait. As you're building your companies, if you're entrepreneur's in the room, don't wait. Do it as you're building the company because it makes for better companies. I also want to call out we have the Salesforce AI for impact accelerator. We're putting money into these nonprofits that are having an impact. We have an incredible company here called Groundswell. Where it's Groundswell? Here we are. Here we are. Groundswell is doing some really cool stuff with solar and AI. They're one of our recipients, so congrats. And you want to know more, just go corner them after the show. You'll learn a lot more from them. So 25 years ago, we started the company. Our vision was connect with your customers in a whole new way. And together with you, we have become the number one CRM by far. We like that scale of that number, by far the number one. And it's been incredible. But we are in a totally new moment right now, a totally new moment. And we know what that moment is. It's a moment about AI. In 2014, Salesforce started its journey in AI. It was great. We started our research group. We had PhDs, and mathematicians, and trying to figure out the future. In 2016, we launched Einstein. And at that time, Einstein was all about predictive machine learning. Help me understand, which lead should I call to close that deal? What's the best one that I should call? Who should handle this case? When should I send this email? Use machine learning and statistics to basically decide what's important. That was state of the art at the time in 2016, and it's still incredible. We will see in our demos Predictive is still very meaningful. But as you all know, we have hit a tipping point. A year and a half ago, generative AI really came into fruition with OpenAI. And we were all shocked, including Salesforce. Our researchers had been involved. We've been building stuff. But something happened there. And then we're going to talk about what happened. Why did it hit that tipping point? So it's about generative AI and this world of autonomous agents that we're moving into, that world of autonomy where maybe the AI is there beside you in the enterprise, always helping you, always doing something. And maybe at times, we're going to let it do stuff for us. It's going to say, yeah, sure. Go and call these-- send these emails out. And if anyone responds, let me know, and I'll follow up with them. That would be an example of, do something for me. And then we're moving into this world of artificial general intelligence. Really cool. And we really do believe that AI is going to augment every single enterprise. It's going to improve your productivity. Of course, it is. It's going to improve your margins. You're going to make more money and spend less doing it. That's cool. We really like that. And it's, of course-- and maybe that should be the highest priority, actually, is build better customer relationships because that's what we're all about, is helping you build better customer relationships. And 84% of leaders agree that AI will serve customers in a better way. That's about the enterprise. But let's go back to how we got here. What happened a year and a half ago, we saw this generative AI happen. And it really is about these three layers. You go to an Anthropic or an OpenAI. And there's a UI for it, and each of them have incredible models. We thought-- I thought a year and a half ago, well, I guess there's only one model in the world that's going to be great, and that was OpenAI. And everybody needs to use it. It still is fantastic. But there's so many more. But how do those models get created? They trained on a whole bunch of data. The more data-- and that's why we have that tipping point. It's the massive amounts of data that we use to train these models, all of a sudden, created these billion parameter models that did incredible things. So it's these three layers of the stack. And in the consumer space, what we saw is go get all the data you can. The more data, the better. Go get every website, every book that you could get access to in the public domain. Go to the New York times, which is not the public domain and a little bit of an issue there. But as much data as possible, let's just voraciously consume it and train these models. That's what happened. And so we have all these different models and all these different user experiences. But in the enterprise, are you going to go and grab all the data on the internet for relevancy in the enterprise for your customers? It's not relevant. What you need is your enterprise data. Who are these customers? What is your business? What is going on? You need to get all that enterprise data. But you don't have an internet in your enterprise where you can go and, oh, let me just go surf the internet of my enterprise. And I'm going to get all the data. No, you don't have that because, in the enterprise, it's all siloed. We all have this problem. The data is all over the place. No matter how hard we try, it seems like, oh my gosh, I've got data in a Databricks and a Snowflake database. I've got my SAP or Oracle ERP system. That's data. I'm using multiple SaaS providers. That's data. I've got maybe some legacy systems. I'm sure there's someone here who has mainframes in their company. I know a lot of banks-- are there any bankers here? You have mainframes, guaranteed. If you're an insurance company, you have mainframes. That is data, and it's all over the place. 72% of companies' applications are disconnected. So that's at the data layer. That's a huge problem. How are you going to move forward in this world of generative AI if you can't get to the data? And then if you get to the model layer-- so let's go up a level. You get to the model laye, there's also a ton of issues you're experiencing. Of course, we talked about the data silo and the systems. But the model layer also security and privacy. These models can be toxic. They're not predictive. Or they're not deterministic. So hallucinations can happen. And they can be very confident liars. You can actually ask them, when it's hallucinating, are you sure? And it'd be like, I am very sure. You're like, OK, great. But it's actually hallucinating. And it's disconnected from your CRM. That's a lack of trust. So at Salesforce, we want to solve these problems for you. We want to solve the problems to get to the AI Enterprise. We want to help you understand how to build the Customer 360. That has been our mission for 25 years. We want you to be able to harmonize and unify all of your data with Data Cloud. We want you to be able to collaborate with AI. And what better way to collaborate with AI than through a conversational interface of Slack. We want you to be able to deliver AI analytics with Tableau. And finally, we want to be able to deploy that AI because you need to get it to everyone. You don't want it to be locked up. We want you to be able to deploy it and deploy that Copilot everywhere in a trusted way. So we're going to walk you through all of these steps. The first step, we want to show you how to build a Customer 360. And with that, I'd like to bring up Patrick Stokes, EVP of product marketing. Patrick, take it away. All right. [APPLAUSE] All right. Thank you very much, Parker. Thank you very much, everybody. OK, so your Customer 360 is your single source of truth. It is your operating system for driving growth, for driving innovation, and ultimately for driving deeper relationships with all of your customers. The Customer 360 is the most robust set of CRM capabilities on the planet. No other platform goes as deep across all of the different customer touchpoints that you might have with your customer as Salesforce. And what makes all of this work is our Einstein 1 Platform. And the Einstein 1 Platform is the foundation for everything at Salesforce. It's what everything is built on. It's the platform that connects everything. It's your trusted platform for driving growth. It's your trusted platform for transforming your business with AI, just like it helped you transform your business with the cloud, with social and mobile before it. It's what will bring all of your data together with AI and, ultimately, all of the different interactions that you have with your customers. Now, it's integrated with Salesforce metadata. It's intelligent and conversational with Einstein and Slack. It's automated with flow. And it's low code, and no code, and open so that you can customize it and extend it to meet the unique needs of your business. And every business in this room is a little bit different. Einstein 1 is what brings all of this together. But at the heart of Einstein 1, in fact, at the heart of almost any of these transformations and especially the AI transformation that we need to make, is data, which brings us to step 2 in our five steps to becoming an AI enterprise. In order to become an AI enterprise, we really need to get all of our data together. And we can see this in our own experience. Parker talked about it a moment ago. If we go ask ChatGPT a question about our business today, it isn't going to give us a very good answer because it doesn't have data and context. So we need to bring all of that data together. And therein lies a really important tension, a tension that really has been around for about 20 years. But we're really seeing it take shape. And it's becoming more and more tense as we try to move to AI. And it's a tension that many in this room probably feel. It's a tension between our business, our business that is building all of these customer experiences. And they need access to data. They need the data that they can trust. And then the other side of the tension is with the folks in the room that are developers, that are admins that work in IT, whose job it is to go and bring all of that data together, to connect that data and harmonize it. This is an incredibly difficult thing to do. And it's difficult, as Parker said, because we have all of these islands of trapped data. We've got silos. We have different systems. And we've been adding more and more and more systems over the last 20 years. And that data, it exists in different formats and different structures. Some of it is structured data, like databases. In other cases, it's unstructured data, like our emails, or conversations in Slack, or our knowledge articles. Sometimes we can get at that data only through batch processes. Sometimes we need to use APIs. Sometimes it's streaming. It's really difficult to bring all of this together. And that's why Salesforce built Data Cloud. Data Cloud is your trusted hyperscale data engine. And it's built right inside of Salesforce. And Data Cloud does something really special. See, data platforms, data warehouses, data lakes, they bring data together. They're really good at bringing data together and unifying that data. And Data Cloud does the same thing there. But Data Cloud takes it even one step further. You see, with Data Cloud, what we really care about is action. We want to help you action all of that data. And that's why Data Cloud is built into the platform. It's integrated with metadata so that we can use all of the data that we connect into Data Cloud across all of our applications. Data Cloud is how customers are building entirely new experiences. For example, you can bring product telemetry data into Data Cloud now, all of the data that's burning off of your digital products. And now we can do real-time proactive service. So your service agents can reach out to your customers if there's an outage before the customer even knows that there's an outage. It's how sales organizations are bringing in website data in real time so that they can give their sales development leaders the information that their prospect is actually on their website right now. This is the power of not only unified data but making that data actionable across the platform. Now, what makes this possible is something called metadata, which we'll talk about in a moment because I'm a slide ahead. But let's talk about how Data Cloud works first. So the way Data Cloud works is really three key steps. We start by connecting your data. We can connect data from Salesforce orgs and applications, from data warehouses and data lakes, like Snowflake, but really from any system. And it works on structured data, semi-structured data, or unstructured data. From there, we want to harmonize all of that data, which is just a fancy way of saying that we're going to build a data model out of it. So we're going to work with you to-- or the product works with you to define how that data exists, what it's related to, what other data it's related to that's in the system. This gives us a sense of how the data is used. And then finally, we can activate all of that data. We can drive insights from it, of course. We can discover new pieces of data by looking at it. But we can also action it across sales, service, commerce, marketing, flow. We can build automations and workflows, and we can use it in AI. Now, what makes all of this possible is metadata. The entire Einstein 1 Platform is built on this concept of metadata. It's something that we pioneered 20 years ago. And it's even more important today. And it's worth taking a moment to describe what metadata is. Metadata is just context about your data. It's a description of how your data is used. And because of that description, it becomes a language that the rest of the platform can read and use. This is how we're able to provide sharing rules. This is how we're able to look at data in sales or service cloud. And it's usable no matter what cloud we're in. But what really is metadata? Well, here on the screen, we have some data, some numerical data. And it looks, as human beings, fairly useless. It's a collection of numbers. It's hard to tell what these numbers are. Are they dollar amounts? Are they some sort of metric? Are they some sort of ID? The reason we can't figure it out is because we don't have the context. We don't have the metadata about this data. But if we add the metadata, suddenly that data becomes much, much more clear to us as human beings. We can see that the 57263 is an account value. That long 555, that's a phone number. The 10708, we can see that that's a zip code. In fact, that's my zip code, where I live, about 10 miles north of here. And then we have a Salesforce ID. Suddenly, as humans now, we can understand this data. We're able to intuit the way that this data might be used. And this metadata is used in the exact same way for the computers. This is how our platform and, ultimately, AI will use all of this metadata as well, which brings us to Einstein. You see, what we've done with Data Cloud is we've built it so that all of that data and all of that metadata can now be used inside of your AI inside of your enterprise. Parker mentioned that we need the AI to be able to understand context about your business. So we do this with Data Cloud. We take all of that data, and we connect it to the question. You want to ask an LLM a question? If you provide more data, if you provide more insight into that question, it's going to do a much better job of helping you-- of providing an answer. This is done through a technique called retrieval augmented generation, which is a mouthful. It's a really important term. You may hear about it. It's called RAG. But it's pretty simple. All we do is we take the question, which is the prompt, and we need to add some more context to it. So Salesforce goes out, and it retrieves that context. And it brings it back in, and it augments something. What does it augment? It augments the prompt. So in other words, all we're doing is adding some hints to your question, giving the LLM a few hints as to what the answer might be. And then we send that off to the LLM. We do that safely, of course. Claire is going to show you what that looks like in a little bit. And we get our answer back. So that's how we bring trusted data to our AI. Now, Data Cloud has been an incredible product for us. It is growing like crazy. We are innovating on it like crazy. In fact, just a few weeks ago in our spring release, we announced an incredible new capability called Data Spaces, which allows you to logically partition your data across different departments, across different functions or geographies, if you want. Our customers are adopting it like crazy. And you can see the growth numbers. We're also incredibly excited to announce that Gartner has just named us a leader in their 2024 Gartner Magic Quadrant for customer data platforms. But the innovation doesn't stop there. We're also incredibly excited today to announce a New Zero Copy Partner Network. And what this does is this is an extension of what the partnerships we've been working on with AWS, and Databricks, and Snowflake, enabling you to mount to virtually bring data to Data Cloud without having to create yet another data silo. You can now virtually mount these tables inside of Data Cloud. Why would you want to do that? Well, you've already invested so much in these data platforms. But remember, what we care most about is helping you activate that data. So keep that data in Snowflake. Keep it in Databricks. But connect it through our Zero Copy Network. And we can help you start activating that data and bringing that data to where the business needs it. We're also super excited about announcing our new data ecosystem partners that are bringing net new data, enrichment data as well, partners like Moody's, and ZoomInfo, Dun & Bradstreet, and Workday, and so many more. So this is incredible. Data Cloud, our Einstein 1 platform, it's all coming together to help companies transform, to help them become an AI Enterprise. Now, we're going to take a look at Turtle Bay. Turtle Bay is an AI enterprise, and we're actually incredibly lucky. We have Robert Marucci here with us today, the gentleman on the right. He happens to be right here on my right as well. [APPLAUSE] He took a break from surfing. If you don't know Turtle Bay, Turtle Bay is an incredible resort out in Oahu. If you're like me, you don't get out to Oahu much from here on the East Coast. But if you ever do, you should and check out Turtle Bay. It's an incredible resort. Robert, I'm told today was also your 32 wedding anniversary. And you're here with us. So thank you so much. I haven't been married as long as you, but I might have some advice for you later. [LAUGHTER] So let's take a look at how Turtle Bay is transforming and becoming an AI Enterprise. Let's roll the video. [VIDEO PLAYBACK] - Change is not the hurdle. Change is the remedy that allows us to win every day. And Salesforce is giving us the ability to change the industry. Turtle Bay is a beautiful, magnificent resort that sits on 1,300 acres of dream landscape in the North Shore of Oahu. - For over 50 years, Turtle Bay Resort has been the largest employer here on the North Shore. And so I take incredible pride in being able to tell that story. - We're in the luxury environment. But to say luxury and to really communicate what luxury is, that's very difficult because all of us look at luxury differently. - Travelers today expect us to know who they are. And so making their experience here more personalized is really critical. - How do we make an easy shopping experience in merchandise that, based on what their likes and affinities are? Trust was the most important thing. I needed a trusted source to secure my data, and Data Cloud was the differentiator and the perfect product to take that journey with me. CRM with AI and data, powered by trust, that's the way forward for us. When sales is talking to service, that's giving the steward the ability to say, wow, this is really cool. Like, I'm actually having fun solving cases with AI auto responses. - I love using the Einstein 1 platform because it gives me everything I need and all the guest details right at my fingertips. We have all of these activities that we can see that they booked. What the generative AI can now do is they can take these activities and then automatically recommend other activities that they know these guests will be interested in. So that way, our associates have all the tools to really sell the guests on what experience they're wanting to do next. - The Einstein 1 platform is really redefining how hospitality can deliver to the consumer. For me, to see where the customer journey happened prior to even being here, to their journey on site, to getting AI to tell me what the next best experience is that they should buy, that's luxury. That's what they want. And the return on investment happens day 1. We've quadrupled our visitation to our website, triple-digit increase in conversion and acquisition, giving us 40% lift in top line revenue here at this hotel. Magic has happened along the way far greater than I ever thought. The aloha here is so deep and so real. We're only as good as our culture. We're only as good as our people. Einstein 1 platform is helping us augment the processes so that we can allow the employee to shine in that regard. We're giving them the time to speak about their place that they love. It's our greatest currency here as a hotel. And people leave here saying, I will be back. [PLAYBACK ENDS] [APPLAUSE] All right. So as you can see, Turtle Bay is becoming an AI Enterprise. And it's really it's so incredible to see how they're delivering these highly personalized experiences. And it couldn't be more important than when you're on vacation. It's the last thing you want to think about is, what do I need to go do? So it's awesome to see this. And we can see exactly why so many Turtle Bay customers are deciding to come back. But as awesome as that is, as inspiring as that is, what I always want to know is, how do they actually do it? How are they actually using our set of tools, Salesforce's tools, to make that a reality? So we're going to spend a few minutes and show you how to do that. And so first, I want to welcome for the first time today our incredible demo team right here in front of me. Hello, demo team. How are we doing? All right. Fantastic. OK, so let's go to the demo. They always come up with something new. All right, so here is Turtle Bay, and we're looking at Salesforce and Turtle Bay right here inside of Salesforce. And what we're actually looking at is Jacqueline Johnson. Or we'll call her Jackie for short. Jackie is a customer of Turtle Bay. And we can see a little bit of information here about Jackie. We can see that she lives in New York, which is cool. I like that. We have her address, some kind of standard contact information that we would expect to have. In fact, we have a little bit more than that. We have some reservation details. In fact, we can see that it looks like Jackie happens to be staying at Turtle Bay right now. But if we look kind of around the rest of this screen, this is maybe a little bit anemic. This isn't a complete view of Jackie and everything that she's doing. I think we can do a lot better than that. But to do better than that, we're going to need more data. We're going to need probably a lot more data. And what we don't want to do is just go ask somebody to manually type all that data into Salesforce. We can do quite a bit better than that. So to achieve that, we're going to use Data Cloud. So let's jump over to Data Cloud. So here we are inside of Data Cloud. And what we're looking at here is a list of all of the different streams that are connected to Data Cloud. And it's not a very big list. We only have our one Salesforce org. We could connect multiple Salesforce orgs and clouds if we wanted to. But right now, we've got our one Salesforce org. And what I really want to do is I want to add more data to that view. One of the simplest things that I would like to know about my customer when they're staying at my resort is, what reservations, what experiences have they booked? What restaurant reservations do they have? And so to do that, we're going to click New. We're going to add some new data sources. And you can see here that there is a whole host of different data sources that we can add. Of course, we can add Salesforce data sources, we can add other data lakes and data warehouses. And the list goes on and on. As we look down through the bottom here, we can see that MuleSoft provides a million more data sources that we can add. And this is really important. Customers have so many different sets of data that they might need to connect. But what I want to do is I want to connect Snowflake. In fact, I want to connect a Snowflake Table because, at Turtle Bay, what they've done is they've taken all of their reservation data from their restaurant reservations. And that actually ends up inside of Snowflake. And I'd like to activate that. I'd like to increase that investment that I've made in Snowflake so that I can activate on that data inside of Salesforce. Let's go ahead and click Next on that. We're going to add to Snowflake. Now, I mentioned earlier this idea of harmonizing. So the next step is this harmonization process, which looks like a fancy UI. And it kind of is a fancy UI, actually. It's really cool. This is what helps you map the data that's sitting in Snowflake to the data model that we have inside of Salesforce, which, of course, is completely customizable. This is how we're able to harmonize and, ultimately, unify data so that we can see that the customer attached to the reservation in Snowflake is the same customer and the identifier that we have inside of Salesforce. So we've done that mapping there. OK, so let's save that. So that step is now done. And through the power of a demo, we're going to go back over to our data streams. And we can see that some time has passed, and we've added a whole host of additional data streams. We have streams from Snowflake, from Oracle, from Salesforce, from a few different APIs. We can see our restaurant reservation table right there at the top. So we have a tremendous amount of additional data that we're able to use. So let's go back over to Jackie and take a look at her profile now. So this is suddenly no longer anemic. This is now a much, much, much more thorough profile. This has made Salesforce immensely more capable and better by bringing all of that data in. We can see we still have Jackie there on the top left. We've got some calculated insights, which is just a fancy way of saying, we're looking over all of that data that we've just connected, and bringing in insights, and adding it right here into the profile with a Lightning Web component. We've got our restaurant bookings up there at the top. We can see she has a booking the 27th. Is that today? No, in two days. She's got a booking at Alaia. We can see her booked experiences. She's going to go on a birdwatching tour. Over in the top right, we can see her status. We can see that she's currently at the resort. We can also see something called a rebooking probability. This is really cool. This is using our model builder to bring in a model that-- an AI model that Turtle Bay has built to look at their propensity to rebook. And this is based on all of this data that we have here. A customer that takes all of these experiences is probably much more likely to rebook. So we're able to bring that in as well. So we now have this much, much, much more thorough view of Jackie, and this is incredible. But remember, this is all about action. What we really want to do is activate on this data. So let's click over and look at what this might look like from Jackie's perspective. So Jackie is on vacation right now. She's probably sitting at the pool. And she gets this highly personalized email. So your personalized Turtle Bay getaway, and we can see that it knows who Jackie is. It knows how long she's staying. But if we go down, we can see that it's suggesting some experiences that Jackie can go take. So with one click, Jackie could click that. And what we've done is we've activated on all of that data in real time. We can see that Jackie's at the resort, and we're able to push her this notification in real time, all through the power of the Einstein 1 platform and the power of Data Cloud to bring all of this data in. Now, we want you all to get started with Data Cloud today. It is an incredible tool. There are so many different ways that you can implement it and bring new capability into your business. If you follow that QR code, we can walk you through some of those ways. You can get started yourself. You can go to Trailhead, and get an org, and get started with Data Cloud today. It's an incredible tool. And we're excited to see how you use it and to see how you use it to transform your business into an action-oriented AI company. And so with that, I'm going to hand it back over to Parker. Parker. All right. [APPLAUSE] Great job, Patrick. Incredible demonstration. Thank you to the demo people here. You guys did a great job. So that was a demonstration of building that Customer 360 and a real deep dive on data cloud, which, really, for me, is the heart of our architecture, is the heart of our future because it's all about data. But now that we have all that data harmonized-- and I did shift my role in January. I'm now the CTO of Slack, actually. [APPLAUSE] So this next section-- yeah, I love slack. And what better way to interact with AI than a conversational interface like Slack. And so with that, we want to talk about collaborating with AI. I'd like to bring up Claire Shih, CEO of Salesforce AI. Here she is. Take it away, Clara. Thank you, Parker. And thank you, all of us, for joining us today. I'm so thrilled to be back in New York with all of you to talk about AI and the AI Enterprise. With everything that's going on and this tremendous opportunity to drive the next level of customer experience, productivity, and efficiency, it's no surprise that everyone in this room and the majority of business leaders wants to become an AI Enterprise. And we've learned firsthand together how to do this already on the predictive side. Just as you heard from Parker, we pioneered AI for CRM together 10 years ago. And now, across self-service bots, across sales forecasting, across data stories, across so many amazing predictive innovations, we're now delivering over 1 trillion predictions across every Salesforce application. And it's all thanks to all of you. And so building on this tremendous foundation, we're going to take it to the next level. And I'm so thrilled today to be announcing the general availability of Einstein Copilot. [APPLAUSE] Einstein Copilot is your one unified AI conversational assistant across every Salesforce application-- sales, service, marketing, MuleSoft, Tableau, Slack. And just as you heard from Patrick, Einstein Copilot out of the box is automatically aware of and grounded in your organization's data and metadata, in your organization's business logic, whether those are flows, apex code, MuleSoft integrations. That's what makes it so smart. And so other AI in the market, they're all talk. Einstein Copilot takes action for you and your employees to drive that next level of productivity and performance. Now, this is why we're seeing tremendous business outcomes from deploying generative and predictive AI. Just as you saw from the video just now, Robert and his team, they're driving an uplift in sales. We're seeing companies like Grubhub increase sales team member productivity by 20%. We're seeing companies like AAA Insurance reduce their response time by 10%, increasing customer satisfaction. We're seeing companies like General Mills increase the number of customer engagements by 3x and so on and so forth across every department, every industry, every workflow in your company. Now, every Salesforce product manager, every cloud, every industry now wants to make it easy for you. And so they're building out-of-the-box, turnkey, copilot actions and prompt templates, whether it's sales account summaries so that your sales teams can walk into customer meetings fully prepared and up to speed on their Customer 360. Or it's customer service with automated reply recommendation suggestions and case summaries. Or it's actions in Tableau or marketing and commerce cloud, product line generation and subject line generation, on and on. Every cloud is supporting your easy deployment of AI in a secure and trusted way. Now, nothing is more important to making this work, of course, than trusted data. And just to talk for a moment about this, I'm going to turn it over to my colleague, Matthew M. [VIDEO PLAYBACK] - If AI's is the Wild West, does that make data the new gold? Huh. [PLAYBACK ENDS] Data is the new gold, and it's central to our Einstein Trust Layer that powers everything that we do in AI, from Einstein Copilot to Slack AI, to Tableau Pulse, and more. You can see it right there, the secure data retrieval. Having that trusted data and metadata about who has access to what data and what business logic is what reduces hallucinations and drives accuracy, relevance, and performance. The Einstein Trust Layer also has data security, elements like data masking, and zero retention prompts, and prompt offense. We have ethical guardrails like toxicity detection, and we keep an audit trail so that you can understand at every step of the way how the AI is performing for your goals. Now, I said earlier, Einstein Copilot is one unified Copilot across every Salesforce application. And that includes Slack and Tableau. And so you're already having your employees conversing and collaborating with their teammates in Slack. Now they have a new coworker they can collaborate with, and it's Einstein Copilot. GA's this summer. In fact, Slack has a slew of AI innovations to make it even easier to drive productivity and connectivity with CRM right from within your favorite collaborative workspace. We have record channels, the new chatter, where you can have conversations about any CRM record, whether that's a sales opportunity, or a service case, or a marketing campaign. We have Sales Elevate that brings all of your Sales Cloud data and KPIs right into Slack, where you can also update any of these records in real time with your coworkers. And then last but not least, Slack AI, which allows you to get quickly up to speed with amazing new features, like thread summaries, AI recaps on any conversation and any channel. Now, the fourth step to becoming an AI Enterprise is about delivering AI analytics and empowering every employee in your organization to become a Data Explorer and a data expert. And that's exactly what we're doing, first with Tableau Pulse. Tableau Pulse proactively delivers AI-driven data insights to every employee in your organization. I love getting my Tableau Pulse. I get it in my email as well as in Slack. And it identifies metrics that I may not have even known to ask about and empowers me to make better decisions right in the flow of work, wherever I might be getting my work done. We're also thrilled to bring Einstein Copilot to Tableau so that you can go deeper and ask more questions of your data and have visualizations, calculations done, simply using natural language, the language of your choice. And so I want to show you how all of this looks and bring back our demo team, [? Rusha ?] and Maximo, and bring us back to Oahu to see the Turtle Bay demo organization. We've got some snorkeling going on there, too. And we're going to walk through two personas. We're going to see how the marketing team uses Einstein Copilot. And then we're going to see how the sales team uses it across both Slack as well as CRM and Marketing Cloud. So let's bring it up. Let's go to Oahu together. All right. We're going to start here as the marketing team. Here we are in Slack. And right away, we see this brand new feature. It's called the Slack AI recap. And it's bringing my attention to occupancy insights, very important if you're managing a resort. So we're going to click into that and see what's going on with occupancy insights. And right away, Robert and his team, they're able to see this Tableau Pulse showing that, unfortunately, occupancy rates are predicted to trend down. Now, as the marketing team, we have to do something about this. And so we start collaborating because we're in Slack. We've got our coworkers here. We're deciding together to focus on the corporate segment. We want to have more corporate events at our resort. And now I'm going to bring in a new coworker to help us to take it to the next step. And that is Einstein Copilot. So I'm going to ask Einstein Copilot to help me figure out what will resonate with this corporate event segment. Now, behind the scenes, Einstein Copilot is referencing everything that you just saw from Patrick. In Data Cloud, it's looking at third-party review sites. It's scanning through all the customer survey results. It's looking at bookings data from these third-party systems, these trapped islands of data in Snowflake that we've now brought into Data Cloud to power this answer. And in an instant, Einstein Copilot is able to tell me that Lu'aus are what's most popular with this corporate event segment. And I'm going to share this insight back with the rest of my marketing team at Turtle Bay. And now, the content and creative team is going to get cracking. They see that insight. They're going to flip over to Salesforce Marketing Cloud and build a campaign for the corporate event segment. But they're not going to do it from scratch. They're going to rely on help from their coworker, Einstein Copilot. This is the same Copilot we've been interacting with in Slack so far. And it's able to, in a split second, generate this landing page and email. Einstein Copilot is grounded in all of Turtle Bay's data and metadata. It understands our brand, our images, what's on message, our brand voice. And it's looking pretty good. I've even asked it to create a section for Lu'aus, given that insight we were given earlier, as well as for sales contacts so that we can really drive leads from this campaign. And in just an instant, I'm able to click Publish. So before we move on, I just want to take a step back and think about what we just saw. In the past, what we just saw would have taken days, if not weeks. Someone would have had to run a report on occupancy rates. Maybe that report would be reviewed once a week or once every other week. Then someone else would have to run data pulls on these different systems and a pivot table to figure out which segment to target and what resonates with that segment. Then to build a campaign from scratch, think about the creating images from scratch or creating copy from scratch. We did all of that in just three minutes together in this live demo. It's just amazing. OK, so we're generating a ton of leads from this campaign. Now let's flip over to one of the sales reps views on Robert's team. Our sales rep, John, has logged into his Seller Home. He can see his typical set of KPIs, what are the group reservations looking like. Well, how is he tracking against his quota for the quarter? Unfortunately, he's a little light. So he's going to ask Einstein Copilot to help him. And instead of having to call every customer and getting a lot of rejections, he wants to know who to focus on. And so Einstein Copilot, again, is crunching all of this data across Data Cloud to figure out who is highly qualified, who has a high propensity to convert and to want to engage with John. And in this case, no surprise. Einstein Copilot is suggesting that the sales rep reach out to Jackie. Jackie is the same customer whose profile that we enriched and unified earlier in Patrick's demo. And Einstein Copilot even explains why it identified Jackie. It's because she just took her family on a vacation there. She clearly has an affinity for the property. She's given it a high customer satisfaction score in her surveys. Again, that's data from a third-party system that was pulled in through the Zero Copy Partner Network. And she doesn't have any open customer support cases. So there should be no sensitivities and reason for the sales team not to reach out. And so at this point, John, our sales person, is going to actually ask Copilot to help him figure out what the next steps are to get an opportunity going with Jackie. And so Einstein Copilot is going across all of the sales policies, the best practices, what's worked in the past to generate this action plan for John. But that's not all. John can even ask Copilot to automate some of these actions for him. And that's exactly what he's doing. He's saying, write that email for me. Personalize it for Jackie based on what we know her preferences are. And just like that, that email is generated, saving John hours of time of researching, and writing, and editing. He can go through, make any edits, and then hit Send. And so that email gets Jackie interested. They have a few calls. Fast forward a couple of weeks, and now John's pipeline is looking really good for the quarter. And he's able to transition from sales planning and prospecting to sales execution and closing deals. So he's on the go. He's meeting with customers. He's in slack, collaborating with all of the other team members at Turtle Bay Resort to help get these deals to a closure. And now, thanks to Slack Sales Elevate, he can see all of his critical sales KPIs right from within Slack. He can see his quota, his pipeline, his revenue closed to date. He also gets a notification that Jackie, his prospect, is actually coming on site today for a visit. So he's going to click in. And he can see there's a number of his coworkers across the resort who've been busy at work to plan the perfect visit for Jackie to close that deal. Now, there's 20 unread messages. And John's a busy guy, so he's just going to ask for a thread summary so that he doesn't have to read every single message. And right away, Slack AI summarizes for him exactly which of his coworkers is doing what so they can stay coordinated for the perfect prospect visit. Now, not only can you view information thanks to-- I'm going to go back into this channel. You may have noticed it's a little bit unique. And it's because this is a record channel that corresponds to this sales opportunity in Sales Cloud. So we can view this information. They can have conversations. He can even update the records in Salesforce right from within Slack. He's going to actually change the stage for this opportunity, given Jackie is coming on site. And as soon as he hits Save, it automatically kicks off a workflow to create a new external channel for John to collaborate with Jackie, with his prospect, to co-create that sales proposal. Just think about how much more higher conversion we can achieve when the customer is part of the proposal creation. And right away, there's a Canvas and a welcome message that are automatically generated based off of a template. And as she goes through her visit, Jackie can help update what activities, what restaurants, what bookings, what the flow is for her event. And because of her co-creation in this process and her buy-in, John closes the deal in record time. And so this is how Slack, and Tableau, and CRM come together, powered by AI data and trust, to break down organization silos, and drive business outcomes, and take action. Back to you, Parker. [APPLAUSE] All right. Big hand for Clara. Great job, Clara. Awesome job. All right, so we built our Customer 360. We've harmonized the data, love those demos, collaborating with AI, the Copilot, Slack, record channels-- Check those out. Those are awesome-- and the analytics. But now we want to geek out a little bit. And the best person to geek out with right now to talk about deploying trusted AI and Copilot is my good friend, Leah McGowan-Hare, SVP of the Trailblazer community. Leah, take it away. Welcome, New York. Thank you so much for hanging out. Just hang with me. I'm the closing chapter. I need your energy. So here's the thing. We all know artificial intelligence is not a moment. This is a movement. It is changing how we move through our lives. It's impacting how we work, how we live. It even is putting an end to busy work. Now, here's the thing. There was a study done that said 62% of our time is lost to repetitive tasks. Now, I don't know about you, but I want to reclaim my time. I could use that 62% time back because, like you, I'm being tasked, like all of us, to do more with less. So we need to make every minute count and really supercharge our productivity. That's why we've built automation into the Einstein 1 Platform so you can work smarter, not harder. And when I say automation, only one thing comes to mind, and that's flow. Where are my Flownatics? [CHEERING] Oh, they're way in the back seat. OK, you're going to have to do a better flow. Get you up closer. So, yes, flow. Flow helps us create and deploy automation to any app. And now, with workflow engine, we can bring workflows to every part of our organization. But get this Now with AI, an automation can take action. It's going to be amazing thing. With Einstein Copilot, we can create dynamic plans that are unique to your business. Now this is really cool. How does all this work, you ask, because I can hear you. How does this work, Leah? Well, it starts basic with natural language. You ask Einstein a simple question. Hey, in this case, can you send a message to Lucy? Immediately, immediately, Einstein gets to work creating a plan. Now, this is not just any plan. This is a plan that's unique to your business. You have trained Einstein on this plan. Now, here's the differentiator. It doesn't just come up with a plan. It takes action. You heard Claire talk about it. Our Einstein doesn't just talk about it. It's being about it. It takes action. You know those folks, you guys have seen them. You've worked with them, who can talk about things and come up with great plans. But they can't execute. That's not our Einstein. That's the differentiator. Our AI takes action, freeing you up to do the things that are important to you, like creating amazing customer experiences. And you don't have to be a developer to build these dynamic plans because Einstein 1 is a low-code, no-code platform. You can build custom apps and workflows with clicks. You can customize and extend Copilot to meet your business needs with custom and standard actions. And what this means on a larger scale, we've democratized development so that you can join our 21 million Trailblazers out there innovating on our platform. Whoo! Yes, Trailblazer. I hear you back there. That's my one Flownatic back there to give you some love. But here's the thing-- we know that everybody is at a different place in their AI journey. And we're not just giving you the technology for this journey. We're giving you the people because we have such a vibrant ecosystem of partners, of our ISV partners, sharing their innovations on AppExchange. You heard Patrick talk about it earlier. If you already have a data lake in Snowflake or databricks, we have partnerships there as well. And you want to use a third-party AI? No problem. We've got partnerships with OpenAI, Cohere, and Anthropic. We will meet you wherever you are on your AI journey. And that's why we're so excited to announce Einstein 1 Studio that lets you bring AI to all parts of your business. With prompt builder, you can create reusable prompts grounded in your data. And we're going to see this in action. Copilot Builder allows you to create those dynamic action plans that I talked about earlier with custom and standard actions. And model builder-- you want to use your own LLM? No problem. You want to use ours? Great. You want to use a third party? You can get with this. You can get with that. The choice is yours. But now I talked about actions, so let's actually be about it and see it in a live demo with our amazing demo drivers, Maximo and Claire. OK, hang loose. I see you. Let's jump on in. So here we are in-- this is we're looking at the concierge dashboard. And I want you to note this is a live Tableau dashboard that is really live. And to the right of it, you can see Einstein Copilot is riding shotgun, helping us create incredible guest experiences. Now, when the concierge goes in there, they ask, hey, who are my high-value guests arriving today? Now, I know what you're thinking, how do they know what is high value? Well, once again, this is unique to Turtle Bay. So that business logic Einstein has been trained on, it can tell. It could be points. It could be spend amount Just get the list of high-value customers. Now, there's Jackie. Remember, she was coming back to the property. And it looks like she missed her flight. Now, as we heard earlier, Turtle Bay wants to know what's happening to their guest even before they arrive because their experience is a holistic picture. And we can see she missed her flight. So we want to ease-- we've all been there. And you arrive. You're not that happy because you missed your flight. So we want to go ahead and give her some resort credit. So we're going to ask Einstein, grant her some resort credit. Now, I want you to note here. Take note what happens. Einstein comes back and says, sorry, I'm unable to do that. Well, that's anticlimactic. But here, really, what he should have said is, I'm not able to do that yet. That's what I tell my kids all the time. You're not able to do that yet. But you will. So we are going to train an Einstein on how to give resort credit within Turtle Bay's business. But then Einstein also offers, hey, we're going to send Jackie this Welcome text. Womp, womp. I don't know about you. But that little welcome text does not scream high value. Hi, Jackie. Welcome to Turtle Bay. So listen, we're going to do two things, and I need you to hang out with me for two things. We're going to give a makeover to that welcome text. We're going to really personalize it. And we're going to teach Einstein Copilot the business of giving resort credit. You all with me on this? [CHEERING] All right. OK, good. I'm not doing this alone. I appreciate that. So in order to do this, we're going to jump on over into Einstein 1 Studio, where we can bring AI to all parts of Turtle Bay's business. And the first stop we're going to make is in Prompt Builder. We're going to use generative AI to create a text message. Now, how many people here have been in ChatGPT? OK, so you all have written a prompt. A prompt is the instructions we give to the LLM to get a response back. Well, what we're going to do here, we're going to create a prompt that is going to be reusable by all concierges and is grounded in our trusted data. So watch this. We're going to go up here and say, we want to write a prompt that's going to personalize this text message. Now, earlier we saw Patrick harmonize all that rich data. We're going to leverage that data in this prompt. We're going to go to that data graph, which is basically the unified profile. We have access to more than just their name. We know dietary constraints, the things that they enjoy doing. And we're going to pull that data into our prompt there. So this is not merge fields we're talking about. This is much better than that. And so then-- now get this. I get super excited about this, super-- because, like Parker says, I geek out. Here's the thing. We are not just bringing in data to this prompt. We have actions. We have access to actions, like flows. So imagine. Our prompt not only has data but has access to actions like flows. So this is going to be a very personalized message because here we're going to recommend activities for this particular guest. And then the bow that we're going to wrap around it is the Turtle Bay tone and voice. So when that text message goes out, whoever sends it out, whichever concierge sends it out, it always has the standard Turtle Bay tone and voice. So we're going to bring that in. That's a pretty awesome personalization message here. But let's test it. So here we're going to have the resolution. And what you're going to see is the JSON. This is the information that's going to be sent to the LLM in that trusted way that Claire talked about earlier. This information gets sent over. And of course, there's some rag in between. Am I right there, Patrick? OK, I got my rag right. So here, this is going to be sent to our LLM, and we're going to get the response back. This looks oh-so-much better than that little 1990s merge field text we saw earlier. So we're going to-- we did that. Check. We did a makeover to the text message. What was the second thing we were going to do? That's right, Sandy. We're going to go ahead and train Turtle Bay's Einstein on giving resort credit. So let's jump on over to Copilot and see what that looks like. So here we are in Einstein Copilot. Now, I want you to notice we automatically have standard actions available to us. But because there's innovation regularly-- you heard Clara talking about that. We're innovating all the time. This list is going to grow every month. But we also have access to Turtle Bay's custom actions. They've been doing things long before we came along. They've got some apex in there. They've got some flows. We can leverage all of these custom actions to train Einstein on the business of Turtle Bay. But this is super cool. I know you're thinking, what? It gets cooler? Yes, stay with me. So we go ahead and we bring over these actions. But here's what is awesome. We don't need to determine the sequence of the actions. I know. Shut the front door. Einstein determines. That is intelligence. It is determining what is the sequence of the actions that we bring it over. So watch this. We're going to go ahead and do a preview. And we're going to see Einstein do its magic as it's thinking and creating a plan. So we ask it, go ahead and give resort credit. And here there's that plan that I was talking about. And there, we see the flow happening that goes and gets an-- from the ERP system gets the reservation. Then we have some apex firing. And it's determining all the things that it needs to do to give the credit to the appropriate guest. That's pretty awesome. So we're seeing Einstein live. All right, so check, number two. We just taught it how to give access-- how to give resort credits. But now let's see it all come together. So let's go back, back to the future, [SWOOSH].. There we are. So we're going to go back here. And now we're going to ask, again, Einstein, will you please give credit to Jackie? And this time, Einstein comes back and says, no problem. Here is the credit. So we have trained it to do to give the credit. This looks so much better. And here's a preview of the text message that's going to get sent to Jackie. But you know what? I'm a little skeptical. I want to see the-- don't you want to see the actual text message? Just shake your head yes. Yes, you do. So we're going to go out here and see the text message. Here we are. This is what Jackie receives. Now, this is a welcome text message. This is giving high-value customer. We're saying, hey, here's a gluten-free restaurant. We know that you are gluten free because we know about your dietary constraints. And we just don't stop there. We say, by the way, we also know that your flight was delayed, showing a little empathy. I'm liking this. We're going to go ahead and give you some resort credit. Now, this is a glow up of a text message. This, my friends, is the power of Einstein 1 and Data Cloud. This is AI that takes action. Back to you, Parker. [APPLAUSE] Whew! All right. Killed it. I knew you would. OK, so the five steps to the AI Enterprise. Very simple, right? We're going to do the Customer 360. We're going to get our data together, unify it with Data Cloud. We're going to collaborate with Slack. We're going to do analytics with Tableau, deploy it with Einstein Copilot in Slack. Very simple, right? Well, there's still a lot to learn. I know. If you didn't catch all of that, we know how you learn it. You learn it as a Trailblazer. You go to Trailhead, and you learn it. You join our community just like all the trailblazers here in the room. It's an incredible community. Trailhead is an incredible resource. We also have these other new communities that are incredible, the brand new ones, Serviceblazer community for service cloud enthusiasts, Salesblazer community if you're focused on sales cloud. It's incredible. And the reason that they're joining these communities, the reason you're going to Trailhead, the reason you're learning those five steps and all those details is because it's good for you. It's good for us. It's why we're all together. 11.6 million new Salesforce jobs will be created by 2028. And those jobs can be for you or maybe your children if they're coming up into the ecosystem. Teach them Trailhead. We would like that. $2 trillion in net gain of business revenue, so that's pretty awesome, too. That's the end of our keynote. But don't go away right away because you can come back in here at 1:00 and come see me and Soledad O'Brien. We're going to do a little 30 minute talk. That's going to be fun. She can ask me anything, so it should be fun. Also, a whole bunch more happening out in the expo, more keynotes happening. Please also give us feedback. We love to make these keynotes better, better, better. They're never perfect. Give us feedback. Go to this QR code. Take the survey. Give us feedback, and with that, enjoy the rest of the day. [MUSIC PLAYING]