Salesforce, Inc. (CRM) Earnings Call Transcript & Summary

Perfect, awesome. So let's start. Hello, everyone, and welcome to today's webinar on Elevate Your Analytics with Tableau AI. Well, I am excited to have you here as we explore how Tableau's AI-powered capabilities can revolutionize your data analytics processes. Whether you're a data scientist or, let's say, you're an analyst or business leader, I mean, Tableau AI can help you uncover deeper insights, helps you automate predictions and make data-driven decisions with more confidence, but in this session, we'll walk you through some of the key AI features and demonstrate how to harness these capabilities to unlock the full potential of the data. Let's dive in into this exciting journey together. To begin with. During this presentation, we may discuss forward-looking statements about future developments and features. As we are a publicly traded company, I mean, I'd like to remind you to consult with your account representative before making any purchasing decisions based on what we cover today. This ensures you have the most up-to-date information tailored to your specific business needs. With this, first thing first. I want to say thank you. Thank you for -- thank you to all of you for taking time to join us today. We truly appreciate you joining from different parts of the country here for this presentation. We are equally excited to share these valuable insights with you. All right, to begin with, let's do a very quick introduction about ourselves. Well, my name is Prashant Keshri. And I have with me my super talented colleague Shilpa Bhatia. Both of us are the integral team members on the solution engineering team here at Salesforce leading the analytics portfolio. I mean both of us comes with over a decade of experience in the data industry primarily, having worked with organizations across different industries and business functions on their analytics journey. Well, we'll be hosts today for this session, and we have some exciting updates for you. To start with. Well, at Dreamforce, we unveiled Agentforce, a revolutionary solution designed to transform how organizations drive customer success. Imagine having a dedicated agent that works tirelessly around the clock resolving inquiries, in natural language, which comes your way; and assisting your sales teams with product questions which they frequently face; and setting up meeting arrangements for their regular activities. Agentforce acts as a personal assistant for both customers as well as for employees, enhancing engagement and efficiency across different business verticals, let's say, sales, finance, marketing, commerce and many more. Well, what sets Agentforce apart is its flexibility. Organizations can easily configure these autonomous agents using existing Salesforce tools like, let's say, prompts, flows, Apex or APIs, tailoring them to meet those specific business needs. Well, this capability not only scales operations but also helps organization reach better satisfaction levels among whoever is actually using these agents for. With Agentforce, businesses can confidently navigate the complexities of customer interactions, ensuring they remain ahead of the curve while delivering exceptional services. Well, this becomes the bedrock for Tableau Einstein. What I mean to say is Agentforce becomes a bedrock for Tableau Einstein, which is built on the Salesforce platform. Let's see how. Well, Tableau Einstein is a cutting-edge, AI-driven visual analytics platform that redefines how your business engages with data. By seamlessly embedding data insights into everyday users' workflow, Tableau Einstein transforms their data usage into a second-nature activity, allowing users to drive actions more intuitively, right? So built on Salesforce's platform, Tableau Einstein enables teams from various departments to harness analytical insights for quicker and more effective strategic decisions. Well, this integration ensures that users can leverage data without the need of extensive training or promoting more agile and informed workforce. The key features of Tableau Einstein actually includes Tableau Agent, which used to be called as Einstein Copilot for Tableau previously. Along with that is Tableau Pulse and Pulse for Salesforce. Together, these capabilities create a unified experience that enhances intelligent insights across our user's workflow. You might have a question how it actually works. You might be wondering how Tableau Einstein actually works. Well, this advanced platform comes with out-of-the-box metrics, along with predictive and generative AI capabilities that allow it to forecast future trends. This forecasting enables users to take informed, actionable steps based on insights derived from the data. Now utilizing Tableau semantics, Tableau Einstein enriches the data with meaningful business context, which enhances the trustworthiness of its insights provided. Its integration with Data Cloud improves the data accuracy as well as the accessibility and further makes insights more useful to everybody across the organizations, right? So additionally, Tableau Einstein features -- I mean it actually features composable and reusable assets that can tailored -- that can be tailored to meet a wide set of use cases across the enterprise. I mean this flexibility ensures that organizations can customize their analytics experience to align with their specific goals and needs, ultimately driving more effective decision-making and operational efficiency. More to this is Tableau Pulse. Well, Tableau Pulse is part of Tableau Einstein journey. Tableau Pulse leverages generative AI to transform analytics for business users, making it personal, contextual and intelligent. Users receive a tailored set of metrics, insights relevant to their specific rules, enabling them to cover new opportunities, which is extremely critical for your business, helps you anticipates issues and enhanced better decision-making process. Well, the platform also offers a guided insights experience, allowing users to track metric trends, filter data on demand and pose questions about their data, right? This opens up the gate or the arena of conversational analytics, where data comes more -- closer to the user wherever they are working from. Let's say, on a mobile device, they can ask their own questions. If they are on laptop, they can ask their own questions using by -- just by typing simple questions. Well, this functionality supports timely data-driven decisions, fostering a more proactive approach towards analytics. Well, enough of talking. You may have a question: How does it really look like? So let me walk you through a quick demo. Now what you see on the screen is Tableau Pulse embedded into Salesforce. The user journey actually starts with a very quick data bulletin at the top, which is generative AI empowered, right? What it helps you understand is your opportunities are dropping by about 13.2%. Your average opportunity size is also significantly low. At the same time, the sales is also declining. Now these set of important metrics or information is normally spread across multiple screens, but via generative AI, you get a very quick digest of what is happening in your business. That is why it is termed as Tableau Pulse. What is the pulse of your business? When I get to understand, when I as a business user get to understand, okay, there is some unfavorable situation happening in my business, I may have a follow-up question. That's when metrics -- cards actually comes into action. It helps you understand, what is the conversion rate. The AI engine actually does a time series comparison to help you understand if there's a growth or a degrowth. Is it favorable to the business. In this case, maybe no. What is impacting, what is happening towards other set of metrics which is if there's opportunity size and this is declining. At the same time, sales is also -- is at a sharp decline. What is impacting sales is also visible in this case. [ Well, the ] supply industries and employee network, my highest-potential customers, are also not doing good business, right, which eventually could be a bigger-impact, root cause for such scenarios. At the same time, I -- being a business user, I would like to keep a track of other metrics as well, which is: How does my open pipe look like? How is the opportunity close situation? At the same time, what is happening towards win rate, right? Win rate, actually it may be transmitting or connecting back to my annual revenue. That's when I click on win rate and I as a business user get to understand what is the win rate situation looking like. And is there a growth or a degrowth? And at the same time, I can apply these filters just like how you apply those regular dashboards and look for more deeper insights. When I do that, I also get to understand how has been the trend over the period of time. At the same time, breakdown actually helps me identify what factors are contributing more towards the current win rate situation. In this case, which account is contributing to the highest? Likewise, what is happening to my industries? Which industry is doing good with respect to the business where I am operating now, right? Now these are those very quick digests which I would need to start my day, right? I'm doing all of it on a mobile device. At the same time, I can operate Tableau Pulse on a desktop browser as well. Moving forward, since Pulse is AI powered, AI/Gen AI-powered, it actually scans my data; helps me with certain preset questions, which is again critical for to understand the situation of my business. In this case, which industry is highly impacted? Which opportunity [indiscernible], right? While AI actually helps me uncover these insights, at the same time, data comes even more -- closer to a business user in this case. I can ask my own questions. A question could be, "I really want to understand how is my account doing. Which accounts are not doing business with me?" Which used to happen in the past, but suddenly these customers have stopped doing business. When I click on it, Tableau Pulse actually populates, the meaning of my question and say, "Okay, it looks good." When I click on it, I get to see the list of customers who have left the business, right? Now in very quick 30 seconds to a minute, I was able to completely understand what is happening in my business. What is actually impacting, positive and negatively, towards my business operation? At the same time, I also get to see some actionable points. All of it comes powered via generative AI in a quick-digest format. At the same time, AI actually helps me have a conversational box to my data as well as a situation, which is actually impacting customers in this case, right? Why we are doing this? Now one of the best part which I want to also highlight here is what we at Salesforce also runs on Tableau Pulse. Well, every day, our sales leaders and reps are relying on these insights to drive their decisions, right, so it has been that useful to us as well as to a lot of customers out there. Now moving forward. Moving forward, you may have a question: How does it work? Or what more can we do? Well, it doesn't stop here. You can use Pulse across any department. In the service sector, let's say, it helps in understanding your customer satisfaction, which is CSAT score, by tracking metrics such as the number of rebounded calls and average waiting times. At the same time, in finance vertical, Pulse aids in monitoring total revenue, gross margin or operational costs. Well, these insights are readily available. Organizations can take action based on real-time data, right? Moving further. I'm sure now you're interested towards understanding how is it really working. How is Pulse engineered? How does the background looking like, right, configuration looking like. So in this case, Tableau Pulse integrates with metrics layer popularly called as Headless BI into its platform, allowing organizations to define metrics and KPIs once and then utilize across teams. Now metrics becomes the foundation. On top of it comes the insights platform, which operates -- which serves as a statistical service that automatically generates these insights about different metrics. It ranks these insights and summarizes them using generative AI in natural language, enabling users with insights; and delve deeper into that data through contextually relevant follow-up questions, using that ask button which you just [ saw ]. Now moving forward, Pulse also offers next-gen experience by presenting data in an intuitive, user-friendly manner. Metrics are typically delivered to users through, let's say, Slack or Microsoft Teams or e-mail or Tableau [ web app ]. Or maybe you can [ weave ] this back into Salesforce, right, which further helps you scale. I mean bring more adoption towards web analytics investment. At the same time, the generative AI-powered insights summarize -- highlight some key data points, trends, outliers and changes, ensuring users remain focused on those critical information. Well, this is not it. And we have introduced -- we have also introduced actually a bespoke version of Tableau Pulse made specifically for Salesforce customers, helping everyone get even more value of Salesforce with AI-powered insights directly into CRM. Well, with Pulse, now you can accelerate your data-driven decision-making process with 9 out-of-the-box metrics for sales and other industry clouds. You can take actions with AI-powered insights natively embedded within Salesforce, exactly what you just saw. It also helps every organizations or user shorten time to value, with streamlined configuration to start delivering insights quickly and faster, the need of the hour, right, and all of this. I'm sure you are extremely excited. You are super energized with this. And you're liking these new features and new updates, right? Now I'll pass on the baton to my colleague Shilpa, who will take us through further details on Tableau Agent in the remaining section of the conversation.

Thanks, Prashant. Hi, everyone. Let's now talk about the next capability of Tableau Einstein which is Tableau Agent. Tableau Agent is something which will help you make data-driven decisions faster with a trusted AI assistant. It will help you kick-start your analytics journey much, much faster with support from an AI assistant. Think of things which you can do with AI. It could be writing calculations. It could be creating visualizations. It could be creating dashboards. It could be doing data cleaning, data preparation part as well, right. But be rest assured all these AI-infused responses also keep in mind the best practices and the design best practices, which are the integral part of Tableau as an overall platform. All the responses that you will get with Tableau Agent are, of course, coming from the large language models, but we have taken care of the safety and security of your data using Einstein's trust layer which makes it much more secure in local [ server ]. And the capabilities which I'll talk about are part of the Tableau+ edition, which is a new SKU we have launched. Let's talk deeper about Tableau Agent. So there are 3 key capabilities which will come with Tableau Agent. One is in the realm of data preparation. Now I'm sure a lot of you are aware of what a Tableau Prep Builder is. It helps you do data cleaning, data massaging operations. Tableau Agent will basically make that journey far more simpler and easier for you. You have to write a calculation as an additional column in your data. You can make use of this conversational AI assistant called Tableau Agent and do that for you. You have a requirement to remove null values. You have the requirement to create some string calculations. You want to just basically clean and massage your data without even doing those drags and drops. You have Tableau Agent for your service. The next area where Tableau Agent will be helpful is cataloging. Now you might have seen that Tableau allows you to bring in the descriptions of the content that you have on Tableau, whether it's your workbooks or data sources. Or you can also write descriptions for the columns which are there in your data. And I know it's the manual process can be quite taxing. So Tableau Agent will automatically write that description for you, but of course, you are fully in control. You can go through it, edit it as acquired. And once you are satisfied, feel free to submit it. How these descriptions helps us? Basically they increase the discoverability. They simplify the documentation. They help create a layer of trust and governance, of course, as well in your organization. Next and, I think, the most liked capability of Tableau Agent by our customers is web authoring. Now think of scenarios where you are an executive, who -- one who gets his insights on a dashboard, but you have some specific questions to be answered because you want to be ready for a meeting. You're doing some planning. You're doing a review, right? You don't want to go back to your team and asking them to write those queries and get the outputs. Instead, Tableau Agent will allow you to, in natural language, converse with Tableau, write your question and get a visual output. Not just that, it again can help you write business logics; calculations; changing visualizations; using filters; and everything which you can do on Tableau, basically, right? It will also give you some recommended questions. Think of scenarios where you have no clue about the data. Tableau Agent will run through your data and give you some recommended possibly easy-to-explore questions for you. And of course, very important, the loop is closed with feedback. If you feel like the responses you've got is not correct, it's not as expected, you can give your feedback. That feedback is taken by our product team, which can help us improve the product. And also, finally, Tableau Agent or Tableau Einstein also improves its responses in future based off the feedback. So here is what we'll do now. We'll look at all of it in a live demo. Let's start with that. Let me just bring up my screen. So let me first explain the scenario of my demo. I have a case, let's say, where I'm an analyst. I've joined an organization and I've been given a task to do the analysis on my business data, on my transactional data, so that my sales leaders can use it to plan some strategies for the next year. This would include finding information about revenues, profits, discounts, products, geographies, et cetera, right? Now as an analyst, I know that the most important part for me to do my analysis will come from the quality of my data, so before I jump on to asking questions and creating -- finding some insights, I want to be very sure that I have to correct it. So what I'll start with is I'll check a Tableau flow. This is a Tableau Prep Builder flow which is created to combine data from multiple sources. It has been already massaged. I just want to be sure if everything is correct here or if I need some modifications before I jump on to it. So you can see this has information coming in from my transactional tables, product information, account information; joints created. And then the final output is here. I'll just quickly, with the help of this grid, go through the columns which I have. You can clearly see, because this is my sales transactional data, this has information from city, state, geography, product category, subcategory, unit price and everything which is quite important for me to do my analysis, but there is one thing which I see missing here, which is revenue. I don't see revenue numbers here, so what I'll do is I'll make use of Einstein to write that logic for me. So I have pre-written the prompts just to save some time, so I'll copy-paste from here. I need a new field for revenue, but it needs to take into account discount. Now this is fair ask, that revenue can be calculated by ignoring discount, but I want to be very specific so that Einstein too take discount into account. So I've mentioned it, and I run the query, right? The moment I do that, it will give me a description that this calculation multiplies the units with the unit price and it also applies discounts. This is the calculation which Tableau Agent has created. If it looks okay for me, I am ready to use it. If you feel like there is some modifications required, this is totally editable. You can change it. And just with that, I can have a new column created in my data. I'll just name it as revenue. And that's about it, right? With that, I have a new column or a new information added in my data which is critical for my analysis; and we are done. Now we'll move to the next step. Now that my data is ready for analysis, I will come to a screen, which I hope a lot of you are familiar with again. It's the typical Tableau canvas, where I have access to this clean and prepared data. It has a revenue -- as well, right? And now I'll start answering my questions. Generally you can do the drags and drops. And there is this VizQL which is converting those drags and drops into queries and you get the visualizations, but I'll now show you how Tableau Agent helps you further make it more powerful. All right, so on the right side, you will see Einstein is in action now. The first thing it is doing is it is basically telling me what all can we do with Einstein: [ build the ways ], filter, sort, group, create a calculator field. It is also giving me a suggestion button. Basically suggestions here mean, if I'm totally new to the data and I'm not sure what we will start with, suggestion could be a good place for you to get some idea about what's happening here. So for example, there are 3 questions which Tableau Agent has given me as a suggestion. Let's just try and see what it is. So the first one is what is the trend of monthly revenue over the current year. I'll just click on that, and let's see what Tableau Agent responds to that. It may not be the exact question which I wanted to ask, but it could be a good starting point. Let's see. Okay, I think the filter has created the problem because maybe the data does not have -- okay, yes, this is historical data, so it does not have information from my latest year, so I'll just remove the filter, right? So it gives me now a trend of my sales over a period of time. This could be one of the questions, but since I have a proper task at hand, I can start with my analysis here, and I will do a fresh sheet. Okay, I'll now write a few questions for Tableau Agent and see how it works, where my use case is to find out information about revenue, about geographies, their performance, the profits and all of that. Okay, so let's start with the first question. Let's say my first question is that I want Tableau Agent to show me how am I performing across different states. So I write a simple statement, a prompt, "Show state-wise revenue," right? It is going through my data, and it is now giving me information on revenues by state. I didn't even ask for sorting, but like I initially said, best practices are taken care of, so it does show a sorted view of state-wise revenue, right? Now this is great. This is a bar chart. It tells me that UP is the best performing. And I can find out the least-performing state also, but I think a better visualization would be to change it into a map, so let me create a map here. The prompt I'm giving is, "Show this as a map. And color code this by revenue." So I am asking Tableau Agent to change the visualization and also specifying how I want my visualization to be brought in, where revenue should be [ on top ]. And just like that, I have the columns, rows, marks all updated. I have a map which shows that UP is the high performing. And these are the states which are my low performing in terms of revenue, okay? I could very easily find out my high-performing and low-performing geographies, right, but I need to find out, let's say, something. This is okay, but I want to further break this down. I have a scenario where I want to, let's say, go deeper across different regions and geographies because there is more information to be seen, right? So let's ask another question. And I'll go to a blank sheet now for this, where the question I'll ask, let me just paste that. "Show me the total revenue and total profit by region," right? I'll also show you -- I removed the "this year" prompt because this data does not have information for this year, but we'll see the total revenue and total profit across different regions. And I see that it is Northeast which has low revenue and low profit. And it is East which has high revenue and high profit. Now in my introduction I also said that this is not -- Tableau Agent is not working independently. You can also still infuse this with the native, very powerful self-service drag-and-drop capabilities of Tableau, so with that, what I want to do is I want to change the visualization type. And instead of writing a prompt, I just want to manually and easily select it from the Show Me button, right? Just in that one click, I could figure I could just change the chart type. And I have my profits and region-wise information available, but there is a bit of change which I need. Now these are profit absolute numbers, right? What I want to rather do is to not see absolute profits but see profit margins, okay? So what I'll do is I'll write a calculation. I'll again write a prompt. The prompt says, "Create a new field called profit percentage." Or let me just call it profit margin. It should take the sum of profit and divide that by sum of revenue. Or I could have written it as, "a ratio of profit and revenue," but I'm just being a little bit more descriptive here. And I am running that prompt now. So I really like what happened in Tableau Prep layer. Something similar should happen here. And I have a calculation. Now you know the -- if there was any error, I'll get it here, but it says the calculation is valid. If you want to find out anything more about any other calculation, you can see it here. If you require any modification, you can do it here, but this looks perfectly okay, so I'll just click okay. And I have a new profit margin created for me, right? Now this is something I want to use for my next question which I want to answer. And let's say that is, "To create a treemap that shows revenue and profit by category and subcategory." Now see what I'm doing. I am creating and specifying a visualization type. I'm specifying the measures which I need. I'm also specifying the categories and subcategories, right, the dimensions basically which I need. And let's have a look at that. Actually, I wanted to use profit margin to -- let's do one thing. Instead of this profit, I'll make use of profit margin. I can -- like I said, sometimes it is simpler to just drag and drop, so I'll swap, right? So just for this, now I have a visualization created which gives me a breakup of category, subcategory. What I find out is my numbers across different categories looks to be very bad in the tables and bookcases. And everything else looks decent, except for scissors, rulers and trimmers in the office supplies, so these are the 3 problematic areas. Now from the previous section, I saw that East is doing great and South is not doing that great, so I want to maybe further filter it down, right? Instead of analyzing the data across the entire nation, let me just filter this for South region. So what is happening now is that I'm passing a filter without calling it -- without bringing manually a filter here. So let's see if Tableau Agent is able to also understand whether a filter is available or -- and just like that, I have a filter. And of course, you would like to -- if you want to modify the type of filter, if you want to change it from a multi select to single select, all of that is very much possible, right? So just to get back. Now I have information only for South region. And I can clearly see that my office supplies is not too bad in the South region. It's actually these tables and bookcases which as the most problem. And so I made use of filters as well, right? Like this, we can keep adding more visualizations, but now I will move to the next step. Say I publish this, right? And I'm skipping this part for now. Again, like, this will work as your standard, typical workbook. You can convert this into a dashboard. It will work as a normal Tableau entity in your project. The data security, the permissions, all of that which you normally do on any workbook on any data can be applied to this also. So Tableau Agent just acted as an enabler and helped to support you, in a conversational manner, getting to data and getting to insights faster, right? Now let me show you the last part of it, which is the next part which is -- let me come back on this field, yes. Now let's say if I want to also write a description to any workbook, right? I did this whole lot of analysis. And I want Tableau Agent to also write a summary for me for that or a description for that workbook. How will I be able to do it? Let's just take this Sales Cloud workbook, right? Let's say this is the workbook where I want to write that description. Like I said, the third scenario was where Tableau Agent is going to help you in cataloging. That's the part we are doing right now. I'll click on Edit Description and I ask Einstein to draft that description for me, so this is a detailed description written by Einstein for that workbook, right? This is how you will be able to use Einstein for one more scenario. And like I earlier said, totally you are in control. You can -- feel free to modify it, change it and save it. All right, with that, let me come back to my slides. So what we saw is we saw the use of Tableau Agent in 3 different scenarios, how it makes the analysis far more simpler, far more intuitive; and keeping your data secure using the Einstein Trust Layer. Let us talk a little bit more about what is happening in the back end while we did all of it, right? So here is what has happened. Initially what you did is on Tableau Cloud you started interacting with your data, right? You started interacting with Tableau Agent basically, to be specific. You asked it to do things like creating visualization by typing the request in natural language. The next step what happens is, once you start conversing with Tableau Agent, the processing starts, right? Now how Tableau Agent processes it is that it tries to understand what you want. Basis that it basically sends a prompt to the large language model to figure out what is the intent of this question, right? Now Tableau Agent also does take care of data grounding, which means it checks that the data such as fields and values which you have added, it tries to find more information about it in your request. And then this communication goes through the large language model, which is of course outside your Tableau environment, but to keep your data safe, it passes through the Einstein Trust Layer, right? Now Tableau Agent gets to work. It sends written prompts or calls the APIs to complete the task which is needed to answer your question or answer your request using these models. And like I said, Einstein Trust will take care of your data security. Back in Tableau, what Tableau Agent is doing is it is doing a coherence check. What it means basically is to make sure that the output of this large language model is consistent with what you asked for. If needed, it can also make adjustments required. Otherwise, in some cases, you might get the prompt or the response which says that it could not understand the question. Or it -- maybe you need -- could re-ask or re-prompt it, right? And finally, the Tableau Agent uses VizQL, which is our very own native technology which is converting that query into the visualization, which is your final response. It also does take care of row-level security. So everything is -- once everything is set, Tableau Agent will respond in the chat with the answer to your request, including an interactive visualization that you can [ break down ], right? So with that, we'll move to the next part now, which is Tableau semantics. This is also another part of Tableau Einstein. Tableau semantics, by the way, let me just start with that, that Tableau Agent is already live right now, but Tableau semantics is something which is the road map, is planned to be launched in the next year, in February. But it is going to be a very, very powerful AI capability which we are getting. So let me start with this. What is semantic, right? Basically, it is an abstract layer which is a business representation of your data, right, which makes you create a metrics layer or a KPI layer where all the business logics can be written which are easy to understand and easy to consume. So instead of people recreating their data layer for every workbook for every use case, this could be your one single source of truth, right? And of course, because it is part of Tableau's Einstein, it is also infused with AI. This layer can also be a good source for your Gen AI strategy. If you are -- you as an organization is working on some Gen AI strategy, then this could also act as a source of data for that as well. I'll talk a little bit more about the specific capabilities in the next slide. So these are all the things which Tableau semantics can do. First thing is the AI-assisted semantic modeling experience. Now this feature, what it will do is it will help you organize your data in business terms with the assistance of AI, which means like -- you want to create a data model. You have data coming in from different applications, different data sources, right? And you want to make use of AI to simplify this data model mitigation. That's what you will be able to do using semantic layer infused with Tableau Einstein. The next is semantic query. Now this is the feature which takes your business questions and turns them into optimized SQL queries while taking business context like time zones, et cetera on account. Now this makes sure that you're getting the right answers based on your query. So when a query has been pushed to this layer, the query can be contextualized based on who's sending it. Who's the user? Who's the source? What is the time zone, et cetera? So that's an additional capability, right? The next one is metrics store and governance. Now like we also mentioned, it is critical for organizations to have consistency across different KPIs. You have different departments working on different use cases, but the KPIs which you are using across your organization should be consistent and should be reusable. That's what this metrics store and governance layer will do, making it a single source of truth for you, right? And the next one, Headless BI. Now I'm sure a lot of you have heard of this term in the industry. What it means is, you invested so much time in creating this semantic model. You created all those business logics, created the single source of truth to be consumed in Tableau, but you have other use cases across other applications. This could be data science applications. This could be your another -- applications. You can actually consume these APIs which are created on Tableau semantic layer for other applications using our APIs. We are also bringing in APIs for you to make this data consumption simpler outside of that, right? I'm sure a lot of customers ask for it, so this would be very useful. And the last one is ML models. Now Tableau's Einstein is in-built, coming with multiple predictive models, but you can make use of your own models as well. You can run those models on this data on the semantic model and get your use cases answered. It -- this would be forecast trend. This would be predicting futures. Basically the end result -- basically the end goal is for you to have the flexibility to choose the out-of-the-box models or make use of your own models on the data which is created on Tableau's semantic layer because this is the most -- cleanest single source of truth which you have already invested your time on. So together, these capabilities will help you manage and analyze your data more easily, providing accurate, meaningful insights that fit into your business context, right? Let's move to the summary of it. Finally, I'm just concluding on what we have heard so far, what we have learned so far in this session, that Tableau Einstein is basically a composable AI analytics platform which is intended to turn your data into actionable insights wherever you work. You saw Pulse in Salesforce, which allows you to bring in insights into not only Salesforce. There will be ways of getting insights in other applications as well, right? With semantics and consistent data built into every workflow for every user for every department, it will give your enterprise the ability to take action on the data whenever you need it, right? With that, I would like to conclude today's session by suggesting you on some of the simpler ways on how you can get started on the Tableau's Einstein journey. All the Tableau Agent capabilities I showed you are along with the [ future ] semantic layer capabilities. They'll be available on our newest SKU, which is Tableau+. You can start your journey with that by trying out Tableau+, right? Or -- and I also would recommend you to try out the Pulse for Salesforce or Tableau Pulse, in general, which is available on Tableau Cloud for you. Feel free to start your trial account on Tableau Cloud and explore these possibilities on our data. With that, thank you so much. Thank you so much for joining us. We hope you find this session insightful and that you are excited to explore all these possibilities with Tableau Einstein. If you have any further questions or you would like any information, feel free to reach out to us. Once again, thank you for your time. We look forward to seeing you in future sessions. Have a great day.