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Nick Otto

General Manager

IBM

Hybrid cloud and AI redefining data integration and automation

As hybrid cloud and artificial intelligence continue to reshape the corporate world, companies are discovering new ways to streamline their operations and stay ahead of the curve.By integrating generative AI with legacy systems such as mainframes and cloud platforms, businesses are unlocking unprecedented access to data, transforming how they function and innovate in an increasingly competitive environment, explained Nick Otto (pictured), head of global strategic partnerships at IBM Corp.IBM’s Nick Otto talks to theCUBE about hybrid cloud and AI

play_circle_outline Partnership between IBM and Salesforce

play_circle_outline Revolutionizing Data Collaboration: Integrating Mainframe Db2, AI, Hybrid Cloud, and Salesforce for Modern Businesses

play_circle_outline Automation and orchestration with Watsonx.orchestrate and Agentforce

play_circle_outline Target industries: financial services, telco, regulated industries

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Nick Otto | IBM

Nick Otto

General Manager IBM

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Christophe Bertrand

>> Hello and welcome to theCUBE from the New York Stock Exchange Office in San Francisco. My name is Christophe Bertrand. I'm a principal Analyst at theCUBE Research. I'm joined today by Nick Otto, who is the head of Global Partnerships at IBM. We're going to be talking about guess what? The partnership between IBM and Salesforce. Dreamforce 2024, great intro this morning. Lots of people. San Francisco is absolutely crazy downtown. Not the usual crazy, good crazy this time. This being said, I can say that. I'm from San Francisco. So Nick, welcome to theCUBE or welcome back, I should say.

>> Thank you. Excited to be here.

Christophe Bertrand

>> Great, so let's talk about the partnership. What are the main areas or technology areas where you actually partner with Salesforce?

>> Yeah, well, I mean, I think you just kind of hit on the fact that everything that was talked about this morning from Mark, it was really exciting that IBM was a part of pretty much every element, and I think that's really just building upon a significant relationship we've had with Salesforce for quite some time. They've been a strategic partner of ours for years. Really what this is all about is, as is most these days, AI. Back at IBM earlier this year, we announced a lot of new collaboration with Salesforce and really everything that we've talked about here is just taking that all to the next level and continuing to help with our customer transformations.

Christophe Bertrand

>> Absolutely. Is it a dream force or Agentforce? I mean, think Mark suggested to change the name himself. Well, let's talk about something I never thought I could say in the same sentence and sound credible, which is Mainframe Db2, AI and Salesforce. What's going on with that?

>> That's very true and it's really exciting to me. I think, and Mark talked about this in his keynote today, but I'll kind of give my simplified version of each of those different pieces. One of the things that IBM has really been focused on for several years now, we've been very consistent around this, is that the priorities around hybrid cloud and AI, that's what our clients need to transform. I think Mark today kind of walked through the history from cloud to mobile to AI to data, and then getting to where they are with agents today, that's very aligned with our mission and our strategy for years around hybrid cloud and AI. Hybrid cloud, to your point, there's lots of data, lots of different places, and I think when you look at any generative AI continuum, it starts with the data. How do we make sure that as you're drawing insights, you have access to all of the relevant data? We announced back at IBM Think earlier this year, a collaboration around zero copy data network with Salesforce that was bringing Watsonx.data and Salesforce data cloud together. What we've announced here is the extension of that to include IBM Data Gate. What Data Gate allows you to do is bring Z data into Watsonx.data, which then through the connection with Salesforce Data Cloud, can really bring insights from a transactional perspective. Running on Mainframe, Watsonx.data also works with Db2 and pretty much everything that you can imagine out there. So when you think about that hybrid cloud element, getting that data available to draw insights from is really foundational, and now being able to connect that to Salesforce Data Cloud is yet another way to draw even faster insights, especially from a sales perspective.

Christophe Bertrand

>> Now, I have to say, this is a very impressive integration. Mainframe will never die. Let's be clear. But I think what you're demonstrating here is that it's really this notion of hybrid, but more importantly, the idea that data is very pervasive. It's not just in Salesforce, it's really across the enterprise. So could you walk me through maybe for this specific example, the type of use cases, or maybe if you have some customer examples. I'm very curious about this because again, Mainframe Db2, lots of data connected to the Salesforce environment. Now, you've got a powerhouse going on right there, right?

>> Yeah, no, for sure, and I think you're right. I mean, what's really exciting to me, I've been at IBM for almost 20 years now, this notion of hybrid cloud and data being pervasive and disparate spread across your organization. Obviously you have clouds, you have on-prem data, you probably are working in most clouds. You've got lots of ISVs involved. Getting all of that data into a format or into a platform where you can draw insights from it is so important. And I think most of the use cases that we're working with Salesforce on today are focused around the customer and particularly transactions around that customer. I think exactly as you said, I mean, the Mainframe continues to be an incredibly pervasive platform. I think the latest stat is 45 of the 50 largest banks in the world are running on Z. That's pretty pervasive, and if you think about being able to bring that transactional level data to life in a broader sales transformation or even lead generation campaign for a seller where not just are you looking at data spread across all of your different systems of records on-prem, on cloud, everywhere else, but also that transactional data, bringing that to life in near real time can draw an entirely new set of insights for that seller to go do something different than they would've done otherwise.

Christophe Bertrand

>> Yeah, especially at Mainframe speeds.

>> That's right.

Christophe Bertrand

>> Something not to forget. We talk a lot about AI. Certainly there's a lot of agentic AI going on right now, lots of conversations about this. So let's walk through what you've done in collaboration with Salesforce around what's next and other agents, and I'm very interested in also in the granite efforts.

>> Yeah, perfect. So I think we kind of focused on data to begin with and what we've done around .data, data cloud and pulling Data Gate into that to allow all of that extension. Once you have that foundation, to your point, then you want to draw insights, and the insights really comes from a lot of different models being built by a lot of different platforms. We believe the granite models are a really, really enterprise ready model. We believe they're fit for purpose. You can see we have a large set of them. What makes them really differentiated is the fact that they're all indemnified, so our models actually come with indemnification, which for the enterprise user is becoming really important. So those insights actually are backed by indemnification. That's point one. Kind of building upon that exactly where you were going with the agent world, there's too many, I think, projects going on across all of our customers, I'm sure many people have seen across their customers, where you're just trying to do something with generative AI. It's a great finding, a great insight. Let me show you a cool use case that we developed. If you're not driving different outcomes, at the end of the day, that value is not going to land. And so what we've done with Watsonx.orchestrate in collaboration with Agentforce, we've been on this, being able to get your insights and then drive different outcomes, and that's all around automation and orchestration, which has been a focus of ours for quite some time. Now collaborating with Salesforce, bringing together Agentforce and Watsonx.orchestrate is really unlocking that value to make sure that different actions are taken and not just you got to need insight that that was kind of fun, but you're actually now driving autonomous activity off of those insights. And that's where our collaboration just continues with Watsonx.orchestrate and Agentforce coming together, our Watsonx.orchestrate skills and actions coming into the portfolio of Agentforce actions, and then finally delivering all of that through a medium. And that medium in many of our internal use cases is Slack. So you really see all of these different Salesforce and IBM and other partner assets all coming together into a single continuum.

Christophe Bertrand

>> Yeah, it seems like the pieces of the puzzle are coming together. Let's talk about some of the typical workflows that you've already automated or orchestrated with this integration. Do you have a couple of examples?

>> Yeah, the cleanest and clearest example is still focused, I mean, we've really zoomed into regulated industries first of all, back to kind of the mainframe of state. You look at financial services, you look at telco, some of those industries where that transactional volume and the speed of that transactional volume is really important. Connecting in those use cases and those industries into what we're doing at Salesforce was kind of point one. And then when you start to look at the spaces that we want to collaborate, the very first one, obviously given Salesforce's expertise and everything they do with Sales Cloud is the seller. How do we help get the seller better insights around churn? How do we get them better insights around cross-sell, upsell? What are those transactional activities happening in the transactional system and how can that then drive different outcomes and different agent actions to kind of drive a whole new continuum much faster with access to data that we never would've had access to otherwise?

Christophe Bertrand

>> No, it makes a lot of sense. So that's accelerating sales cycles potentially. Hopefully augmenting the average sale-

>> Sales training comes up often. Exactly.

Christophe Bertrand

>> Exactly. Sales enablement was going to be my next question, but before we go there, I'd like to know, obviously you're dealing with a lot of data here from many sources. How do you keep this data compliant? What type of mechanisms have you put in place? Because if you're just at the receiving end, you may not know that some data should not be used or reused.

>> Yeah, no, that's a great question. I think it's really foundational to the approach IBM has taken on this space from the beginning. When we launched Watsonx last year now, there's three foundational elements. There's .data, which we've talked a lot about today, around getting access to all those different data sources. There's .AI, which is really that platform where you can come in and work through the AI insights that you're trying to derive as well as where our granite models reside. And then .governance. To your point, that to us is the part and to our clients is the part that gets the most attention by far, because at the end of the day, you can get the best insights, you can go drive some different autonomous actions off of it. But if it's not properly governed, if you're not accessing the right data with the right controls, if you're not controlling for all of the things Mark talked about today around bias and being able to ensure that you have trustworthy AI data, that outcome that comes out the back end really doesn't matter, and it's the first question all of our customers are asking us today. So anytime we engage with a customer on this entire space, where we start the discussion is usually around governance.

Christophe Bertrand

>> It makes a lot of sense. Plus you've mentioned the banking industry where-

>> Governance is important.

Christophe Bertrand

>> A little bit of governance, but pretty much every industry has, especially in the enterprise, has a ton of governance between national or transnational type of compliance regulations as well as industry-related regulations. Are there some verticals you're targeting with this partnership? Is there a priority list? Where do you think the business will come from originally or where is it coming from today?

>> Yeah, no, as we talked about earlier, I mean, the three industries that we're seeing the most interest out of the gate, not to be confused with Data Gate, but at the beginning here is financial services for sure, which is interesting, sitting in the New York Stock Exchange building, but that's absolutely a space where we're seeing tremendous interest from the customer side. The other is telco. Our telco relationships that both IBM and Salesforce have together really bring this to life. And also the mainframe estate that exists there is key. And then any other regulated industry where you have complex data requirements, complex data governance needs, bringing that to life and being able to pull that out through the Salesforce transformation project that you're driving is where we're starting all of that initial Agentforce work.

Christophe Bertrand

>> So that's covering quite a bit of the economy right there. You haven't mentioned airlines, but I'm sure, I would love some agents with-

>> I think everyone would. Yeah.

Christophe Bertrand

>> Yes, with airlines for all sorts of reasons. Let's talk a little bit about the future. So what are you going to be doing next? And maybe put this in a context also of your channel partners and your services partners and your own organizations. How are you deploying this moving forward? How are you going to make the combination more pervasive beyond the original three?

>> Yeah, no, great question. I think first of all, one of the key elements, if you think about Mark's presentation today, when he kind of started to talk about Agentforce, there was four different types of partners. The first was agent partners. We're collaborating very closely to co-create some of these new agents together. The second was an action partner. I mentioned Watsonx.orchestrate and a thousand plus skills slash actions we have in that portfolio that we're going to pull into Agentforce. The third was all around zero copy data and making sure that we have access to all of this data. And then to your point, across the bottom was implementation partners. Those implementation partners, we obviously with the IBM consulting team, very large significant Salesforce practice doing great transformation work, really prioritizing this Agentforce space because a lot of our customers are looking to do work in that area. However, we're not stopping there. I mean, a lot of the collaboration that we're driving with IBM right now is really opening up to the entire ecosystem. Working with other GSIs out there, making sure that there's a full understanding of the differentiation that comes from our product set and how that coincides with the Salesforce product set. And so I think you'll see more and more as we look forward, the unlocking of that ecosystem. We'd give a lot of signals of this on a regular basis at IBM Think earlier this year. We announced a lot of new partners, a lot of expansion to our partners area. The IBM ecosystem is growing at rates it never has before, which is really exciting. And I think activities like this, what we're doing with our most strategic partners just further unlocks that opportunity for the long list of service partners as well as technology partners. Within my portfolio, I also have the AWS relationship. We're seeing a lot of synergies between what we're doing with Salesforce, what we're doing with AWS, some of the other discussions you've had here today. Really pulling all of those things together unlocks, I think an incredible new set of value for the end customer.

Christophe Bertrand

>> Right. So let's talk about the requirements in terms of skill sets. Now, enablement obviously is not to be taken lightly.

>> Right.

Christophe Bertrand

>> So or have you created specific certifications where I could be this sort of Watsonx, Salesforce, Agentforce-certified architect or specialist? Is that something you're doing?

>> The consulting team is absolutely launching an entire track around that. I think on our side today, we have Watsonx certifications and there's Agentforce certifications coming. I think we need to work together on what that intersection looks like, and I think that'll really be brought to life by what the customers need. So we'll be fostering and curating those as we move forward together.

Christophe Bertrand

>> Well, I mean, we've covered a lot of ground, and before we close, what recommendations would you have now if you think about end users, because I'm sure you spend a lot of time in the field as well with end users. What recommendations would you have in order for them to be successful with their next project? Because you mentioned that AI, well, everybody's doing something, but are they really doing anything? But clearly, some people are. So what recommendations do you have? What should they do if you engage with leaders of these end users, what is the first thing you should think about? What is the second thing you should think about?

>> I'll do first and second. That sounds good. The first thing that I encourage every one of my customers to think about is back to the first point that I made, which is value. Why are you doing this? What are the KPIs that you're looking to improve? What's the actual outcomes you're looking for? Whether that's Agentforce-related and actually deploying agents increasing productivity, or are there other insights you're looking to get that you're going to be able to quantify? I think that right from the beginning, understand what is the thing that I'm trying to improve is something that unfortunately today, the vast majority of projects don't start with. They start with, "We have a huge problem here. We're going to go do a bunch of experiments to see if we can make it better," versus, "Here's the specific KPIs we're going to go out and try and improve." The second goes to your last question, which is, how can I do this and what should I be thinking about as I go into this project? I think first of all is you're probably going to need help. These are incredibly complex things to solve. So whether it's just help to figure out what is that problem you're trying to solve, what are the KPIs you're going to go after, or the entire ecosystem of help that can be there. And that can include, I've got cloud partners that we've got data spread across multiple clouds. That can include an ISV partner, like a Salesforce. We're all in on that platform. That can include a long list of ISV partners, most likely services partners. And where we like to kind of come and help solve that is to wire those things together. So everything I talked about from a Watsonx.data perspective, a key differentiator around that is your data can exist anywhere. On-prem, on whatever cloud it is, we will pull that together and help you draw insights. Then the layer on top of that, Watsonx.ai, you can draw insights using not just our granite models, but partner models. Partner models can come in. Doing that in an open way is absolutely fundamental. And then finally, the governance piece. Don't forget about governance. You can start with value, but the very second thing you should think about is governance, and how am I going to make sure that I'm doing this all in a way that follows the regulations and needs and requirements of my business, my enterprise, my market? I think those are the important pieces, and the fact that you can't do it alone is probably one of the most important, that too many of our customers have tried it on their own, and then they come and ask for help. But those are a few quick thoughts.

Christophe Bertrand

>> Great. Well, thank you so much for your time, Nick. This was great. I would like to thank everybody for watching theCUBE. We're here at the New York Stock Exchange Office in San Francisco covering Dreamforce 2024. Thank you, Nick.

>> Thank you so much.

Christophe Bertrand

>> Thank you everyone.

Nick Otto | IBM

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Christophe Bertrand

>> Hello and welcome to theCUBE from the New York Stock Exchange Office in San Francisco. My name is Christophe Bertrand. I'm a principal Analyst at theCUBE Research. I'm joined today by Nick Otto, who is the head of Global Partnerships at IBM. We're going to be talking about guess what? The partnership between IBM and Salesforce. Dreamforce 2024, great intro this morning. Lots of people. San Francisco is absolutely crazy downtown. Not the usual crazy, good crazy this time. This being said, I can say that. I'm from San Francisco. So Nick, welcome to theCUBE or welcome back, I should say.

>> Thank you. Excited to be here.

Christophe Bertrand

>> Great, so let's talk about the partnership. What are the main areas or technology areas where you actually partner with Salesforce?

>> Yeah, well, I mean, I think you just kind of hit on the fact that everything that was talked about this morning from Mark, it was really exciting that IBM was a part of pretty much every element, and I think that's really just building upon a significant relationship we've had with Salesforce for quite some time. They've been a strategic partner of ours for years. Really what this is all about is, as is most these days, AI. Back at IBM earlier this year, we announced a lot of new collaboration with Salesforce and really everything that we've talked about here is just taking that all to the next level and continuing to help with our customer transformations.

Christophe Bertrand

>> Absolutely. Is it a dream force or Agentforce? I mean, think Mark suggested to change the name himself. Well, let's talk about something I never thought I could say in the same sentence and sound credible, which is Mainframe Db2, AI and Salesforce. What's going on with that?

>> That's very true and it's really exciting to me. I think, and Mark talked about this in his keynote today, but I'll kind of give my simplified version of each of those different pieces. One of the things that IBM has really been focused on for several years now, we've been very consistent around this, is that the priorities around hybrid cloud and AI, that's what our clients need to transform. I think Mark today kind of walked through the history from cloud to mobile to AI to data, and then getting to where they are with agents today, that's very aligned with our mission and our strategy for years around hybrid cloud and AI. Hybrid cloud, to your point, there's lots of data, lots of different places, and I think when you look at any generative AI continuum, it starts with the data. How do we make sure that as you're drawing insights, you have access to all of the relevant data? We announced back at IBM Think earlier this year, a collaboration around zero copy data network with Salesforce that was bringing Watsonx.data and Salesforce data cloud together. What we've announced here is the extension of that to include IBM Data Gate. What Data Gate allows you to do is bring Z data into Watsonx.data, which then through the connection with Salesforce Data Cloud, can really bring insights from a transactional perspective. Running on Mainframe, Watsonx.data also works with Db2 and pretty much everything that you can imagine out there. So when you think about that hybrid cloud element, getting that data available to draw insights from is really foundational, and now being able to connect that to Salesforce Data Cloud is yet another way to draw even faster insights, especially from a sales perspective.

Christophe Bertrand

>> Now, I have to say, this is a very impressive integration. Mainframe will never die. Let's be clear. But I think what you're demonstrating here is that it's really this notion of hybrid, but more importantly, the idea that data is very pervasive. It's not just in Salesforce, it's really across the enterprise. So could you walk me through maybe for this specific example, the type of use cases, or maybe if you have some customer examples. I'm very curious about this because again, Mainframe Db2, lots of data connected to the Salesforce environment. Now, you've got a powerhouse going on right there, right?

>> Yeah, no, for sure, and I think you're right. I mean, what's really exciting to me, I've been at IBM for almost 20 years now, this notion of hybrid cloud and data being pervasive and disparate spread across your organization. Obviously you have clouds, you have on-prem data, you probably are working in most clouds. You've got lots of ISVs involved. Getting all of that data into a format or into a platform where you can draw insights from it is so important. And I think most of the use cases that we're working with Salesforce on today are focused around the customer and particularly transactions around that customer. I think exactly as you said, I mean, the Mainframe continues to be an incredibly pervasive platform. I think the latest stat is 45 of the 50 largest banks in the world are running on Z. That's pretty pervasive, and if you think about being able to bring that transactional level data to life in a broader sales transformation or even lead generation campaign for a seller where not just are you looking at data spread across all of your different systems of records on-prem, on cloud, everywhere else, but also that transactional data, bringing that to life in near real time can draw an entirely new set of insights for that seller to go do something different than they would've done otherwise.

Christophe Bertrand

>> Yeah, especially at Mainframe speeds.

>> That's right.

Christophe Bertrand

>> Something not to forget. We talk a lot about AI. Certainly there's a lot of agentic AI going on right now, lots of conversations about this. So let's walk through what you've done in collaboration with Salesforce around what's next and other agents, and I'm very interested in also in the granite efforts.

>> Yeah, perfect. So I think we kind of focused on data to begin with and what we've done around .data, data cloud and pulling Data Gate into that to allow all of that extension. Once you have that foundation, to your point, then you want to draw insights, and the insights really comes from a lot of different models being built by a lot of different platforms. We believe the granite models are a really, really enterprise ready model. We believe they're fit for purpose. You can see we have a large set of them. What makes them really differentiated is the fact that they're all indemnified, so our models actually come with indemnification, which for the enterprise user is becoming really important. So those insights actually are backed by indemnification. That's point one. Kind of building upon that exactly where you were going with the agent world, there's too many, I think, projects going on across all of our customers, I'm sure many people have seen across their customers, where you're just trying to do something with generative AI. It's a great finding, a great insight. Let me show you a cool use case that we developed. If you're not driving different outcomes, at the end of the day, that value is not going to land. And so what we've done with Watsonx.orchestrate in collaboration with Agentforce, we've been on this, being able to get your insights and then drive different outcomes, and that's all around automation and orchestration, which has been a focus of ours for quite some time. Now collaborating with Salesforce, bringing together Agentforce and Watsonx.orchestrate is really unlocking that value to make sure that different actions are taken and not just you got to need insight that that was kind of fun, but you're actually now driving autonomous activity off of those insights. And that's where our collaboration just continues with Watsonx.orchestrate and Agentforce coming together, our Watsonx.orchestrate skills and actions coming into the portfolio of Agentforce actions, and then finally delivering all of that through a medium. And that medium in many of our internal use cases is Slack. So you really see all of these different Salesforce and IBM and other partner assets all coming together into a single continuum.

Christophe Bertrand

>> Yeah, it seems like the pieces of the puzzle are coming together. Let's talk about some of the typical workflows that you've already automated or orchestrated with this integration. Do you have a couple of examples?

>> Yeah, the cleanest and clearest example is still focused, I mean, we've really zoomed into regulated industries first of all, back to kind of the mainframe of state. You look at financial services, you look at telco, some of those industries where that transactional volume and the speed of that transactional volume is really important. Connecting in those use cases and those industries into what we're doing at Salesforce was kind of point one. And then when you start to look at the spaces that we want to collaborate, the very first one, obviously given Salesforce's expertise and everything they do with Sales Cloud is the seller. How do we help get the seller better insights around churn? How do we get them better insights around cross-sell, upsell? What are those transactional activities happening in the transactional system and how can that then drive different outcomes and different agent actions to kind of drive a whole new continuum much faster with access to data that we never would've had access to otherwise?

Christophe Bertrand

>> No, it makes a lot of sense. So that's accelerating sales cycles potentially. Hopefully augmenting the average sale-

>> Sales training comes up often. Exactly.

Christophe Bertrand

>> Exactly. Sales enablement was going to be my next question, but before we go there, I'd like to know, obviously you're dealing with a lot of data here from many sources. How do you keep this data compliant? What type of mechanisms have you put in place? Because if you're just at the receiving end, you may not know that some data should not be used or reused.

>> Yeah, no, that's a great question. I think it's really foundational to the approach IBM has taken on this space from the beginning. When we launched Watsonx last year now, there's three foundational elements. There's .data, which we've talked a lot about today, around getting access to all those different data sources. There's .AI, which is really that platform where you can come in and work through the AI insights that you're trying to derive as well as where our granite models reside. And then .governance. To your point, that to us is the part and to our clients is the part that gets the most attention by far, because at the end of the day, you can get the best insights, you can go drive some different autonomous actions off of it. But if it's not properly governed, if you're not accessing the right data with the right controls, if you're not controlling for all of the things Mark talked about today around bias and being able to ensure that you have trustworthy AI data, that outcome that comes out the back end really doesn't matter, and it's the first question all of our customers are asking us today. So anytime we engage with a customer on this entire space, where we start the discussion is usually around governance.

Christophe Bertrand

>> It makes a lot of sense. Plus you've mentioned the banking industry where-

>> Governance is important.

Christophe Bertrand

>> A little bit of governance, but pretty much every industry has, especially in the enterprise, has a ton of governance between national or transnational type of compliance regulations as well as industry-related regulations. Are there some verticals you're targeting with this partnership? Is there a priority list? Where do you think the business will come from originally or where is it coming from today?

>> Yeah, no, as we talked about earlier, I mean, the three industries that we're seeing the most interest out of the gate, not to be confused with Data Gate, but at the beginning here is financial services for sure, which is interesting, sitting in the New York Stock Exchange building, but that's absolutely a space where we're seeing tremendous interest from the customer side. The other is telco. Our telco relationships that both IBM and Salesforce have together really bring this to life. And also the mainframe estate that exists there is key. And then any other regulated industry where you have complex data requirements, complex data governance needs, bringing that to life and being able to pull that out through the Salesforce transformation project that you're driving is where we're starting all of that initial Agentforce work.

Christophe Bertrand

>> So that's covering quite a bit of the economy right there. You haven't mentioned airlines, but I'm sure, I would love some agents with-

>> I think everyone would. Yeah.

Christophe Bertrand

>> Yes, with airlines for all sorts of reasons. Let's talk a little bit about the future. So what are you going to be doing next? And maybe put this in a context also of your channel partners and your services partners and your own organizations. How are you deploying this moving forward? How are you going to make the combination more pervasive beyond the original three?

>> Yeah, no, great question. I think first of all, one of the key elements, if you think about Mark's presentation today, when he kind of started to talk about Agentforce, there was four different types of partners. The first was agent partners. We're collaborating very closely to co-create some of these new agents together. The second was an action partner. I mentioned Watsonx.orchestrate and a thousand plus skills slash actions we have in that portfolio that we're going to pull into Agentforce. The third was all around zero copy data and making sure that we have access to all of this data. And then to your point, across the bottom was implementation partners. Those implementation partners, we obviously with the IBM consulting team, very large significant Salesforce practice doing great transformation work, really prioritizing this Agentforce space because a lot of our customers are looking to do work in that area. However, we're not stopping there. I mean, a lot of the collaboration that we're driving with IBM right now is really opening up to the entire ecosystem. Working with other GSIs out there, making sure that there's a full understanding of the differentiation that comes from our product set and how that coincides with the Salesforce product set. And so I think you'll see more and more as we look forward, the unlocking of that ecosystem. We'd give a lot of signals of this on a regular basis at IBM Think earlier this year. We announced a lot of new partners, a lot of expansion to our partners area. The IBM ecosystem is growing at rates it never has before, which is really exciting. And I think activities like this, what we're doing with our most strategic partners just further unlocks that opportunity for the long list of service partners as well as technology partners. Within my portfolio, I also have the AWS relationship. We're seeing a lot of synergies between what we're doing with Salesforce, what we're doing with AWS, some of the other discussions you've had here today. Really pulling all of those things together unlocks, I think an incredible new set of value for the end customer.

Christophe Bertrand

>> Right. So let's talk about the requirements in terms of skill sets. Now, enablement obviously is not to be taken lightly.

>> Right.

Christophe Bertrand

>> So or have you created specific certifications where I could be this sort of Watsonx, Salesforce, Agentforce-certified architect or specialist? Is that something you're doing?

>> The consulting team is absolutely launching an entire track around that. I think on our side today, we have Watsonx certifications and there's Agentforce certifications coming. I think we need to work together on what that intersection looks like, and I think that'll really be brought to life by what the customers need. So we'll be fostering and curating those as we move forward together.

Christophe Bertrand

>> Well, I mean, we've covered a lot of ground, and before we close, what recommendations would you have now if you think about end users, because I'm sure you spend a lot of time in the field as well with end users. What recommendations would you have in order for them to be successful with their next project? Because you mentioned that AI, well, everybody's doing something, but are they really doing anything? But clearly, some people are. So what recommendations do you have? What should they do if you engage with leaders of these end users, what is the first thing you should think about? What is the second thing you should think about?

>> I'll do first and second. That sounds good. The first thing that I encourage every one of my customers to think about is back to the first point that I made, which is value. Why are you doing this? What are the KPIs that you're looking to improve? What's the actual outcomes you're looking for? Whether that's Agentforce-related and actually deploying agents increasing productivity, or are there other insights you're looking to get that you're going to be able to quantify? I think that right from the beginning, understand what is the thing that I'm trying to improve is something that unfortunately today, the vast majority of projects don't start with. They start with, "We have a huge problem here. We're going to go do a bunch of experiments to see if we can make it better," versus, "Here's the specific KPIs we're going to go out and try and improve." The second goes to your last question, which is, how can I do this and what should I be thinking about as I go into this project? I think first of all is you're probably going to need help. These are incredibly complex things to solve. So whether it's just help to figure out what is that problem you're trying to solve, what are the KPIs you're going to go after, or the entire ecosystem of help that can be there. And that can include, I've got cloud partners that we've got data spread across multiple clouds. That can include an ISV partner, like a Salesforce. We're all in on that platform. That can include a long list of ISV partners, most likely services partners. And where we like to kind of come and help solve that is to wire those things together. So everything I talked about from a Watsonx.data perspective, a key differentiator around that is your data can exist anywhere. On-prem, on whatever cloud it is, we will pull that together and help you draw insights. Then the layer on top of that, Watsonx.ai, you can draw insights using not just our granite models, but partner models. Partner models can come in. Doing that in an open way is absolutely fundamental. And then finally, the governance piece. Don't forget about governance. You can start with value, but the very second thing you should think about is governance, and how am I going to make sure that I'm doing this all in a way that follows the regulations and needs and requirements of my business, my enterprise, my market? I think those are the important pieces, and the fact that you can't do it alone is probably one of the most important, that too many of our customers have tried it on their own, and then they come and ask for help. But those are a few quick thoughts.

Christophe Bertrand

>> Great. Well, thank you so much for your time, Nick. This was great. I would like to thank everybody for watching theCUBE. We're here at the New York Stock Exchange Office in San Francisco covering Dreamforce 2024. Thank you, Nick.

>> Thank you so much.

Christophe Bertrand

>> Thank you everyone.