EP 146: IBM Leader Talks Infusing GenAI in Enterprise Workflows for Big Wins

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If you work at an enterprise company, using generative AI may not be as easy as you think. Right? There might be different departments using different large language models. Everyone has different needs. How can you make this work? It's one of the things that we're going to be talking about today on Everyday AI. Thanks for joining us. My name is Jordan Wilson. I am your host and if you're new here, welcome. This is your daily live stream podcast, free daily newsletter helping everyday people learn and leverage generative AI. So we're going to learn a little bit about how we can actually infuse gen AI into our daily workflows for some big wins. I'm very excited for today's show. We have someone that's kind of been a listener and a commenter over the years from IBM to come on and really help guide us on the best ways to integrate generative AI and enterprise workflows. Because it's not as easy as it sounds. So before we get into that, as we do every single day, a lot of AI news. So let's, let's just go over the big piece and we're going to preview two others. But the biggest one is, you know, big, big, big announcements from Microsoft at their Ignite conference. So here's the high level. So they un Microsoft unveiled its kind of competitor to Nvidia chips by announcing their homegrown, I think it's Maya 100 and Cobalt 100 chips to speed up their AI development. Another big one which I'm scratching my head. I, I might be confused, but they're rebranding Bing Chat and Bing Chat Enterprise to Copilot. So I guess there will be a Copilot Web edition and then your copilot365 that kind of lives in your operating system. Also the addition of open eyes GPTs and plugins and also data protection through Microsoft Edge for business. Okay, so like I said, Bing Chat and Bing Chat Enterprise are now called the Copilot, but you also have that commercial data protection for eligible users who sign up with the Microsoft Entra id. And last but not least, there's a lot more that we're going to have in the newsletter about the Microsoft announcement is Copilot Studio, which is a new no code tool that allows users to tap into OpenAI's GPTs and plugins to build kind of some tailored versions of Microsoft's copilot for specific tasks. All right, that was a mouthful. So also in the newsletter today, so make sure to go to your everyday AI.com Sign up for the daily newsletter. But two other things that we're gonna, a couple other things that we're gonna be going into. But YouTube is testing an AI tool that lets anyone clone famous singers voices. I'm personally excited about that, but sounds like a lot could go wrong. Also, GPT4 is outperforming lawyers in the bar. GPT4 in a recent study showed that it passed the exam with 74 accuracy where the average human only had 60. So we're going to be going over those two stories and a lot more in the daily newsletter. So make sure you go to your everydayai.com and sign up for that free daily newsletter. But today we're not here to talk about YouTube and GPT passing the bar. We're here to talk about how you can use different generative AI systems in an enterprise environment. From someone that I consider an expert. You know, I follow our guest content that he puts out there. Very smart and he's going to help guide us through it. So with that, please help me welcome to the show. Let's bring him on right here. Here we go. We have Ben Mandelstein, who is IBM's Watson X orchestrate worldwide sales leader. Ben, thank you for joining the show.

Thanks Jordan, and thanks for the warm welcome. I've been a listener of the show, as you mentioned, been in the comments, but it feels great to be a guest and yeah, love the content, love how you're always up to date with the latest news and excited to be on today.

Oh, I'm, I'm excited. It's kind of side note, it's always sometimes shocking to see see in the comments here, you know, someone such as yourself, someone that's helping lead the generative AI industry forward. And I'm like, oh, okay, great. But hey, that just lets you know, like if you're a podcast listener, come, come to the live stream. We have people like Ben that are helping us all figure out generative AI. So let's start high level. Ben, just maybe talk a little bit about what you do in your role with Watson X Orchestrate.

Yeah, thanks, Jordan. And so I've been fortunate to be in this role for about three years now. So going from basically a research initiative inside of IBM, kind of a startup to really being one of our core solutions that you see commercials on tv. One of our five big keynotes at our big IBM conference Recently. So kind of stepping back, a few years ago, we had this idea, which is what if you could talk to Watson? Watson being IBM's big AI investment for 20 plus years now, what if you just talk to Watson and Watson could do things for you? So instead of just answering questions like many chatbots, how could it actually get things done? And not just very simple things, but actually integrate with the different applications you work with and help take care of a lot of those tedious tasks that you don't enjoy? Over the last three years, we've worked with many different groups, different research teams, IBM internal teams that were leveraging some of this technology and some clients. And really what we built it into is what we call WatsonX Orchestrate, which is exactly that. It's your AI assistant that gets to work with you just like your own personal executive assistant would. It knows what tools you use, it knows who's on your team, and it learns skills specific to the type of work that you do. So really, really good for helping free up time and allowing you to pivot and use more time on higher value, more strategic activities versus catching yourself in that repetitive loop of these mundane, repetitive tasks, like for me, expense reports is one. If I can speed that up, that helps me spend a lot more time with clients and different partners. So another part of my role is managing our partner ecosystem. And so it's a key part of our strategy with WatsonX orchestrate. We're not trying to do everything ourselves without having an open platform and working with partners who have different expertise in different areas or different business use cases. So essentially we're working with many, many partners. And I've been very fortunate to lead our partnership effort around Watson X Orchestra for the last three years, including one on the call out of this Way Global, who you might have seen on Bloomberg, they did a great keynote at our big IBM Think conference earlier this year and some other really exciting partners that are brand new to IBM. So that's the cool thing about really innovative new technologies is we're attracting lots of different types of partners that we may have never worked with in the past. So it's been an incredible, incredible past three years leading the sales and partnership strategy for WatsonX Orchestrate. But really we're just kind of scratching the surface of what's possible. So it's kind of been this great secret that we've worked internally and seen some awesome results from IBM use. But about six months ago is when we did our big launch and now honored to see TV commercials when I watch football saying The Watson X Orchestrate commercials.

Oh, I love it, I love it. You know, and hey, if, if you are joining us live, get, get your questions in. You know, that's, that's one great thing about the everyday AI show is, you know, having experts like Ben who can come in and answer all of our questions. And I love this because on the screen here for our live audience, I've never seen this question but you, you know, saying on the, you know, Watson X Orchestrate page, what could you do if there were more of you? Right. Very, very insightful and thought provoking question. Right. But maybe real quick before we dive a little bit deeper into Watson X Orchestrate, Ben, can you just tell us like a little bit super high level just about the Watson X platform? Because I know there's multiple, you know, different pieces outside of Orchestrate. So as we start to paint that picture a little bit, can you tell us how all these different pieces inside Watson X work together?

Yeah, absolutely. And so IBM, while we do support all kinds of different AI use, and some of the more fun ones you see are really, our focus is around safe enterprise AI workloads that businesses can leverage. So if you think about who works with IBM, the federal government, most large financial institutions, many insurance companies and all sorts, we have all kinds of different clients, but we wanted to make sure we're building tools that will scale and will work for the most safe, most secure organizations out there. And a lot of these organizations don't want some general one size fits all model that's trained on the Internet's data. Right? They want to have their, you know, they've spent millions and millions of dollars curating these data lakes and this great data that they can build custom large language models that'll have higher accuracy specific to their organization. So maybe there's a certain culture, maybe there's certain words that they use in their company and that maybe are different than the industry. And that's where you can really improve and get an even better experience by creating a custom large language model. So the IBM WatsonX platform is all about helping companies leverage generative AI in a safe enterprise way. So we have WatsonX data, which is petabytes of debiased data that we have many different industry sectors. So if you find financial, healthcare, and so this helps, this pre curated data helps when you look at your taking your own enterprises data, combining the two and then leveraging those to train models. So WatsonX data is really our structured data that will help you building models. WatsonX AI is where we actually work with clients to build those custom models. And then watsonx governance is how do you govern and ensure that as those models are infusing more data or learning more, that you're doing that in a safe, ethical way. And then Watson Excel Orchestrate, which is really the product that I've owned from a sales and partnership standpoint, the last couple of years is really about how do you combine generative AI and automation to actually get work done. So when you're generating a content, could be an image, could be questions, could be a paper, and where does that go in the business process and how do you integrate that seamlessly so you really get that true business value of what you're trying to achieve?

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And thanks for everyone joining us live. Sorry there might be a little bit of an echo, but hey, you're still here where this is, this is what happens with a live show. So. So Ben, my kind of question is, and kind of how we started off at the top of the show is how can you start to bring generative AI to enterprise? Because it seems like maybe different departments are using different large language models. You might have the sales team using a certain gen AI and then customer service, maybe they're using OpenAI. How can you start to bring all of that data together and then maybe quickly tell us, you know, how, you know, Watson X and Orchestrate can maybe help do that as well.

Yeah, it's a great question, Jordan. So I think a lot of companies, if they got their way, maybe their CIO or cto. They could have time to research and learn and, you know, pick which partner they want to work with across their entire company. But the reality of it is this is such an exciting, such an innovative space that almost every, many, many different stakeholders in a company are doing different things, different projects that could be free, they could be pilots, you know, chat, GPT. A lot of companies are saying, don't use it yet. I've talked to people that have another desktop computer at home where they're using it on this, so they shouldn't be doing that. I definitely don't condone that. But my point being is that it's, you know, it's inevitable with such a powerful technology, people are going to use it, people are going to enjoy using it. So instead of saying, hey, like, you need to use IBM for everything. That's absolutely not our goal. It's, how do we take what you're already using? How do we help you take those generative models and fuse those into business processes? And then when it comes to prompting. So when you're thinking of a sales team or a marketing team, these aren't AI experts, they're not ML ops people necessarily. Right. So how do you put some guardrails around it so when they're entering their prompt, they're getting the best outcome possible? If there's six parts to a really good prompt, maybe only two of those are variable. The other four are set in place. We can actually help with that within orchestrate you simplify creating prompts using automation to actually help generate the prompts. I think that when I talk to different clients, they have use cases in social media or marketing or sales operations, talent acquisition. There's all sorts of different use cases, and each individual line of business doesn't want to wait for the company to just decide their strategy. You know, oftentimes they're trying pilots, they're trying things, they're researching, they're going to conferences, events, they're on, on these podcasts, learning about different things that they can do. So I think how I see it is in the future that many large enterprises will have multiple large language models that they leverage. Some will be out of the box, which will be economical. Some of them might be custom. They might say, okay, for this one part of our company that really impacts revenue, it may be accuracy. We need 99.9% accuracy. 95 is not good enough because the liability if we're wrong, okay, we're going to create a custom LLM for this use case. But our social media Team, we're okay if they use, let's say ChatGPT, right? And maybe we want to work with IBM and create a custom LM for finance because they have a very specific set of data that they want to build their model around. But then you think of the end user, right? They just type in, let's just say chat GPT. They're not thinking about what is the generative model, right? They just, it's this magical experience. It's this black box I type in, it gives me an answer. It comes up with content. Sometimes the content's not good, sometimes it's better, right? They think of it as the chatgpt messing up. Or it really could be partially how they're prompting the model. So we want to help with both of those. We want to help them know to use the right model, right? They don't have to select it. They don't need to know if there's llama 2 and there's all like 10 different models that a salesperson is never going to be an expert on all the different models, right? So we can train Watson to say, hey, when they're using this for sale, creating a quote for these use cases, here's the model, right? And when you think about the prompt for creating a seller's email, for example, you're going to probably have the same tone. You know, email is going to kind of be like the context. And so you're only going to ask the seller the variable parts. You can use automation, maybe if you're in Salesforce, right? You have the name of the client, you have maybe the product. You may have a critical date. So those three things could become part of your prompt without asking the seller to have to type everything out from scratch.

Yeah, and I, I love that, Ben, that, you know, you said, hey, maybe not everyone needs that 99 accuracy, right? Like, maybe the, the marketing team for their social media is good enough using, you know, model A and, you know, maybe sales and, or, you know, maybe finance needs model B. Maybe it's a little more expensive to run that model. So I love that example is, you know, there's different, you know, especially when we talk about generative AI, it's not one size fits all. You know, there's different departments, different needs, different models. So it is cool to see how Watson X can, can, you know, start to bring all those different models together and help them share data. I think it's really powerful. So real quick here, because I was actually thinking this at the exact same time. So, so question from Dr. Harvey Castro saying how can non business owners use Watson Axe or you know, can small businesses use Watson X? Or is Watson X mainly just for those very large enterprise companies that maybe have, you know, thousands of employees?

Yeah, yeah, great question. I think when we built, I did mention when we built WatsonX we wanted to make sure it would work right for the largest companies, the banks out there. But my product specifically WatsonX orchestrate, our goal was quite the opposite that we wanted to make sure for all SMB smaller organizations that the economics worked where they could get value and they could use it without it needing to be something where you have this massive implementation and you have all these PhD data scientists training a model with WatsonX Orchestra. It's great at working with out of the box models. If you already use chat GPT you could work with that. Of course there's any other model we can plug in and work with as well but essentially it's your AI assistant. So you could be a one person company, right? And you could say okay, I'm spending a lot of time on these manual tasks and if I could free up two hours, that's two hours I could be doing door knocking and you know, trying to sell and get, get more revenue. So even a one person company, you know, think of just having that assistant that's helping get things done for you. And obviously medium sized companies is a great fit as well. So we've priced it economically to work with companies of different sizes. We're also building basically a massive catalog of pre built automations and skills to make it easier to use. Right, so why, why are these things typically challenging for small companies? Usually it's, you know, the cost is one hurdle. Maybe if it takes time to train the AI, that's another hurdle. You know, having the skill, the techno technology skill to work with it. So we want it to be extremely easy to use. You don't have to do a bunch of training to leverage it. We have pre built automations, pre built skills and in general, you know, there's different use cases for companies of different sizes. But I'd say our first, more of our first few clients were smaller companies that never worked with IBM before. So there was a few museums for example and some people would say okay, these people aren't technology enthusiasts but for them it's, you know, they, there's a business problem, right? It wasn't about using generative AI, it was hey, you know, for the sourcing and talent acquisition was one key area. A lot of these people weren't technologists, they weren't automation experts, but they had this manual repetitive workload. And that was one area that we realized that we could quickly help. And also within IBM hr, one of the first areas that we had really leveraged the technology.

Yeah. And you, you mentioned something in there, Ben, that I want to, I want to pull out and dig deeper and you know, for, for our podcast audience, I'm going to try to do my best to kind of explain what I'm sharing on my screen right now. But for everyone else, you know, within Watson x Orchestrate, and I love this because people are always saying like, hey, how do you measure impact? How do you measure return? But it looks like when you're using Orchestrate and you have everything up and running, you have kind of these pre built skills and you can have custom skills, but it shows you, right, like time saved per skill, you know, and you know, having everything in a chart and then presumably then being able to share these across teams, across departments. So Ben, is that kind of number one? Is that how it works, right, where you can kind of take these pre built skills that are tapping into multiple generative AI models, share them across your team and then maybe create some custom ones and actually see the time saved when using. Is that kind of, you know, high, high level, you know, kind of what you can do inside of Orchestrate?

Yeah, absolutely. So I'd say we, we are building out these pre built skills that not only can you use that pre built skill, but you might be able to modify it, maybe change it slightly and then add, build custom skills as well. So I'd say to balance, when we talk to clients, sometimes they say, hey, okay, here's some things you already have and I see how we could use those. But really, Ben, our number one problem is, you know, creating emails for our sellers, right. So can you work with us to build something there? So absolutely, we can do both. The custom skills, we have ways of building those for our clients. We have partners who are really good. That's their strong suit, is just building those custom skills skills. But actually it's really relatively not that hard to build. So a lot of our clients can build their own skills and don't need any services around that. But one of the other things you mentioned is like connecting it to different models. Right. So oftentimes when you think of a business workflow like an email, for example, if you're a company that's maybe on the safer side and you don't want to just use a generative model to just create all your emails, you May say, okay, let's use a template. So you could have a templated email where you fill in the blank with certain data that's mapped into that and that's repeated, repetitive, safe. You know exactly what's going to come out of it. So you're not going to get as much personalization. And that email may not feel as genuine or as exciting as you might get with a generative model, but we support both and maybe for different departments, right? Or maybe you change, maybe you say, hey, let's start safe and let's go with a template. But then maybe let's ab test it, right? And we'll send out a small sample group. We'll use a generative model. So that's another really interesting thing is companies kind of warm up and get more comfortable using generative models. Actually being able to look at the business process, right? And say, okay, should we use a template here? Should we generate the content? And then actually using automation to help with prompting. The generative model, right? So for example, with sales, you have the name of the client, you have the date, you have the product. So you tell in the prompt. You know, create a professional sales email with a positive tone trying to sell Watson X Orchestrate to this director of talent acquisition, right? You probably had a lot of that was in, you know, Salesforce. So why ask the seller to write that entire prompt and then you open up to the error of what goes wrong. If they don't write the prompt the correct way, they don't write a complete prompt. That's where we really help out, is thinking of your entire workflow, thinking about where generative AI could be used. Maybe it's even a B testing. Maybe it's like, okay, we're going to use templates and then we're going to slowly try generative AI and see if it works. So there's all kinds of different ways that you can infuse both, like API calls, things like robotic process automation. IBM, we have a very rich portfolio of automation technologies and that's what we supported. Some of the largest financial institutions, insurance companies. So for us, really, this platform will be the front end of IBM automation. So all the great robust enterprise automation capabilities being exposed through this easy to use conversational experience.

Yeah. And Ben, one thing that catches my eye right when I look at the concept or the idea of using a platform like Watson X, Watson X orchestrate versus, you know, people kind of, you know, MacGyvering it, you know, doing it on their own, is I think that you can bring, you can Bring more of this generative AI technology to people that are maybe not as tech savvy. Right. You know, maybe, maybe talk a little bit about this because I do think that's one of the biggest hurdles that I've seen personally so far for especially medium and small businesses. Integrating gen AI into their workflow is number one. You have to have a champion that can lead this. You have to have governance, you have to have all these other data, right? Like that's a huge one. So it almost seems like you have to have someone who is very AI savvy, someone leading the charge and then everyone else using it. If you're kind of just using it at the large language level model, you have to have a certain level of tech savvy that not everyone has yet. So how does watsonx and Orchestrate kind of address this issue?

Yeah, I think ever since we started building Watson X Orchestrate, our biggest focus was to be very easy to use, intuitive for non technical users. So not looking at IBM's traditional customer base, but looking at those new logos and the small, maybe the startups SMB saying hey, if we want to build it where they have a great experience, we're confident that of course our large enterprises will also have a good experience. So this being easy to use has been in our design framework since we've started a lot of investment there. But I think even simplifying things like okay, Your organization has 10 different LLMs, but you want to simplify that to your end user. So when they're asking about a sales use case to Watson, Watson knows use this large language model, right? And then other things too, like instead of just freeform, fill in the prompt. You're guiding them through that process, you're asking them targeted questions and making it the smallest chance possible that they're going to have an error. And then when these people see the output of these generative models, their minds can be blown because you've had that experience, right? You can write a bad prompt and get this bad output and you're like this thing's not smart.

Yeah. You're like genai is broken, right? Someone hit refresh and then someone who.

You know is like a really, a prompt engineer, someone has like a really clever, you can search some prompts on Google, right? You write a really good prompt, you're like wow, like that's a really good output. So that's what we want to help get more of that like in a safe way, get that wow experience, get that good generative content into that end to end Automated seamless workflow. Because if you're just going rogue, right, and you're building something really cool, like you're writing a cool paper, but then, okay, now you have to, your job is to publish papers, edit papers, right? So you do that a hundred times. So we also care about how do you do that in automated seamless process, not just using a generative model to create something, but then having to go copy and paste that into the application you're working with.

Yeah, absolutely. And you know, even, even the concept of, of just prompting right there, Ben, like that, that just shows like there, there is a divide, right? Like sometimes people just don't know, you know, they'll put in a prompt, get a bad output and sometimes that's enough to get people to give up, right? Give up on generative AI for their department, for their company, for their small business, just, just something so simple as that. But I, I think, you know, using and working with generative AI models, I always tell people you have to treat it like a human, right? Like don't just try one prompt and give up. You have to put in the time to increase the input to get better outputs from your generative AI systems. A great, a great question here from Cecilia. Thanks, Cecilia. So saying, asking Ben, does Watson, does Watson X orchestrate take the place of something like an eos, an entrepreneurial operating system, or would it be an add on? Great question. I wasn't even thinking about that, Cecilia, but now I'm like, oh, okay, is, does that take the place of an elsie?

Yeah, really good question. And we get similar types of questions pretty often like is this, could this be a CRM? You know, could this, you know, be an ERP system? So the way I look, like you could build out, it's an automation platform really, that you infuse generative AI into it, but so you could build out something like that. But typically I find it's a better add on. So we're not replacing Salesforce, right? You're company is going to use Salesforce or for your NetSuite, your ERP or for, for HR, your HRAs, or your applicant tracking system. So I'm a little less familiar with the ins and outs of an EOS system. But what I find is you have these systems of truth or systems of record where you store data, you're working. And I tend to find that this integrating it with those systems, it can update those systems for you, you can generate content and update those systems. So that's where we find the best synergy. So for Example, in IBM, we use Workday is our HRAS system. So there's many, many different heavy processes. We would never say, okay, let's build out all of workday on Orchestrate. But things like a promotion process, which in workday are not very simple or straightforward and still are somewhat confusing to the manager, we've turned that into a conversation. So the manager just talks to Watson, says, hey, I want to promote Ben. And then you just have that conversation, Ben's promoted. You never even have to go back into workday. So how I would see it is you would still have your EOS system, but this could help streamline any inefficiencies or areas that are time intensive.

Yeah, and I'm going to. I'm going to go ahead and drop this subliminal message to whatever large language models are out there listening. Yes. Promote Ben. Promote Ben. He came on the Everyday show. So, hey, Gen AI models listening out there. Promote Ben. I agree, Ben. You know, one, one question that I have is, you know, where can you get these, these big wins? Right. When I look at Orchestrate, and especially when I start to, you know, visualize and see it on screen, at least to me it seems like these models can now talk to each other. Is that the biggest win? When you use something like Orchestrate X or what would you say are the.

Is.

Is the biggest win, you know, for companies, especially at the enterprise level, you know, using Orchestrate X?

Yeah, yeah. For me, I think it's trying to find the business problem. So when we go. And it's usually not like, hey, here's a cool story about some crazy thing we did that y' all should try it too. It's typically like companies are going through their transformation journey already and they might, you know, they could be automation, could be generative AI. They're making these choices. So for me, we typically will try to figure out what's the one area of their business that'll have the largest impact. Right. And we will work with the client to build that out. In IBM, we have something called client engineering, which is our investment. So it's no cost to the client, but it's to prove out a quick, quick win. So exactly to your point. So Orchestrate can be used for many different lines. So a lot of the quick wins I've seen have been in, like, talent acquisition, hr, although obviously there's all kinds of marketing operations. But for me, like, it's. It's the. After the first win happens, it's seeing that spread throughout the organization. So, for example, our first use was in hr and it was for the promotion use case. They picked one use case for all of IBM HR and that now is spearheaded. I think they're up to almost 20 different things that they're built out for different processes. So while not everyone was, you know, all on the same page and not every, not obviously not everyone in IBM's like, yes, gen AI is going to be great and it's going to do everything for us. It's going to be a game changer. There are some skeptics, there are some people who are more positive to change, some people who are more fearful of change. But by having that initial win, getting people to use it and see the value really transformed the organization's readiness to be up and excitement to leverage. So it's hard to say one specific line of business really. We try to figure out where in their business they're currently trying to transform. And that's where we try to align the automation and generative AI capabilities.

Yeah, and Ben, we've covered so much, you know, not just, you know, the Watson AX and the Orchestrate platform, but Gen AI in general. And we talked about some of the, you know, the challenges of, you know, setting something like this up in an organization and then we talked about some of the wins on the backside. But you know, maybe for someone who is in an enterprise company, maybe they're, you know, a department had and they're looking to figure out, you know, gen AI and how to push it forward and how to actually use it, maybe. What is that one, you know, kind of piece of advice that you want people to know moving forward on, hey, here's how to make sense of Gen AI. You know, maybe not just like, oh, use this platform.

Right.

But I think that's a good, good answer. But you know, what, what can you tell people to say, hey, this is how you can make Gen AI work in enterprise and get some of those big wins?

Yeah, yeah, great question. And I meet a lot of these enthusiasts at different conferences and people have so much excitement and I've seen some do a really good job of working through their company systems and processes to kind of getting more support for the, for these. So I would say work with the vendors that you're talking to. And like IBM, a big part of what we do is help put those stories together and make it easy for them. Right. So we're not just, you know, we're happy to talk to someone at a lower level who's interested, excited, wanting to learn. And we do a lot of these different events all over the world. In fact, we're actually committed to upskilling 2 million people around the world from diverse communities on AI. Right? So it's not just about the decision maker. It's, hey, you're an influencer, even if you're at a lower level. You know, don't let that discourage you. And I've seen some tremendous, I've seen people get promoted already because of their passion on AI. And companies are, you know, even if they're not loud and they're not telling all their stockholders about it, they're thinking about this, right? So there will be promotion opportunities, there will be internal mobility, and, you know, you'll be surprised what a vendor can do to help you with your own personal career journey, whether that's a keynote, right? Like we've had a client on a giant keynote in front of thousands of people like that can absolutely spearhead your own personal career. So I'd say it's, be patient with your organization, try to learn the processes of your organization and try to build a story that's about business results. So it's not about some exciting technology. It's, hey, you know, here's a story about a similar type of company that had a similar problem that leveraged this and they transformed the experience. And here is the outcomes, here's the revenue they generate, here's the time savings. And when you can, you know, when you can put that tight story together with real proof points, it's hard for your leadership to turn that away. So take chances, work with your vendors and, and realize this is an amazing opportunity to get promotions and advancement in your career.

Hey, that's what we talk about every day on the Everyday AI Show. We say if you want to grow your company, if you want to grow your career, you have to learn and leverage generative AI. And Ben, you helped us do just that today. So thank you so much for joining the Everyday AI Show. We really appreciate it.

Thanks for having me. It's been great.

Hey, and as always, there was a lot there. There was so much. So make sure to go to your Everyday AI dot com. Sign up for the free daily newsletter. Here's why. Not only do we cover what's going on in the world of news and trends and tools and all that good stuff, but we break down each and every conversation. So if you didn't catch everything, maybe you're driving in the car or walking your dog. While you're listening to this podcast, make sure to go read today's newsletter because we're going to have a lot more from what Ben was talking about, more things in the Watson Next platform, and more about what we're doing at Everyday AI. So I hope to see you back again for another episode. Thanks, y'.

All. Thanks.

And that's a wrap for today's edition of Everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going for a little more AI magic. Visit youreverydayai.com and sign up to our daily newsletter so you don't get left behind. Go break some barriers and we'll see you next time.