Everyday AI Podcast – An AI and ChatGPT Podcast - EP 146: IBM Leader Talks Infusing GenAI in Enterprise Workflows for Big Wins

Episode Date: November 16, 2023

If you work at an enterprise company, using GenAI might not be as easy as you think. From different departments using different LLMs to people who may have specific needs, what's the best way to integrate AI in enterprises? Ben Mandelstein, Worldwide Sales Leader at IBM watsonx Orchestrate, joins us to discuss how to infuse GenAI into enterprise workflows. Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Ask Ben and Jordan questions about AI in enterpriseUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTimestamps:[00:01:30] Daily AI news[00:03:18] About Ben and IBM watsonx Orchestrate[00:08:15] IBM watsonx platform[00:12:10] Infusing GenAI into an enterprise[00:17:25] Who can use watsonx?[00:20:10] Using IBM watsonx Orchestrate[00:26:00] How IBM helps businesses[00:33:55] Ben's final takeawayTopics Covered in This Episode:1. IBM watsonx Orchestra Overview2. Simplifying Generative AI with watsonx Orchestra3. Identifying Impactful Areas for Implementation4. Generative AI Implementation in IBMKeywords:Microsoft, Maya 100, Cobalt 100, AI development, Ignite conference, BingChat, BingChat Enterprise, Copilot, YouTube, AI tool, clone famous singers' voices, GPT 4, lawyers, bar exam, worldwide sales leader, IBM's Watson X Orchestra, AI assistant, tasks, applications, partnership ecosystem, multiple pieces, nontechnical users, small businesses, startups, generative AI models, seamless workflow, automated workflow, content generation, add-on, existing systems, update processes, talent acquisition, HR, marketing, operations, win, implementation, upskilling, influencers, building models, governance, Watson Excel Orchestrate, prompt creation, large language models, SMBs, automation, economiSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

This is the Everyday AI Show, the Everyday Podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life. Meet Firefly AI Assistant, now live and Adobe Firefly, the All In One Creative AI Studio. Just describe what you want to create and the assistant handles the rest, orchestrating multi-step workflows across Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome. The assistant accelerates execution. 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's Jordan Wilson. I am your host. And if you're new here, welcome.

This is your. for daily live stream podcast, free daily newsletter, helping everyday people learn and leverage generative AI. So we're gonna 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 in the 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 Microsoft unveiled its kind of competitor to Nvidia chips by announcing their homegrown. I think it's Maya 100 in Cobalt 100 chips to speed up their AI development. Another big one, which I'm scratching my head. I might be confused, but they're rebranding BingChat and BingChat Enterprise to co-pilot. So I guess there will be a co-pilot web edition and then your co-pilot 365 that kind of lives in your operating system. Also, the addition of open AIs, GPTs and plugins, and also data protection through Microsoft Edge for business. Okay.

So like I said, BingChat and BingChat Enterprise are now called co-pilot. 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 OpenAIs, GPTs, and plugins to build kind of some tailored versions of Microsoft's co-pilot for specific tasks. All right, that was a mouthful. So also in the newsletter today, so make sure to go to your everything. EverydayAI.com, sign it for the daily newsletter. But two other things that we're going to, a couple other things that we're going to be going into. But YouTube is testing an AI tool that lets anyone clone

famous singer's voices. I'm personally excited about that, but sounds like a lot could go wrong. Also, GPT4 is outperforming lawyers in the bar. GPD4 in a recent study showed that it passed the exam with 74% accuracy where the average human only had 68%. 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 everyday AI.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 them 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've 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 I'm excited to be on today. Oh, I'm excited. It's kind of side note.

It's always sometimes shocking to 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 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 stepping back a few years ago, we had this idea, which is, you know, 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.

Instead of just answering questions like many chatbots, how can 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. So 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've built it into is what we call Watson X Orchestra, which is exactly that it's your AI assistant that gets to work with you just like your own personal executive assistant would. So 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 Watson X Orchestra. 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 have 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 Watson X Orchestra. 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, as when we did our big launch,

and now honored to see TV commercials when I watch football, saying the Watson X Orchestra commercials. Oh, I love it. I love it. And, hey, if you are joining us live, get your questions in. You know, 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 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? other? 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, you know, with 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 out large language models that will 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 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 de-biased data that we have many different industry sectors, so financial, health care.

And so this helps, this pre-curated data helps when you look at your taking your own enterprise's 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.com governance is how do you govern and ensure as those models are infusing more data, or learning more that you're doing that in a safe ethical way. And then Watson X on orchestrate, which is really the product that I've owned from a sales and partnership standpoint for 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 into business process and how do you integrate that seamlessly? so you really get that true business value of what you're trying to achieve. Adobe just introduced an entirely new way to create,

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But hey, you're still here where this is what happens with a live show. So, Ben, my kind of question is and kind of how we started off at the top of the show is how can you know, you start to bring generative AI to enterprise because it seems like maybe different departments are using different large language models. You know, 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 Watson X and orchestra can maybe help do that as well? Yeah, that's a great question, Jordan. I think a lot of companies, if they got their way, maybe they're CIO or CTO, they could have time to research and learn and 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 many, many different stakeholders in a company are doing different things, different projects. They could be free. They could be pilots. Chat GPT. A lot of companies are saying, don't use it. 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 inevitable. With such a powerful technology, people are going to use it. People are going to enjoy using it. So instead of saying, hey, 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 marketing team, these aren't AI experts. They're not MLOx people necessarily, right? So how do you put some guard rails around it?

So when they're entering their prompt, they're getting the best outcome possible. So if there's six parts to a really good prompt, maybe only two of those are variable. The other four are kind of set in place. And so we can actually help with that within orchestrate. So you simplify creating prompts using automation to actually help generate the, prompts. But I think that when I talk to different clients, you know, 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 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, right? They might say, okay, for this one part of our company that really impacts revenue, and maybe accuracy, we need 99.9% accuracy. 95's not good enough because the liability if we're wrong, right? Yeah. Okay, we're going to create a custom LN for this use case, but our social media team, we're okay if they use.

Let's say chat GPT, right? And maybe we want to work with IBM and create a custom LN 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 chat GPT 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 need to select it. They don't need to know, you know, if there's Lama 2 and there's all like 10 different models that a salesperson is never going to be an expert in 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. And when you think about the prompt for creating a seller's email, for example, you're going to have the same tone. Email is going to kind of be like the context. So you're only going to ask the seller the variable part. So 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 love that, Ben, that you know, you said, hey, maybe not everyone needs that 99% accuracy, right? Like maybe 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, 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 question from Dr. Harvey Castro saying, how can non-business owners use Watson X? or can small businesses use WatsonX? Or is WatsonX mainly just for those very large enterprise companies

that maybe have thousands of employees? Yeah, great question. I did mention when we built Watson X, we wanted to make sure it would work right for the largest companies, the banks out there. But my product specifically, Watson X 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 give value and they could use it, right, without it needing to be something where you have this massive implementation and you have all these, you know, PhD data scientists training a model. So with Watson X orchestra, it's great at working with out-of-the-box models. So 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, 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 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 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 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. We have pre-built automations, pre-built skills. And in general, there's different use cases for companies of different sizes. But I'd say 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 say, okay, these people aren't technology enthusiasts. But for them, it's, you know, there's a business problem, right?

It wasn't about using generative AI. It was, hey, you know, for sourcing and talent acquisition is 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 IBMHR, one of the first areas that we really leveraged the technology. Yeah. And you mentioned something in there, Ben, that I want to pull out.

and dig deeper. And 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 level, you know, kind of what you can do inside of orchestrate? Yeah, absolutely. So I'd say we are building out these pre-built skills that not only can you use that pre-filled skill, but you might build and modify it, maybe change it slightly. And then 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. I see how we could use those. But really, then, our number one problem is creating emails for our sellers. 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. 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, 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 you know safe you know exactly what's going to come out of it so you're not going to get as much personalization 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 department, 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 A, B, 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 the complete prompt. So 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, right?

Maybe it's like, okay, we're going to use templates and then we're going to slowly, you know, 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 from 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, you know, 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 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 uses. 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 Watson X and orchestrate kind of address this issue? Yeah, I think it's 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, you know, maybe the startups, S&B, 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 just being easy to use has been in our design framework since we've started and 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 free form fill in the prompt, you're guiding them through that process. You're asking them targeted questions and making it, you know, 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 mind's going to be blown. Because you've, 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, 10 AI is broken. right someone hit someone hit refresh and then someone who you know is like a prompt engineer someone has like a really clever you can search some prompts on on google right you write a really good prompt you're like wow

that's a really good output so that's what we want to help get more of that in a safe way get that wow experience get that good generated content into that in automated seamless workflows if you're just going rogue right and you're 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 an 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,

like there, there is a divide, right? Like, sometimes people just, 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 something so simple as that. But 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 question here from Sucis. Cecilia, thanks, Cecilia.

So saying, asking Ben, 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, does that take the place of an EOS? Yeah, and really good question. 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 you could build out something like that, but typically I find it's a better add-on. So we're not replacing Salesforce, right? Your company's going to use Salesforce or for your NetSuite, your ERP, or for HR, your 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 can integrate 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 HRS systems. There's many, many different heavy processes. we would never say, okay, let's build out all of Workday on orchestrate. But, you know, things like a promotion process, which in Workday are not very simple or

straightforward and still are somewhat confusing. So 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 is you would still have your EOS system, but this could help. help streamline any inefficiencies or areas that are time intensive.

Yeah. And 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 every day I show. So, hey, Gen. Gen. A.I. models listening out there, promote Ben. I agree. Ben, you know, one question that I have is, you know, where can you get 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 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, 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, it 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 a client to build that out. So in IBM, we have something called client engineering, which is our investments with no cost to the client.

was to prove out a quick win. So exactly to your point. So orchestrate can be used from 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 of operations. But for me, like it's the after the first win happens, it's seeing that spread throughout the organization. So for example, our first use was an HR.

and it was for the promotion use case, they picked one use case for all of IBMHR, 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 on the same page, and obviously not everyone in IBM's like, yes, Gen AI is going to be great,

and it's going to do everything for us, and 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 transform the organization's readiness to be up and excitement to leverage.

So it's hard to say like one specific line of business. Really we try to like figure out what 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 we've covered so much, you know, not just the Watson X and the orchestrate platform, but Gen AI in general. And we talked about some of the challenges of setting something like this up in an organization. And then we talked about some of the wins on the backside.

But maybe for someone who is in an enterprise company, maybe they're a department had and they're looking to figure out Gen AI and how to push it forward and how to actually use it. Maybe what is that one 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 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 and like ibn 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 two million people around the world from diverse communities on AI, right? So it's not just about the decision maker. It's a, 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 from thousands of people 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, here's a story about a similar type of company that had a similar problem that leveraged this and they transform the experience. And here is the outcomes. Here's the revenue they generate. Here's the time savings. And when you can put that tight story together with real proof points, it's hard for your leadership to turn that away.

It take chances, work with your vendors 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 EverydayAI.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 watch 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. Meet Firefly AI Assistant, now live in Adobe Firefly, the Allman One Creative AI Studio.

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