Everyday AI Podcast – An AI and ChatGPT Podcast - Ep 420: Why scaling AI is a people-first challenge with Lenovo's Rick Kreuser

Episode Date: December 11, 2024

You think AI is a technical implementation? 🤔  Nope.  It's about the people. Join Jordan and Lenovo's Rick Kreuser to find out why scaling AI is actually a people first challenge, not a technical one. Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Ask Jordan and Rick questions on AIUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTopics Covered in This Episode:Misconceptions about AI scaling as a technical challengeIntroduction to Lenovo's AI Center of ExcellenceImportance of defining business outcomes in AI implementationAI as a people-first challenge and its implicationsThe 'unlearning' concept in AI adaptationThe impact of AI on productivity and workforceImportance of transparency in AI adaptationDiscussing Lenovo's internal AI use case (Studio AI)Future predictions for AI: on-device AI and multi-agent environmentsAI's impact on the future of workConcepts of responsible AIBrief overview of Lenovo's AI policy and governance committee.Timestamps:00:00 Visit website for free AI content and resources.04:24 Start AI with a clear outcome in mind.07:21 Embrace AI to transform and enhance work.11:52 Leadership alignment is crucial for AI implementation.14:20 Personal productivity successes not always shared internally.19:30 Organizations must consider the human side when scaling AI.21:10 AI now dominates US market's top companies.24:32 On-device AI becomes accessible; alternatives emerge.27:04 Sign up for informative AI newsletter recap. Keywords:Scaling AI, technical challenge, generative AI, inference cost, organization, people first challenge, AI in businesses, daily podcast, livestream, AI content, AI news, AI center of excellence, leadership, productivity, efficiency, Lenovo, technology, security, process, responsible AI, AI discover, AI policy, governance, AI outcomes, AI deployment, on-device AI, language models, motorola, business strategies, AI journeyEp 420: Why scaling AI is a people-first challenge with Lenovo's Rick Kreuser Send 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 in 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 think that scaling AI is a technical challenge, I'm going to go ahead and say you're kind of wrong.

I mean, here we are, you know, two years into the generative AI wave. Yes, AI has been around for many decades. But the generative AI way is still, the wave is still very new. People are still riding that wave and trying to surf it. But so many people think it is a technical challenge. What model? They're worried about inference costs. They're worried about all these things, but if you actually want to scale AI in your organization and get it to work for you, let me tell you something. It is a people first challenge. I'm excited to be talking about that today and a lot more on everyday AI. What's going on y'all? My name's Jordan Wilson and I'm the host in this thing is for you. It is your daily live stream podcast and free daily newsletter helping everyday people like you and like me not just keep up with AI. It helps us get ahead, right? We say this is how you outsmart the future with our daily podcast, live stream, and free daily newsletter. So if you are tuning in for the

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This is technically a pre-recorded show, but we're debuting it live. All right. Enough chit-chat, y'all. So I am excited for our guests for today. So please help me welcome to the show. We have Rick Cruiser, who is the AI. Center of Excellence Director at Lenovo. Rick, thank you so much for joining the Everyday AI show.

Hey, thanks, Jordan, for having me. It's a pleasure to be here. Looking forward to talking through everything AI. Oh, can't wait, can't wait. So this is going to be a fun conversation. I have hot takes on this one, Rick, but I'll leave the insights to you. But, you know, before we dive into deeply, first, let's actually start. I mean, probably mostly everyone knows Lenovo, but for those that don't, maybe tell us

a little bit about Lenovo, right? but most people are probably listening to this on a Lenovo laptop probably yeah it's well everybody i think knows Lenovo and think pad and Motorola and things like that we're a you know a 70-odd billion dollar what people know us as is a device company but we're trying to working with that to sort of reinvent ourselves around outcomes and services and different things like that so we're pivoting so we can help our clients with uh with business problems not just being a device supplier so yeah absolutely And I'd say that's pretty much a trend, right, in the industry.

Like any company that you would think is hardware from the 90s, you know, they're obviously on the service side as well because their clients need it. But Rick, maybe can you tell us a little bit about what the AI Center of Excellence even is? Sure. And the Center of Excellence was founded a couple years ago. And really what it does is it tries to bring the best of one another to our clients. So we really start by understanding devices. And if you think about AI, there's a lot of layers, right? There's devices.

There's infrastructure. There's organization or orchestration. There's data. There's LOMs all the way up to stack to use cases and change management. We try to bring the best of all of that to our clients because every client sort of presents and solve a different way. Everybody's got a different reality. And you have to find a way to meet clients where they are.

And that's at our core, that's what we did. And so you said it's been out for a couple of years now, the AI Center of Excellence. And so I'm wondering, what have you all learned so far, right? Because I'm sure you've seen, you know, certain enterprise clients adapt very well. And then I'm sure there's been some that may have taken a little longer to get up to speed. But what have you all learned so far in that experience? There's a ton there, which is we explored the depths of complicated things, costly things, et cetera. But really what it comes down to is we've learned first and foremost, you have to start AI with an outcome.

If you're building AI for the technical experiment, okay, that's an outcome. That's great. You learn something. You did some technology. What is the business outcome we're trying to do? What are we trying to achieve? And that's probably the biggest thing that people forget is they start doing a use case or an experiment. And they get into it and go, okay, what we were trying to do again?

We kind of missed the boat. What is the business going to say about this in a year? So that's probably the biggest learning that we have amongst millions. And, you know, let's just jump to the end here, right? Let's give people what they came for, right? I made a claim in the beginning that I think, me personally, that people sometimes think that, you know, AI success is all about the technical implementation. Is that right?

Or is it more of a people first challenge? It is people first challenge. And I can tell a quick story about this that illustrates that. So I was working for a big tech. company, consulting for them. And they had access to a ton of data. So what they basically said was, hey, with all the access to data that we have, we can actually create a next best move system. And that's telling the salespeople, you know, on Tuesday, you should go see Joe with presentation number two and get these entitlements, you know, figured out. And so we built the tech. The tech worked.

It took about a year to build it. But we built the tech. And you said, okay, great. Now the tech works. it improved sales closure rate by 30%. So we took that exact same solution out to the field and said, okay, how fast can we implement this? And we got to the field sales leader, and he said, that's great. We'll follow exactly what the computer tells us to do. However, you're going to retire my sales quota right now for the year.

Because if you say the computer knows better than I do, what I should be doing with my clients, I'm no longer responsible for my quota. It took two years to unwind the people side of it, policy in the process. That's one example where if you think about the outcome and the people upfront, you'll scale much more quickly. That's an example of it. That's deep, right? I could talk about that for hours, but I agree. And I think it's something that I always like to refer to as this concept of unlearning, right? We have to kind of unlearn human behaviors. We have to

unlearn good habits. You know, what are maybe some of the challenges on, on the people side, right? I think on the technical side, most people know the challenges of AI, right? It's inference cause. It's compute. It's on-prem versus off-prem. Data security, all this. What are the people challenges?

People challenges are, there are a lot of them. So let's start at the very top level. AI changes the way people work in your job, whether it's 2% or 70%, or 100% or 50% it's going to change the way you do your jobs and I can give you just a quick anecdote tells you if you use AI, your chat GPT, whatever your engine is this year,

whatever it is. If you use that and just use it as a replacement for your Google search bar, you will not change the way you work and you will not get the benefits of it. However, if you embrace the change and surf as the analogy goes and use it to change the way you approach your work,

the way you deliver your work, the productivity gains and the scale are infinitely better if you go better that way. But if you just use your Google search bar, it is going to become your Google search bar. That's it. That's a great way to think about it, right? Because I think, yeah, even with, you know, chat GPT as an example, right? They just came out with a Chrome extension and that's what they're trying to do, right? They're trying to replace your traditional search with, you know, chat GPT or using perplexity, which I think is a great entry point for maybe AI skeptics to learn about the potential time savings, but how can us humans take it a step further, right?

And what does that scaling AI actually look like from the human side past that first step? And I think you have to get into the actual work that's getting done. So we all go to work and we work in a department, everybody's got a department of some kind. And really, if you just take it individually, you're gonna get your 20, 30, whatever percent productivity

by using chat GPT or whatever your tool. perplexity, pick your tool. But if you really take it at the work group level, how people interact with each other with the benefits of AI, I think the productivity and the benefits are multiplied. The quality is multiplied. And I think that comes down to an organizational change management mandate or a change management mandate, if you will. We spend a lot of time with customers talking about adoption. How can I increase adoption? I'll just give you a perfect for instance. Let's say you used ChadGBT and you were 30% more productive on a daily basis. Did your boss know that?

Did you tell your boss that? Probably not. So how do you realize those gains? How do you understand them? That's very much a human thing because you're asking a human to either admit they're more productive and take on more work potentially or whatever, or you're saying keep it to yourself. This is purely a change management and a human thing.

you have to understand the impact that it has on humans and have a way of seeing it transparently. I literally, Rick, had this conversation with someone from EI recently, you know, about, hey, once you do get that 30 to 40 to maybe 50% personal efficiency gains, you know, people are wondering, why is in that leading to even a 10% 20% measurable boost in revenue? Why can't you tie that, you know, ROI of a. AI. Why is it? Is it because maybe those that are finding the most use out of AI are maybe keeping it to themselves, not sharing with their team. We see that in space. And I think I think the solution is, honestly, transparency and trust. You have to be transparent with your people about what you're

trying to achieve, the outcomes that you're looking for, and enroll them in the process. And if you enroll them with a sense of trust where they understand the outcomes, they're much more likely to share their realities with you, whether it's gains, whether it's productivity, whether it's, hey, I got another three hours this week. Give me some more work or let me go on vacation or I'm going to go take another coffee break. But it all comes down to trust and engendering trust and being transparent about the outcomes you're looking for. Because the fastest way to the bottom here is tell somebody you use AI, have them get productivity and then cut 30% of your workforce. That's how you will not scale AI.

So what should what should business leaders be looking at? Because I think Rick, what you described there is, yeah, I've seen a lot of enterprise companies fall in that trap, right? They find some productivity gains. They say, oh, we don't need to hire new people. We say AI a lot in our earnings call. Stock goes up. We lay people off.

Things are fine, right? So how should they be approaching that then? Well, and I think it all comes down to leadership. You have to have your leadership aligned. top to bottom on what you're trying to achieve and how you're going to go about it. At Lenovo, we have an AI policy and a governance committee, which looks at questions like this and says, how are we going to approach this?

And IT has different approaches. They may want productivity and coding. Marketing may want faster collateral production, so we're not using as much agency time, whatever it might be. But I think, getting leadership aligned by how you're going to address the human element of AI. What is it going to do to your workforce? What do you want it to do to your workforce? How is it going to help your team compete better in the business that you're in? And I think if you align on that up front, early in our process, we have something called AI Discover,

which really assesses four elements of everything that we do with AI, which is security, people, process, and technology. So we think about it very early in the process to make sure that you don't get down the line and then either by lack of clarity or lack of focus, you end up making some decision that you don't want to make down the line. Addressed it up front. Be clear. I like that. AI discovery.

So just so our listeners get this right. So you said security, people process and technology, right? Yes. Okay. Is that the order? Because like I'm looking at it. I'm like, that's not a bad order if that's the order, right? Yeah, it is.

I think security is required and I include responsible AI as part of that. But I think that's that's table sticks. You have to have that, period, because otherwise it's not going to, is the technical solution that's not going to work or the people aren't going to believe in it. Like, where's my, when I type my data into co-pilot, where's it go? You have to be able to answer questions like that. And it's just, it's the simple stuff. But people come next because it is, in our view, a people's work.

We have to have people enrolled, whether it's in the design of the AI system, whether it's into deployment, the use, the monitoring of the AI system, people are an integral part of it. And I think it's very commonsensical today with hallucinations, especially in generative AI. You have to have a human in the loop at some level. You have to. It doesn't work by itself. It doesn't today. That's how, that's a reality for better or for worse. You have to have a human in the loop to have it function. Yeah. And Rick, so what you said a couple of minutes, I think is the reality for many companies is, you know, you have a lot of individuals that are finding big productivity gains and maybe or maybe not there, you know, sharing that

with the rest of their department or their higher-ups, right? You know, I'm curious, what have you all found successful or how have you all found success in a similar process of even internally, right? Because I know with clients, you can't always talk about those things, right? But how have you all at Lenovo, you know, taken or, you know, been able to parlay some of that personal productivity success and really have that filter out throughout the rest of the organization? So let me tell a quick story about that because it's one of our use cases. We have an internal use case called Studio AI and basically it's producing marketing collateral. So the old process would have been you get a technical specification for a new computer or a PC, whatever it is.

somebody drafts an idea for a brochure for financial services. It goes to the agency. The agency either does wireframes or creates a mock-up with some kind. It comes back to you. And the back and forth goes back and forth and it takes weeks, weeks, weeks, and months. Lots of agency fees, lots of creative type things. But using AI, internally, we've basically said, hey, you can use Gen AI in this case to push a button. It reads the text back.

It reads the format that you want. It produces it and it translates in 16 languages. And you say, and make it for financial services. And it knows how to weave that in there. 80% productivity gain. But we measured it. And we made sure the people understood that this doesn't mean that you are now, 80% of you are going to lose their job.

It means you can do better collateral production and more event production and better communications with the market. So we've retaken a lot of the effort that was going into man. managing the agencies, and we've redirected that for higher value activities. And that's what we advise our clients to do as well. I mean, they can always pocket the gains. But at the end of the day, if you pocket the gains in terms of cost reduction, the trust factor might not be there for the next time you'd like to make a change to your organization.

So we ask clients to consider it pretty close to. Adobe just introduced an entirely new way to create, bringing the power and precision of its creative suite into one conversational experience. Meet Firefly AI Assistant, now live in the Adobe Firefly app, the all-in-one creative AI studio. Powered by Adobe's creative agent, Firefly AI assistant lets you start with your vision, just describe what you want, and shape the outcome as it takes form with the assistant. The assistant orchestrates multi-step workflows, drawing on 60 plus pro-grade tools across Adobe Creative Cloud apps, including Photoshop, Illustrator, Premiere, Lightroom Express, and more to help

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You stay in the driver's seat as the creative director. Adobe Firefly AI assistant now in public beta. See it today at Firefly. dot adobe.com. It's a great point. Yeah, you can you can pocket the gains, but you know, especially if the gain is only time, right? Sometimes you're not going to pocket any actual gains, right? Actually, what you're gaining is the employee, the personal employee, maybe just has a lot more time on their plate to think creatively, right? But you have to do something with it. You know, let's talk Rick a little bit about responsible AI because I think when we talk about people,

first, you know, after safety, right? I love that, but, you know, at least with scaling AI being more about people than the technology, how does responsible AI play into the equation? Responsible AI is incredibly important. Lenovo actually has a very good story to tell here. But Lenovo has foundation for everything that we do. We have a responsible AI committee that's been in existence for at least five years before my time at Lenovo.

And really, what it does is it takes the policy that we have at Lenovo. And it makes it so that you can execute against that and you have some kind of idea about how it will actually land in the world. Because you've got to have it kind of in three levels. You've got to have the policy. You've got to have the governance and the interpretation of that policy. It's almost like courts, right? You have Congress that makes the laws. Then you have judges that interpret the law.

And then everybody else abides by them because now we have an interpretation of it. The same thing for responsible AI. You have a policy. here's what it means to us in Lenovo or you as a client. And then here are the things that you can go do on a daily basis to stay within those policies and do that. And I think you have to have all three levels and all of them working together. And then it becomes actually something that will allow you to scale much more quickly.

Speaking of scaling quickly, right? And looking at the human side, right? And this being people first. Because I think there's so many different roles, right? If you're a decision maker, right, that people challenge is going to look a lot different than you if you are an entry level frontline worker. How should organizations be having the conversation, having the human side conversation when it comes to scaling AI?

Because, yeah, like what happens when companies all of a sudden realize, oh, wow, once we do roll this out to the, right? So I think a lot of companies in 2024 went from their little pilots and they're like, oh, wow, we're gaining a lot here. What's going to happen next? What happens when AI works? How should different people at different levels be talking about it? Well, and I think it's got to be an ongoing dialogue.

As you know, it changes quickly, what it's capable of, how it deploys, what we use it for, changes weekly, if not monthly, whatever. I think the important part is that at board levels, that you have alignment on how you're going to have that dialogue with your people. I don't think there's one formula that says, okay, here's how you do it. Clunk, it lands on your desk and you do it that way. I think it is legitimately a cultural issue within a company about this is how we want to engage our people through this journey.

And you have to, and that's a complicated, it's a complicated dialogue. It's messy. There's probably not an easy button. But I legitimately think the value is in having a good, clean dialogue with your people. transparent from top to bottom. Yeah. Transparency is huge, right? You know, speaking of top to bottom and transparency, I think, you know, here we are, you know,

two full years into this generative AI wave, right? A lot of people point at, you know, the introduction of chat GPT as the start of the wave, including, you know, Nvidia CEO, Jensen Wong said that. But, you know, as we look at the next two years, I'm not going to ask you to look into a crystal ball, but I want to get your point because I think a lot of, of people had hesitations, right? They thought large language models in AI were going to be a fad, and they're like, let's sit this one out, right? You can't sit it out anymore, obviously, right? I think for the first time in U.S. history, you have the top six companies by market cap in the U.S. all in the same

sector, which has never happened. They're all, you know, working on AI. So what as companies now are looking forward in planning for a future where AI is inevitable, how should they be planning that future out because the technology scales so quickly and changes so quickly. But the people's fears, hesitations or question marks around AI are only going to, I think, increase and grow. I tend to agree with you. I think things will be getting more complicated and easier all in the same motion. So things that we think are complicated today will get more, they'll get easier. Right now you have for Gen AI specifically, you have to turn wrenches at all level. of your staff to get it to get it to work.

That will get easier. You can see that because people are putting out platforms. Lenovo will put it out of platform. But it gets all the components for you to work together. Yet it will continue because you're going to get more multimodal. You're going to get more NLP. You're going to get bigger models. And you're going to get agents.

And that will actually bring to a head to people side of it. because an agent is, in essence, somebody that goes and does a task for you. So think of yourself as the quarterback of all these agents. That's a different skill set than actually what people do today. So you're going to have to be able to assemble your workforce consisting of people and agents to do all this things. So the people side of it is actually going to get magnified as you go. Or some things get simpler. Some things are going to get more complicated.

Yeah. I think that's a great way to approach it, right? Yeah. Things that we maybe thought two years ago. would be so complicated, are going to seem so simple now. You know, speaking of new technology, right? You just talked to Rick there about agents and, you know, everyone's talking about on-device AI, right?

So a couple of weeks ago was at the Microsoft Ignite conference. I got to see a lot of cool tech from Lenovo. I got to see a lot of cool stuff yet to be released from Microsoft. But it seems like the future of work is going to very quickly change, right? And maybe two of those big changes are what we just referenced there. always having AI on your device in some way, shape or form, whether you're using a Lenovo laptop or something else, and then a lot of agents and probably multi-agent environments. Are those kind of too safe kind of assumptions to make about the future of work?

And if so, how can the average non-technical human even look at those kind of two different challenges? Because I think they're pretty radical, actually, when it comes to hands-on keyboard. Yeah, I think those are two things you can kind of bank on over the next two years, which is on-device AI, as well as people trying to figure out how do I do on-device AI. Today, it's complicated. It'll get easier. But I'll just give you a perfect, for instance, the days of going to chat GPT as the one and only option you have, and you can substitute any, but any other, you know, Azure or whatever, AWS, substitute. any of them in the days of going there is a one stop or a place this is your only option are dying you will have the option to inference in the cloud on prem or on your your laptop depending on the language model language models are getting smaller so now they fit on machines so i think the optionality is coming

there and i think there's going to have to be a whole ecosystem that develops to figure out how to manage that optionality because it's very expensive to use chat and dccD for everything for instance Yeah. Great points there. Yeah, like I think that where AI happens is always changing. And I think it's going to be faster and, you know, more and more things are going to be happening on device than, you know, than we thought a couple of years ago would have been possible. So I mean, Rick, we've covered a lot in today's conversation, you know, both on why and how a scaling AI is actually a people first challenged. And we've actually talked about some of the technology side as well. But, you know, as we up today's conversation. What's your one most important takeaway for people, at least when we talk about scaling AI being a people challenge? For people, be intellectually honest and realistic with yourselves as you start out on your AI journey about the outcomes that you want and how the people are going to play into that, whether that's at an organizational level, a project level, a use case level, or even your employee or customers, and how they're going to be.

involved. Consider it early because it deserves the attention and it will make the journey on the back half of deploying AI at scale much easier. Great advice from someone that knows. Rick, thank you so much for taking time out of your day and joining us and helping us all better realize why scaling AI is actually more of a people first problem than a lot of us think. Thank you for your time. We appreciate you coming on the show. Thank you, Jordan. It was great being here. Love the conversation. And I'm sure we'll talk soon. All right. Hey, everyone, that was a lot of great knowledge that Rick just dropped on us.

If you couldn't catch it all, don't worry. We've got you. If you haven't already, please go to your everyday AI.com. Sign up for our free daily newsletter. We're going to be recapping everything that we just talked about in today's conversation, as well as giving you a lot more information. So you can take what you learned and take it one step farther. Because learning things isn't enough.

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