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Scaling AI: Unveiling the People-First Challenge
In the rapidly evolving digital landscape, AI is often mistaken as a technical obstacle, a challenge involving models, inference costs, and implementation details. Yet, two years into the acceleration of generative AI, a new perspective is emerging. The scalability and the effectiveness of AI in an organization is not a question of advanced algorithms or powerful machinery. Rather, it's a challenge centred around people.
Change management, new roles, and cultural shifts are indicating a paradigm where AI becomes a tool augmenting human intelligence. The first challenge when scaling an organization's AI pursuits involves nurturing a workforce equipped and ready to leverage this technology for significant improvements.
People First: Why AI Success Is About More Than the Algorithm
The scaling of AI technology isn't a solo performance exclusively led by data scientists or IT professionals. Instead, it requires an ensemble of your entire workforce, spanning across departments and hierarchical structures, all learning to converse in the language of AI.
Building and leveraging AI pivots on the principle of people-first. Shaping a productive AI-based organization demands a robust understanding of how AI will change people’s jobs, their workflows, and their interactions with each other and the technology. Successful AI integration necessitates a recalibration of corporate culture around transparency and trust.
From Outcomes to Adoption: Breaking Down the ‘People Challenge’
Starting with an outcome-based approach is pivotal. Each entity needs to explicitly define what business outcome it seeks through the integration of AI. Once this purpose is clear, the organization needs to focus on how to achieve these outcomes. This involves examining how people interact within the organization and leveraging AI to enhance these interactions.
However, ensuring a productive workforce in an AI-centric environment means overcoming several challenges. One of them is how to increase the adoption of AI tools and how to translate personal productivity gains into measurable increases in efficiency and revenue across the organization.
The process demands transparency - openly sharing the objectives of the AI implementations, managing expectations correctly, and maintaining an open dialogue throughout the journey. Leaders must engage their teams, taking into account the human impact of AI, and strategically plan their path forward.
Responsible AI and The Future of Work
As the AI wave continues to rise, the principles of responsible AI rise with it. At the forefront of the conversation about the future of work with AI, new strategic relations will include agents and the integration of on-device AI. Organizational leadership needs to align on these implementations and steer the conversation towards transparency and trust.
The road ahead isn't without its obstacles. New AI technologies will likely complicate the process initially before making things easier. However, with the right approach - one that puts people first - organizations stand poised to reshape their business landscape, navigating the AI wave successfully.
Topics Covered in This Episode
- Misconceptions about AI scaling as a technical challenge
- Introduction to Lenovo's AI Center of Excellence
- Importance of defining business outcomes in AI implementation
- AI as a people-first challenge and its implications
- The 'unlearning' concept in AI adaptation
- The impact of AI on productivity and workforce
- Importance of transparency in AI adaptation
- Discussing Lenovo's internal AI use case (Studio AI)
- Future predictions for AI: on-device AI and multi-agent environments
- AI's impact on the future of work
- Concepts of responsible AI
- Brief overview of Lenovo's AI policy and governance committee.
Podcast Transcript
Jordan Wilson [00:00:16]:
If you think that scaling AI is a technical challenge, I'm gonna go ahead and say you're kind of wrong. I mean, here we are, you know, 2 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 cost. 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.
Jordan Wilson [00:00:53]:
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 is Jordan Wilson, and I'm the host, and this thing is for you. It is your daily livestream 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 first time, awesome. You are in the right place.
Jordan Wilson [00:01:24]:
If you're listening to the podcast, we appreciate that. Make sure to check out the show notes. You'll see a link to our website at your everydayai.com. On our website, it is like a free generative AI university. So you can sign up for our newsletter there where we will be recapping today's episode, as well as we literally have thousands of hours of free AI content on our website from leaders in the world, in the AI space. Whatever you wanna learn about, we have it there. So, if you are tuning in for the AI news, which I know a lot of you do, we're gonna have in our newsletter. This is technically a pre recorded show, but we're debuting it live.
Jordan Wilson [00:01:59]:
Alright. 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.
Rick Kreuser [00:02:17]:
Hey. Thanks, Jordan, for having me. It's a pleasure to be here. Looking forward to to to talking through everything AI.
Jordan Wilson [00:02:23]:
Oh, can't wait. Can't wait. So this is gonna 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 in too 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.
Rick Kreuser [00:02:44]:
Yeah. It's well, everybody, I think, knows Lenovo and ThinkPad and Motorola and things like that. We're we're a, you know, a $70 odd $1,000,000,000. What people know us as is a device company. But we're trying to we're working with that to sort of, reinvent ourselves around outcomes and services and different things like that. So we're we're pivoting so we can help our clients with with business problems, not just being a device supplier. So
Jordan Wilson [00:03:09]:
Yeah. Absolutely. And I I'd I'd say that's pretty pretty much a trend, right, in the industry. Like, any any company that you would think is hardware from, you you know, the nineties, you you know, they're 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?
Rick Kreuser [00:03:27]:
Sure. Center of Excellence was, was founded a couple years ago. And, really, what it does is it tries to bring the best of Lenovo 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.
Rick Kreuser [00:03:46]:
There's data. There's LOMs, all the way up the 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 themselves 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.
Jordan Wilson [00:04:04]:
And so you said it's it's it's been out for a couple of years now, the a AI Center of Excellence. So I'm 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 to get up to to speed. But what have you all learned so far in that experience?
Rick Kreuser [00:04:24]:
There there there's a ton there, which is we we we explored the depths of complicated things, costly things, etcetera. But, really, what it comes down to is we we've learned, 1st 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 learned 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 we kinda missed the boat.
Rick Kreuser [00:05:01]:
What what is the business going to say about this in a year? So that's probably the biggest learning that we have amongst 1,000,000.
Jordan Wilson [00:05:09]:
And and, you know, let's just jump to the end here. Right? Let's 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?
Rick Kreuser [00:05:32]:
It is people first challenge. And, I can tell a quick story about this, that 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 2 and get these entitlements, you know, figured out. And so we built the tech. The tech worked.
Rick Kreuser [00:06:03]:
Took about a year to build it, but we built the tech. And he said, okay. Great. Now the tech works. It improves 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 we got to the field sales leader, and he said, that's great. We'll we'll follow exactly what the computer tells us to do.
Rick Kreuser [00:06:23]:
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 2 years to unwind the people side of it, policy and the process. That's that's one example where if you think about the outcome and the people upfront, you'll you'll scale much more quickly. That's an example of it.
Jordan Wilson [00:06:48]:
That's that's deep. Right? Like, I could I could talk about that for for hours, but I agree. And I think it's something that I always like to refer to as this concept of 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 of AI, right? It's inference cost, it's on prem versus off prem, data data security, all this. What are the people challenges?
Rick Kreuser [00:07:21]:
People challenges are are there there are a lot of them. So let let's start at the at the very top level. AI changes the way people work in your job, whether it's 2% or 70% or a 100% or or 50%. It's going to change the way you do your jobs. And from a and I can give you just a quick anecdote that tells you. If you use AI, your chat GPT, whatever your your your engine is, this year, you know, 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 serve, 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 are infinitely better if you if you go about it that way.
Rick Kreuser [00:08:09]:
But if you just use it as your Google search bar, it is going to become your Google search bar. That's it.
Jordan Wilson [00:08:15]:
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 the Chrome extension, and that's what they're trying to do. Right? They're trying to replace your traditional search with Yep. 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 us humans take it a step further? Right? And and what does that, you know, scaling AI actually look like from the human side past that first step?
Rick Kreuser [00:08:45]:
And I think you have to get into the actual work that's getting done. So we we all go to work, and we work in a department. Everybody's got a department of some kind. And and it's really if you just take it individually, you're gonna get your 20, 30, whatever percent productivity by using chat g p k 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 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.
Rick Kreuser [00:09:24]:
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 chat gbt 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 how do you realize those gains? How how do you understand them? That's 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.
Jordan Wilson [00:10:08]:
Mhmm. 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
Rick Kreuser [00:10:19]:
Right. You
Jordan Wilson [00:10:20]:
know, people are wondering why isn't that leading to even a 10%, 20% measurable boost in revenue? Why can't you tie, that, you know, ROI of AI? Why is it? Is it because maybe those that are finding the most use out of AI are maybe keeping it to them themselves, not sharing
Rick Kreuser [00:10:38]:
with their beliefs. We see that in spades. 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 3 hours this week. Give me some more work, or let me go on vacation, or I'm gonna go take another coffee break. You get but it it all comes down to trust and engendering trust and being transparent about the outcomes you're looking for.
Rick Kreuser [00:11:18]:
Because the 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 is that's how you will not scale AI.
Jordan Wilson [00:11:31]:
So 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 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.
Jordan Wilson [00:11:49]:
Right? So how should they be approaching that then?
Rick Kreuser [00:11:52]:
Well, and 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 a 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 encoding. Marketing may want faster collateral production, so we're not using as much agency time, whatever it might be. But I but I think aligning 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's it gonna help your team compete better in the in the in the business that you're in? And I think if you align on that upfront, early in our process, we have we have something called AI Discover, which really assesses 4 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.
Rick Kreuser [00:12:57]:
And then either by lack of clarity or lack of focus, you end up making some decision that you don't wanna make down the line. Address it upfront. Be clear.
Jordan Wilson [00:13:07]:
I like that. AI discovery. So just just so our listeners, get this right. So you said security, people, process, and technology. Right?
Rick Kreuser [00:13:14]:
Yes. Okay.
Jordan Wilson [00:13:15]:
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?
Rick Kreuser [00:13:19]:
Yeah. It is. I I think security is required, and I include responsible AI as part of that. But I think that's that's table stakes. You have to have that, period. Because, otherwise, it's not gonna either the technical solution is not gonna work or the people aren't gonna believe in it. Like, where's my when I type my data into Copilot, where does it go? You have to be able to answer questions like that. I mean, it's just it's it's the simple stuff.
Rick Kreuser [00:13:43]:
But people come next because it is, in our view, a people's work. We we have to have people enrolled, whether it's in the the design of the AI system, whether it's in the deployment, the use, monitoring, of the AI system, people are an integral part of it. And I I I think it's very common sense of all 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 does today. That's our that's our reality for better or for worse.
Rick Kreuser [00:14:15]:
You have to have, a human in the loop, to have it function.
Jordan Wilson [00:14:20]:
Yeah. And and and, Rick, so what you said a couple of minutes ago, 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? Yeah. 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?
Rick Kreuser [00:15:02]:
So, let me 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 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 or creates mock up of some kind, comes back to you. And the back and forth goes back and forth, and it takes weeks weeks weeks months. Lots of agency fees, lots of lots of creative type things.
Rick Kreuser [00:15:42]:
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 then it translates into 16 languages. And you'd say, and make it for financial services, and it knows how to weave that in there. 80% productivity again. 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.
Rick Kreuser [00:16:10]:
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 managing the agencies, and we've redirected that for higher value activities. And that's what we 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, to make a change to your organization. So we ask clients to consider it pretty closely.
Jordan Wilson [00:16:47]:
Hey. This is Jordan, the host of Everyday AI. I've spent more than a 1000 hours inside Chat GPT, and I'm sharing all of my secrets in our free prime prompt polished Chat GPT course that's only available to loyal listeners like you. Here's what Lindy, who works as an educational consultant, said about the PPP course.
Jordan Wilson [00:17:26]:
Everyone's prompting wrong, and the PPP course fixes that. If you want access, go to podpp.com. Again, that's podpp.com. Sign up for the free course and start putting ChatGPT to work for you. 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.
Jordan Wilson [00:17:55]:
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 use 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?
Rick Kreuser [00:18:23]:
Responsible AI is incredibly important. And we Lenovo, actually, that's a very good story to tell here. But, Lenovo has a foundation for everything that we do. We have a responsible AI committee that's been in existence for at least 5 years, before my time at Lenovo. And, really, what it does is it takes the 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'll actually land in the world. Because you gotta have it kinda in 3 levels. You gotta have a policy. You gotta have the governance and the and the interpretation of that policy.
Rick Kreuser [00:18:57]:
It's almost like courts. Right? You have congress that makes the laws, then you have judges that interpret law, and then everybody else abides by them because now we have an interpretation of it. Same thing for 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 to stay within those policies and do that. And I think you have to have the all three levels and all of them working together, and then it becomes actually something that will allow you to scale much more quickly.
Jordan Wilson [00:19:30]:
Speaking of scaling quickly, right, and and looking at the the the human side, right, and this being, people first. Because I think there's so many different roles. Right? If if you're a decision maker, right, that people challenge is gonna 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 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 we're we're gaining a lot here. What's gonna happen next? What happens when AI works? How should different people at different levels be be talking about it?
Rick Kreuser [00:20:19]:
Well, and I think it's it's gotta be an ongoing dialogue. As you know, it changes quickly. What what what it's capable of, how it deploys, what we use it for changes weekly, if not monthly, whatever. I 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 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 wanna engage our people through this journey.
Rick Kreuser [00:20:54]:
And you have to and that's a complicated it's a complicated dialogue. It's messy. It's there's probably not an easy button. But I I I would generally think the value is in having a good, clean dialogue with your people, transparent from top to bottom.
Jordan Wilson [00:21:10]:
Yeah. Transparency is huge. Right? You know, speaking speaking of, top to bottom and transparency, I think, you know, here we are, you know, 2 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 Huang said that. But you know, as we look at the next 2 years, I'm not gonna ask you to look into a crystal ball, but I wanna get your point because I think a lot of people had hesitations. Right? They thought, large language models and AI were gonna 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 US history, you have the top 6 companies by market cap in the US, all in the same sector, which has never happened. They're all, you know, working on AI.
Jordan Wilson [00:21:57]:
So so what as as companies now are looking forward and 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.
Rick Kreuser [00:22:19]:
I tend to agree with you. I I think things will be, get more complicated and easier all in the same motion. So things that we think are complicated today will get more it they'll get easier. Right now, you have for Gen AI specifically, you have to turn wrenches at all level of of your stack 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 out a platform, but it gets all the components for you to work together. Yet, it will continue because you're gonna get more multimodal.
Rick Kreuser [00:22:54]:
You're going to get more NLP. You are gonna get bigger models, and you're gonna get agents, and that will actually bring you to a head the 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 these things. So the people side is actually gonna get magnified as you go. Where some things get simpler, some things are gonna get more complicated.
Jordan Wilson [00:23:28]:
Yeah. I think that's that's a great way to approach it. Right? Yeah. Things things that we maybe thought 2 years ago would be so complicated are gonna seem so simple now. You know, speaking of of new technology, right, you just talked, Rick, there about 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 2 of those big changes are what we just referenced there.
Jordan Wilson [00:24:03]:
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, you know, environments. Is that 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 2 different challenges? Because I think they're pretty radical actually when it comes to hands on keyboard.
Rick Kreuser [00:24:32]:
Yeah. I think those are those are 2, things you can kinda bank on over the next 2 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, you could substitute in any but any other, you know, Azure, whatever, AWS, substitute any of them. 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 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 GBT for everything, for instance.
Rick Kreuser [00:25:31]:
Yeah.
Jordan Wilson [00:25:33]:
Great great points there. Yeah. Like, I think that where AI happens is always changing, and I think it's gonna be faster and you know more and more things are gonna 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 scaling AI is actually a people first challenge, and we've actually talked about some of the technology side as well. But you know, as we wrap 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?
Rick Kreuser [00:26:11]:
For people, be 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 your employee or or customers and how they're gonna be involved. Consider it early because it it deserves the attention, and it will make the the journey on the back half of deploying AI at scale much easier.
Jordan Wilson [00:26:41]:
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.
Rick Kreuser [00:26:58]:
Thank you, Jordan. It was great being here. Love the conversation, and I'm sure we'll talk soon.
Jordan Wilson [00:27:04]:
Alright. 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 a i.com. Sign up for our free daily newsletter. We're gonna 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've learned and take it one step farther because learning things isn't enough. You have to actually apply them and leverage them to grow your company and career.
Jordan Wilson [00:27:34]:
Thank you for tuning in. If you found this helpful, please, share this with a friend. If you're on the podcast, please subscribe and follow the show, and we hope to see you back tomorrow and everyday for more everyday AI. Thanks y'all.
AI [00:27:47]:
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 your everydayai.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.