Everyday AI Podcast – An AI and ChatGPT Podcast - The AI Training crisis: Why companies are spending money on AI but not educating

Episode Date: December 12, 2025

AI investments? Everywhere. AI training? ....... 🔭Why are enterprises so quick to throw insanely large stacks of cash at any AI project or software, but neglect ongoing employee training and education? And what should we do about it? Join us to find out. The AI Training crisis: Why companies are spending money on AI but not educating -- An Everyday AI Chat with Jordan Wilson and Lucid Software's Dan LawyerNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion:Thoughts on this? Join the convo and connect with other AI leaders on LinkedIn.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTopics Covered in This Episode:The AI Training Crisis in EnterprisesCompany Investment in AI vs. Employee EducationCultural Shifts Required for AI AdoptionImportance of Domain-Specific AI TrainingGrounding Large Language Models With Company DataProcess and Workflow Documentation for AI SuccessRapid Experimentation With New AI ModelsMeasuring ROI and Outcomes of AI TrainingCreating Cultural Moments for AI LearningPreparing Employees for Fast AI Technology ChangesTimestamps:00:00 AI Training Crisis in Business05:32 Generative AI and Family History07:55 Grounding AI for Effective Use11:49 "AI as a Co-Collaborator"16:38 "Grounding AI for Better Trust"19:29 "AI Needs Procedural Knowledge"22:33 "Measuring AI's Value and Speed"23:49 Rapid Experimentation and Continuous Learning26:58 "Maximizing AI Through Education"Keywords:AI training crisis, AI education gap, enterprise AI adoption, AI in business, employee AI training, workplace AI integration, generative AI, large language models, AI-powered workflow, cultural shift with AI, organizational AI culture, domain specific AI training, AI-driven process documentation, AI experimentation, AI safety, securing company data with AI, best practices for AI implementation, nondeterministic AI, grounding AI in company data, procedural knowledge for AI, AI as coworker, AI collaboration, AI trust building, AI productivity, training AI like employees, measuring AI ROI, quantifiable AI outcomes, AI-powered decision making,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. I literally can't tell you the number of times that I've talked to business leaders

who have spent their companies anyways are spending usually millions of dollars on AI. Yet they haven't formally trained their people. And it's almost baffling to me, right? Because here we are with this generative AI technology powered by, large language models, arguably some of the biggest technological shifts ever. And it changes almost daily. Yet, why aren't companies investing in their people to make sure that they understand what the technology does, understand what it can do, and the cultural and process changes needed

to actually get a return on AI? So that's what we're going to be talking about today, going over the AI training crisis and why companies are spending so. much money on AI, but not spending the time and the resources to educate their people. All right. I'm excited for today's show. I hope you are too. What's going on?

My name's Jordan Wilson. Welcome to Everyday AI. This is your daily live stream podcast and free daily newsletter, helping everyday business leaders like you and me, not just keep up with the AI changes because they're happening literally every single day, but how we can make sense of them and grab the important insights to grow our companies and our career. So it starts here with the unedited, unscripted live stream podcast. but to take it to the next level, make sure you go to our website,

your EverydayAI.com. There, make sure you go sign up for the free daily newsletter. We're going to be recapping the highlights from today's podcast, as well as all of the other daily AI news. You need to get ahead. All right. You don't got to listen to me rant about this one. I've done that enough.

I'm excited for our guests for today. So live stream, audience, please help me welcome to the show. Dan lawyer, the chief product officer at Lucid Software. Dan, thank you so much for joining the Everyday AI show. Jordan, thank you. I'm thrilled to be here. I'm pretty excited for a chance to talk with you and to share some thoughts with you in your audience.

All right. So before we get into the topic and yeah, this is going to be a fun conversation, I think. Tell everyone a little bit if they're not aware, what does Lucid Software do? I'd love to. Lucid Software, we are a work acceleration and visual collaboration platform.

It's used by more than 100 million people around the world. So a lot of people probably know about. about Lucid, you know, we're on its mission to help teams see and build the future. And we do that through a portfolio products, things like Lucid Char, which is in television, programming, Lucis far virtual whiteboarding, air focus, and AI powered product management and roadmaping platform. So a suite of products that work together to just really help people solve some hard collaboration problems.

And I'll ask you this, and I'm sure we're going to get into a little bit. But even for you all personally, right? You know, like you said, one of the larger companies in the world when it comes to putting AI products out there for people to use. What have you all even learned internally, right? When it comes to investing in AI products, in AI offerings for yourself and for your customers, yet training. What's been some of your biggest takeaways internally? Yeah, there's a couple of things internally that we see. One is it's actually much more of a cultural shift than it is.

is just a retooling of the team. And of course, it's important to provide tools and provide space and time. But it actually has to be treated like a cultural shift and an evolution of the culture of the company, to be a company that embraces AI. It knows how to use it has expectations and normal things. And even has like, I think of them as cultural moments

where AI comes to the forefront that highlights it for people and give them permission and expectation and things like that. So the cultural change has to be very well managed in addition to like the security of locations, the data, the tooling, the train that people talk about. I think that's the biggest surprise is how much of the culture impacted actually is. So you have a deep background working in product at some large companies.

So I like asking people this, right? Because I think sometimes you can learn through personal stories. I've shared mine plenty. But can you talk a little bit maybe about when was the first time or if you remember, when was the first time that you looked at an AI system and you were like, wow, kind of taken aback? but almost like not taking it personally, but when was it at the point where you were like, okay, this piece of software or LLM just produced something that I didn't think it could.

And this is something, maybe some knowledge that I thought I was kind of special at knowing something at this level. Do you remember that or, you know, do you have any anecdotes kind of like on that kind of point of realization? Yeah. Well, like maybe maybe I'll do that a two-part answer. The first time was actually a long time ago when I worked at Ancestry.com and back then like we would have probably just talked about machine learning or stuff like that but

what we're doing is closer to what we think about today is AI than that and so like we like what we're able to do to like automatically generate stories and information about people's families and helping find people is amazing but if you fast forward to like the more recent generative AI world things like that I think the first time that I was able to go to an AI and something that Lucid had built and and give it a prompt basically asking it to you know diagram out for a very close complex system. And it did it and got it, you know, 95% right. I was like, okay. Like, that's pretty cool. Like, like, I can see how that could change and speed up the way that I work. Being able to just like the time to understanding. So it was someone's faster when I could do

that. So that was that was like a first unlock. There's been many locks now. I think. So I kind of want to jump to the end here. So this big AI training crisis, because depending on what this, you know, the stat, the study that you look at, there's so many. But I say for across the board, most stats say that, you know, 90 plus percent, you know, of executives say that AI is a top priority, right? And, you know, obviously the amount of money that companies are investing in the AI, you know, it's in the billions, right? Yet most studies show that only a third org less are properly training their employees on how to use it. Why? Why is this big gap? Why is everyone saying this is the most important thing and we'll gladly spend millions of dollars yet why are employees not getting trained? Yeah, I think there's there's several gaps in there. One of the gaps for employees not getting trained is it actually like takes a little bit in time for a company that thinks how do I safely provide access to the tools in a way that it doesn't compromise our data. It doesn't compromise RIP. There's been there was initially a lot of fear and concern about that. The concern. The concern.

are still there, but there are a lot of playbooks now for how to, like, do that. So that part is accelerating. Then the second part is, like, what should I train them on? Like, like, there's a broad general training up just like, well, you give a prompt and pitch you an answer and things like that. That actually doesn't take you very far in being able to get your work done. You have to go deeper and think about how to train in domain specific areas. You have to actually have a fair mind understanding of the domain and social matter expertise to really extract the highest value. And I actually think one of the biggest gaps in how people think about, you know, getting value from AI, it's the combination of training, but also the expectations that are like,

in order to get a good out from AI, you know, generative AI is nondeterministic. Businesses don't survive that very well. They need to, you know, predictable outcomes. And so you have to teach people that to get good outcomes from AI, they actually have to ground the AI in what a good job looks like. You have to ground the AI in, you know, the reality of like, this is how work gets done at our company if you want to automate that work. And so you need to, like, actually back people up and teach them, okay, you have to actually have a fair amount of documentation that you can provide to the AI about how your company works and about what a good job looks like before you can then get the highest value from AI. And so it takes some preparation and some forethought and some domain specific knowledge to be able to do it well. Yeah, and an analogy I love, especially since, you know, I interviewed the guy who, you know, came up with the easy button, right? Like way back at Staples at HP now. You know, but it seems like that's the expectation that a lot of business leaders have. They're like, okay, well, you know, especially larger enterprises. If they have tens of thousands of employees and they're like, all right, well, we'll pay the, you know, the $20 or $30 a month for tens of thousands of people, which adds up to, you know, usually seven plus figures annually.

They're like, all right, well, there's the investment. Now it's an easy button. That's wrong, right? Yeah, it takes more. Like, there is there. There is a there. There is an outcome there. But it takes more forethought in preparation, maybe than people initially thought.

And we think about this a lucid a lot. We talk about it as the last mile problem, right? Which is, there's, you know, if you think of logistics, right? You build a bunch of distribution centers. That doesn't matter, actually, unless you can get from the distribution center to people's homes. And similar to AI, it's like you can have the AI. You can license the tool. But if you can't actually, you know, pass to the AI information about how your company works,

which requires you to actually go through and document your processes and document things and get the knowledge that's scattered across many people's heads and get it all together where people can see it. And then to make it worse, like if you pass the AI bad process, you will still get a bad outcome. So you actually have to document how your company works and then you have to refine that. And that's part of the essential trainings. Like you have to teach people that that part of what they need to do to get the most value from AI is to document how they work so that they can share that with AI to have a good examples of good outcomes so they can share that with AI. And there's a fair amount of like teaching an expectation setting, I think that has to happen there.

And I'm glad you brought that up because I felt weird, you know, back in like early 2023 saying like, hey, you need to talk with an AI. You need to teach it. You need to train it just like you would with an employee. because I think back then everyone was looking at large language models like Chad GPT or or Gemini or Claude as co-pilot input, right? And not necessarily a coworker. Yet here we are, you know, rolling into 2026. I think it's a little different now. I think people are looking at AI, especially agentic AI, as true coworkers, right? How can people get through that mindset shift of, hey, this is actually something I need to sit down with.

I need to iterate like your example. I need to ground it, not just in our company's data, but ground it in, you know, what good work looks like. How can people get to that shift? Because it is hard to treat a non-human thing with a human-esque characteristic of working with it, being patient and sharing. Yeah. And it even shows that like you see these like, you know, like we've done a bunch of surveys

and you see gaps between like an executive or a leader's mindset and how they interact with AI and how an individual contributor might interact with AI. And it really has to do with this comfort of delegating work and this comfort of like having this subordinate. Like, you know, I'm very used to asking other people to do things for me and expecting high outcomes from that. They're, you know, I think if you, you know, take Dan 20 years ago, I didn't even think the same way.

And so it takes some of that. But I think there's a, there's an evolved model that come to. And it's actually how we tried, like, incorporate into our own products. is like the idea of like AI as a co-collaborator, co-collaborator, not as a subordinate. And I think many people probably feel more comfortable with that. It's like, hey, like we've got, you know, a sixth man on the team now that we can turn to, who we can trust, who can get things done.

But we still have to give them feedback like with any other team member. We have to do that. So I think, and I've even like, I wouldn't go all the way there maybe, but mentally sometimes like I tend to personify, not my AI systems quite a bit. I tend to talk to them as if they're real people. I but then I have to back them off because then they tend to talk to me like like I worry that you know my like AI assistants try to flatter me and I have to tell I'm like look I don't want I don't want you to flatter me I want critical thinking like I don't I don't

need you to tell me this is good or like I need honest to stuff so I actually have to have to say things to AI to get it to give me more critical of feedback otherwise it's just telling me everything I do is great which is not true yeah so so But there's just like a whole work pattern that we have to think so. Yeah, just like humans, you know, the AI is being a little too sycophantic to try to, you know, suck up to us. So you said something there that I want to dig a little bit deeper on, just this concept of, you know, AI and maybe treating AI like a subordinate. But maybe that's not the best way in the future. But real quick, before we get into that, a quick word from our sponsors.

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creative skills, a growing library of pre-built workflows for common creative tasks, like batch editing photos, creating mood boards, portrait retouching, and creating social variations. Every step the assistant takes is visible, so you can refine, redirect, or take over at any time. You stay in the driver's seat as the creative director. Adobe Firefly AI assistant now in public beta. See it today at firefly.adobie.com. You know, Dan, I liked what you were saying kind of about, you know, treating AI like like a sixth man, right? So for basketball fans or maybe for not a basketball fan, right? The sixth band is important. That's the person that comes in first, you know, in basketball. And usually they can play a variety of positions. And, you know, they're good enough to be a starter.

but for whatever reasons, they're not a starter yet. What happens in 2026, Dan, when the models themselves are all starter worthy, right? And maybe they're better than all of the starters. How do we, number one, get over that mindset shift, right? And I'm starting to see that a lot personally, not just handing tasks off that I would like a subordinate, but, oh, this is if I had someone running my own company, right?

And I'm taking, you know, those big picture, you know, answers or outputs from it. And now I'm the subordinate. Are we getting to that point? And if so, how do we prepare for that and how do we train for that? Yeah. I think we are getting closer to that. But it's actually really interesting because it's very similar to how you treat another person right. You have to earn a certain amount of trust. And someone with AI, like it's on a like a skill by skill or use case by use case basis. You have to gain trust that it can do a consistently good job as at something. And so part of how that trust can be built is, you know, to inspect what it's doing.

It's one of my pet peeves on my team with somebody hands me something that's AI generated. And I'm like, did you even read this? Like, it's not quite right. But so you have to, you have to do that. And to increase the likelihood of growing that trust and get there, you know, AI does so much better. It has like broad context and broad knowledge of the world. But when you can grow out of specific context about your business, about your domain, about what? way you do, it'll do so much better. And if you can keep feeding it a constant stream of content, just like you would another person on your team, so it stays very aware of what's happening. It's going to do better and better. So I think, you know, you gain trust on a use case by

use case basis. That probably then starts to build awareness on the team. Like Kay would actually discover that if we, you know, ground AI with this knowledge of how our company works in this particular use case, it can give us a consistently good outcome. And then that insight needs to be shared across the team. Then the team can start using that and get that in there. But I'm skeptical that you'll ever get the strong outcome you need without providing specific context to the AI. So I want to go a little bit deeper. And maybe this would be a little technical and dorky.

But one thing we keep talking about here is grounding. And obviously that's extremely important, right, when working with non-deterministic generative AI, large language models. right, that are in theory just next token prediction, right? But when you ground it, right, in your company's data, and if your data is clean and if it's organized, right, I'll say in 2024, that's a big part of what humans did, right? They made sure to feed company information to a large language model, right? Especially when we're talking about front end chatbots, right? So leaving the API and the dev talk, you know, at the door here.

But on the front end, now in 2025, all the major systems, right, they essentially have, you know, two clicks. And now these systems are grounded in your company's data. Whereas in 2024, that was a big part of what, you know, AI native organizations, what the humans did there, right? They were just making sure. So now that it's, you know, it's not solved, sure, right? But grounding is relatively simple and straightforward now. So moving forward into 2026, now that. that these large language models, it is much easier to ground them in your data. How should we

be thinking about working with large language models when they do have access to that? And if your data is in a row, right, how does that change the role of your everyday business leader going forward? Yeah. So there's still a missing piece. So you've got the data. The data is not the workflow. The data does not explain this is how you go from A to B to C to D to get the outcome that you want. And so you, in addition to the data, to having the data, you have to actually ground it in the process. This is how work gets done. This is how, you know, this is like, I'll be really practical, right? So like, how at your company, do you reconcile a wire transfer, right? Like, data will not tell you that. And, but, but, and,

in fact, there's probably not a single person or company that will say, you probably have to get 10 people together to answer that question. But if you can document that and pass that to AI, then the combination though, this is this is procedurally how we work, how you get it done. This is the data set and this is what a good job looks like. Then you can do it. So there's actually a missing component beyond just grounding in the data, is grounding it in the process, grounding in the procedural knowledge of how to get things done.

And you can imagine world where you have like MTP servers and strong API between all types of systems. You still have to have an orchestration that says this is how to progress the work. This is the proper sequence. Now, I think over time, yeah, I can be trained and it can learn that. But there will still be like specific knowledge in a company that is their secret sauce. So, well, we're better because we do it this way and we don't want the world to know that. And so I think for a long time, it's going to be important for companies to augment the data,

procedural knowledge that this is how work gets done. And then you'll get good outcomes. You know, Dan, I think you've been spot on just the amount of times that you've mentioned documentation, process documentation and having your data in order, I think those are two keys that you know, you can't overlook. But one of the biggest issues, I think, you know, when it comes to educating employees is the rate of change, right? If we just look at from, you know, mid-November until now, every single week, it started with Open AI and then it went Google and then it went GROC and then it went Claude. And now we're back to OpenAI, releasing a new best model.

week over week. We've had it for five straight weeks now. So, you know, especially when companies are maybe using one or two systems and they're changing all the time. And, you know, a lot of people don't think, oh, when you go from a GPT 51 to a GPT 5 to, oh, I can just do things the same. Well, you can't always. How can companies possibly keep up when the technology they're using and maybe everything they've learned can change very quickly without very much notice? Yeah, I think there's two critical components to that. One is you have to think through how do you make it easy for your employees to rapidly access the new technology in a safe way?

And so it's like, you know, what is your procurement process? What is your policy around what I can install on my machine? How can I do that safely and quickly? Like you have to have like, you almost have to have like, here's a fast path. And these are, you know, the guidelines of how you can do fast path experimentation. You can't use customer data. You can't use PII. You know, like that kind of stuff.

But we do need you to rapidly experiment with new things. And then, you know, so you need like those fast paths of how you can, you know, get people exposure to the new things that are coming. You need to provide them time and space and cultural moments to highlight it. So that's the one piece to it. The other piece is you have to know what you're getting value from. And so you have to figure out for like every kind of business function or domain we're trying to get by for me. What like what is the central measure of speed? You know, how did for example like and I you know, I'm attempted like should I tell you our measures, but like you like you know, think of like a software attorney team.

Is there a number that measures whether AI is speeding you up that is beyond just like, you know, the sentiment, but like an actual, you know, quantitative view of is, is this new. tool speeding us up or on a product team like product and the UICs team like that's my play right where I spend most of my time like how do I measure if AI is actually making us faster at getting good outcomes and we we spend a lot time figuring that out for our company like what are those quantitative measures that so that gives you like a way to evaluate and say is it is it just new and shining or is it actually creating value and speeding us up toward better faster outcomes for our customers and so like that I think that's the comment right is like allow for rapid access and experimentation but have a quantifiable way of knowing if it's

healthy so rapid experimentation what one of my favorite things right yeah don't don't spend you know hours or days or weeks on something if you aren't ready for plan B plan C or experimenting with them at the same time right and Dan I think what you said there just about having those kind of you know internal benchmarks and quantitative measurements extremely important to especially when you're working on something a little bit more finite, right? But what about for everyone else? What if there is no one benchmark? If there is no one measurement, you know, on one system to see if there's, you know, a good return.

You know, maybe for those that are looking at training their entire company and maybe this is something that you've all learned internally, maybe what's been some of the most successful ways that you've seen even internally on, hey, here's good ways that we can educate our people in a space that is changing weekly. Yeah. So I think, you know, we create cultural moments that are like, and I think those like, you know, so there's things like like all hands meetings or staff meetings or things like that. And we create space in all those places to highlight what we're learning about AI. All right.

So like in my, you know, all hands meeting, I have a, you know, an AI moment. where we are having people highlight various ways that they're seeing new value or new experiments, what's working, what's not working around AI, to share the knowledge broadly because you have many people touching them. And we need the knowledge of your leverage. And so creating those cultural moments, it does two things for one that shares the knowledge, too. It gives people permission to play. And it's highlighting, hey, this is a good job.

This is somebody who went and tried something new with AI. And it did or did not work. but we're like, you know, giving them air time and highlighting that's what a good job is, is to go do this type of experimentation at a safe way. And so I think like, like, that is a critical thing. And pretty much any part of any company can figure out, like, where are there cultural moments where we give the airtime to AI to like start working on the behavioral change and help people realize it's safe to play.

Now, you also have to create the space, right? like expecting people to just like go home and you know spend their after hours doing all the learning life some people will do that but i think you have to give in time of space at work uh to do that and you know so like can you take hackathon style approaches or can you say hey we're going to have half the fridays where you're free to experiment or things like that so you know it'll be different for every company how you do it but it should be i think intentional how they do that so uh dan we've covered a lot in today's conversation you know everything from going over the, you know, cultural changes and talking about data and process documentation

and having the right quantitative measurements internally so you can know even what to educate people on. But for those business leaders right now who are planning out there 2026 AI education, how they're going to get it done, what's your one most important piece of advice for them to get education right in 2026? So one, think of the prep work that has to be done to educate. And like there's layers to education. There's the broad general AI awareness. The real value will come when you get domain specific and talk about like in this domain,

in this part of my company for this business function. This is best way to leverage AI. And then part of that training is in order to get the highest values, you have to ground the AI in both the data and the procedural knowledge of how to. how things work, you'll get better outcome. So it's like it's, you know, getting from general to domain specific to very pragmatic around how to get the highest returns from AI will help. And then creating a culture around that that reinforces and supports the idea of learning

and training and rapid experimentation. All right. Some great pieces of advice as it's, you know, a big topic. We're all trying to tackle. And Dan, your time today, I think helped us, you know, tackle this thing. a little bit better. So Dan, thank you so much for taking time out of your day to join Everyday AI. We really appreciate it. Of course, thanks, Jordan. Have a good one. All right. And if you missed anything, y'all, don't worry. We're going to be recapping it all in today's newsletter. So if you haven't

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