AI Proficiency: From Users to Builders

As AI continues to reshape how organizations work, companies are increasingly asking what AI proficiency should look like across their workforce, and how they can help employees adapt without simply mandating AI adoption. Our returning guest Mike Lewis, Chief AI Architect at TiER1 Performance, joins Dan and Chris to explore AI proficiency through the L0–L3 framework, with a particular focus on the role of non-technical builders. They discuss AI resistance, identifying the right people to build AI-powered solutions, turning tacit knowledge into durable processes, and finding measurable business value from AI. Mike Lewis was previously on Practical AI episode 289: https://practicalai.show/289 Featuring: Mike Lewis – LinkedIn https://www.linkedin.com/in/aisolutionist/Chris Benson – Website https://chrisbenson.com/, LinkedIn https://www.linkedin.com/in/chrisbenson, Bluesky https://bsky.app/profile/chrisbenson.bsky.social, GitHub https://github.com/chrisbenson, X https://x.com/chrisbensonDaniel Whitenack – Website https://www.datadan.io/, GitHub https://github.com/dwhitena, X https://x.com/dwhitenaLinks: TiER1 Performance https://tier1performance.com/Your New Job Is to Onboard AI Agents: How AI Native Companies Actually Operate https://creatoreconomy.so/p/your-new-job-is-to-onboard-ai-agentsSponsors: Midwest AI Summit: Join AI practitioners on October 15 in Indianapolis for practical sessions, hands-on discussions, and real-world AI solutions. Use code PracticalAI20 to save 20% on your registration. https://midwestaisummit.com/#ticketsPrediction Guard: A self-hosted AI control plane for running agents in high impact environments. predictionguard.com/practicalai https://predictionguard.com/practicalaiResources and Events: Register for upcoming webinars here https://practicalai.fm/webinars! Prior Webinars from our partner Prediction Guard https://www.youtube.com/playlist?list=PLRpRdFmBfdF6boDgAD3nNaLm8Yx4KN2VtMidwest AI Summit 2026 https://midwestaisummit.com/

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:02Speaker 00

  Welcome to the Practical AI Podcast, where we break down the real world applications of artificial intelligence and how it's shaping the way we live, work, and create. Our goal is to help make AI technology practical, productive, and accessible to everyone. Whether you're a developer, business leader, or just curious about the tech behind the buzz, you're in the right place. Be sure to connect with us on LinkedIn, X, or Blue Sky to stay up to date with episode drops, behind the scenes content, and AI insights. You can learn more at practicalai.fm. Now, onto the show.

• 0:41Daniel Whitenack?

  Welcome to another episode of the Practical AI Podcast. This is Daniel Whitenack. I am CEO at Prediction Guard, and I'm joined, as always, by my co-host, Chris Benson, who is a Principal AI and Autonomy Research Engineer. How are you doing, Chris? I'm doing good today. How's it going? It's going great. I'm continually impressed by just the amazing AI work that's happening around, not only on the coast, but in kind of the, I guess, the heartland, the middle of the country, where a lot of, actually, the large enterprises of our country are located. And we have a guest related to that today. But I do want to remind folks,

• 1:26Daniel Whitenack?

  we're also involved and a sponsor of the Midwest AI Summit, which is coming up October 15 in Indianapolis. Really cool event, Chris. You were there. You saw what was going on. There's tables where you can sit down with actual AI practitioners. And rather than just hear a bunch of talks, you can actually get feedback on what you're doing, suggestions, design, et cetera, and hear great talks. So recommend people check it out, Midwest AI Summit. You can use the code PRACTICALAI20 for 20% off. But we have an amazing AI practitioner from close by my area, more towards Cincinnati.

• 2:10Daniel Whitenack?

  Mike Lewis was on a previous episode with us. He got a ton of great feedback on that episode. He's Chief AI Architect at Tier One Performance. Welcome, Mike.

• 2:18Speaker 02

  Thank you, Daniel. It's great to be here again.

• 2:20Daniel Whitenack?

  Yeah, like I say, I have multiple people comment on just the utility and insights that they got out of our previous discussion. And just for context, because I think it's so impressive what you're doing and what you're involved with. Could you just set a little bit of context for kind of the types of projects that you work on? Like, give an example of kind of some of the types of companies that you work with in relation to AI initiatives?

• 2:50Speaker 02

  Yeah, sure. So Tier One Performance is an end-to-end organizational performance and transformation partner. What does that mean? We kind of help some of the most of the largest organizations in the world rethink transformation. I would add in, though, that we have a non-traditional approach. And so, and a multi-decade track record of success there. So the types of clients we'd work with day to day would be like Google, Eli Lilly, Takeda, Air Force. I mean, just most of the Fortune 100s. And we see everything related to transformation.

• 3:33Speaker 02

  So obviously, AI would just represent a sliver of the types of challenges we encounter as an organization day to day. But that sliver is my whole life. You know, so I don't really see all the other stuff. I kind of get a glimpse into it every now and because more and more AI touches everything. But we are sort of a transformation partner. And so you can just imagine how the phone starts ringing when a massive disruptive technology is birthed into our lives, whether we want it or not, right?

• 4:08Daniel Whitenack?

  Yeah, and a part of that, I think people are struggling with on the transformation side is there's just so many sources of information. There's so much noise around like what you should be paying attention to, what people are actually doing, what people are saying they're doing, what is hitting the news? Like, there's all sorts of a range of things. And obviously, a lot of that can be noise. And one of the things I've appreciated in interacting with you over time is that ability to kind of pick out some of that signal from the noise and develop some kind of structural ways of thinking about AI and transformation in an actual, real-world organizational environment.

• 4:53Daniel Whitenack?

  In your kind of day to day, obviously, you're hearing a lot of things, both from customers that you're working with, the news cycle, the things from Anthropic, OpenAI, Hugging Face, whoever that's coming out with things. What does your process look like? Or how do you feel like your rhythms day to day help you kind of isolate some of that signal? Maybe kind of distill down some of what you should be paying attention to? Any suggestions? I think it's something on all of our minds, certainly on my mind.

• 5:24Speaker 02

  Yeah, well, maybe I'm very good at this because I am just like the weirdo, oddball guy who stumbled into this at the perfect moment. I mean, Daniel, you know this about me, and your audience might remember. I was a portrait painter for almost 20 years. I mean, from when I graduated college till 2016, I painted portraits. And I owned a fine arts company and apprenticed a bunch of artists. And I only got into this because I had heard about the Dali model from OpenAI and applied to become one of the commercial arts beta testers. And I lucked into that.

• 6:08Speaker 02

  I got an email one night at dinner and I was like, oh, wow, I just got access to this thing no one had heard of at the time. And so, and I knew a little bit of like Python and coding or whatever. So enough to fumble around and get things installed. And I fell in love with language models. I mean, almost instantly. I just couldn't believe all the new things you can make computers do. And this is before we really thought about them as chatbots or whatever. In my mind, it was like something you plug into code to just manage edge case weirdness, to pick a path and make your app keep working. And context models were minuscule back there. I'm running things locally on my gaming computer, you know? And then, you know, medical records companies start calling.

• 6:52Speaker 02

  And I build this book of business and Tier One acquires my company. Here's the only reason that's worth unpacking is to say, like, I don't have this muddy, long history in the industry where I had opinions about everything or like a solid way of thinking about it. It was all just fun toys for me. And then, I think the way I've made it through business my whole life, I've owned maybe a dozen businesses, all small. But I've always just had a firm rule. It's like, I do not concern myself with things outside of my sphere of influence. So there may be this big thing going on at the edge of the AI sphere. But the reality is, like, if I don't have influence or if it doesn't impact like the way I'm interacting with my clients,

• 7:36Speaker 02

  if it's not, then I just ignore it. These things are not going away. Like, I know for sure, whatever this is, it will be here probably forever. And in my experience, I'm a much better worker. I mean, I can fool almost anyone into thinking I'm a smart guy when I'm using AI and I'm aligning context to like whatever my work project is. I mean, it just works so much better using these tools. And I just have that conviction that's probably true for most people. So, Daniel, my trick is really only focusing on like, what directly impacts like the things going on in my day to day? And at Tier One, what that is, is workforce enablement, performance.

• 8:17Speaker 02

  You know, basically, not worrying so much about tools or whatever, but just outcomes and performance.

• 8:28Daniel Whitenack?

  Yeah, that makes a lot of sense. And one of those things, you know, last time we talked, I looked up the date. It was like 2024, right? Yeah, sometime in 2024. Obviously, a lot has changed. It's impacting all of us in different ways. At that time, we had a discussion about what's, I think, doable, scalable, et cetera. You've, I know, spent a bit more time recently thinking about this concept of AI proficiency. And I know myself, you know, obviously, if you're doing AI things, you're searching them, you get targeted online. I'm all the time seeing these ads

• 9:12Daniel Whitenack?

  for whatever it is, Harvard School, like what the engineering or what becoming AI proficient in your business or whatever the course or thing is. And so, there's a lot of people trying to understand this because of, like you say, the wide-reaching impact across the workforce. So next week, our company's doing an AI accelerator with one of our customers. I know that there's going to be a whole range just from discussions of, quote, AI proficiency. But general, like, other than thinking, well, I know that there's that range, right?

• 9:59Daniel Whitenack?

  It's another step to go and say, well, in light of that range of proficiencies, what do I need to change for different proficiencies? How do I advance people through proficiencies? Do they even need to advance through different levels of AI proficiency? What is enough for some people? What, what is too much, right? So I don't know if you want to help us launch into this topic. Maybe just starting with a little bit of definition of what we mean by proficiency would help us just so that we're all talking about the, the same thing.

• 10:33Speaker 02

  If you don't mind, Daniel, I'd like to even go a step further back.

• 10:36Daniel Whitenack?

  Yeah.

• 10:37Speaker 02

  And just give you a sense for how and why I even started thinking about this. So if, if inside of our, if at tier one, we have, you know, most of the largest, we and three or four other companies are helping the largest companies in the world navigate this disruption. And we also see the same story playing out inside of every company. Like, I mean, I could, I could tell you 95% of the companies, I could walk through the timeline and you'd say, yep, I saw that at fill in the blank, fill in the blank, fill in the blank. They've made all the same mistakes. That I, it's kind of watching that happen, sort of helpless at the periphery. You know, as a consultant, you, you don't have a lot of control, but you know, and you don't have perfect ideas either, you know?

• 11:19Speaker 02

  So everyone's just sort of watching this thing unfold and like, oh, I really thought that was going to work, but that didn't stick. And, and the big thing that I focused on early on was developing tools that help people learn and get good with AI. So I, I developed our, you know, solution archetypes framework where we kind of like tried to pin down, we talked about that on the last episode. And then I built our super user habits index, which is basically, it's like, hey, these are the 24 habits. We see super users displaying and, and, and then I built a coaching tool that people could use. And, and in my mind, it was like, I'm going to put all this together and turn people into super users. And guess what? It just kind of didn't really happen. For some people it did, but I couldn't help but feel like most of them, it was going to happen anyway.

• 12:04Speaker 02

  And at the same time, I'm watching, um, I'm, at the same time, I'm sort of reading all the Substack articles on it, watching things unfold at companies, looking at my work history, you know, thinking through the people, looking at the statistics we create or, you know, stuff in the, in the literature. And then, um, I think the breakthrough moment for me, I was invited to a, a bachelor party with some young guys who used to work with me. I, you know, never again, but, um, I brought 200 pages of, um, research citations and synthesis. So this is not 200 pages of research, it's 200 pages of like the name of a paper, a URL,

• 12:44Speaker 02

  and maybe a sentence about why it might be relevant to my work, just to read, um, during that weekend.

• 12:50Daniel Whitenack?

  During the bachelor party.

• 12:52Speaker 02

  I thought I was probably going to need to pull away a lot. These are all younger guys. And I did, I read it all. And there were just, there were, there were just patterns in there that answered a lot of questions for me. And, and the breakthrough moment with all of that just sort of happening and within like 30 to 60 days, that stew forming, um, I read a Substack article, it was Peter Yang. I'm kind of skimming my screen here for the details on it, but basically it was called, uh, your new job is to onboard AI agents, how AI native companies actually operate. And, and they just had this, they, this way of thinking through the L zero through L three. So I didn't invent that. I read it in that Substack, L zero, um, through L three classification.

• 13:37Speaker 02

  So L zero, L one, L two and L three. And the L zeros are the people who aren't using it. And the L threes are the ones who are, um, you know, just making a difference across the entire enterprise. And we all know those, and then they labeled L one and, and, um, L two though, like knocked me off my chair almost. When I, when they talked about the concept of a non-technical builder and they just gave a name to a thing that I, I just thought, wow, this is, this is something we need to really focus on is like the idea of a non-technical builder. We're all focusing on converting L zeros, people who are disengaged. And we'll unpack what that is, I think we need to, because there's so much important stuff there. And they're trying to get people from L zero to L one.

• 14:22Speaker 02

  And L one is what everyone is, what everyone knows, it's the user. It's the one who's like, I couldn't live without this. Like, and they kind of feel like it ends here. And then that leap, you know, the big jump from like, okay, someone who like uses AI to, wait a minute. No, this is a non-technical builder. This is a person building durable solutions that emulate the company's work in an uncanny way.

• 14:44Speaker 02

  That jump is actually the one that now I sort of run around evangelizing like this is actually where you need to focus your energy. But so, so Daniel, I just, I guess I unpacked all that to say, like for me, I had the, like, the stew, I guess is the way to say it of like, you know, the, the guy who like didn't have a dog in the fight, built a company on accident, it was bought by tier one. Now I'm here, I'm, I'm inside of all these companies, trying to help them make sense of this thing. And then, you know, all these external inputs sort of just clashed. And, and so now we, this is, this is the framework we, we push and advertise and help ask people to think through.

• 15:24Daniel Whitenack?

  If you've been listening to the show over the past few months, you realize how transformative agentic AI is. Whether it's Claude Code or Hermes Agent, or your own custom software that you're deploying for operational efficiencies, or as a product into the market. These systems are transformative and it's where the industry is going. But agents have a lot of risk associated with them. They have agency, they take action within your environment, within your infrastructure, within your systems. And that's why security teams and governance teams view it as risky to deploy these agents

• 16:02Daniel Whitenack?

  within high impact environments and industries like manufacturing, logistics, financial services in the public sector. And that's why I've personally been spending my time working on a product called Prediction Guard with a great team of AI engineers. Prediction Guard is an AI control plane that you host within your own environment, whether that's on-prem, hybrid, or in your cloud VPC, even air gapped scenarios. You can set custom AI policies or those that are aligned with NIST and OWASP standards. And those are then enforced automatically across every handshake, across every agent,

• 16:43Daniel Whitenack?

  whether that's being powered by your own self-hosted models or models in your cloud environment, like those from Azure or AWS Bedrock. And all of that telemetry goes back to your monitoring and observability system. I would love for you all to check out what we're doing. Schedule a demo and a call with our team to find out more at predictionguard.com-practicalai. Like I say, schedule a call today. We're happy to show you how this works and why we think it is so beneficial to the market. People already have this deployed in production, powering transformative agentic systems across a number of industries.

• 17:26Daniel Whitenack?

  So check us out at predictionguard.com-practicalai. That's predictionguard.com-practicalai.

• 17:38Speaker 03

  So Mike, uh, wanted to, wanted to ask you a question. As you are, uh, thinking about how you get the employees of one of your client companies, you know, moving from the L zeros up to the L threes, um, want to throw another variable in. And that is, is the attitudes that people have, which are, you know, very varied in terms of like, there's, there's always people who are excited. As you, you mentioned before, there are people that will naturally want to go to that L one, L two and up to L three. But there are also people that are going to push back hard on that. And so when you're, when you're kind of coming in and you're trying to start your process of helping them achieve that, how do you bring along the L zeros that don't really want to move?

• 18:21Speaker 03

  Uh, or even the ones that have done a little bit, but they're also pushing back, uh, on that. Maybe they fear for their jobs or something like that. So there's that kind of L zeros and maybe some L ones. How do you get over that process as you're trying to bring everybody into this to, to kind of level up through those? Any, any thoughts? Can you kind of throw that in as you're, as you're talking about your process?

• 18:45Speaker 02

  Yeah, I love that you're, you're asking me about L zeros because, uh, the reality is we don't, we don't try to move an L zero. Here's, here's, here's the thing. Uh, the, the article I mentioned, the Substack article, I disagreed with 98% of what I read in that article. I just latched on to the concept of an L two and non-technical builder. That name resonated with me. The L zeros in that article, um, at least at Ramp, the way I interpreted what I read, they were saying that, hey, if you're an L zero at Ramp, of course, it's a software company. You know, if you're an L zero at Ramp, that's grounds for dismissal. You have until this date for our system to assess you as an L one. And man, that just broke my heart because it's kind of like, okay, wait a minute.

• 19:29Daniel Whitenack?

  Terrifying.

• 19:29Speaker 02

  Let's back up, let's back up. Name another technology that we would say that's true. But can you imagine you just go company wide? Hey, by the way, everyone, we want you to geek out about Smart Sheets and be great at Smart Sheets by the end of this, you know, year. Or, or, you know, AWS, or, you know, like just, it's ridiculous. This is just, it's another technology like that. Yet I think a lot of executives are kind of scrambling and panicking and they're saying, hey, guess what? We're going to be AI enabled, we're going to be AI activated. Everyone in our company needs to speak AI by X date, or there's not a place for you here.

• 20:04Speaker 02

  And so first of all, you remember I read 200 pages of science and a lot of it was like, it was just about learning. It wasn't even about AI. And, and, and so, um, threat framing actually does slow down adoption. So, you know, if you put, if you back someone into a corner and say, learn this, or there's not room for you here, they will not learn it as well as if you came to them and said, let's figure this out together if it interests you. And I will acknowledge that there are some positions where you absolutely need an AI-enabled individual. It's true, like, you know, and so there, there are cases where an L zero is not going to work in that role. But I think we're going to realize in 10 years, looking back at all this, that it's just not true in every role. And I think the one that hit me the hardest is my wife, probably the smartest person I know in the world.

• 20:49Speaker 02

  My wife, smartest person I know. And she has no use for AI in her job. She's a nurse and, um, she kind of just, I, I know what she does. She pokes people in the arm with needles and, you know, some other things. And there's medical records, maybe someday it'll be useful to her. It's just really not right now. It would be in the way. And I started doing research on the concept of L zero, people who are disengaged. And I realized there are multiple buckets. So when we talk about an L zero, it's not a thing. So an L zero is not just someone who doesn't use AI. I found five major categories. So one is the performative user. They've been doing this.

• 21:34Speaker 02

  They've been nodding their head in all the trainings, the meetings, they've logged into the thing, they've submitted a chat, and they've gone about their job, like, you know, like, they're not really getting, and, and hey, they're still getting their job done. There's the disinterested.

• 21:49Speaker 02

  I think the moment when this hit me the hardest was when I was looking at all the research and I saw a chart that showed the adoption rates of personal computers in the office space back when PCs were first released and AI. And there's almost the exact same adoption curve over the same period of time. It looks like it's happening at the same rate. And when I saw that, I thought, how silly is it like to think back to that? And, and, and like, would, no one is in a panic because the people in their company haven't figured out personal computers now. Like, it took care of itself because everyone wound up with one. And the same thing is happening with AI. Everyone went home and just created a chat GPT account or whatever. It's free. This is not true with your CRM.

• 22:31Speaker 02

  This is not true with, like, everyone didn't run home and create a smart sheet account because, you know, like, they just gotta have one. And so, you know, but the disinterested are still detached there. Okay, here's another bucket, the too busy. I could give you 10 names at tier one. We almost don't want them running around trying to figure out AI because they are just so critical in their role. They're so good at what they're doing. It's just like, it's like, it's a don't break it or don't touch it. It ain't broke type role. And, you know, who cares if they're using AI or not? And I think that is also really hard for the people who are here in communication. Use AI or you're out of here. And then they can look around and see the people who like, very often it's some of the highest performers in the company who don't really care about it because they've, they're in a rhythm.

• 23:16Speaker 02

  So those I kind of push past, but the next two, these are still buckets in L zero. These are people who aren't using it. Really matter. And there's something you can learn from these people. And I think when you say like, hey, you're gonna be doing this, you're gonna be using AI at this company. They will just zip their lips and they will not, they will nod their head and, you know, and they'll say, yes, you know. But here's the thing, first of all, we have the job fearful. And, and Chris, I think you mentioned this, you might have mentioned this earlier, the job fearful. What do we do? What do we do about the job fearful? First of all, very often these people don't really understand how the tool work. And they're under the impression that if they use AI to do their work, it's going to learn how to do what they do and replace them. And that's almost never true. I mean, just if you kind of know how these things work, I mean, you know, maybe self-driving cars.

• 24:01Speaker 02

  I could think of a few examples of like, the AI is actually learning from watching this, but my vote doesn't really impact whether or not it's going to replace that profession someday, you know? And so I think with the job fearful, there's an opportunity to help them see like, hey, you might be in the middle of a self fulfilling prophecy here. Like, if you continue to push back against this, you might lose your job, but it won't be because the AI replaced you. Because Joe, who's willing to use AI will replace you, you know? So, so that's one way to address an L zero. But another one, and this is actually six out of 10 people that we talked to. And I've seen this number in research hover around 60% in more than one place are the quality disappointed. This is where I think every executive's ears should turn on.

• 24:44Speaker 02

  They should lean forward in their seat and they should realize, do not dismiss these people. If they are resistant to AI, there could be some very valid reasons why. And I met a person at a, at a company who her complaint was, it's not good enough for what I have to produce for my work. And she kind of showed me and she's just like, humans do this better. And it was, it was a marketing role. It was copy generation and with some other stuff. And, and she was just right. And it may not stay true forever, but no one had ever actually just, no AI expert had ever just sat and listened to her. And I, I walked away from that conversation with just a brand new perspective on how much value there is to mine from the group of people who say, it's not ready to do this work yet.

• 25:29Speaker 02

  Or either it's not, or they need better tools or better training. But you, you can't really do that until you listen first. And so, and I also don't think about it like a ladder. I don't think an L one is better than an L zero. And I don't think an L two is better than an L one or an L, actually, if any,

• 25:48Speaker 02

  I don't see it that way. I just see it as, as kind of a division within an organization for how different ways of thinking about the toolkit.

• 25:57Daniel Whitenack?

  And maybe one follow-up question on that. I'm wondering, you mentioned maybe part of the, part of the stumbling block here is the quality disillusion or however you put it, you know, getting into these tools. I, sometimes I also wonder about the way in which, like, I would put myself into the category of the AI people. Like, yeah, I'm building a product, right? How much of this is us providing AI in a way that is just another thing that they have to add? Like a different tool that they have to add in their workflow versus something that actually works in their workflow?

• 26:37Daniel Whitenack?

  And what I mean by that is, you know, if, if I'm, to give an example, you know, we're working with an organization now. They have teams of translators. We have essentially a team of digital agents that helps translate with, with an agent harness. And they use existing tools. They go through the, you know, drafting and quality checking and post-editing. They do these cycles, but we, we don't actually say, hey, go to this different interface that you've never used and do things the AI way. The, the sort of agents work and then stuff pops up in the translation management system that they already use, right?

• 27:20Daniel Whitenack?

  And so what they see is, yes, there's change, right? But it's actually like allowing them to do the thing that they want to do with the tools that they're using in a, in a way that actually they, they want to do that more. Now, that may not always be possible, like, there may need to be a shift of like, how people, you know, the interfaces that people use to do that. But I don't know, do you, you have any experience with that? Like, how much of this is, we're asking people to use yet another tool? And they've already got tools that they like, whether they're AI or not, right? And how much of it is the actual AI output? I don't know if you have any sense of that?

• 28:04Speaker 02

  No, I, I hear, I think I hear the, the sort of the spirit of the question. And so this is where I land, you know, when it comes to, you know, how do, who do we train to do what? And what do we give them? And how do we, you know? And, and honestly, at this point, I just feel like that is part of the noise to me. And, and all the conversations, the main thing it all traces us back to is executives are complaining, we're spending too much on all of these licenses. We don't even understand what we're buying. I can't measure, I mean, yeah, I see this or that anecdotal piece of data, but I can't measure like the real impact. Like, what is the value of the, this investment compared to the cost? And so we, we just say like, hey, whoa, let's zoom way out. Let's zoom way, way, way, way, way out.

• 28:48Speaker 02

  And let's think about where we should actually focus. I mean, if we, if we've already said, okay, L zero, we understand some people just really aren't going to be using it maybe ever, but definitely for a while. Okay, let's not even think about them right now. We got the L ones. This is kind of who you're talking about, Daniel. It's, you know, it's the people who like, believe it or not, it's the people who would say, you could pry it from my cold head hands. I could not live without this. You'd think that must be like an L three or whatever. No, no, actually, it's just people using it, but we can look at that and we don't really understand the value there. I mean, they, they don't want to lose it, but they're just doing their email faster or they're interpreting technical documentation. But the company can't look at that and say, there's $4 million we, you know, saved or whatever.

• 29:31Speaker 02

  It's just, so, so we say, that's noise too. That's going to happen. It's going to take care of itself. I wouldn't focus too much on that. When we talk about L twos, so at tier one, we have, we have a process for assessing and identifying quality L two candidates. Now, Daniel, I'm going to work my way backwards to your actual question from here. So the idea is with an L two, this is a person who you might think, oh, great. How do you assess them? So they have some aptitude for AI? No, we really don't even kind of start with any sort of technical aptitude assessment. What we care most about is company DNA. Do they understand the work in an uncanny way to the, to the company? Do they know which cell of a spreadsheet to like, fight over?

• 30:11Speaker 02

  Do they, like, do they know how to deliver the work in a way that the company wants it to deliver? Is their work style uncanny to what we want? Like, that is, is absolutely necessary before we screen anyone for L two training candidacy. And here's the reason why, right now, the wrong people are in the driver's seat. It's the, we call them the AI excited. The AI excited are the ones who are getting all the attention in the company. They're running down the hall screaming words like nano banana and meet those class models. And everyone's just like, well, they must be the, they must be the person, you know, who should be building things. When in reality, what we see, Daniel, is these people who maybe by definition are often distractible

• 30:55Speaker 02

  and not very focused on the work,

• 30:59Speaker 02

  don't know exactly how to align an agent to work in a way that is absolutely uncanny to what the, the smartest and best and most qualified smees inside of that function would call good or accurate or what we want done. And so we say, we want to start with, it doesn't need to be the SME necessarily, but it needs to be someone who, who in the Venn diagram, like, can replicate the work the company needs done in the way they do it, that honors the brand. And has the aptitude to learn these models, the interest. And though, Daniel, we only want one of those on each team. We find that it is not, it is not a one plus one equals two thing. Too many chefs in the kitchen.

• 31:44Speaker 02

  If you have two L twos on a team, they are not necessarily as good as just an L two, a few L ones, some L zeros. And we want to make sure every L two, every non-technical builder has an L three within arm's reach so that they can double check the work. The L two can make it uncanny, but the L three can make it scalable, durable, make a thing that won't break or doesn't break laws or violate governance policies. And Daniel, this is like your whole world, right? But so this is kind of what we tell. And the other nice thing about thinking about it this way is if you could see like, well, we really only want one L two inside of each, you know, like high performing team.

• 32:24Speaker 02

  That also means we don't have to think about as many expensive licenses and we can focus training on pre-qualified candidates. And so if we're just letting the L ones happen naturally, we're saying, okay, L zeros will deal with them someday, but what can we learn from them now? And, and L twos are really the ones building the machines that we want to work in a way that honors, honors the work. And think about it, how many tools have you used? It's like, who built this? Where did this come from? Like, you know, these things tend to last for years or decades and it better work right, you know? Anyways, particularly when you're building something that works at scale.

• 33:04Daniel Whitenack?

  If you're listening to the Practical AI Podcast, I'm guessing that you value practicality, not just the hype around AI, which is why I think you should check out the Midwest AI Summit. This is an amazing event. I'm going to be there this year. It's happening October 15th in Indianapolis. This is an event like no other I've been to. There's actually an AI engineering lounge where for free, you can go up and get expert advice from practitioners and get feedback on your architecture, your design, your agentic harness, whatever you're looking at, you can get feedback on in real time in between amazing speakers that are on the main stage. So don't miss this event.

• 33:47Daniel Whitenack?

  Again, October 15th in Indianapolis and you can use the code PRACTICALAI20 for 20% off. So go to Midwestaisummit.com and grab your ticket today. Use code PRACTICALAI20 for 20% off. Midwestaisummit.com.

• 34:08Speaker 03

  So Mike, you got me pretty interested and I'm really thinking about, you know, this, this kind of L two process that you're describing. And, and I have sort of a, I want to rephrase a little bit in my own words and kind of finish with a, a question from there. And that is, it feels from what you're discussing, what you're describing there, that that L two kind of has the knowledge of, you know, the value that the company is producing locked in their heads. And so they may not be, you know, the L one that's running down the hallway screaming, hey, AI is cool, we should do it. But they're the ones that are fundamentally and historically bringing that core value into the products and services the company's trying to produce. And that, and that's kind of how it sounded to me.

• 34:53Speaker 03

  And they, they, whether or not they're into AI, they, they have that. So it seems like you're trying to get kind of the L twos to be able to best use these capabilities to enhance their ability to drive value creation in the company. Is that a good way to interpret that? And, and, you know, depending on what your answer is, can you kind of give me a course correction or can you kind of, you know, kind of go down that path and explain it more? Because I'm, I am pretty keen on that idea.

• 35:27Speaker 02

  Yeah, great. So you, you know, an L two is, is a good one when the SMEs in the area, and don't complain about whatever the AI tool they created, you know, like whatever its output is, you know? You know that, you know that they are a good L two candidate when,

• 35:49Speaker 02

  when they can align an agent so that, you know, when it creates outputs, it feels familiar to, to the type of work we do and doesn't require, you know, a whole bunch of babysitting and, and handholding. But also to, to, to back up a little bit, if you, if you think about what that actually means to an organization, because and in a minute, I'll give you an example of a recent, a project I'm on right now, where, that I think would help you see the value of this instantly. A good L two is also not only building tools that, you know, can sort of address like just repeatable work at scale, they're also converting tacit knowledge to documented process.

• 36:30Speaker 02

  Because once, once an AI model is aligned, now you have documentation. Maybe it's in code, but it is, it is documented. And so that's a big problem in like every industry right now. It's like, oh, you know, the aging workforce and, you know, what are we going to do? We got, don't get hit by bus guy over here. And if, you know, if that's me, if we lose them, you know, the plant shuts down, you know, kind of, kind of thing. And so I think in my mind, like that's how you know you have an L two there, that you don't have to worry about the tool they're building and whether or not it's, it's going to work well. So, but here's the other piece of it. And, and really like we, executives lean forward in their chair at this part of the conversation, because when we say,

• 37:16Speaker 02

  you know, if, if you're wondering, you know, about the, the value of this investment, let's, let's stop thinking about this as AI work. And really, you know, like the work of yesterday is going to look like the work of tomorrow. Believe it or not, the more things change, the more they stay the same. And I know it feels like everything is shifting beneath our feet, but the reality is we will be doing the same things before tomorrow. Whenever I talk to AI teams, it feels like what they want to build is agents, what they want to build is tools. But if you actually go look inside of the work at companies, they don't, they're not thinking about AI. They're thinking about this particular problem in front of them. So with one of my clients, uh, let's just say one of the top four pharma companies in

• 37:58Speaker 02

  the world, they were sitting on a stack of, uh, I can't remember how many thousand documents. They needed it converted to look from, to look like this, to look like this. And, um, I, I can't, I can't say too much more about that project, but you know, it was a $4 million job. It was a $4 million job. They, they knew exactly how much it would cost to convert each one and it's requires SMEs.

• 38:21Speaker 02

  And an L two took a look at it and said, this feels like a Claude skill. Had the Claude skill built within three hours, um, we drug one of their documents onto that skill, you know, out the other end squirted, the almost exactly what we were hoping these things would look like at the end of the process. You know, of course, everyone's jaw hit their desk, like, wait a minute, you're telling me that was going to be $4 million? We, it needs to be done. And now our Claude, which we've already paid for, you know, you just drag them on and, and wait, you can also run a thousand concurrently and like this whole thing can be done in hours? Like, wait, what?

• 38:58Speaker 02

  That is measurable, you know? And so we, we kind of push people to thinking toward like, you know, you, you're not going to find out the value of this investment based on how many tokens people are spending or, you know, like you're going to find it when you start intentionally forming teams with a couple of strategic people in there who know how to spot an AI opportunity and just make the headache vanish. We call it doing their laundry, you know? And so like, this is where the real money is, and this is where the real savings is. And Daniel, I know you know about this because I've heard you kind of almost complain about it like, you know, it's like, this stuff's not fun, but it's like, it's real work. Chris, is that helpful?

• 39:38Daniel Whitenack?

  It is. No, that, that helps frame it very well for me. I appreciate that. Yeah. And I, I would be curious of your take on this, Mike. I think you're the right person to ask for a critique on some examples that I've, I've been using even personally. But I've been, I've been trying to use this example of, you know, how in like leadership retreats, you, they always used to show like the, the F1 pit stop and how it advanced from like 20 seconds to two seconds, right? And how that happened was like, everybody knows their job in the pit lane, right? And one guy's job is just to move the tire from here to there. And that's all they do. Like that's their full responsibility.

• 40:21Daniel Whitenack?

  And it almost seems like in some of these use cases that I'm seeing at least, those kind of very targeted jobs.

• 40:31Daniel Whitenack?

  Or outcomes, right? But like you're talking about this document from here to there, right? That that needs to be done. And that is maybe a good candidate for however you frame it, a use of AI, an agent to take care of it, however you word that, like, that's a, that's a thing. And it kind of, in my mind, then it doesn't remove the human, but it actually kind of elevates, in some cases, the dignity of the human from being the person is like, all you're gonna do every day is move the tire from here to there, or to, you know, do this task. And certainly, I recognize there is legitimate, you know,

• 41:16Daniel Whitenack?

  jobs will shift, right? But, um, and, you know, people will, will struggle around that. But it seems like now, kind of the human, you're not framing your team of humans as, hey, don't go, don't get out of your lane, just move the tire from here to there, to actually being able to leverage AI in these individual tasks. And you kind of coming into the orchestrator mode, or the team principal, or the strategist mode, and actually thinking about outcomes. Well, what, what is the outcome I want here? And what are the individual things associated with it? Many of those which can be individual tasks that AI accomplishes. I don't know if that, that rings true at all. I've been

• 41:59Daniel Whitenack?

  trying to think about, um, yeah, how, uh, people are very used to thinking now, I, I think, or many people are very used to thinking of the one-to-one interaction between a human and AI tool, let's say, or a chat interface, and maybe less familiar with this, or familiar with this way of thinking of maybe one human is actually aided by multiple different instantiations or manifestations of AI that help them work towards, towards outcomes. But I don't know. Any critique on that rambling example of kind of the,

• 42:40Daniel Whitenack?

  the F1 pit stop and some of these tasks? Um, you know, what are the tasks in your, in your pit box that need to be accomplished? And what of those are good candidates for AI? How do I think about managing this set of AI workers or tasks, or whatever you, however you might frame it?

• 42:59Speaker 02

  Yeah. One of, one of the things that popped in my head while I was listening to you, Daniel, which I always enjoy doing, is, um, yeah, I'm very often thrown into rooms with people who, you know, live on a spectrum of, you know, attitudes. And, um, you know, one of the big ones is just like, all the fear wrapped up in this. And, um, I kind of always open with, hey, guess what? If you're worried about losing your job to, I think there's thunder in the background. I don't know if you can hear that.

• 43:27Daniel Whitenack?

  Yeah, it's definitely, it's thunderstormy in the Midwest right now, for sure.

• 43:32Speaker 03

  It's giving the answer extra trauma, right there.

• 43:34Speaker 02

  Yeah, that's the fear. No, really, you know, I, when I go in a room and I know there are people who, who are dwelling on that, hung up on that. I, I mean, you know, I just open with, guess what? You are losing your job. You, like, your job is going to change. And that's true for all of us, always, whether or not AI was invented or not. Like, I don't know how many careers I've had, and I think the average person has seven. And even if you stuck with one, your job would change year over year in time. And if, if you're, if someone is uncomfortable with the concept of change, guess what? You're not cut out with the, for the workforce. I mean, like, the reality is, um, the reality is, like, if, if my daughter was scared there's a monster under her bed, I wouldn't go and say, there's no monster on your bed. I'd say, you're stronger than a monster. Like,

• 44:19Speaker 02

  and monsters aren't real. You know, like, um, you know, and, and it's kind of like that in this job. It's like, hey, um, uh, adapt or die, and I don't think you're gonna die. And in, and maybe some of this, it might sound a little heartless, but it comes out of the research is that, um, disruption is always, it turns out in hindsight, it was not as hard, um, to adapt to as you thought it was going to be. And you adapt more quickly than you thought you could. And so this is just, you know, documented, um, truth. And so, but it never feels that way. Um, I think I'm just the weird bird

• 44:58Speaker 02

  who, uh, really likes the idea of like, uprooting my entire career and just trying something new, you know, like, I just, I just have done that so many times. And I almost enjoy it. Um, so, but then I think the rest of what I heard you talk about, Daniel, yeah, I addressed that. But like, remind me, like, what else? What was the?

• 45:19Daniel Whitenack?

  Yeah, I think it's this idea of, one element of the change, I think, is, is understanding that maybe your, your position, which might be consumed by this task right now, might not be just come into work and do this task, but it, it sort of elevates more to an outcome, kind of creative orchestration mode, where you're actually orchestrating work that needs to be accomplished. Like you say, the same functions, the same roles, like, the same outcomes need to be accomplished in your, in your business, right? But, um, but yeah, it's, it does take a different level of thinking to think about, well, how

• 46:01Daniel Whitenack?

  do I orchestrate these things to get the outcomes versus how do I do this task? Because I know how to do the, I know how to get the task done, right?

• 46:09Speaker 02

  Yeah. Yeah, I hear you. Um, maybe, maybe part of why I struggle with just, just rapid firing and answer to this is, my mind darts back and forth from the hundreds of, like, work environments and situations I've had to kind of inspect and solution for.

• 46:25Speaker 03

  Let me, let me frame, let me frame it for a moment, because I, I think I see where Dan's going. And, and I think you are the right person to answer this. And that would be, you know, if you go back to that analogy of the F1 pit stop that he was describing, and the nature of the jobs that everyone had there. And you're kind of looking forward. And so, you know, we're kind of, what is our expectation of the future? And, you know, at least, I think, I think one of the things that probably the three of us would agree on at some level, is the fact that the human that's in that process, it may, uh, may have these AI agents that are able to take over some of those very specific jobs along the

• 47:07Speaker 03

  way. And you're almost becoming the pit stop manager or orchestrator overall. So you're still doing the pit stop, because that's the value of what your organization's doing. But the nature of the humans may change to better do it, to do it faster, and to grow, rather than be kicked out of, you know, rather than loss of job. It's a change of job that's actually more valuable in a lot of ways. Could you talk a little bit about what that future might look like for those individual employees that are adjusting their pit stop job, but maybe, you know, maybe moving a little higher in the abstraction layer to owning that whole process, where they are valuable as humans, and yet they're taking

• 47:49Speaker 03

  full advantage of, of the agents? Any thoughts, like, as you take companies through the process? How, how you would do that? And what your expectation of the future will be, um, on how that progresses?

• 48:02Speaker 02

  I don't, I don't think you're gonna like my answer.

• 48:04Daniel Whitenack?

  It's all good.

• 48:06Speaker 02

  I've decided to stop worrying about it. I don't even think about it anymore. Like, and maybe, maybe it's just because I don't fully understand the question. I think I do. I, I spent probably two years inside of all of these organizations trying to like, lead and organize and, you know, um, and implement, uh, enablement and activation campaigns. And in the end, it just kind of felt like I was just watching water run down a river. It was gonna go down the direction it was gonna go. The river is, is just winding the way it winds. And, and, and I think I'm always just searching for, like, where can I actually make an impact? Where can I actually activate change in an organization that is

• 48:49Speaker 02

  measurable? And, and something that we're all glad we did after we're done doing it. And, you know, when I come in and, you know, and we try to just, you know, really analyze and think through, you know, like, well, what's the impact of this gonna be after we do that? And, you know, what's, in the end, like, what, what it's gonna be is what it's gonna be. And people tend to adapt to these tools very quickly, once they've used them once or twice and seen the result. And so in my mind, it's kind of like, I'm gonna do everything I can to get them to do that. But beyond that, if I can't get them to, I don't care. What I care about is, can I find that L2? Can we get them equipped and activated? And, and, and synergizing, that's a better word than that, um, effectively

• 49:32Speaker 02

  cooperating and contributing inside of their team, uh, in a way that, like, I want to know the difference before and after. And, and so it's not just that, you know, maybe the pit crew thing was a distraction for me, because I was, I was focused on like, well, it's faster now. And it drives me nuts when people say, I want things faster. At tier one, we say like, faster's not always better, especially when it's, it's, it's a system with a whole bunch of parts, and you've just created whiplash and angst for everything around that fast piece, you know, like, nothing else is ready to go that fast. So that's not necessarily better. Cheaper? Sometimes that doesn't even matter, believe it or not. A lot of the companies, that's, that's not like, the main thing we're talking

• 50:15Speaker 02

  about. Like, but new emergent types of work, like, that is interesting. So what can we do now that human minds just really couldn't do well before? And, and equipping these language models inside of like, really clever harnesses. So again, it's kind of like Daniel's world, you know, like, whoa, uh, new kinds of work. Um, and so what's that mean for, you know, the product, or this, you know, department, or function, or whatever it is? I mean, Um, so, um, so I guess I put that, that whole concept just right outside the periphery of what I can control. And so therefore not something I worry about and I'm sort of done thinking about.

• 50:57Daniel Whitenack?

  Yeah, that, that makes sense. I actually do like that answer.

• 51:00Speaker 02

  I did, I did too.

• 51:02Daniel Whitenack?

  I, I think there, there, there are a lot of people and may, maybe this is, um, I don't know, a, a little bit too personal, but certainly like a mode that I think all of us as humans get in is not being present in the moment and thinking about, of course, the past, you know, dwelling on that, but also the future and the, all the stories that we tell in our head about how things might go, right? Which won't, there, there may be elements of that that are true and it's not, of, of course, I don't mean that we shouldn't innovate and look at new ways of doing things. I just mean, we often dwell on eventualities in the future that will, that will never take, take place. And there's dynamics that are

• 51:47Daniel Whitenack?

  happening that we need to dig into and think about how they matter in, in the moment, which I, I, I really, I really like that, that perspective. As, as we close out here, Mike, um, after kind of working across these different levels of proficiency, and I know also you're thinking about structured ways of thinking about the, the actual things that can be done by AI, the outcomes, that sort of thing. What, what's on your mind, you know, today as you're going into your next meetings as like the, the, the kind of challenges or the things maybe like you had done this work to parse through all of these, this research and, and, um, work on AI proficiencies, right? Um, what's that

• 52:32Daniel Whitenack?

  next kind of, or is there a next kind of area in your mind where you're like, I don't, I don't totally have a grip around this bit of it yet, but I'm really curious to dig in more and see if I can, see if I can parse through some of it. Anything that comes to your mind as, as we close out here?

• 52:51Speaker 02

  Yeah. I think, I think the thing that's been most exciting to me as like an idea or like a rallying cry when it comes to the rank and file inside of organizations or leaders who look at me and say like, what's, you know, like, where is their energy? Where is their momentum? Like what, you know? Um, one, one idea that I've kind of had lately and I've seen payoff over and over and over is the reality inside of, of most of these massive organizations. It's mostly, I'm talking about big business now, um, is that everyone's kind of got a toolkit in place. Like most, if you're, you know, uh, Eddie Punch Clock, Procter and Gamble, you probably have access to one, two, or three AI tools and they don't

• 53:36Speaker 02

  work well together and they don't necessarily work well. And it is very easy to try to use them and just see it doesn't work, you know, super well for the thing that you need to get done and to give up on it. And I think like the thing I'm realizing is that there is massive, massive value in, in the people who are willing to lean against the brick wall and push and push and push. And you, believe it or not, you'll start to find ways to use these tools that are, are hugely transformative, informative in spite of the fact that they're so burdened by necessary governance and, you know, rules and restrictions and why can't we turn that connector on and why can't, you know? But, um, you know,

• 54:21Speaker 02

  when Claude tells you something's not possible, you know, ask it to get creative and, you know, and, or, um, you know, think through, you know, alternate approaches. I think creative solutioning is going to be really, really valuable over the next, uh, two years, three, four, five years before, you know, all the wrinkles are ironed out and who knows, you know, Terminators or whatever, whatever comes after that. Um, but, um, I think that, yeah, the thing, Daniel, that like I've been preaching lately, you know, with, with like my trusted colleagues and, um, with different leaders and organizations is like, um, do your best to, to encourage, um, uh, that type of thinking and behavior of like, okay,

• 55:05Speaker 02

  I know they aren't perfect. Figure out what you can get done though. Um, so.

• 55:09Daniel Whitenack?

  Yeah, that's, that's I think a great rallying, rallying cry to, to end with. Appreciate you joining us again, Mike. Um, we look forward to having you, having you on again, uh, to, to learn from you in hopefully not too long again. Um, but yeah, appreciate you taking time. It's been a great conversation.

• 55:28Speaker 02

  Thank you.