Enterprise leaders say they're "using AI" but most are unprepared for what's coming.
In this AI Answers episode, Paul Roetzer and Cathy McPhillips tackle 15 questions from business leaders navigating the messy middle of AI adoption, from the growing divide between power users and everyone else to why AI strategy keeps failing at the starting line.
Paul shares his take on job displacement, why AI literacy is the most overlooked foundation of any AI strategy, and what the future of knowledge work might actually look like when senior leaders can do the work of entire teams.
Listen or watch below—and see below for show notes and the transcript.
Over the last few years, our free Intro to AI and Scaling AI classes have welcomed more than 50,000 professionals, sparking hundreds of real-world, tough, and practical questions from marketers, leaders, and learners alike.
AI Answers is a biweekly bonus series that curates and answers real questions from attendees of our live events. Each episode focuses on the key concerns, challenges, and curiosities facing professionals and teams trying to understand and apply AI in their organizations.
In this episode, we address 15 of the top questions from our March 18th Scaling AI class, covering everything from tooling decisions to team training to long-term strategy. Paul answers each question in real time—unscripted and unfiltered—just like we do live.
Whether you're just getting started or scaling fast, these are answers that can benefit you and your team.
00:00:00 — Intro
00:06:09 — Is Amazon slowing its AI rollout a sign of maturity?
00:08:58 — Are large enterprises structurally disadvantaged in the AI era?
00:12:14 — Who owns the AI adoption and data readiness problem?
00:14:56 — Is there a growing AI divide between power users and everyone else?
00:21:16 — What AI take do most people disagree with?
00:22:47 — Can companies automate too much too fast?
00:26:19 — Does automation eventually take over, or do we land in the middle?
00:28:24 — What does the average knowledge worker's job look like in three years?
00:35:02 — What are companies still getting wrong about AI strategy?
00:36:27 — How should leaders decide what matters versus what’s noise?
00:40:21 — What separates AI councils that drive progress from ones that don't?
00:41:47 — Where is governance necessary, and where does it get in the way?
00:45:17 — Should you show leadership the AI system or the results?
00:47:19 — What's a no-brainer AI use case most companies still haven't tried?
00:49:36 — Why do people wait to be told how to use AI instead of experimenting?
This episode is brought to you by AI Academy by SmarterX.
AI Academy is your gateway to personalized AI learning for professionals and teams. Discover our new on-demand courses, live classes, certifications, and a smarter way to master AI. Learn more here.
Disclaimer: This transcription was written by AI, thanks to Descript, and has not been edited for content.
[00:00:00] Paul Roetzer: Based on all the conversations I have had with leaders of major companies, I have yet to find one that's prepared for it, and that worries me. a lot. Welcome to AI Answers, a special Q&A series from the Artificial Intelligence Show. I'm Paul Roetzer, founder and CEO of SmarterX and Marketing AI Institute.
[00:00:19] Every time we host our live virtual events and online classes, we get dozens of great questions from business leaders and practitioners who are navigating this fast-moving world of AI, but we never have enough time to get to all of them. So we created the AI Answers Series to address more of these questions.
[00:00:37] Share real-time insights into the topics and challenges professionals like you are facing, whether you're just starting your AI journey or already putting it to work in your organization. These are the practical insights, use cases, and strategies you need to grow smarter. Let's explore AI together.
[00:00:58] Welcome to episode [00:01:00] 206 of the Artificial Intelligence Show. I'm your host, Paul Roetzer, along with my co-host today, Cathy McPhillips, our Chief marketing Officer at SmarterX. Welcome back, Cathy.
[00:01:09] Cathy McPhillips: Thank you.
[00:01:10] Paul Roetzer: if you are a regular listener, you know that we do these special AI answers additions in addition to our weekly, and Cathy is my co-host for these.
[00:01:18] So if you're expecting to hear Mike's voice, tune in for episode 2 0 7, our next weekly episode. AI Answers is the series we introduced probably about a year and a half ago now, I guess. 'cause this is our, well, no, probably not. This is our 17th. We do like two a month. I don't know, last year. Sometime
[00:01:32] Cathy McPhillips: last have summer sometime, I think.
[00:01:34] Last spring.
[00:01:34] Paul Roetzer: Yeah. And so the basic premise here is, we teach two free classes every month. And if you haven't attended them or if you've got someone in your organization that's. Trying just figure this stuff out is a great entry point for them. We teach Intro to AI every month. We've been doing that one for, we're going number 57.
[00:01:53] Cathy McPhillips: 57 is next. Yep.
[00:01:54] Paul Roetzer: Yeah, so we've been doing that one every month for 57 months. You can do the math and how many years that is, [00:02:00] probably close to 60,000 people have registered for that class alone. It is, it is a great way. Again, like I said, just like a intro level for everybody. We do 30 minute presentation, then we do about 30 minutes of questions.
[00:02:11] And then we do the same thing with, five essential steps to scaling ai. And that one's more for like leadership level. but both of those are completely free. They're done through Zoom webinars. We will put the links in the show notes if you wanna attend one of those. We've got two of 'em coming up in April, but each time we do that, we get dozens of questions and Cathy and I usually get through, I don't know, maybe 10 to 12 of those questions in the live sessions.
[00:02:36] And so what we do with this AI Answers podcast series is we take the unanswered questions, we curate them. Sometimes we'll handpick some of the best ones from the webinar as well, because, you know, it's good to repeat those answers if people weren't there. And we go through those. So Claire and Cathy on our team go through, they curate the questions, they put them together, and then Cathy sends me a link five minutes before we're getting on.
[00:02:56] And I have not looked at these questions. and we just [00:03:00] kind of. Answer them the same way I would live just unscripted and however I'm kind of feeling at that moment, if I don't have a great answer to it, I'll be honest with you and tell you that. But we do our best to just try and provide as much context and perspective for the non-technical audience.
[00:03:15] Again, you know, if you're new to the show, most of what we do is we try and cater to the actual practitioners and business leaders on the non-technical side. So we're talking to marketers, salespeople, customer success people, company leaders, ops, finance, things like that. So, the questions today that we're gonna go through are from our scaling ai, the 15th version of that class that we taught from March 18th.
[00:03:38] So we're recording this on March 25th. sometimes that's relevant, depending on what the question is. I may throw in something like current event news that I haven't even talked about the podcast yet. So that, that's it. Am I missing anything there, Cathy? On? no, I don't think so. Okay. Alright. So this episode is brought to us by AI Academy by SmarterX that helps individuals and businesses accelerate their AI literacy and transformation.
[00:03:59] [00:04:00] Through personalized learning journeys and an AI powered learning platform, new educational content is added weekly, so you always stay up to date with the latest AI trends and technologies. Our AI four industries collection is one of the great features of the platform. We have six core series right now with professional certificates of completion, that are designed to jumpstart AI understanding and adoption across industries.
[00:04:22] there, as I mentioned, there are six, it's professional services healthcare. Software and tech insurance, financial services and retail and CPG, which actually just launched last Friday. so these series are ideal, ideal launchpad for organizations that wanna level up their teams and accelerate AI adoption and impact.
[00:04:41] individual and business account plans are available now. You can also buy single course series for one time fees. So if you're not ready to do the AI Mastery membership and do the annual program and take advantage of all those, you can just do an individual, thing. I will say on the business account side, it's actually cheaper to buy [00:05:00] the annual thing than it is for the single course series.
[00:05:03] but if that's, you know what you're looking for, those are both available. So go to academy.SmarterX.ai. You can learn all about not only the industry series, the department series, the foundations collection, AI Academy Lives, gen AI app reviews, everything that's a part of AI Academy. So check that out again, at Academy.SmarterX.ai.
[00:05:22] Okay, Cathy, let's do it. We've got, we're gonna try and do this in about 40 minutes, 45 minutes to get through. It looks like we have 15 questions, so I'm gonna try and be, efficient with my answers today.
[00:05:33] Cathy McPhillips: But real quick on the academy stuff, you know, I just think of my agency days. Yeah. I would've loved to have taken the professional services.
[00:05:39] Totally, obviously, but then I had a CPG client, a financial services client like that would've just been so helpful.
[00:05:45] Paul Roetzer: So, yeah. And yeah, for, for context, so I owned an agency for 16 years. Cathy also comes from the agency world. She did her own business for a while as well. So like both of us are very deep in that.
[00:05:54] And yeah, when you're in the agents world, you, you need this depth of knowledge across industries, across your client [00:06:00] portfolio. So, yeah, it is a, it's a great thing for agencies as well as people who already, you know, work on the corporate side within, in the brand side, within those, those industries.
[00:06:09] Cathy McPhillips: Okay. Question number one. We've been talking about Amazon slowing parts of its AI rollout due to quality issues. My gut says that might actually be a sign of maturity, but how do you see it?
[00:06:21] Paul Roetzer: So this question, I think become, it's tied to, amazon had issues recently with AI agents kind of going rogue and doing some things that shouldn't have done.
[00:06:33] So I'm thinking that's the context of this question. Yeah. I think it is, I think there's just growing pains for everyone right now. Not, not just like on the brand side, not just the people who are trying to figure out how to do just the basic stuff within a corporation and drive pilot use cases and get an AI council going and get support from the C-suite.
[00:06:52] Like we're all fighting that fight. But even the tech companies themselves, like the big frontier lab companies, the cloud [00:07:00] companies like Amazon, they're all trying to move really fast, like the tech is advancing so quickly in the capabilities, like the ability to build these agents and these agents swarms and give them access to files and the ability to make decisions and take actions, and it's really messy.
[00:07:16] I mean, we had an issue we talked about on episode 2 0 5 with meta had a similar problem where the agents just sort of went rogue and did something crazy. So I do think that. It could be a sign of maturity, but I think it's probably more likely a sign of everyone is moving really fast and trying to keep up with the competition, especially in the technology industries.
[00:07:39] and it's hard and there's lots of unknowns about this tech, but you don't want to be caught sitting back and not experimenting with it. I think it's more the people have to realize how to experiment responsibly and safely within their enterprises, and that's a ongoing learning curve, and it's why.
[00:07:56] All of us need to lean on our technical partners within the [00:08:00] organization or outside consultants who can make sure as we're experimenting with the latest and the greatest in ai, we're doing it in a way that doesn't put our data or our companies at risk.
[00:08:11] Cathy McPhillips: Right. I think that's, you know, I think back to when ChatGPT came out.
[00:08:14] And the other models were like, wait, we have something too. And they rolled it out when they weren't quite ready. Yeah. They were iterating in real time. And I think just the risk is so much greater now.
[00:08:23] Paul Roetzer: Yeah. Especially with the agent stuff, you know, a lot of the generative ai, just being able to create things, use reasoning models, the human is still very, very much in the loop and in control.
[00:08:33] As we start to experiment with these things like OpenClaw, where you're just like giving it access to files or even Claude Cowork to a degree, like you're giving it access to a bunch of stuff and we don't really understand how these agents do things or why they do things. And so it's, it's, it's just a whole nother surface area of risk and it's why most enterprises are gonna be very, very slow to like move aggressively into this space.
[00:08:58] Cathy McPhillips: Right. [00:09:00] actually I'll just skip, I'll flip flop three and two. Okay. number two, do you think large enterprises are structurally disadvantaged in the AI era or do they have assets that will ultimately let them win?
[00:09:12] Paul Roetzer: So this is a mix I wrote in 2023, an article called The Future of Businesses AI Are Obsolete.
[00:09:19] And in that article, which we can put a link in the show notes. I basically, my theory was there's gonna be three types of companies in the future, ai, native ai, emergent, and obsolete. The AI natives are built smarter from the ground up. They don't have legacy systems, legacy talent. They have to, you know, convince to use ai.
[00:09:36] they don't, they aren't stuck in legacy pricing models. So they have all these advantages where they can just use the smartest tech and they can build the organization on the fly around what it enables. so that's the I native ideas. You look at a opportunity in an industry and say, we can build a smarter version of that company.
[00:09:53] Take a, you know, a law firm, a marketing agency as we were just talking about a software company and just say, let's just build from the ground up with [00:10:00] fewer people, be more efficient and use AI and everything we can do. Then you have the AI emerging companies, which is basically everybody else, all the other existing companies who now have to figure out.
[00:10:09] How to adapt, how to change their pricing model. So if you're in a services industry and you're charging by the hour, doesn't work like you, you can't do that. You're just gonna completely undercut yourself and destroy your financial model. if you have a legacy tech that's hard to move p people off of, or it's not AI native and you're trying to force fit AI capabilities into an existing software stack, really hard to do if you have customers.
[00:10:33] We, we experienced this when I started launching AI services back in 2017 at my agency. We had all these legacy customers who wanted nothing to do with it. They didn't understand it, they didn't like, they didn't get why we would be building AI capabilities into a service company like we were doing. And so you're known for something and to try and change that perspective and become known for something else is really hard in any industry.
[00:10:57] And so the AI emergence though, they, they have, [00:11:00] they have talent, they have a customer base. they have more likely to have financial strength. And if they can move fast enough through a combination of vision from leadership and then, you know, a strategic approach to change management, they can push off the AI native competitors that are gonna merge from everywhere.
[00:11:19] But it's hard and we're definitely seeing a lot of organizations struggle. We're starting to see like Adobe just had a turnover at their top with the CEO. I think you're gonna see a lot of that. I think you're gonna see a lot of. Leadership, C-suite in particular that just aren't getting it, aren't moving fast enough with a, you know, high enough sense of urgency.
[00:11:37] And I think it's gonna cause a lot of shifts. and then we also see it in the stock market when you start looking at the valuations, the market caps of these companies that just haven't figured it out yet, even Apple like is a great example now, apples that somehow managed to keep their stock price, relatively strong.
[00:11:54] It's just an organization that has really, really struggled and they have everything you could ever want from an AI emerging company. [00:12:00] More money than anybody, incredible talent and amazing brand that we all are envious of. And yet, for three years now, they have yet to figure out how to infuse AI properly,
[00:12:12] Cathy McPhillips: right?
[00:12:14] Cathy McPhillips: Number three, if we say the bottleneck is something like adoption or data readiness. Who actually owns that problem inside an enterprise and why hasn't it been solved yet?
[00:12:23] Paul Roetzer: Part of the reason it hasn't been solved is because nobody knows who owns it. Like, so I think what, what happened? and I say I think, but I can't say confidently.
[00:12:33] Like I know this in many enterprises when ai, when Gen AI shows up in late 2022, and then we get GPT-4 in spring 2023, and it starts becoming very apparent to enterprises that this is a shift in not only consumer behavior, it's gonna be a shift in the way we do. Everything from the ideation of products to the marketing of those products, to our sales and our success.
[00:12:54] All these things are gonna shift. And a lot of C-suites turned to the IT department turned to [00:13:00] the CIO and said, go figure this out. This is a technology problem, which it wasn't, wasn't at a macro level. it is part a technology problem, but it was treated as a pure technology problem. And so what then happens is they didn't take the initiative to educate and empower the leaders of each of the different.
[00:13:19] Business units or teams within an organization and then let them like democratize the ability for them to then build their plans, figure out what tech stack they needed, figure out how it was gonna evolve their org chart. So you have people like chief marketing Officers, heads of sales, chief chief customer success officers, CROs, like if they don't understand AI deeply and aren't using it themselves and becoming very competent in it.
[00:13:44] Then they can't own the diffusion of it across their departments and teams and business units. Right. And so I think that's largely what happened is we had this lack of adoption in part because we didn't prioritize literacy and competency of the tools. [00:14:00] The data readiness is, is a separate but related issue because some of the most important highest value use of AI in enterprises are going to retire require.
[00:14:10] Clean data that is infused into the AI processes and workflows. But there's, that's a, it's often like a red herring in terms of why adoption slows, because what happens is, like the IT department will say, well, we're just not ready. We gotta get the data. We make sure it's safe. You can't touch this because it puts these things at risk, this data at risk.
[00:14:31] When in reality, like in most organizations, take a marketing team as an example, 90% of the use cases they would tackle in the first 12 months have nothing to do with the data. You don't even need any data access, and that's a misconception. I see time and time again when I talk to enterprises, they think they have to solve data first and they don't.
[00:14:49] It can happen in parallel while you're stacking all these use cases that don't touch the data. Absolutely.
[00:14:56] Cathy McPhillips: Number four. I keep thinking about this idea of an AI [00:15:00] divide inside companies, between power users and everyone else. Are you seeing that too? And what actually happens to the people who don't keep up?
[00:15:07] Paul Roetzer: We see this every day. this is a major problem. So the idea here, and I, I'll try and give a tangible, tangible example. So let's say our AI academy is a good example. So we will have companies come in and they'll buy, let's say, a hundred licenses for their, their team, you know, one of the divisions of the company.
[00:15:24] So you buy a hundred licenses. That gives them access to all this education. You know, piloting ai, scaling AI fundamentals by industry, by department, all this stuff. It's like sitting there and they can, they can learn it in, in a short time, become highly competent with ai. And yet, if you take that segment of a hundred people and you start breaking it down, there's gonna be 20 to 30% or whatever the number is, of people who hate ai, want nothing to do with it.
[00:15:47] Find it threatening, find it abstract, think it's gonna destroy the environment like they have some reason why they want nothing to do with it. Then you're gonna have like a middle of the road who are like, yeah, I'm dabbling in it. I'm, I'm using the co-pilot a little bit. The company gave us, but [00:16:00] mainly for like summarizing meeting notes and doing some emails, but they would answer the question, do you use AI regularly As Yes, like I use it weekly.
[00:16:07] So you're gonna have this misconception that they actually know what they're doing, when in reality they're just doing these like surface level things. And then you're gonna have a portion of the company who are racing ahead, like the day you give them access to academy, they're in there, they're doing their first three certificates in the first week.
[00:16:21] You give them a co-pilot license and they are like daily active users grinding all day, and it's like doing all kinds of amazing things. And so they are racing ahead, becoming infinitely more productive than their peers, while still getting paid the same as their peers. By the way. They're actually leveling up the organization because they're uncovering all these use cases and ways to infuse AI that drives efficiency, productivity, innovation.
[00:16:45] So the difference becomes you have these people who sort of are intrinsically motivated to solve ai, even with all its challenges and you know the negatives it's gonna have on the economy and jobs and like, they're just like, okay, we get that, but like, let's figure out how [00:17:00] to do this in a responsible way.
[00:17:01] And so those power users really start to separate themselves in a way we, we've almost haven't seen in a very long time. I mean, the only thing I can think of is back in like, say 2000 when the Internet's really started to take off within, you know, corporations, you have people who figured that out, knew how to use Google, knew, you know, got really good at doing email, and the people who didn't, who, you know, refused to do those things.
[00:17:24] and it's, that's the closest analogy I can probably get to where you're just gonna have the people who do it and the people who don't. And what we are seeing, what I've heard behind closed doors many times over the last two years and what we're now seeing happen publicly is the people who don't, won't have jobs.
[00:17:39] And it is, it's like one of the hardest realities, and it is, I don't want to say it in that way, to seem like crude or, inhumane about it. It, the reality is if you run a company and you know that a tool or a capability enables that company to grow more efficiently, to accelerate its [00:18:00] growth, and you have people who refuse to use it, they won't be employed at your company anymore.
[00:18:06] And so what we're telling people is like telling CEOs and leaders is give them a runway. Like tell them that that's the case. Right? Don't just like spring it on them in three months and say, okay, everybody who said on this survey that you didn't like ai, you're no longer employed. No. Tell them from the CEO on down, we are going to move in this direction.
[00:18:25] We're gonna become an AI forward company. We are gonna empower you with general AI applications. We're gonna give you, you know, ChatGPT, Google, Gemini, Anthropic, Claude co-pilot, whatever. It's, we're gonna give you the tools. We're gonna personally train you on those tools so you know, the use case, the more value to you.
[00:18:39] We're gonna provide AI Academy, courses to you so you can go through and take this training and do the live events and like learn every week. we want you to listen to the, I, you know, the artificial intelligence podcast. Like we're gonna tell you the blueprint to become more valuable in this company, and you're going to be assessed based on it.
[00:18:56] It's gonna be part of your performance reviews annually. [00:19:00] Now, if you've done that and you've clearly integrated that into the business and they still don't do it, then there's nothing you can do as a leader. But help transition them to somewhere else where they would prefer to be, right? Because they obviously don't wanna be there.
[00:19:14] And so I think that that's the only way to do it. I don't think most companies will take that human-centered approach to it. I think a lot of companies are just gonna cut people, but my hope is more and more companies take the approach of, at least be transparent about what you want from them. Set expectations, clearly.
[00:19:31] Give them the resources to meet those expectations. And then if they don't do it. There's nothing you can do. Yeah, I mean, this is replace AI with any technology advancement over the last 50 years. The same thing would be true. You're a salesperson. We're gonna give you Salesforce, okay. For 12 months, the person refuses to use Salesforce and they still manage their sales leads in an Excel spreadsheet that no one else has access to.
[00:19:53] You're fired. Like, it just, it's, it's not just an AI thing, it's a empowerment of tools and [00:20:00] education. And if you choose not to take advantage of that, then you don't have a job there.
[00:20:05] Cathy McPhillips: Yeah. and you said, you know, we're going to be doing this. Like, we must be doing this. We, our company, we, in order for our company to succeed, we need to be doing these 'cause our competitors are doing it.
[00:20:14] Paul Roetzer: Right. And this goes back to that first question about the, or one of the first questions about the difference between, you know, these kinds of companies and how these enterprises can evolve. the enterprise have a really hard time with this because you're going to have a large percentage of your employees who either think it's too abstract and technical.
[00:20:31] They don't like it. They find it threatening. Like there's fear and anxiety. We're AI native companies are like, we're only hiring AI Ford people. Like, we're not even bringing you in unless you already listen to a podcast you work with, you know, ChatGPT daily. Like, you're not getting a job here unless this is you.
[00:20:47] So in that case, the AI native companies have a massive advantage from a hiring and development of talent perspective.
[00:20:53] Cathy McPhillips: Yep. We talked about, email and Google back in the day made me think like, we, we don't need [00:21:00] interoffice mail like we talked about
[00:21:01] Paul Roetzer: this week. Yeah. That's funny. We were, we were at a, we were at a gala together on Saturday night for my kids' school, and this, this came up about interoffice mail, which I didn't even, I, I, I totally forgot that kind of thing, but yes, it was, it's not a job now.
[00:21:15] Cathy McPhillips: Right. So funny.
[00:21:16] Cathy McPhillips: Number five, what's an AI take you have, you have right now that most people would disagree with?
[00:21:23] Paul Roetzer: I mean, my take for the last two years was that we were gonna lose millions of jobs, and most people, including leading economists, argued me about this, thought I was insane. I think it's, people are coming around to this idea, but I do still get a lot of pushback on this nowhere near as much as I did six months ago, but I think it's, in the end, I think AI is a net positive for the economy.
[00:21:48] I'm not sure exactly. How, from a jobs perspective, 'cause I think there's just gonna be fewer jobs. but I feel like we're gonna go through a very, [00:22:00] challenging period in an, from an employment perspective, both, both unemployment and underemployment. And I actually am more concerned about underemployment, meaning, your kid graduates college in May and they take a job at a retail store even though they have a double major in economics and marketing.
[00:22:19] That kind of thing. Yeah. Because just, you gotta get out into the world and start making a living. But I think a lot of jobs are gonna be hard to come by and so I, I get less disagreement now than I used to, but I think that the next few years there's just a ton of unknowns about how this plays out.
[00:22:37] And I, based on all the conversations I have had with leaders of major companies, I have yet to find one that's prepared for it, and that worries me a lot.
[00:22:47] Cathy McPhillips: Yep. Number six. Is there a world where companies look back and feel like they automated too much too fast? And has that already happened?
[00:22:55] Paul Roetzer: I think it's gonna happen all the time and it's, it is probably just gonna be [00:23:00] part of business moving forward.
[00:23:01] I think there's always gonna be this push the limit of what we think this AI can do and then realize, oh, I couldn't do that. one prominent example we've talked about on the podcast is Klarna. They're like, Hey, we're never hiring people again. We're just gonna do everything through AI agents. All our customer success can be agents and then now they're hiring a bunch of people.
[00:23:17] You have openAI's who you would think would be the perfect example of they're gonna need as few people as possible. We just talked on episode 2 0 5 about the fact that they're planning to like double their staff from like 4,500 to 8,000 in the coming months. So. I think that, and then the AWS example, we talked about the agents, the meta one, about the agents going rogue.
[00:23:36] Like I do think a lot of companies are just gonna try real hard to use this. and then there's gonna be pullback. Another example I could think of is companies that race ahead to use AI avatars because they're cool and they, you know, save you time and people can just talk to the AI avatar in a customer success call.
[00:23:54] Or you build your online learning with AI avatars instead of the human. I think you're gonna, that's gonna [00:24:00] snap back fast. Like, I think that a lot of humans want to know they're actually talking to a human or hearing from a human. And while it's kind of like fun and efficient to the, you know, push the limits of this tech and try to file these ways to do automation it at the end, I think a lot of it's gonna fail and we're gonna fall back to the importance of the human element of business.
[00:24:23] And I think that's just gonna be a constant learning curve for organizations as they experiment. And the ones who. Are out on the frontiers of this trying all the things, you know, they're going, we're gonna learn a lot of lessons from them and they're gonna be painful lessons for them, and hopefully, you know others.
[00:24:38] So a fast follower is probably where most companies want to be here. I don't think very many are gonna want to be on the true edge finding. Like I saw a tweet last night from Andrej Karpathy that there was like some, like a Trojan horse in essence, put into this code base that was downloaded like 95 million times [00:25:00] and it ended up exposing all this stuff.
[00:25:02] And so you had all these developers using this thing and it created this massive, security risk that apparently is like blown up in everybody's face.
[00:25:11] Cathy McPhillips: Hmm.
[00:25:12] Paul Roetzer: And so again, if you were out there and you were doing the thing and trying all the OpenClaw stuff and all these new agent things. You know, you're, it sounds great in an ex post that you're doing these things and then all of a sudden it's like, oh shit.
[00:25:22] Like, you know, probably shouldn't have done that. Right. So that is why you know it. And legal, as much as they can be roadblocks to things, you, you gotta, you gotta work in alignment with them, especially as it comes to using these frontier technologies. There's lots of risks ahead.
[00:25:37] Cathy McPhillips: And I think even just very simple things, you know, like we turned on a chat bot a while ago.
[00:25:42] Yes. And then we're like, turn it off. And we had to go through a lot of due diligence and things before we could turn it back on. So it seemed on paper to be like, great. And then it wasn't. So we had to self, you know, self-correct on that. And the other things. Now we're like unsurfacing all these ideas on [00:26:00] ways we can streamline our processes with our, some of our free classes.
[00:26:04] And Jeremy, who's heading up marketing for academy, he and I have been working on this and had this great idea and we tried it and we're like, stop.
[00:26:11] Paul Roetzer: Yeah,
[00:26:11] Cathy McPhillips: so we just need to, I love the ideas, but we need to make sure that they're, they're ready to actually roll out.
[00:26:17] Paul Roetzer: Definitely.
[00:26:19] Cathy McPhillips: Number seven, we've talked before about augmentation versus automation.
[00:26:25] Do you still think we land somewhere in the middle, or does automation eventually take over?
[00:26:29] Paul Roetzer: It's, it's not gonna be. Like evenly distributed in terms of this, I think it's gonna depend on your role, what the tasks are that make up your role, what workflows you know you do on a regular basis, what industry you're in, what kind of company you're in.
[00:26:42] Is it in a highly regulated industry? So I think everybody's gonna experience the augmentation versus automation, spectrum differently depending on what you do. For me as a CEO, you know, I can say that, it is almost. I don't know. I'm just gonna pick a number. I would [00:27:00] probably change it if you asked me tomorrow, but I wanna say like 95% of the way I use AI is augmentation.
[00:27:06] you know, it truly is just an enhanced strategic partner. Like it's enabling me to think more intelligently to, you know, think more broadly about the implications of decisions before I make those decisions. There are definitely pieces of my work I'm automating, but for the most part it's really just enabling me to do more, do more, work better, more thoroughly.
[00:27:30] And so I think of it that way, but you know, then if you move down the channel in terms of like the roles within an organization, I think, I think entry level is gonna probably flip. It's gonna, it's gonna take 90% of what an entry level person would do and automate it. Like it's, so I think. And again, I'm kind of thinking out loud here.
[00:27:48] I think augmentation at the senior level is the more likely scenario. Automation is more likely at the entry to mid-level where you do the tactical work. And I think maybe that's a distinction is if your job is to [00:28:00] do tactical work as part of a strategy or a campaign, a lot of that tactical work, regardless of what industry and is, is gonna be automated, with minimal human in the loop in the coming, you know, one to two years.
[00:28:13] Where the senior level people are gonna be using it more as a strategic thought partner, assisting in decisions and problem solving, building strategies, stuff like that.
[00:28:21] Cathy McPhillips: Yeah, that makes total sense.
[00:28:24] Cathy McPhillips: Number eight, if we fast forward three years, what does the average knowledge worker's job actually look like because of ai?
[00:28:32] Paul Roetzer: And if I, if I had the perfect answer to this, I, you know, would be in a different financial state. I would say overall like, this is the multi-trillion dollar question. Like I, nobody really has the answer to this. It's why openAI's and Google and Anthropic are hiring economists. Like they're, they're trying to model this stuff.
[00:28:55] It's why, you know, you can look to places like, you know, Brookings Institution does some really good [00:29:00] stuff in this area. And, some of the content we're actually gonna plan for MAICON is gonna focus on sort of this future of work, future of economy kind of stuff. My keynote at Make On, I don't think we've announced it yet, so I'm not gonna like, I'm not gonna like sh share exactly the plan behind there.
[00:29:17] 'cause I'm not a hundred percent sure how I'm gonna do,
[00:29:19] Cathy McPhillips: don't me, I wanna announce that.
[00:29:20] Paul Roetzer: Yeah. So I, I, I have, I have a, I have a working hypothesis of what I think the future looks like, but to be quite honest with you, it was literally at a bar on a Friday night, two weeks ago. Where I was like picking up food for my family, and I had this thought of like what I thought it would be, and it was based on a couple of conversations we'd had that morning a meeting.
[00:29:42] Cathy and I had actually been in that morning with a leader of a major university and we're talking about like the future of work for students coming outta college. And then there was, and something else that happened that day that made my mind kind of go to this. And so I developed this hypothesis of what I thought the different roles in an organization would be.
[00:29:59] [00:30:00] I wrote it down in like five minutes and then I actually sent it to the team while I was sitting at the restaurant and I was like, Hey, I think this might be like the MAICON keynote, but I have to play with it a little bit more and like think it through myself. So I think without divulging like the whole concept, I think at a very high level you're gonna have leaders with extensive experience and expertise who, who oversee.
[00:30:27] Agents and a swarm of agents and a team of people, and those leaders can do most of the work that their, lower level employees, associates and things like that used to do the tactical stuff. It goes back to the previous automation versus augmentation. So if I'm the CEO and I wanna like launch a new product.
[00:30:48] I can build the product myself and Claude code. Like I don't, I don't need to hire designers and developers. I can actually go in and just do the thing in like 20 minutes and then if I wanna launch that, rather than [00:31:00] turning over to the marketing team and saying, okay, go build the landing page and write the emails and do all the things, I'll just tell Claude to do it.
[00:31:05] Alright, great. We're locked in. This is what it's gonna be. I wanna launch it in 30 days. Build me a game plan to like launch this thing. Here's, you know, all the past game plans we've done great. Three minutes later, I have the game plan. Great. That looks awesome. Like let's go start building all the components to it and build all the components.
[00:31:19] Finish it up, package it, turn it over to the marketing team. Say, here you go. Like got drafts ready to go. Like you guys do your thing now and edit, vet it, do whatever. But like, imagine that scenario for all knowledge work. Like there's, there's nothing stopping a senior level person from doing the work of the lower level people.
[00:31:38] especially as the AI models start to learn that business and learn the preferences of that senior level person, and, you know, you don't have to teach it to the entry level people. So then the question becomes what happens to the entry level people? That's the part I think I may have cracked the code ish on, but I, I'm not ready yet to like explain it deeply, but I'm, what I'm trying to solve for is how do we [00:32:00] create entry level employment at scale when the senior people can do the work of those entry level people.
[00:32:05] That's like the fundamental. Problem statement I'm trying to solve for, and I think I have a direction, so to be continued, but I think the first part is the way I just explained. I think senior level people are doing most of the tactical work themselves, and then they're turning it over to people to get it to the finish line.
[00:32:21] Cathy McPhillips: Yeah.
[00:32:21] Paul Roetzer: Versus building the strategy and then hoping someone else figures out how to do it. Right.
[00:32:24] Cathy McPhillips: Right. And again, it goes back to that thing about even when we were talking about, Sora and things like that, it's like. Hitting that idea out of your head is a very valuable part of the process, and if AI can help you get to that point versus passing off somebody else, that's huge.
[00:32:40] Yeah. so Katie Robbert from Trust Insights, we try to talk once a month and we're about five months behind, so we talked on Wednesday. And it was just like catch up, but it turned into like I have notes from Claude Cowork that she was talking about and I was like, I can't wait to get in there.
[00:32:54] Paul Roetzer: I know.
[00:32:54] Cathy McPhillips: So it's amazing. I have one right now I'm working
[00:32:56] Paul Roetzer: on that I've been trying to do for at least two [00:33:00] years, and it's a visual thing. And I'm not like the best at it. I've shown different drawings. I've worked in freeform on my iPad, sketching it out on flights. I've got paper drawings. Like I, I've tried every different way to do this.
[00:33:11] It's a very complex idea that needs to be simplified into a simple, interactive visualization. And it came up again, roughly in one of the meetings we were in yesterday, Cathy. And so like last night, I'm literally just like, okay, I just need to write this as a prompt. Like I need to explain the challenges I've tried, like run into the.
[00:33:29] Why it's not been working the way I've tried to do it. I tried to work with the designer to do it and they, they didn't get it either, so I just like, I don't know what it needs to be, but I'll know it when I see it. And so I started working on this, this prompt last night. I continued this morning and it's like all I can think about right now, like in a perfect world, I'm gonna finish it by the end of the day.
[00:33:47] But it's one of those, like, once you get the prompt written. You literally just give it to like Claude and Chet, GBT, and then you just sit back and pray for three minutes. Like, you know, maybe it'll do it, maybe it'll nail it. Maybe it's like a one shot thing. I'm [00:34:00] gonna give it this prompt and like, there it is.
[00:34:02] Like this happened to us last week with a couple of visualizations I was building for the team retreat. and so I'm like. And by the time this podcast episode comes out, I may be like the happiest person in the business world tomorrow if it works. Like it'll be transformational for, for me and maybe for the company, and maybe even for some of our AI Academy customers, but I have no idea.
[00:34:24] Like I might put the prompt in like now, now on that, and then could take a while. But like this is an example of rather than me. Having this idea and going and hiring designers and like trying to do a project with brief, brief with them and like waiting for, you know, maybe months to try and nail it and going through 15 revision rounds.
[00:34:42] I'm just gonna see if the prompt works and if it does, like, it'll be amazing. But that's an example of what the future work looks like is I don't need all those people, I just need 'em to get it to the finish line once I have it. And I have like endless of these things. I have a sandbox of, you know, five of these things a day I would love to bring to life that I just didn't have the [00:35:00] resources or capacity to do before.
[00:35:02] Cathy McPhillips: Yep. Number nine, what are companies still getting fundamentally wrong about AI strategy right now?
[00:35:08] Paul Roetzer: They don't have one. Look, it's, i I, it all starts with, and again, like I don't ever want it to feel like I'm. I'm saying AI education is the core because we offer AI education, like it's what we do as a company.
[00:35:24] But even if I wasn't doing that, if I was just consulting and speaking, which, you know, I could be doing, if we weren't building SmarterX, I would lead with AI literacy every single time I gave a talk. Because it's the most fundamental thing to understand how can you build a strategy if you don't understand the technology.
[00:35:41] So if you, like, let's just say the chief, you know, chief marketing officer, you're saying. Okay. CMO you are in charge of the I strategy for the marketing team. Great if that CMO is using AI as a chat bot and an answer engine and has no idea of its reasoning capabilities, has never done a [00:36:00] no-code app development project.
[00:36:02] never done deep research, doesn't understand multim modalities and like the ability to do video and image and like how the social team can be using all the, if they don't know those things. How in the world are they ever gonna build an optimal AI strategy of the people they need, the technology they need, how to reimagine workflows, what the future of the org chart looks like.
[00:36:21] So the way everyone gets AI strategy wrong is that they don't start with AI literacy. And I'm, I can just stop talking at that, but like it is literally the answer to almost every Yeah. Flawed AI strategy is they didn't start with a deep understanding of the technology itself.
[00:36:36] Cathy McPhillips: Okay.
[00:36:37] Cathy McPhillips: Number 10. One of the biggest challenges I hear is just.
[00:36:41] Keeping up, since everything seems to change weekly, how should leaders at decide what actually matters versus what's just noise?
[00:36:49] Paul Roetzer: I think about this a lot. This is actually, so this is kinda related to the visualization project I was just alluding to. I think that there's, like, the way I, when I was designing [00:37:00] the product roadmap for AI Academy, when I sort of like reinvented what our AI academy was back in fall of 2024.
[00:37:06] This was actually the problem I was trying to set out to solve. It's what is the fundamental knowledge everyone needs to know, like what is the base level of understanding they need to have about artificial intelligence so that they can then figure out how to apply it to their department, their, their personal role, their industry, and then how do they keep up with the stuff that's relevant to them.
[00:37:26] So if you look at how we've structured the learning journeys and the collections within our academy, it, this is, it was meant to answer this question. Mm-hmm. So I'm gonna start with taking the Foundations collection. I'm gonna go through like fundamentals. I'm gonna take piloting, I'm gonna take scaling, then I'm gonna take AI for marketing because I'm in marketing and I'm in, you know, the insurance industry.
[00:37:43] Then I'm gonna take AI for insurance and then the Gen AI apps that drop every week. Like when one pops up that's relevant to my job, I'm gonna take 20 minutes. I'm gonna watch that Gen AI app. And then, you know, I'm gonna attend some Mastery Live classes because I wanna do the AMAs and you know, be able to ask some questions.
[00:37:57] I wanna get a trends briefing every quarter. I want, like, [00:38:00] that was how we thought about it, was truly like, what is a learning journey you need to go through? And so I think everybody has to figure out their role and it, and it doesn't mean like having to buy academy from us. It's like, come to the free intro class.
[00:38:11] Like start there. If you need to listen to the podcast each week, that'll keep you like, what are the 10 things you actually need to know each week you're gonna, you're gonna get that. Find a couple books that you know are super relevant. Find some people that follow on LinkedIn or X that you, you know, really trust, find a couple other podcasts you like.
[00:38:26] You can do all this for free. That's the beauty of all this. Like, and you can even go take some course on like LinkedIn Learning or Coursera. You don't have to just do our stuff. Our stuff is meant to be complimentary to whatever else you need to do to learn. All of us learn in very different ways. I used, notebook LM last night with my son.
[00:38:42] He was studying for a Spanish test. And he needed to like, you know, I would give him the Spanish word, which is funny 'cause I took Spanish, but I can't pronounce a lot of Spanish words, so I'm trying to say the word in Spanish. Then he's supposed to tell me what it means in English. So I instead went into notebook, lm I took a picture of the thing he was studying.
[00:38:57] I said, make me flashcards where we want, you know, show me the [00:39:00] Spanish and then turned it to English. It did it. And so like I, we prepared for his quiz through flashcards. And so I think. That's the kind of thing, like you have to understand what the tech is, and then you have to find the ways you best learn.
[00:39:12] And in some ways, you're using the tech itself, like Notebook, LM to create quizzes, flashcards, mind maps, like whatever you do. So I don't know. You need to think about a personalized learning journey for yourself. You need to think about what education you have access to, and then what are just those free, amazing resources that are gonna keep you up to date and on the leading edge of where you want to be.
[00:39:32] It's not for everyone to try and consume everything every week like it's. It's a lot. Like, there's times I've said on, I was like, I personally get overloaded by it. Like it's, I have days where I'm like, I want it to stop. Like I want it to shut off. I wanna not think about everything today. I want, I don't wanna think about the political shit.
[00:39:50] I don't wanna deal with like the negative impacts on humans. Like I don't want to think about it today, but I do because that's my job. But I get it. If like, [00:40:00] you can't do that or you don't wanna do that, you wanna dip in, know what you need to know and then. Get out and go on with your life. So everybody's gotta figure out what their goal with this stuff is, and then you can adapt your learning journey based on that.
[00:40:12] Cathy McPhillips: Absolutely. Okay, we're starting a new segment on AI answers called Rapid Fire because we have five questions left and not a lot of time.
[00:40:19] Paul Roetzer: We got time, we got 14 minutes,
[00:40:21] Cathy McPhillips:
[00:40:21] Cathy McPhillips: number 11. We hear a lot about AI councils, but I've also seen them slow things down. What separates the ones that drive progress from the ones that don't?
[00:40:30] Paul Roetzer: So I teach an entire class on AI councils. There's an amazing, channel within our Slack community actually, of people who have built AI councils within their organizations. Every council's different. And you know, I think it all probably starts with what is the mission of the council and what's within the charter, their responsibilities and their goals and how it's discovered and things like that.
[00:40:51] So I think you have to contemplate that from the beginning of the formation of the council, or if you're trying to evolve a council. What do you need to have to [00:41:00] actually move the organization forward? And if you find the overall council is slowing things down, maybe you can split off a subcommittee that's focused on a specific thing that isn't hindered by, you know, the politics of the overall council.
[00:41:12] yeah, it's like anything else, especially in an enterprise, everything gets bloated and too many people get involved and there's too many meetings and too many emails. Nobody actually owns anything or has the responsibility to move anything forward. And you just gotta, you gotta try and avoid that however you do it.
[00:41:26] Whether it's split off a center of excellence, that is allowed to be more innovative and take some more risks. 'cause you can test things in a sandbox and it is cool with that. Like you just gotta find the thing in your organization that allows you to keep moving it forward. And if you find the council is slowing things down, then find a spinoff of that that allows you to keep moving.
[00:41:47] Cathy McPhillips: Okay.
[00:41:47] Cathy McPhillips: Number 12, where do you think governance is necessary right now? And where is it actually getting in the way?
[00:41:53] Paul Roetzer: This isn't probably gonna be a complete thought, but the two things that came to my mind the second you were reading this was, anywhere that [00:42:00] touches data and anything that has to do with agents automating, outcomes, actions, and decision making.
[00:42:07] So if you're at a point where you're actually allowing agents to do things, whether it's, to your point Cathy, like a, a customer interface where it's the chat bot and it's doing things and it's making decisions and. Giving recommendations and actions. It could be personalization of emails based on behavior, and you're just letting the e, you know, the AI, write the emails and send them, and you're just kind of overseeing these things, like you need governance when it touches, you know, high value instances.
[00:42:31] It touches stakeholders that are, you know, important to the organization. So the more prominent the use case is, the more it accesses data, the more it has decision making or autonomy. The higher the risk it becomes and the more you wanna welcome that governance and you want guidance on how to do it properly because you do not wanna screw that up.
[00:42:50] And we're in a whole new world where we don't even know what the precedents are around liabilities and insurance. And it's like, it's a whole, like it is a whole new world right [00:43:00] now. And all the ecosystem and infrastructure is being built around this. And you gotta be safe when it call, when the use cases call for responsibility.
[00:43:11] Cathy McPhillips: That's one of the things that scares me the most is my, the relationships that I've built and then setting up automations that
[00:43:19] Paul Roetzer: Yeah.
[00:43:20] Cathy McPhillips: Would come across as an automation to people that I have relationships with. And I know there is a solution. I just, it just still, it's just that that worries me.
[00:43:29] Paul Roetzer: Well,
[00:43:30] Cathy McPhillips: you know, I think like do they just need an answer to something?
[00:43:32] Right. Will they know it's automation? Do they care? It's automation. If they just want an answer to something, there's just a whole gray area that I'm still trying to wrap my head around. And I'm probably being overly cautious, which I don't think is a bad thing.
[00:43:43] Paul Roetzer: No, I don't think it is either. And you know, we've talked about it before.
[00:43:46] I, I wrote this in one of my books. I have no idea if it came from somewhere else. I read it. So, you know, if, if you've heard it before, you know, I, I'm not claiming that I was the originator of it, but you know, a brand takes a lifetime to build in a moment to lose. And that [00:44:00] could be a personal brand or it could be a business brand.
[00:44:01] So yeah, you could invest in relationship building and, you know, you could spend time on call at Katie Robbert. You talk to her all the time and you know each other and you know the families and you do all these things. And then Katie gets like some crappy automated email. It's like, what is this
[00:44:16] Cathy McPhillips: right
[00:44:17] Paul Roetzer: now Katie's going to give you the grace of like, okay, I get what they're doing.
[00:44:20] But if you think about your customer base as a whole and you try and install some new, you know, AI powered personalization, which is top of mind. 'cause I was actually working on a strategy for that right before we got on this podcast. And that goes haywire or it just like doesn't land the way you intended it.
[00:44:35] And now all those people in your database. Who are people to you, but to the ai, they're just data points. you, you could start to chip away at that, the brand equity you have and the trust and the goodwill, and that terrifies people like you and me, Cathy, or like CEOs, CMOs, like, that's your life. You know, the company is based on that brand trust and if you ruin it.[00:45:00]
[00:45:00] What do you have left? Like, it's, it's hard and I think people give a little bit more grace as we move forward on these kinds of things when they realize companies are experimenting. But that's a fine line and I don't know that you're gonna know when you crossed it. You know, it might be too late by the time you realize you went too far.
[00:45:15] Right,
[00:45:16] Cathy McPhillips: right
[00:45:17] Cathy McPhillips: Number. 13. When you're showing AI to leadership, is it more important to show the system itself or real outputs and results, and especially you Paul, I know that you are,
[00:45:26] Paul Roetzer: yeah,
[00:45:27] Cathy McPhillips: more hands on with AI than your average CEO, but what do you want to see? Do you wanna see the outcomes or do you wanna see, like, tell me how you did it.
[00:45:35] Paul Roetzer: This goes back to knowing your audience and. Who, who is the CEO and what is their familiarity with ai? And do they care? Do they not care? Have they already given, you know, resources, support to AI initiatives? Are they still trying to be convinced that it, you know, matters? So you gotta know your CEO and what, what actually is relevant to them?
[00:45:53] I would say at a general level, like if we approach it as a CEO, who is skeptical of what [00:46:00] AI can do and the, you know, the need for urgency and to invest in gen AI platforms and to invest in AI education. Show 'em the results. Like they don't, you know, how you did it is not maybe as relevant to them maybe after the fact.
[00:46:13] But if you say, listen, we've been working on this, you know, initiative using some, you know, new capabilities, new technology, and we took this thing that used to be 50 hours a week for the sales team and we condensed it down to seven minutes and the outcomes actually like the deliverables. A, a better value and here's what it looked like before.
[00:46:33] Here's what it looks like after, here's what we think that means to the business. We think we can scale this across, you know, other teams. We can save the company, you know, 700 hours a month, which equates to this amount of value. And we think we can redistribute that to launch two new products next quarter that we wouldn't have launched otherwise.
[00:46:49] Sold. Like I don't even, I don't even care which tool you're using to do it, but it now if that, the CEO's like, that sounds amazing. How, like, show me how you did this.
[00:46:57] Cathy McPhillips: Right?
[00:46:57] Paul Roetzer: Great. Now show the demo. But if [00:47:00] you lead with, yeah, we're doing this cool AI thing, let me show you some prompts. And they're like, eh.
[00:47:05] But again, it know it, you gotta know the leader. And if, if they love the tech and all their peers are like bragging about all their AI and they wanna be able to brag too, then show 'em the ai. Like, I don't, you just gotta know who they are and what, what moves them.
[00:47:17] Cathy McPhillips: Totally.
[00:47:19] Cathy McPhillips: Number 14, what's one AI use case?
[00:47:22] That feels like a no-brainer at this point, but most companies still have not implemented
[00:47:26] Paul Roetzer: strategic thought partner. Like I, if you're not using the thinking versions of Chad GPT, if you're not, you know, playing around with like, depending again, which platform you have license to, but one has to be a paid license.
[00:47:38] Like you need paid licenses, pay the 20 bucks a month. So if you're using like Gemini Pro, sonnet 4.6 in Claude, or, Opus or Sonnet and then chat, GPT, you know, the 5.2 or 5.4, whatever the hell we're on with now, with thinking, you gotta use the reasoning models. They're, they're just, [00:48:00] it's a cheat code.
[00:48:01] Like if you're not using the reasoning model, you're just leaving so much intelligence on the table, that you're not applying to what you're doing. So. Using it as a thought partner to help you with decision making, problem solving, strategy building. It is an absolute game changer. It has been, for me, it is the dominant use of how I work with AI every single day.
[00:48:22] And as I've said on other shows, like I work across three models every day. Like if it's a high value situation, I will put the same starter prompt into ChatGPT, Gemini, and Claude. I will monitor the outputs of them. Cathy can attest to, we're working on a project right now that we met about yesterday. I put a high value prompt into, six different models.
[00:48:43] I actually tried variations. I did Claude Opus, Claude on it. I did Gemini, I did ChatGPT with and without my Co CEO. Like, I will try everything on these high value ones and then the model that seems like it's best suited for that use case. I'll, then that'll become the dominant thread, and I will work within that [00:49:00] one.
[00:49:00] But then I'll use the other models as a critic. To test the outputs of the primary model. So I get to a final product and I'll say, Hey, what do you think about this final strategy? And I'll let the other models critique the primary model.
[00:49:12] Cathy McPhillips: And you might think that's taking a lot of time, but it's still saving you.
[00:49:15] Paul Roetzer: Oh my God,
[00:49:16] Cathy McPhillips: hours and weeks of your life.
[00:49:17] Paul Roetzer: It might take me like four or five hours to do it that way instead of 50 hours,
[00:49:22] Cathy McPhillips: right?
[00:49:22] Paul Roetzer: So yes, it's like, but we, because we can just prompt something we think, like we should just be able to get it done super fast and move on with our lives. It's like, no. Sometimes you, you have to.
[00:49:31] Finish the process and be patient. Correct. And like see it through.
[00:49:35] Cathy McPhillips: Yep. Okay.
[00:49:36] Cathy McPhillips: Last one, number 15. I still see people waiting to be told how to use AI instead of just experimenting. Why do you think that is and what actually works to change that behavior?
[00:49:47] Paul Roetzer: It's human nature. That's just, just how we are. Like that's not, that's never gonna change.
[00:49:51] Like ha. I mean, I haven't said it in a while to anybody, probably since Chad, GBT, but like. Correct to 2020? Like how often were you saying just Google [00:50:00] it? Like what are you, what are you asking me for? Like the answer is three seconds away by just putting it in the search engine and yet 20 some years after the invention of the search engine, you had people who still wouldn't Google it.
[00:50:11] Like, I don't know how to use Google. So, okay. so I think it's just human nature, that there's just people who don't wanna learn the new thing. They're not going to naturally experiment with it. Because it's abstract or because it's just not what they're comfortable with or because they hate ai or like, I don't know any number of reasons.
[00:50:27] The only way I found a change behavior is to show them a use case that changes their life in a positive way, like solves a pain point that they have that they didn't know how to solve otherwise, or gives them the ability to do something creatively they couldn't do before. And so, you know, hold their hand through those first few use cases until they realize like, this isn't that bad.
[00:50:49] This isn't that hard. and this is how you do adoption in an enterprise too. Go back to the how I started. Like those a hundred people that buy licenses for AI Academy, you, you have to give them personalized use [00:51:00] cases. So you, you could do the training and like show them how to do it themselves, but when you assign copilot licenses or ChatGPT or whatever it is, you should assign those licenses with the first three to five use cases baked in for the people that you're giving them to.
[00:51:13] So show the sales team how to use it to do. SDR work, like to do the outreach and things or to segment databases or write better proposals, like give 'em a GPT trained to do those things. I'm the company policies and you know, the brand, like the easier you make it for people who still need to learn it, the, the way faster you're gonna get adoption and buyin and actually people wanting it in their life.
[00:51:37] The example I always give is like, no one wants to spend their Sunday night away from their family for two hours because they have to write the report that's gonna get turned into. C-Suite Monday morning that they know the C-Suite isn't gonna read anyway. You like hate that. All of us hate that. Well, what if that's the thing you can take off someone's plate like, Hey Cathy, I'm gonna, I'm gonna give you your Sunday night back.
[00:51:56] Like, we're gonna build a GPT that's actually gonna do this. Then we're gonna set up an automation where just [00:52:00] emails it to you Monday morning, you edit it and then you just send it to me. With like the summary of what I need to know. How about that? Like, okay, like you can do that. That sounds amazing. Now do that five times for somebody and there's no way they don't start to like experiment themselves.
[00:52:14] Cathy McPhillips: That is the one thing I remember from MAICON 2019 when I came as an attendee, was sitting in Keith Moehring session about automated reporting with ai and I left and I was like, we were doing
[00:52:23] Paul Roetzer: automated
[00:52:23] Cathy McPhillips: insights time, nothing else from this. That's amazing.
[00:52:27] Paul Roetzer: Now, funny enough, that is the service that we introduced in like 2017 that our clients wanted nothing to do with.
[00:52:34] We were using a tool called Automated Insights. I don't even know if they're still in business. And we were doing rules based automation of analytics reports. So Google Analytics reports, storytelling that was automated was the first thing we built back in 2017, 2018. And then Keith did that talk in 2019 about how we were doing it.
[00:52:50] And we had zero clients paying us to use it at the time. It was shocking, but that's. That's where we are.
[00:52:56] Cathy McPhillips: Yep, that's where we are. Alright, another 15 questions through [00:53:00] AI answers
[00:53:00] Paul Roetzer: and we're done right in time. Excellent. Make my next appointment.
[00:53:04] Cathy McPhillips: Thanks Paul.
[00:53:05] Paul Roetzer: Alright, thank you Cathy. Thanks everyone for joining us.
[00:53:07] We will be back with our regular weekly as scheduled, which would be, I don't know, March 31st, whenever that Tuesday is.
[00:53:13] Cathy McPhillips: Yes. March 31st.
[00:53:14] Paul Roetzer: And then just warning everyone in advance, I am on Spring Break with my family April 1st to the 10th, so we will not have a weekly. The day after Easter, whatever that week is.
[00:53:25] Cathy McPhillips: the seventh, I think
[00:53:26] Paul Roetzer: we'll be back on the 14th. Yeah, so the April 7th, we will not have a weekly, we will be back with a weekly on April 14th. So Sounds great. Some amazing stories to share of our travels. I'm really excited. All right. Thanks everyone. have a great weekend, right? Let's come out on Thursday.
[00:53:42] Yeah. Have a great weekend. Thanks for listening to AI Answers to Keep Learning. Visit SmarterX.ai where you'll find on-demand courses, upcoming classes, and practical resources to guide your AI journey. And if you've got a question for a future episode, we'd love to hear [00:54:00] it. That's it for now. Continue exploring and keep asking great questions about ai.