Transcript
Intro
0:00 · 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. [music] Welcome to AI Answers, a special Q&A series from the artificial intelligence show. I'm Paul Ritzer, founder and CEO of Smarter [music] X and Marketing AI Institute. Every time we host our live virtual events and online classes, [music] we get dozens of great questions from business leaders and practitioners who are navigating this fastmoving world of AI. But we never have enough time to get to all of them. [music] So we created the AI answers series to address more of these questions and share real time insights into the topics and challenges [music] professionals like you are facing. Whether you're just starting your AI journey or already putting it to work in your [music] organization, these are the practical insights, use cases, and strategies you need [music] to grow smarter. Let's explore AI together.
0:58 · Welcome to episode 206 of the artificial intelligence show. I'm your host Paul Ritzer along with my co-host today, Kathy McFillips, our chief marketing officer at Smarter X. Welcome back, Kathy.
1:09 · Thank you.
1:10 · Uh if you are a regular listener, you know that we do these special AI answers editions in addition to our weekly and Kathy is my co-host for these. So if you're expecting to hear Mike's voice, uh tune in for episode 207, our next weekly episode. AI answers is a series we introduced probably about a year and a half ago now I guess because this is our well no probably not this is our 17th we do like two a month I don't know last year sometime last summer sometime I think last spring yeah and so the basic premise here is uh 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 to 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 57 is Yep.
1:54 · Yes.
1:54 · We've been doing that one every month for 57 months. You can do the math on how many years that is. Um 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 and then we do the same thing with a five essential steps to scaling AI and that one's more for like leadership level. Um but both of those are completely free. They're done through Zoom webinars. We will put the links in the show notes if you want to attend one of those. We've got two of them coming up in April. But each time we do that, we get dozens of questions.
2:30 · And Kathy and I usually get through, I don't know, maybe 10 to 12 of those questions in the live sessions. 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 people weren't there. And we go through those. So Claire and Kathy on our team go through, they curate the questions, they put them together, and then Kathy sends me a link five minutes before we're getting on. And I I have not looked at these questions. Uh, and we just kind of answer them the same way I would live, just unscripted and 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 and tell you that. But we do our best to just try and provide as much context and perspective for the non-technical audience. 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, sales people, um customer success people, company leaders, ops, finance, things like that.
3:30 · So, uh the questions today that we're going to go through are from our scaling AI, the 15th version of that class that we taught are from March 18th. So, we're recording this on March 25th. Um 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.
3:47 · So that that's it. Am I missing anything there, Kathy, on uh format? I think so.
3:51 · Okay.
3:51 · All right. So, this episode is brought to us by AI Academy by Smarter X that helps individuals and businesses accelerate their AI literacy and transformation through personalized learning journeys and an AI powered learning platform. New educational content is added weekly so you always stay upto-date with the latest AI trends and technologies. Our AI for industries collection is one of the the great features of the platform. We have six course series right now with professional certificates of completion uh that are designed to jumpstart AI understanding and adoption across industries. Uh 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. Um so these series are ideal ideal launchpad for organizations that want to level up their teams and accelerate AI adoption and impact. Um individual and business account plans are available now. You can also buy single course series for onetime fees.
4:48 · 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 um thing. I will say on the business account side, it's actually cheaper to buy the annual thing than it is for the single course series. Um, but if that's, you know, what you're looking for, those are both available. So, go to academy.smarterx.ai.
5:08 · You can learn all about not only the industries series, the department series, the foundations collection, uh, AI Academy lives, Genai app reviews, everything that's a part of AIA Academy. So, check that out again at academy.smartx.ai. Okay, Kathy, let's do it. We've got we're gonna try and do this in about 40 minutes, 45 minutes to get through. Looks like we have 15 questions. So, I'm going to try and be uh efficient with my answers today.
5:33 · But real quick on the academy stuff, you know, I just think of my agency days. I would have loved to have taken the professional services obviously, but then I had a CPG client, a financial services client. Like that would have been so helpful.
5:45 · So, yeah. And yeah, for for context, so I owned an agency for 16 years. Kathy 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.
5:54 · And yeah, when you're in the agents world, you you need this depth of knowledge across industries, across your client 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 the brand side within those those industries.
Is Amazon slowing its AI rollout a sign of maturity?
6:09 · 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? So the this question I think become it's tied to um Amazon had issues recently with AI agents kind of going rogue and doing some things it shouldn't have done. So I'm thinking that's the context of this question. Um yeah I I think it 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 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 sea suite. Like we're all fighting that fight, but even the tech companies themselves like the the big frontier lab companies, the cloud companies like Amazon, um they're all trying to move really fast. Like the tech is advancing so quickly and the capabilities like the ability to build these agents and these agent swarms and give them access to files and the ability to make decisions and take actions and it's really messy.
7:16 · I mean, we had an issue we talked about on episode 205 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 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. Um, 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 that people have to realize how to experiment responsibly and safely within their enterprises and that's a ongoing learning curve and it's why all of us need to lean on our technical partners within the 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.
8:11 · Right.
8:11 · I think that's you know I think back to when chat GPT came out and the other models were like wait we have something too and they rolled it out when they weren't quite ready they were iterating in real time and I think just the risk is so much greater now 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 as we start to experiment with these things like open claw where you're just like giving it access to files or even clawed co-work to a degree like you're giving giving it access to a bunch of stuff and we don't really understand how these agents do things or why they do things.
8:47 · And so it's it's it's just a whole another surface area of risk and it's why most enterprises are going to be very very slow to like move aggressively into this space, right?
Are large enterprises structurally disadvantaged in the AI era?
9:00 · Um actually I'll just skip I'll flip-flop three and two. Uh 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?
9:12 · So this is a mix. I I wrote in 2023 uh an article called the future of businesses AI or obsolete. And in that article, which we can put a link in the show notes, I basically my theory was there's going to be three types of companies in the future. AI native, AI emergent and obsolete. The AI natives are built smarter from the ground up.
9:31 · They don't have legacy systems, legacy talent. that they have to, you know, convince to use AI. Um, they don't, they aren't stuck in legacy pricing models.
9:39 · 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. Um, so that's the I native ideas. You look at an opportunity in an industry and say we can build a smarter version of that company. 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 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 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 can't do that. You're just going to completely undercut yourself and destroy your financial model. Um, if you have uh legacy tech that's hard to move 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, 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. And so the AI emergence though they they have they have talent, they have a customer base, um 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 strategic approach to change management, they can push off the AI native competitors that are going to emerge from everywhere. But it 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. Um I think you're going to see a lot of that. I think you're going to see a lot of leadership seuite in particular that just aren't getting it aren't moving fast enough with a you know high enough sense of urgency and I think it's going to cause a lot of shifts and 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, Apple's somehow managed to keep their stock price uh relatively strong, but it's just an organization that has really really struggled and they have everything you could ever want from an AI emerging company, more money than anybody, incredible talent and amazing brand that we all are envious of and yet for 3 years now, they have yet to figure out how to infuse AI properly.
12:12 · Right?
Who owns the AI adoption and data readiness problem?
12:14 · 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? Part of the reason it hasn't been solved is because nobody knows who owns it. Like so I think what what would happen and I say I think but I I can say confidently like I I know this in many enterprises when AI when Jennai shows up in late 2022 and then we get GPT4 in spring 2023 and it starts becoming very apparent to enterprises that this is a shift in not only consumer behavior it's going to 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. all these things are going to shift and a lot of sea suite turned to the IT department turned to the CIO and said go figure this out this is a technology problem which it wasn't um wasn't at a macro level um 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 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 going to evolve their org chart. So you have people like chief marketing officers, heads of sales, chief chief customer success officers, um uh CRO's like if they don't understand AI deeply and aren't using it themselves and becoming very competent in it, then they can't own the diffusion of it across their departments and teams and business units. 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. The data readiness is is a separate but related issue because some of the most important highest value uses of AI in enterprises are going to retire require clean data that is infused into the AI processes and workflows. But there's that's a um 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 got to get the data.
14:26 · Make sure it's safe. You can't touch this because it puts these things at risk, this data at risk." 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. It can happen in parallel while you're stacking all these use cases that don't touch the data.
14:54 · Absolutely.
Is there a growing AI divide between power users and everyone else?
14:56 · Number four, I keep thinking about this idea of an AI divide inside companies between power users and everyone else.
15:03 · Are you seeing that too? And what actually happens to the people who don't keep up?
15:07 · We see this every day. Um, this is a major problem. So the the idea here I'll try and give a tang 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. 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 100 people and you start breaking it down, there's going to be 20 to 30% or whatever the number is of people who hate AI, want nothing to do with it, find it threatening, find it abstract, think it's going to destroy the environment, like they have some reason why they want nothing to do with it.
15:54 · Then you're going to 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 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 I use it weekly. So you're going to 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 going to 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 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 and 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. 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 going to have on the economy and jobs and like they're just like okay we get that but like let's figure out how to do this in a responsible way. 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, um, and the people who didn't, who, you know, refuse to do those things. And, and it's that's the closest analogy I can probably get to, where you're just going to 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. 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 um 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 growth um and you have people who refuse to use it, they won't be employed at your company anymore. 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. Don't just like spring it on them in 3 months and say, "Okay, everybody who said on the survey that you didn't like AI, you're no longer employed." No, it tell them from the CEO on down, we are going to move in this direction.
18:25 · We're going to be become an AI forward company. We're going to empower you with Geni applications. We're going to give you, you know, Chad GPT, Google Gemini, um, Anthropic Claude, C-pilot, whatever it is, we're going to give you the tools. We're going to personally train you on those tools so you know the use cases that are more valuable to you.
18:39 · We're going to provide AI Academy uh courses to you so you can go through and take this training and do the live events and like learn every week. Uh we want you to listen to the you know the artificial intelligent podcast like we're going to tell you the blueprint to become more valuable in this company and you're going to be assessed based on it's going to be part of your performance reviews annually. Now, if you've done that and you've clearly integrated 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 because they obviously don't want to be there. 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 going to 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, give them the resources to meet those expectations, and then if they don't do it, there's nothing you can do. I mean, this is replace AI with any technology advancement of the last 50 years, the same thing would be true. You're a saleserson, we're going to give you Salesforce. Uh, 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. You're fired. Like, it just it's it's not just an AI thing.
19:58 · It's empowerment of tools and education. And if you choose not to take advantage of that, then you don't have a job there.
20:05 · Yeah.
20:05 · And and you said, you know, we're going to be doing this. Like we must be doing this. We our company, in order for our company to succeed, we need to be doing these because our competitors are doing it, right? And this goes back to that first question about the um or one of the first questions about the difference between, you know, these kinds of companies and how these enterprises can evolve. Um, 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. They don't like it. They find it threatening. Like there's fear and anxiety. We're AI native companies. They're 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, chat GBT daily. Like, you're not getting a job here unless this is you. So in that case, the AI native companies have a massive advantage from a hiring and development of talent perspective.
20:54 · Yep. We talked about um email and Google back in the day made me think like we we don't need inner office mail like we talked about this week.
21:01 · Yeah, it's funny. We were at a we were at a gala together on Saturday night for my kids school and this this came up about inner office mail which I didn't even I I I totally forgot that kind of thing. But yes, that was it's not a job now, right? So funny. Number five, what's an AI take you have you have right now that most people would disagree with?
What AI take do most people disagree with?
21:23 · I mean, my take for the last two years was that we were going to lose millions of jobs. And most people, including leading economists, argued me about this, thought I was insane. Um, I I think it's people are coming around to this idea, but I do still get a lot of push back on this. nowhere near as much as I did 6 months ago. But I I think it's in the end I think AI is a net positive for the economy. I'm not sure exactly how from a jobs perspective because I think there's just going to be fewer jobs. Um but I I feel like we're going to go through a very uh challenging period in from an employment perspective both both unemployment and underemployment. And I actually am more concerned about underemployment. Meaning um 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 like that kind of thing because just you got to get out into the world and start making a living.
22:25 · But I I think a lot of jobs are going to be hard to come by. And so I I I get less disagreement now than I used to. But I I think that the next few years there's just a ton of unknowns about how this plays out. 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.
Can companies automate too much too fast?
22:47 · Yep.
22:47 · Number six. Is there a world where companies look back and feel like they automated too much too fast? And has that already happened? I think it's going to happen all the time and it's it is probably just going to be part of business moving forward. I think there's always going to be this push the limit of what we think this AI can do and then realize, oh, I couldn't do that. Uh, one prominent example we've talked about on the podcast is CLA where they're like, hey, we're never hiring people again.
23:12 · We're just going to do everything through AI agents. All our customer success going to be agents. And then now they're hiring a bunch of people. You have Open AI who you would think would be the perfect example of they're going to need as few people as possible. We just talked on episode 205 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 I think that and then the AWS example we talked about the agents the meta one about the agents going rogue. Like I I do think a lot of companies are just going to try real hard to use this. Um and then there's going to 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 and people can just talk to the AI avatar in a customer success call or you build your online learning with AI avatars instead of the human. I think you can that's going to 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 let, you know, push the limits of this tech and try follow these ways to do automation, at the end, I think a lot of it's going to fail. And we're going to fall back to the importance of the human element of business. And I think that's just going to 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, uh, you know, they're going we're going to learn a lot of lessons from them and they're going to be painful lessons for them. and hopefully you know others. So a fast follower is probably where most companies want to be here. I don't think very many are going to want to be on the true edge finding like I I saw a tweet last night from Andre Karpathy that there was like some um uh like a Trojan horse in essence put into this code base that was downloaded like 95 million times and it ended up exposing all this stuff. off and she had all these developers using this thing and it created this massive uh security risk that apparently is like blown up in everybody's face.
25:12 · And so again, if you were out there and you were doing the thing and trying all the open claw stuff and all these new agent things and you know it sounds great in an expost that you're doing these things and then all of a sudden it's like oh [ __ ] like probably shouldn't have done that. So that is why you know IT and legal as much as they can be roadblocks to things you you gota you got to work in alignment with them especially as it comes to using these frontier technologies there's lots of risks ahead and I think even just very simple things you know like we turned on a chatbot a while ago and 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 right and then it wasn't. So, we had to self, you know, self-correct on that. And other things, you know, we're like uncsurfacing all these ideas on ways we can streamline our processes with our some of our free classes. 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. We're like, stop.
26:11 · Yeah.
26:11 · 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.
26:17 · Definitely.
Does automation eventually take over or do we land in the middle?
26:19 · Number seven, we've talked before about augmentation versus automation. Do you still think we land somewhere in the middle or does automation eventually take over?
26:29 · It's it's not going to be like evenly distributed in terms of this. I think it's going to 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. Is it in a highly regulated industry? So, I [clears throat] think everybody's going to experience the augmentation versus automation. uh spectrum differently depending on what you do. For me as a CEO, you know, I can say that um it is almost I don't know, I'm just going to pick a number. I would probably change it if you asked me tomorrow, but I want to say like 95% of the way I use AI is augmentation. Um 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 any implications of decisions before I make those decisions.
27:18 · There are definitely pieces of my work I'm automating, but for the most part, it it's really just enabling me to do more um do more work better um more thoroughly. And so I think of it that way, but you know, then if you move down the the channel in terms of like the roles within an organization, I think entry level is going to probably flip. It's going to it's going to 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 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 do tactical work as part of a strategy or a campaign a lot of that tactical work regardless of what industry in is is going to be automated um with minimal human in the loop in the coming you know one to two years where the senior level People are going to be using it more as a strategic thought partner, assisting in decisions and problem solving, building strategies, stuff like that.
28:21 · Yeah, that makes total sense. Number eight, if we fast forward 3 years, what does the average knowledge worker's job actually look like because of AI?
What does the average knowledge worker's job look like in three years?
28:32 · And if I if I had the perfect answer to this, I you know am'd be in a different financial state. I would say overall like um this is the the multi- trillion dollar question. Like I nobody really has the answer to this. It's why OpenAI and Google and Anthropic are hiring economists. Like they're they're trying to model this stuff. It's why, you know, you can look to places like, you know, Brookings Institution does some really good stuff in this area. And um some of the content we're actually going to plan for MECON is going to focus on sort of this future of work, future of economy kind of stuff. My keynote at Mon, I don't think we've announced it yet. So, I'm not going to like uh I'm not going to like share exactly the plan behind there because I'm not 100% sure how I'm going to I want to announce that.
29:20 · Yeah.
29:20 · 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 2 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 Kathy 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 out of college. And then there was and something else that happened that day that made my mind kind of go to this. And so I I developed this hypothesis of what I thought the different roles in an organization would be. Um I wrote it down in like 5 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 Makeon keynote, but I have to play with it a little bit more and like think it through myself." So I I think without divulging like the whole concept, I think at a very high level you're going to have leaders with extensive experience and expertise who um who oversee agents and a swarm of agents and a team of people and those leaders can do most of the work that their uh 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 want to like launch a new product, I can build the product myself and claw 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.
30:57 · And then if I want to launch that rather than 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.
31:05 · All right, great. We're locked in. This is what it's going to be. I want to 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. 3 minutes later, I have the game plan. Great. That looks awesome.
31:15 · Like, let's go start building all the components to it and build all the components, 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. um especially as the I 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 codeish 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 create entrylevel employment at scale when the senior people can do the work of those entry-level people. 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 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 versus building the strategy and then hoping someone else figures out how to do it. Right.
32:24 · Right.
32:24 · And again it goes back to that thing about even when we were talking about um Sora and things like that. It's like getting 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 it off to somebody else, that's huge.
32:40 · Um, so Katie Rober 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 catchup, but it turned into like I have notes from Claude Co-work that she was talking about and I was like, I can't wait to get in there.
32:54 · I know.
32:55 · So it's amazing. I have one right now I'm working on that I've been [clears throat] trying to do for at least 2 years and it's a visual thing and I'm not like the best at it. I've tried different drawings. I've worked in free form on my iPad sketching it out on flights. I've got paper drawings. Like I've tried every different way to do this. It's a very complex idea that needs to be simplified into a simple interactive visualization.
33:17 · And it came up again roughly in one of the meetings we were in yesterday Kathy.
33:21 · 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 why it's not been working the way I've tried to do it. Um I tried to work with a designer to do it and 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.
33:45 · Like in a perfect way I'm going to finish it by the end of the day. But it's one of those like once you get the prompt written, you literally just give it to like Claude and Chad GBT and then you just sit back and pray for 3 minutes. Like, you know, maybe it'll do it, maybe it'll nail it, maybe it's like a oneshot thing. I'm going to give it this prompt and like there it is. Like this happened to us last week with a couple of visualizations I was building for the team retreat. Um, and so I'm like, by the time this podcast episode comes out, I I may be like the happiest person in the business world tomorrow.
34:15 · 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 like I might put the prompt and like no now on it 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 with them and like waiting for you know maybe months to try and nail it and going through 15 revision rounds. 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 of work looks like is I don't need all those people. I just need them 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 resources or capacity to do before.
What are companies still getting wrong about AI strategy?
35:02 · Yep. Number nine. What are companies still getting fundamentally wrong about AI strategy right now?
35:08 · They don't have one.
35:11 · I I it all starts with and 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 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 Smarter X I would lead with AI literacy every single time I gave a talk because the it's the most fundamental thing to understand how can you build a strategy if you don't understand the technology. So if you like let's just say the chief you know chief marketing officer you're saying okay um CMO you're in charge of the eye strategy for the marketing team great if that CMO is using AI as a chatbot and an answer engine and has no idea of its reasoning capabilities has never done a no code app development project um never done deep research doesn't understand multiodalities 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 going to 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. So the way everyone gets AI strategy wrong is that they don't start with AI literacy.
36:26 · And I I can just stop talking that like it is literally the answer to almost every flawed AI strategy is they didn't start with a deep understanding of the technology itself.
How should leaders should decide what matters versus what’s noise?
36:36 · Okay, number 10. One of [clears throat] the biggest challenges I hear is just keeping up since everything seems to change weekly. How should leaders decide what actually matters versus what's just noise?
36:49 · I think about this a lot. This is actually so this is kind of related to the visualization project I was just alluding to. Um, I I think that there's like the way I when I was designing the product roadmap for AI Academy, when I sort of like reinvented what our AI Academy was back in fall of 2024, 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 so if you look at how we've structured the learning journeys in the collections within our academy it this is it was meant to answer this question so I'm going to start with taking the foundations collection I'm going to go through like fundamentals I'm going to take piloting I'm going to take scaling then I'm going to take AI for marketing because I'm in marketing and I'm in you know the insurance industry that I'm gonna take AI for insurance and then the Genai 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 geni app and then you I'm going to attend some mastery live classes because I want to do the AMAs and you be able to ask some questions I want to get a trends briefing every quarter I want like 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 doesn't mean like having to buy academy from us it's like come to the free intro class 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 going to you're going to get that. Find a couple books that you are super relevant. Find some people to follow on LinkedIn or X that you you know really trust. Find a couple other podcasting.
38:25 · Like 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 Corsera. 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 I used um notebook LM last night with my son.
38:43 · He was studying for a Spanish test and he needed to like you I would give him the Spanish word which is funny cuz I I took Spanish but I 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. I said make me flashcards where we want you know show me the Spanish and then turn it to English. And 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 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 going to keep you up to date and on the leading edge of where you want to be. 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 the podcast like I personally get overloaded by it. Like it's um I have days where I'm like I I want it to stop. Like I want it to shut off. I want to not think about everything today. I want I don't want to think about the political [ __ ] I don't want to 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 you can't do that or you don't want to do that. you want to dip in, know what you need to know, and then get out and go on with your life. So, everybody's got to figure out what their goal with this stuff is, and then you can adapt your learning journey based on that.
40:12 · 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.
40:19 · We got time. We got 14 minutes. We got 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? So I teach an entire class on AI councils. There's an amazing uh channel within our Slack community actually of people who have built AI councils within their organizations. Every council is different. You know, I think it it all probably starts with what is the mission of the the council and what's within the charter, their responsibilities and their goals and how it's governed and things like that. So I 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 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. Um, yeah, it's like anything else, especially in an enterprise.
What separates AI councils that drive progress from ones that don't?
41:15 · Everything gets bloated and too many people get involved and there's too many meetings and too many emails and nobody actually owns anything or has the responsibility to move anything forward.
41:23 · And you just got to you got to try and avoid that. However you do it, whether it's split off a center of excellence that is allowed to be more innovative and take some more risks cuz you can test things in a sandbox and it's cool with that. Like you just got to 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 spin-off of that that allows you to keep moving.
Where is governance necessary and where does it get in the way?
41:47 · Okay.
41:47 · Number 12. Where do you think governance is necessary right now and where is it actually getting in the way?
41:53 · This isn't probably going to be a complete thought, but the two things that came to my mind the second you were reading this was uh anywhere that touches data and anything that has to do with agents automating uh outcomes, actions, and and decision-m. So, if you're at a point where you're actually allowing agents to do things, whether it's to your point, Kather, like a customer interface where it's the chatbot 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 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 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-m or autonomy, the higher the risk it becomes and the more you want to welcome that governance and you want guidance on how to do it properly because you do not want to screw that up. 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 it's a whole new world right now. And all the ecosystem and infrastructure is being built around this. And you got to be safe when it call when the use cases call for responsibility.
43:10 · 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 Yeah. 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 just that that worries me.
43:29 · Well, they just need an answer to something. 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.
43:43 · No, I don't think it is either. And, you know, we've talked about it before. I I I wrote this in one of my books. I have no idea if it came from somewhere else.
43:50 · I read it. So, you know, if you've heard it before, you know, 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 could be a personal brand or it can be a business brand. So, yeah, you could invest in relationship building and you know, you could spend time on call like Kate Rober like 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 right now? Katie is going to give you the grace of like, okay, I get what they're doing. 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, cuz 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. And now all those people in your database who are people to you, but to the AI, they're just data points. um 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 Kathy or like CEOs CMOs like that's your life you know the company is based on that brand trust and if you ruin it 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 going know when you crossed it. You know, it might be too late by the time you realize you went too far, right? 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 more hands-on with AI than your average CEO, but what do you want to see? Do you want to see the outcomes or do you want to see like show me how you did it? 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?
Should you show leadership the AI system or the results?
45:43 · 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 got to know your CEO and what what actually is relevant to them. I would say at a general level like if we approach it as a CEO who is skeptical of what AI can do and that you know the need for urgency and the invest in Gen AI platforms and to invest in AI education show the results like they don't you know how you did it is not maybe as relevant to them maybe after the fact 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 here's what it looks like after um 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 sold like I don't I don't even care which tool you're using to do it Now, if that the CEO is like, "That sounds amazing. How?" Like, "Show me how you did this." Great. Now, show the demo.
46:59 · But if you lead with, "Yeah, we're doing this cool AI thing. Let me show you some prompts." And they're like, "But again, it know it's you got to know the leader. And if if they love the tech and all their peers are like bragging about all their AI and they want to be able to brag too, then show them the AI." Like, I don't you just got to know who they are and what what moves them.
47:18 · Totally.
What's the no-brainer AI use case most companies still haven't tried?
47:19 · Number 14. What's one AI use case that feels like a no-brainer at this point, but most companies still have not implemented?
47:26 · Strategic thought partner like I if you're not using the 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 like you need paid licenses, pay the 20 bucks a month. Um, so if you're using like Gemini Pro, uh, Sonnet 4.6 six and claude or um opus or sonnet and then chat GPT you know the the 5.2 or 5.4 for whatever the hell we're on with now with thinking, you got to use the reasoning models. They're they're just it's a cheat code. 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-m, problem solving, strategy building, it is an absolute gamecher. It has been for me.
48:17 · It is the dominant use of how I work with AI every single day. 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 Chad GPT Gemini and Claude. I will monitor the outputs of them. Kathy can attest to we're working on a project right now that we met about yesterday. I put a high value prompt into uh six different models. I actually tried variations. I did claude opus claudet. I did Gemini. I did chatbt with and without my co-CEO.
48:50 · I will try everything on these highv 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 one.
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.
49:12 · And you might think that's taking a lot of time but it's still saving you oh my god hours and weeks of your life. it might take me like four or five hours to do it that way instead of 50 hours. 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. Like, no, sometimes you you have to finish the process and be patient and like see it through.
49:35 · Yep.
49:35 · Okay, 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? It's human nature. That's just just how we are. Like that's not that's never going to change. Like how I mean I haven't said it in a while to anybody probably since Chad GBT, but like go back to 2020. Like how often are you saying just Google it? Like what are you what are you asking me for? Like the the answer is 3 seconds away by just putting it in the search engine. And yet 20ome years after the invention of the search engine, you had people who still wouldn't Google it. Like I don't know how to use Google. So okay. Um, so I think it's just human nature that there's just people who don't want to 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. The only way I found to change behavior is to show them a use case that changes their life in a positive way. Like solves a painoint 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. This isn't that hard.
Why do people wait to be told how to use AI instead of experimenting?
50:50 · Like, and this is how you do adoption in an enterprise, too. Go back to how I started like those hundred people that buy licenses for AI Academy. You you have to give them personalized use cases. So, you you could do the training and like show them how to do it themselves. But when you assign co-pilot licenses or Chad GPT 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. 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 them a GPT train to do those things on the company policies and you know the brand like the the easier you make it for people who still need to learn it the the the way faster you're going to get adoption and buying and actually people wanting it in their life. The example I always give is like no one wants to spend their Sunday night away from their family for 2 hours because they have to write the report that's going to get turned into the seauite Monday morning that they know the seauite isn't going to read anyway. You like hate that. All of us hate that.
51:51 · What if that's the thing you can take off someone's plate like hey Kathy I'm going to I'm going to give you your Sunday night back. Like we're going to build a GPT that's actually going to do this and we're going to set up an automation where just 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?
52:05 · 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 that is the one thing I remember from MCON 2019 when I came as an attendee was sitting in Keith Moing's session about automated reporting with AI and I left and I was doing something else from this that's amazing now funny enough that is the service that we introduced in like 2017 that our clients wanted nothing to do with 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.
52:47 · And then Keith did that talk in 2019 about how we were doing it. And we had zero clients paying us to use it at the time. It was shocking, but that's that's where we are.
52:56 · Yep. That's where we are. All right.
52:58 · Another 15 questions through AI answers and we're done. Right in time. Making my next appointment.
53:04 · Thanks, Paul.
53:05 · All right. Thank you, Kathy. Thanks for joining us. We will be back with our regular weekly as scheduled, which would be I don't know, March 31st or whatever that Tuesday is.
53:13 · March 31st.
53:14 · 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. Um, 7th, I think.
53:26 · We'll be back on the 14th. Yeah. So, the April 7th, we will not have a weekly. We will be back with the weekly on April 14th. So maybe some amazing stories to share of our travels. I'm really excited.
53:38 · All right, thanks everyone. Uh have a great weekend, right? It's come out on Thursday. Yeah, have a great weekend.
53:44 · Thanks for listening to AI Answers. To keep learning, visit smarterx.ai where you'll find on demand courses, [music] 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 it. That's it for now. Continue exploring and keep asking great questions about AI.