AI Adoption Isn't One Size Fits All: 3 Archetypes Every Leader Should Know
Hey, everybody. Welcome back to Make Others Successful, a podcast where we share insights, stories, and strategies to help you build a better workplace. My name is Mitch. If we haven't had a chance to meet yet, I'm one of the partners here at Balb, and I'm joined with
Matt:Matt. Matt. Hey, everybody.
Mitch:Yeah. We we're doing a two person today. Mike is enjoying some time away, so we're gonna jump right into it. We're gonna be talking about AI today. Surprise, surprise.
Mitch:It's quite the the ecosystem right now, the landscape right now. We work with a lot of different clients and everyone has a slightly different perspective on how they should or could be using AI. And so we wanted to to kind of dig into a couple common scenarios that we've seen, maybe buckets that we would put people into and and how they view AI, and then talk about kind of our perspective on on those sorts of things. So the overarching thought is how do you approach AI and how do you view AI and the usage of it for your team? And how how kind of hungry are you to to jump in?
Matt:And this this is an interesting thing because it can be you've seen at the organization level. So we're gonna talk about it in the context of being this is what our customer thinks of it, the the leadership, the business. But it also can go down to individual teams, individual groups, and Right. It's useful to think about these things and recognize where you fit into this, and also where you want to be, maybe.
Mitch:Yeah. Yeah. So there's probably a lot of individual natures or perspectives that boil up into an organizational guidance, an organizational kind of way of of working. So Yep. Let's talk about these these buckets.
Mitch:So I'm just gonna kinda go through them quickly, and then we're gonna go into each one in more detail. So the first bucket is the holdouts. So the people that say, uh-uh, that AI thing, it's a little too
Matt:What's the point? It's too scary. I'm worried about what it does for jobs. I'm worried about how it fits in my business, wasting a bunch of money and time on something that maybe doesn't provide me a lot of value or maybe gets me in trouble from a legal or from a compliance perspective. That's holdouts.
Mitch:Yeah. So the holdouts, they are waiting or they're saying, I don't want it. We'll talk about that more in a second.
Matt:Yep.
Mitch:The next is the Wild West. So these are the people that say, hey, let's use AI. Let's let's make use of it. Go ahead. Go get your subscription.
Mitch:Put it on the company card and and and use it as as much as you need. And that probably leads to a lot of, you know, sporadic usage, but a lot of people kind of on their own little adventures figuring things out along the way. And then the joke is, what what's a token? Like, they probably don't even conceive of how much something actually costs or how does it affect budgets and things like that. The next bucket is the playbook.
Mitch:So these are the folks that say, we have a program, we know exactly how we want to use AI, here's the licenses we're gonna buy, here's what they should be used for, And here's what we should tell the people that want AI that we don't wanna buy AI for. Yep. And so very structured and very calculated.
Matt:Rigid.
Mitch:Yes. Yep. So everybody all these buckets have their own pros and cons. We want to go into each. So we feel like we have a pretty good grasp of how to adopt AI, but let's dig into the pros and cons of each type and we'll tell you our own thoughts at the end.
Mitch:We'll see if you're surprised by the answer or not. So let's talk about section one, the holdouts, group one. So AI is risky. We're not touching it. What are people worried about that might hold them off from do do you feel like it comes from a fear line of thinking?
Matt:Yes. Definitely from a fear, the question is whether or it's real or perceived. And I think in many cases, it's a little bit of both. You see articles, news articles in the in industry journals and that kind of thing about bad things that have happened with AI. Right?
Matt:Either copy route and freight infringement or secrets being leaked or stealing of data or repurposing of data that you weren't that that people weren't aware of.
Mitch:I remember the early story of a Samsung employee Oh, yeah. Put their code on Dumping their somebody's code
Matt:that yeah. A 100%.
Mitch:That kind of kicked off this fear side of things.
Matt:Think Another one another piece of the fear is job loss. Right? People thinking that if we just go get a bunch of AIs, my manager, my team, someone's gonna think that that was just gonna replace employees and we can do this like for like swap for humans versus AI tools. Right? Like, there is a it definitely, I think, comes from a fear perspective.
Matt:I think it also comes from a to some people, AI isn't that new. And people have wasted money on AI for twenty years. And when I say wasted, some people could view it as that. They maybe they were part of an organization that invested in this new AI technology that that's gonna solve their problem and and make this streamline this process. And in reality, they spent, you know, $500,000 and got nothing out of it.
Matt:Right? Had to redo it, rework it. Right? So when when I say out of fear, I don't mean that as a negative thing. There are real scenarios, real situations that are not positive about AI.
Matt:And so that's not necessarily wrong. But that is definitely where they're where they're coming from, their primary feelings about it.
Mitch:Yeah. It just sort of reminds me of the the standard IT approach of if we don't have a plan for this, we're going to turn it off.
Matt:Turn it off.
Mitch:Yeah. And that's the the safest way to to behave. I think the other thing that we want to call out is that some industries, they just take long to do things. They're not super fast paced like finance and like healthcare in particular and and legal companies. And they they are intentionally structured to move a little bit slow.
Mitch:Mhmm. And so how I mean Talk about that.
Matt:Oh, I mean, the the bottom line is there are these fears are real. There are things that happen with AI that are bad. And if you do not have and are not willing to recognize that and address that in a way that is meaningful, maybe you shouldn't be doing it. Right? And a lot of those are exactly what you're saying.
Matt:In the case of health care, in the case of privacy, and some of these things are of a, you know, high concern, your choices are either a, come up with a plan and manage it, which might be expensive and time consuming and all of these things, or just don't do it. And of the two, don't do it is could be could be a legitimate and valuable and correct approach. In fact, for some of our customers that have are play in that space, even in a world where they maybe fall into some of these other buckets in some areas, some, it is a no go zone, period. You're dealing with sensitive financial data? Nope.
Matt:No AI. We're not You cannot input that information into AI, period. Does not matter. I don't care. And that's probably appropriate.
Mitch:Yeah. Yeah. Lots of lots of factors going into that. So let's let's talk about kind of a reality check that we would give these people in the fact that I am hesitant to even talk about AI because everybody is talking about AI. It is this, like, foregone conclusion that everybody and anybody needs to know about it, needs to learn about it, and use it.
Mitch:Otherwise, they're gonna get left behind. And then there's the other side of things where if there is not a blessed way to do it, you might have some, we'll call them bad actors, but they're probably not intentionally bad actors who are going to explore and try these things without guidance and without any sort of perspective from the business. And they will inherently be breaking the status quo of not using AI and staying away from it. And we just see that risk growing and growing all the time. Because it's tempting.
Mitch:People are saying, oh my gosh, look what this thing could do for me.
Matt:As I said before, AI is a lot of different things. You cannot get away from it. AI already was here before the current wave of AI, and it's not gonna go away. Every tool, every toolset is building in AI features and building in AI capabilities. You cannot get away from it.
Matt:Even if you wanted to live in this world where you say you don't you don't you don't wanna do AI, that's going be an active choice. Right? It used to be just don't buy the AI tool. And now it's I need to make sure I turn off all of the AI features in all of the different places in order to really do that. It takes work.
Matt:It's no longer just the you might think when we said that, you know, this these are people who put their head in the sand. That's not the case anymore. If you are a organization that is like, no AI, you're doing it purposefully now because you cannot get around it.
Mitch:Mhmm.
Matt:Because even if I say it at the top level, some feature somewhere is probably turned on and somebody might be using it unless I actually do something to turn it off.
Mitch:Yep. Yep. So, our prompt for a leader in this situation, say you are finding yourself in this you're out on AI, just something to kind of ponder is is this caution? Is this holding out doing something to protect your business? And be like, it's it's just.
Mitch:It it makes sense. It's too risky, and so we need to actively have a stance against it. Or is this something that is just easier to hold off on and easier to say no and easier to protect your job or somebody's job by saying, let's not do that thing. And if it's the latter half, we'll talk about the other the other perspectives that that maybe you could you could hold later. But that's our our question, our prompt to you.
Mitch:Make sense? Yep. Cool. Let's move on to group number two. So this is the Wild West where they just don't really understand AI.
Mitch:They they know all the things that you were talking about, where it's like it's powerful, it's integrated, it's up and coming. So, sure, guys, we can use it. Let's try it out. Go have fun with it. There's a few different ways that this mindset works.
Mitch:First is it's flexible. Right? You you don't have to be the person to say pause. Matt Dressler likes to tell me to pause and and not go ahead and do things, but it's flexible when when you could say, hey, yeah, let's go explore. And it also allows your people who like to solve problems and get their their sink their teeth into something, they can attempt to solve problems, which is good.
Mitch:And then third is you're not tied to one system or rule. Yep. So, that's like the most positive spin that we could think to put on this Wild West reality.
Matt:Yeah. The only other thing I'll say is that you framed it very much like it's someone who doesn't know AI.
Mitch:The organization doesn't know it.
Matt:Even if the organization does know it, they have they have bought into the idea that bleeding edge is the most important thing. Having the latest, greatest, most updated, available option in AI is the thing that's going to make them a winner
Mitch:Mhmm.
Matt:Right, in the AI landscape. That's the mindset that you're seeing. Right? That could be because they don't understand the challenges with related to that. That could be because it truly is valuable to them.
Matt:I don't know. We've had some customers where the having access to the the latest and greatest was enough of a a value add for them that the risk of letting people do more is good.
Mitch:Yeah. Where they chose a completely different platform than they might have if if they had a different bent. Yep. And, like, that's okay by definition. Yep.
Mitch:It's just different. I think there is a few ways that we've seen this problem go wrong. One, it can cost a lot of money.
Matt:So A lot of money.
Mitch:AI platforms in general want to get you to like, it's like any other business. Sign up for a bulk discount on all these tokens, and then your people are spending less money. So, like, if you know you're gonna be spending a lot of money, it can make sense to have a little bit more of a collective offering. Right?
Matt:Yeah. But it also is a problem of usage. Everything is going to usage base at some level, at some stage. And that is an unpredictable thing, number one. And number two
Mitch:Let's define that. So, instead of buying a bank of you can spend you can use these these many tokens, who which who knows what tokens Well, or or
Matt:just saying, like, what for a little while, it's like, I pay for this per user license and I can use as much I want. Yeah. Unlimited. Yeah. Use as much AI as you want.
Matt:Right? And it literally was like, you could be right at doing stuff in AI almost unlimited and no problems. Right? Now, it's almost everything is, yep, great. You bought this much for a month.
Matt:That gets you this many tokens or this many credits or this many whatever that might be. And then you can use that, and that that goes a pretty long ways. But when you run out, you run out. You gotta get more.
Mitch:Yeah. Or or true usage
Matt:Or of total usage of Yeah. You you pay
Mitch:for what you use. Do what you wanna do. We'll send you a bill. Yep. Don't worry about it.
Matt:And you don't even know when you put in the prompt how much that's gonna cost. That could be a $5 prompt. That could be a
Mitch:two separate We'll chat after. We'll let
Matt:you know. No problem. But but it's the hidden cost is the other piece that is is missing as well. Right? Because once you start using this AI platform, your data starts going into a place that you now need to manage.
Matt:And that is a area and a way that people don't often think about it. We talk about it a little bit later, and we're gonna get into it more detail. But it's not just the cost of the license, training, adoption, management. Like, there are real costs when you go from it's one thing to have one person, one time, use a cool thing to do a cool project and then be done. That's one thing.
Matt:To then have that turn into a real AI tool that you're gonna send out to your 200 employees, that's a whole different thing.
Mitch:Mhmm. Yeah. And most of the time, people want that guidance. Right? Like, the worst thing that can happen that we see is, hey, we enabled this for you.
Mitch:Go ahead.
Matt:Go use it. Whatever you want.
Mitch:Yeah. And so all those things that he's calling out are as much as, like, me and my personality don't like the formality of they can make sense at scale in in a lot of ways. So I know you you mentioned data, but only a little bit. Like, that is probably the number one thing that I hear from you when I go try I tried Claude Fable the other day and Matt's like ears perked up and he's like, what are you doing? Like, talk about your data privacy side.
Mitch:Like, if you can nerd out real on that.
Matt:The real problem is that the way that people engage with the with these tools, this is probably the best way to think about it, they the feeling that the tools give you is that you're talking to your private helper.
Mitch:A therapist. Right? Executive assistant.
Matt:And in some ways, that's true. Most of the models, when you're paying for them at the right level, which we haven't gotten to yet, but this is another problem with this, is that if you're paying at the cheap level, because you're like, do we just wanna try this and I don't wanna spend a lot of money, you might be training a model, which I think everybody knows probably what that means and it's bad and everything. But let's assume that you're actually spending some money and you're not paying you're paying enough that it's not training the model. Right? It still gets your prompt.
Matt:If your prompt has private data in it, that prompt just went somewhere. How do I know who was looking at it? How do I know when it got destroyed? How do I know and so if your order if your if your team members are putting in PII or financial or other protected data into their prompts and sending out to this model, you've just moved this data outside into this other system. Does it have the same level of protections?
Matt:Does it have the now, if you can trust your team to not do that, or if you can trust the model, because a lot of the models have protections to stop, says, hey. No. No. No. I'm not gonna I'm not gonna accept that, or I'm not gonna store that, or you know?
Matt:If you can trust all of that, great. But how do you trust that on every model, in every case, for every application, for every feature that's used across all these tools?
Mitch:You're gonna push me right back into group number one Group number one. And say lock it
Matt:all down. Don't do But not it. Like, the real deal is you just need to be aware and be training and be knowledgeable. Because quite frankly, it takes for a lot of for a lot of data data elements, it doesn't take very much to scrub the data and, like, remove some of that information. And you can still ask the same question to get the same information back.
Matt:Yeah. It just takes a few more minutes.
Mitch:So would you recommend your strategy to everybody where you just sit over my shoulder and make sure that my model selection is correct?
Matt:Every single time? And that I check my token usage. That's not appropriate. I do recommend my strategy of shaming you when you do something bad.
Mitch:Even if I unknowingly did it, I didn't mean to.
Matt:I mean, it's okay one time. The second time, though.
Mitch:Sorry. Sorry. I didn't mean to. That aside, so as we're reflecting on this reality that we've seen people choose, the question to a leader then is, does letting people go crazy with this stuff just cause more problems than than it solves, basically? It's something for them to ponder.
Mitch:I don't know that we need to to answer. It's it's what sort of leads us to the next bucket of people. Right?
Matt:Yeah. It's the question that at the end will get resolved.
Mitch:Okay. Okay. So, yeah. Let's let's kind of continue that line of thinking and say, okay, I should use it. I can't let my people go crazy with it.
Mitch:I need to have a really structured playbook that people can use when they're using AI. So we want to adopt AI tools. We want to make sure all the people are on the same page. When they're in their work, we want them to be able to find the right thing, apply AI to their specific scenario, and it all just makes sense. Right?
Mitch:So couple things that this enables is it ensures continuity. So when people see each other working in their their workdays, they know how things go and they know what tools people have at their disposal and they can kind of apply their problem solving approach and recommend it to other people. Another one is that you make sure everyone is using it and hopefully no one is falling behind. Like, you have a, say, a spreadsheet of people. These people all have licenses.
Mitch:They have checked the box of of training and hopefully, it's it's making sense. What are your what are your thoughts on these scenarios where, like, these are theoretically areas where this approach works well?
Matt:Yeah. I mean, it is these key component. When you need consistency across an organization in the use of AI, and that is where you're gonna get the best benefit, this really helps. Right? This is where getting everybody be on the same page is a huge benefit.
Matt:Right? Because now everybody's running from the same playbook. You don't have one person who's your superstar and did some crazy investigation and came up with a new way to do it, but then they're guarding it themselves. And they just that they use this thing, and that's what they do. And you also don't have somebody in the corner with their head in the sand going, I can't use this, I can't use this, I can't use this.
Matt:You bring them you're bringing them forward. You're giving them the tools that they need to make that happen.
Mitch:Mhmm.
Matt:It is by far the best way to do this in mass at a at at scale for large groups of people. Mhmm. And by that, I don't mean thousands, even 10 or 15 people having this type of thing is is what you want.
Mitch:I think that all makes sense. Right? Like, when we are doing this from a business standpoint, do whatever you want personally. Like like, I I'll go adventure in my personal life and go try out different models, and I just used it to help me build a little gaming system for my kids in the corner of the living room. And it's super cute.
Mitch:And I I love it. And I I used ChatGPT to help me do that. I didn't use Copilot or Claude or I might be right or wrong for that, but guess what? It it worked. But when we talk about organizationally, it probably makes sense to have some sort of perspective.
Mitch:But it doesn't work all the time. We have seen it go wrong. The first one that comes to mind is, I think in our experience, certain groups of people got missed in the strategy. They had such a particular perspective on how to use it and what it should be used for that they forgot like half of their users. You wanna expand on that?
Matt:Yeah. I mean, it's difficult to get that right for everyone across all of the things. If you don't focus on the common goal that you have for your organization and for your team, and then provide outlets to be able to go, oh, this group of people, they are gonna be the the explorers, the the trailblazers in what they're doing. It creates problems. You end up either trying to create something that only works for the trailblazers or something that only works for the people who are working day to day or only the people who can never use AI because they're using always private data.
Matt:And then you you kinda miss the miss the mark. So you need to have a a multi pronged approach. And if you choose just one, you likely will end up with a problem.
Mitch:Yep. And I think there's also an aspect of when you write something in stone like this Mhmm. And and take the time to formulate an opinion and a playbook and training, and all of a sudden, new model gets announced tomorrow and What do you do? All of a sudden five times better, and you're like, shoot, do we want to do all of this again? Right?
Mitch:Like, that will continue to happen for the foreseeable future. Yep. That's it's a tough reality to to sign up for. And then I think, like you were talking just about flexibility, like some people need to try different things and try different skills, different models, different tools that might work better with either their specific job or data that they need to work with or different tools that they need to integrate with and things like that. And then it all obviously comes to to the ground when training isn't involved.
Mitch:When people say, great. This is how we're doing it. We have signed off. We bought all of these licenses. Here's what we're gonna use them for.
Mitch:Go ahead. Without the actual, like, hands on, here's what you need to do in order to feel what it's like to use these tools. We we have seen that that fall down. And usually it comes out as a symptom of, oh, shoot, people aren't really using this. Maybe we need training.
Mitch:I think we're we're suggesting attempts to not make it an afterthought.
Matt:Yep.
Mitch:Okay. So the question for leaders that we have for this group of people is, did you make an informed decision to choose the right tools or did you pick something quickly because you had to? And sometimes speed, like I was talking about, sometimes this is a little bit of a trick question. Like, you do have to pick quickly, but it should be a certain amount of informed. Right?
Matt:Yeah. Well, it also is a part of the whole conversation is here is that the AI world is changing so fast. You need to be positioned in a way that allows you to change it. Yeah. Choose quickly enough so that you make the right choice right now, but also choose something or or implement in a way that allows you the ability to evolve.
Matt:Because if we've learned anything in the last three to five years, it's gonna evolve.
Mitch:Is it gonna change? That long?
Matt:I mean, yes.
Mitch:Was it yeah. Five? I don't know about five, but I remember yeah. Years
Matt:ago GPT? Three years ago three years ago was GPT was
Mitch:That's scary. That shows you how fast.
Matt:It was not
Mitch:it was not it was
Matt:not in the way that it is today. It was mostly developer tool,
Mitch:mostly. GitHub Copilot. GitHub Copilot. I find some
Matt:Yeah.
Mitch:Some, like, help help posts or whatever in the forum and
Matt:From long ago. Yeah. Like, it's been around a very long time. And if you think about where it was then and to where it is now, and where it will be in six months or in a year, it is frightening a little bit. Scary.
Mitch:Okay. So let's close with how do we hold all of these in our minds? Oh, Will, pull it up for us. 11/30/2022. It was launched as a free research preview to the public.
Mitch:That's, like I said, I'm actually like having a moment of holy crap, this
Matt:move Yeah. And so
Mitch:it's been out for a while, like, this has been a little bit of a crazy ride. And we're just now coming out with this podcast about three different groups. Like, for anyone on the bleeding edge of this stuff, props to you because it's it's it's kinda crazy. Yep. Let let's close out with our amalgamation of these approaches.
Mitch:Like, if we could wave a magic wand and say, you want this part of of group one, this part of group two, and this part of group three, what would you what would you prescribe?
Matt:I mean, it really comes down to leveraging all three. Like, the reality is you need a group of people who, within the constraints, are allowed to be those trailblazers and have a little bit less control. I'm a little bit less. It is for sure.
Mitch:You give me permission
Matt:to go You
Mitch:You won't bother me about it anymore.
Matt:You have explored. Mhmm. And you will continue to explore, just like I will continue to explore.
Mitch:You've heard it here.
Matt:Yeah. He's he's now thinking he's authorized to do anything. Yeah. But it like, it is that small group of people who are like, they're gonna be the one that learns about the new model that will have a transformation part of your business that helps you understand how that might come into your business, that helps build out this thing that might be used by other people. Right?
Matt:Maybe they start out with it very unstructured and and, you know, very little control, but then they prove the value. And then it turns into, oh, we should invest in that and make that something for the rest of the organization. And that's the second p group.
Mitch:Hold on. Historically, we would label those people like champions.
Matt:Champions, trailblazers, whatever And it we're not saying it's not all the way the Wild Wild West. It's not Yeah. You can do anything Well anywhere.
Mitch:I'll say
Matt:But it's way less controls.
Mitch:From from my experience, the hardest part that I have found in that is because I wanna try things and figure things out. I am way less likely to stop and articulate a perspective and say, here's how this should apply to our business.
Matt:Yeah.
Mitch:And that part is really, really hard.
Matt:Yeah. Yeah. There it's work. Yeah. But I think that's an important part of this.
Matt:Right? The second group is probably the other end of it, which is like so in some organizations, this may never be a group of people. But I think in most organizations, there are gonna be a group a smaller group of people that are like they deal with our specialized information, and I never really want there's very few scenarios where I want that to get out anywhere else. And so there's gonna be way more controls, and they're not gonna be using the bleeding edge technologies. And they're not gonna be they're gonna be doing what is needed to keep this thing going.
Matt:Right? And it's and and having it be secure and in a box. Right? And I think that's a totally fair group of people within an organization. Like I said, I think in most organizations, there may be very few or none of them.
Matt:But I think for lots of medium to large organizations, there's going to be this group of people that need to sit in the it's not that they've got their head in the sand. It's not that they're trying to resist change. They rightfully so, it should move slower for them. Right? Then there's everybody in the middle.
Mitch:Well, I wanna give an example of that group. Like Yeah. Some people are in a ditch with a shovel. I don't think they need to understand bleeding edge AI. Right?
Matt:But they may need to use it for something. Yeah. Expense reporting or for something else.
Mitch:To write up their next But it
Matt:should be only it should be only certain features that they're doing. On the others No.
Mitch:I'm gonna keep Okay. Keep adding. Are they missing something? Would you ever say somebody should never use AI? Or they are not missing out from using AI?
Matt:I think it is I'm gonna hot take. I think it's impossible for anyone who uses any form of technology, phone or otherwise, to not use AI. Straight up. Period. But I think you can't do it.
Matt:Whether or not it's you know that you're using AI is a whole another question. Sure. Right? But I'm telling you right now, when you order online for something or, like, you're using like, AI is involved in that at some level.
Mitch:Mhmm.
Matt:Right? So everyone in the world at this point that's using any form of technology is 100% having some interaction with AI. But, to your point, the people at that end, it's not gonna be obvious to them that that's what they're using. They're just using a thing
Mitch:Yeah.
Matt:That has AI built into it. They type in some things, and it does a thing, and it does what they want, they move on. Mhmm. Right? They're not doing it to use AI.
Matt:They're just doing it to get their job done.
Mitch:Sure. Yeah.
Matt:And then you got everybody in the middle. Right?
Mitch:Yeah. Now, I'll let you continue. I
Matt:wanna continue. This is probably the bulk of people. Like, this is where everybody should be, which is this this prescriptive playbook, whatever you wanna call it, way of using AI. And I'd say everybody in the middle, much to like you're saying, I don't know that this applies to everyone that works in a in a manufacturing facility. I don't know that it applies to the bulk of people.
Matt:But definitely knowledge workers, definitely people who are interacting on calls and and doing managerial style tasks. Right? Their need of being able to use AI for the purposes, that's that's who it's targeted at. Mhmm. There's a lot of them.
Matt:They need to have structure around it. They need to be provided options that are safe and are okay for them to use, so that they can get do the best job that they can do, but they also aren't putting anything at risk. Mhmm. And that's what they need. And so that there's a place for all three of these.
Matt:Right? Different Yeah. Zones.
Mitch:And I think that group is the group that needs, like, I don't wanna generalize too much, but training the most where they need to see something applied to their daily work so that they can say next time that they need to do that.
Matt:Really interesting because I would well, I would hesitate to call it training as much as they need to gain a new personal skill. Sure. They need to learn how just like people in that same space probably learned how to Google.
Mitch:Mhmm.
Matt:Learned how to search. Learned how to think about their day from a calendar and from an email perspective. If you think about long ago, back when they were sending inter inter department memos using a
Mitch:Right.
Matt:You know, inter company mail system.
Mitch:The tubes.
Matt:Right? Yeah. And then now they're doing email. Like, that whole group, it's not like because the reason I have a problem with calling it training is, like, they just need to use the tech they need to learn how to use the tech. They need to wear all the buttons are.
Matt:No. No. No. They need to know how to rethink about their work in the context of what AI can do for them. Mhmm.
Matt:That's what it really is.
Mitch:Not simple. No. And it's easy to just leave that to the side and say, they'll they'll figure it out. Well, yeah.
Matt:It's this is where people are talking, like, there's lots of discussion around the legality isn't the right word. Concept of I'm gonna fire you unless you use AI. You must use x number of AI credits, otherwise, you get a pip.
Mitch:I I have I have a lesson in one of my courses that talks about this. It says, be an adult and recognize we're all adults. And I can tell you, you need to do this or else I'm gonna fire you.
Matt:Yeah. It's an interesting way to think about trying to force
Mitch:Yeah. I don't like it.
Matt:The situation. Because it doesn't it doesn't get at the heart of the problem.
Mitch:So No. When you want to enable someone with something, the last thing you want to do is take a stick and Beat them with it.
Matt:Yeah.
Mitch:Yep. Yeah. So welcome to our world, folks, where we need to encourage people to use these tools, but not beat them in the head with a stick.
Matt:Yep.
Mitch:That's that's our bread and butter. Anyway, I appreciate the perspective. This is, yeah, again, our take on a couple different buckets that we've seen people fall within in this AI ever evolving world. And it's exciting. It's intimidating.
Mitch:It's overwhelming in lots of ways, but we gotta show up for it, and we gotta continue to try new things and bring people along for the ride. So, Matt, any closing thoughts before we go?
Matt:No. I think we covered it.
Mitch:We got through our list here. If you have any thoughts, I think I say a note in a minute, but we would love your feedback on this. What sort of perspective does your organization have? Yeah. Do they are they jumping in with both feet?
Mitch:Are they overwhelming with process and security and IT administration or somewhere in between? We'd love to know. Please share your your thoughts over at ballblooddigital/feedback. But for now, we'll let you go. Thanks for listening.
Mitch:Thanks, Matt.
Matt:Thanks, everybody. We'll see you soon.
Mitch:Hey. Thanks for tuning in to Make Others Successful. If you enjoyed this episode, we'd love to hear from you. There's a couple ways you can do that. One, we'd love for you to rate our show on your favorite podcasting app.
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