Often you get a different type of burnout from doing generative AI over and over and over again on the same thing.
SPEAKER_02What are the commonalities that you're hearing where they're getting it right, but also where they're getting it wrong?
SPEAKER_00There's a train of thought that treats it more like an IT project when it's actually more of a transformation project.
SPEAKER_02So how do I navigate this? Hey IT guys, we need you, but we don't need you need you.
SPEAKER_00The short answer is centralizing the decision making from a tool perspective, but decentralizing the decision making and deployment from a use case perspective.
SPEAKER_02I mean you give them what they want or do you give them what they need?
SPEAKER_00They have to get their hands dirty. They have to be fully immersed in using AI in their own work.
SPEAKER_01Justin Trombold is the founder and president of AntiSyn Advisors, a Gen AI strategy leader who helps companies answer the big question: what are we going to do about AI? Moving teams beyond pilots into measurable results through readiness diagnostics, practical workflows, and culture first transformation.
SPEAKER_02Welcome to Using AI at work. I'm your host, Chris Dagle. Each week we'll be learning how today's business owners, entrepreneurs, and ambitious professionals are getting more done with smart use of tomorrow's tech. Let's get started. Right now, every business leader is asking the same question. What are we going to do about AI? If this is you, ChiefAIOfficer.com has the answer. We give you a simple path forward where we provide executive and team training so your people know exactly how to safely use generative AI in their day-to-day. We also manage the deployment and implementation to make sure tools actually get adopted and deliver results. And we'll also guide company-wide transformation so AI becomes part of your operating system, not just another shiny object. The companies that act now will increase productivity, cut costs, and grow faster than their competitors. Those that wait will get left behind. So if you want to make AI work in your business, visit chiefaiofficer.com and see how we're helping companies of all sizes finally get results from AI. Hi, everybody. Welcome to another exciting episode of Using AI at Work. My name is Chris Stagel, and I'm the host of the show. And today our guest is Justin Trombold. He's the president and founder of Antison Advisors, and he is talking to a lot of businesses that are asking the question, what are we going to do about AI? And I'm always excited to bring other people who are out there, like not just coming up with ideas, but actually dealing with clients in the wild when it comes to, hey, we got to do something, help us. We've got these ideas or we've got these misconceptions or whatever. And there's a couple of threads that I'm going to pull on for sure, Justin. But before we get started with that, maybe just um give a little introduction about what your career has looked like that so that you've arrived on this podcast today.
SPEAKER_00Yeah, and I I love the way you said that of being out there in the wild, because I think uh it's certainly like that more than it's even felt like before with with consulting and the work that I do. But I'll keep a very long, boring short or long story and boring story short and perhaps still boring, but just so people know who I who I am. But I was uh an academic uh teacher and researcher for 10 or 15 years in in biological sciences and you know, doing a lot of various different work. Um, your listeners might be thinking, well, why is this guy now talking on an AI at work podcast? I about a decade ago, I switched and and started a new career path working in in consulting. And so your typical management consulting, you know, big four strategy firms, that those types of cultures, had a chance to work at a number of of those different firms and over the last three years transitioned out of that lifestyle into more independent consulting. Some of that had to do with just my proclivity for you know liking to go my own way a little bit, but some of it uh I have a three and a four-year-old uh at home and you know, the big firm consulting lifestyle, while independent consulting is is busy, it's a different kind of busy. So I have a little bit more mental bandwidth for those for those guys. And I think in short, you know, there are a lot of things that I can do, but the relevant part for your audience is I work with a lot of organizations, whether it's you know, your larger Fortune 500 type organizations, all the way down to, let's say, five, 10, uh, 10-person family shops that do something. And even solo, solopreneurs, uh, individual entrepreneurs, helping to understand not just what generative AI is, but a way of thinking about generative AI and AI in such a way that they can consume it in the context of their business and deploy it in the context of their business. So that that takes that, yeah, that that monster takes on a lot of different shapes and sizes, but there are some central tenants that that string together that I find are are true for the biggest companies you you hear of, down to, again, a single individual uh that that's building, that's trying to build something. So it's it'll be an interesting conversation and you know, a lot of different experience in different industries, both in and out of AI. So excited to talk to you about that.
SPEAKER_02You know what? Well, I think that's a great place to start. Like, what are the commonalities that that you're hearing where they're getting it right, but also where they're getting it wrong? Like your your clients or these prospects that are interested in getting help with AI?
SPEAKER_00Yeah, I I think the the the easy one, you know, we'd say there are obviously companies that are getting certain things right, but we'd say, like, well, what's the what's the common thread that people are getting right? And it's usually the fact that they have this is something they have to pay attention to, right? They they have a degree of focus on. Now that, like most things, putting a little bit of focus into something is very different than putting a lot of focus into something. Yeah. And when you put a lot of focus into something, you're always at risk of of overinvesting or misallocating resources in in certain ways. And of course, in in this case, um, you know, getting you know confidently wrong answers or perhaps getting lazy and the type of thinking you're doing and so forth. But you know, so what organizations get get right, I think is the the understanding that there has to be adoption. Now, what typically gets wrong, and and we can pull on a bunch of different threads, but I think one area that's notably wrong, I'll say two that are notably wrong is one, there's a a train of thought that tr treats it more like an IT project when it's actually more of a transformation project. So that's that that's one part. And then the other part where there's uh a lot of mistakes and it's it's a little bit of a different flavor of the first one, but it's it's the concept that there is this technology solution, but it isn't a technology solution that can solve every problem. And and what it what most of the time organizations get wrong is they assume if we plug in solution X into workflow Y, we're gonna get outcome Y better and faster than we did before. But what usually happens is it improves some aspect of process Y. You know, and so maybe that's accelerated and that's improved, but the process itself just gets gets stopped, basically, at that at that point. And and we can dig into that in more detail, but just think of an idea where you know a typical bottleneck in a process, oftentimes what AI will do is it'll just shift the bottleneck to a different part in some sort of workflow. So that's a little bit more of a detailed version of the first one, but yeah, the same, you know, it's it's change is one, and two, it doesn't solve a whole process or a whole problem. It tweaks certain aspects of a process or a problem.
SPEAKER_02Okay, I I like that a lot. And I am gonna to dig in on that, but I want to start with this this IT conversation because for uh okay, I'm an executive, I've got maybe a free account to chat GPT. I'm kind of using it. I know we've got some people scattered throughout the organization who I hear chatter that they're power users, but I have no idea what they're doing. I'm too busy to sit down and watch a bunch of YouTube videos. I've heard some you know, some presentations at our industry events over the past year and and I'm intrigued, but like uh I've never really got gotten, quote unquote, gotten what AI is. So to me, as that executive, it seems like, hey, bring the IT guys in here, let's talk about this. It seems obvious, right? This is a technology, it's artificial intelligence, machine learning, data science, but generative is not that. So how do you because here's what I don't want to do as the executive obviously I want the right information, but I don't want to alienate or I don't want to create this um uh contrast between, oh, well, it's not the IT guys, they're gonna feel left out. And I know how protective IT is of you know their role and their domain. So how do I navigate this? Hey, IT guys, we need you, but we don't need you need you.
SPEAKER_00Yeah, you know, it it it the you know going back to the Dilbert cartoons, you know, IT is always a a point of you know, some some jokes and so forth, and there's always a a nugget of of truth in that. I I think it's as as a leader, you can set the precedent. Well, we see like with like anything else, if you set the precedent early in your organization, and and perhaps as a leader, you know, someone listening has already done this, but what happens is if you make it an IT first priority and you bring in the IT, IT leads and put them in charge of the let's just say the generative AI agenda, that that starts with what you typically think of of selecting tools, and yeah, that very much could be in the domain of the IT team, right? Of which tools can you can and can't you use and security parameters. So picking the tools, but then what we see is that the the the further the IT team starts creeping down into use cases and deciding where investment should and shouldn't happen, that's where you start to get a fundamental conflict. And it's a conflict for a couple of reasons. Because there is an interpersonal aspect of it, you're gonna start seeing solutions that are deployed that don't necessarily address business problems. And you're gonna see what you typically see with when when anyone in any business gets a new technology solution, they're like, like, this doesn't have anything to do with anything I do I do. Like, I don't care. So you get that that natural friction that will start emerging. And and and what we see in that case is you know the people that you really need to have engaged. And and it isn't, we were talking about this a bit a bit before it, it's uh before the conversation. It's a mindset of wanting to empower and engage your people and make them feel like they have, and for them to truly have ownership over how it's being used, where it's being used. And so what we typically see that works very well is you do have an IT thread, right? But that's more about the the toys that are in the sandbox, right? Okay, yep. But in terms of, let's say, picking which toys are getting played with, how big that sandbox is, what game's being played in the sandbox, you know, whatever, you know, everything you're that you're doing. So that's an analogy for deploying the tools. What we see is having an individual in charge at the business unit level, you know, somebody that's in the business. Yeah, so this could be a business unit lead, a functional lead. In a smaller organization, it could be, well, this is the person that typically manages HR or typically manages um, you know, product product for a company, whatever the roles are and however it's organized. You make them the lead. And what they do, and and this can this can show up in a lot of different ways, but they're the person that makes sure there's connectivity between business problems and the way the tools are deployed. And on occasion, there'll be an upstream conversation. Hey, we need a different tool. In that case, of course, there has to be some conversation, but there shouldn't be a conversation in terms of what the use cases are unless there really is a true feasibility, technical feasibility bottleneck, which is almost never the case in a lot of these tools. Yeah. And so the the short answer is centralizing the decision making from a tool perspective, but decentralizing the decision making and deployment from a use case perspective.
SPEAKER_02Yeah, that makes sense. Okay, so IT's involved when it comes to now he here's here would be my concern if I was uh you know a listener to this and exploring this. We're you know most companies are in Microsoft. Uh oh, well, we've got Copilot baked in, and Copilot has all of these things. They've got a partnership with OpenAI, they've got access to Cloud Now, we've got agents. However, our experience in working with clients that are in the Microsoft environment is that that tool as of today is falling short when compared to the other options that are out there. And as an executive, I don't know that, right? Because I I it just all is the same to me. Let me ask you, has that been your experience?
SPEAKER_00It it it has a little bit. I to be honest, I do find it's it it's often a little bit more of a of a theoretical question than one that comes up too often. So what I'll I'll say that in larger organizations where they have, let's say, preferred vendors and they have a stack in place, provided that there's access to some form of LLM, and you know, there's there's some trade-offs with different ones, but let's just you know put them in that you know, Gemini, Claude, ChatGPT, Grok, you know, the four or five that are the core, you know. As long as you have access to those and you have enterprise licenses, and perhaps there's there's a reasonable amount of of restriction on what you can and can't do in using the model, right? So obviously if the restrictions are too high, that can create another set of problems. As long as you can clear that hurdle, as a person in the business, you just you might have to be okay with, look, I'm not gonna win this battle. You know, we're we're always we're typically, you know, we go with this type of vendor. Good. You know, we have relationships that led, yeah, led to us having anthropic instead of let's say um, let's say grok or instead of your ChatGPT or Gemini. It's more of just accepting that as a reality and then just getting used to using that system. I think what what I found in in my own work and working with clients is once you you spend a half day with any of these models, you almost forget the model that you're working in. Yeah. And so you'll you get past that. And but at a smaller organization, you have a different type of problem, which is oftentimes there's a hesitancy just to get over that first hurdle, particularly if it's, let's say, a family shop and the leader is, you know, let's say a more senior person, hasn't really been around much, and maybe they're uncomfortable with even deploying like an enterprise license at Chat GPT for enterprise use, that's a different that's a that's a different argument. So these are all like more political aspects, but if you can clear that LLM bar, you can do most things you would need to do in the way that at least we we talk about. Now, now you might be restricted in certain applications you could purchase, sure. Right, that may or may not talk to those models. But in terms of deploying those LLMs and exploring use cases and testing and scaling, you could almost do anything with any of them that that's technically feasible at this point.
SPEAKER_02I'm satisfied with that answer, and I'll tell you what I just realized is that I'm because of my like I guess my my depth in this subject, I'm jumping the gun. Most of these people, like this is their first experience with any type of LLM. So the reality is you're right. It doesn't matter what they start with. Once they exhaust the capabilities of that model, then maybe we talk about well, this let's add some more, let's get let's get Chat GPT enterprise licenses or whatever. But I think I think that's really good advice is don't get caught up in oh, but this one's better at this and this one's better. Just you know, ride the horse you got, right? I think that's great advice actually. Um okay, so who needs to be in the room when we're we're ready to have that conversation? I I like the idea of the the business unit or the the domain kind of heads because they're gonna talk about the problem from the bottom-up approach. We know the pains that are happening with the people on a daily basis, but we may not be thinking about the strategic alignment of the use cases that we think are important, right?
SPEAKER_03Yeah.
SPEAKER_02So how do I, when we're ready to say, okay, guys, we got to do something, let's figure this out, what is that how are you structuring that conversation with clients?
SPEAKER_00Yeah, so it's it's one of those things that's very easy to say and always kind of hard to do, but but the two the two buckets, and it's always it is always helpful to clarify what it is that's being discussed, right? Right. Because if you're having a discussion about how to use an LLM, and it isn't clear if you're talking about, let's say, realizing the enterprise strategy and some broader vision versus solving some more precise problem, it's gonna be an impossible conversation. And so the way that we we sit around this is split it into two areas. You know, the first area is okay, we have our enterprise strategy, we have our business unit strategy, we have some strategy, and it could be at a more local level within the company, right? But you have that strategy, then you start asking yourself the questions of within a given strategy that we have, where are those opportunities to leverage generative AI to help realize and facilitate that strategy? Now, there's a lot that goes beyond that, but just conceptually, it's good to think about it as a separate bucket. Now, as you get further along, that can be reimagined where you go back and say, should we change our enterprise strategy because of those tools? But it's premature to have that conversation before you've even deployed anything. You know, you have to have your hands in it and understand what it can and can't do before you do that. But the second bucket is okay, you know, person X, Business Unit X, Team X, how can you deploy generative AI tools? And the way that we recommend is always starting with more of an open forum LLM approach. So going in and deploying those tools in your work to first maybe solve a specific problem, accelerate something. And as part of that, a key thing that that I always like to talk about, and we we alluded to this a little bit earlier, is that start with something simple. Start with one thing. Maybe it's one process, it's one thing we do. Ideally, it's important. It doesn't necessarily have to be, but ideally it's important. Map out that workflow. Find the place where you think it doesn't, you don't even have to know for sure if it's a generative AI solution at that point. It's just, hey, this is something that's very manual, yeah, it's very text heavy. Those series of questions to identify a play, or you could you could even put it in LLM and say, here's my process, what's a candidate, right? You can you can use so so you find that point, yeah, but you have one step before you start experimenting, and that is okay, if this was faster, or if we got this different output or result from this step in the process, what has to be true downstream or upstream for that to be realized, right? And so like yeah, the example, you know, we were working with a client, and I think it generally just resonates even if someone isn't even even if someone isn't in sales, because it conceptually made sense. We were working with a a client to to deploy a a lead qualifying and and lead um you know, qualifying scoring. So it was about like, is it good? Can you warm it up a bit? Like, how do you create custom content? What was that? And what that was doing in that case, that was asset management. Okay. In that case. And so the the these leads, this this flow is coming through. And but this holds, I think it's a pretty simple, it holds through to you know most anything. And yeah, the leads did accelerate, but what what wasn't different is the salespeople either didn't know how to use what they were consuming, or perhaps more importantly, there wasn't a change in the incentive structure in order to incentivize the salespeople to sell more. Yep. And and so, and in in in that business with that company, they were more incentivized to expand existing accounts than to sell the new ones as well. And so they just got this log jam of qualified leads. And so ROI from that solution? Absolutely not. Yeah, because it's not going to do anything. So the the the that step there that's in between is look at it from a non-generative AI lens, from a first principles lens, and say, okay, the bottleneck was maybe in finding good leads before, but now it's in actually selling to the leads. What has to change to now address that new bottleneck? So you you do that, then you have it could be an out-of-the-box solution. Yeah. But it's it's it's more likely that you're going in, particularly with smaller companies, and you're like, okay, well, we have this process of qualifying leads. Let's start experimenting with putting together really good prompts. So we work with clients a lot. What does a good prompt look like? How do you then actually use the LLM to make the Prompt better and better because you can do that as well. And then how do you get better at directing it to good information sources? You know, that that's a that's a very important thing because you know I'd ask advice your audience to just Google the typical information sources for any of the major LLMs, and that'll make the case for me in terms of where this that comes from. And then they start experimenting and testing. But what's key is there has to be a very clear set of parameters that are set up on the front end? Yeah, it's not just about what models you can use, it's also about well, if this starts working well, what does this look like?
SPEAKER_03Yeah, what does it mean?
SPEAKER_00Yeah. Well, well, what is it what even what does it mean for the person that develops the solution? Yeah, if it works, how do we know if it works? If it works, is there a light at the end of the tunnel in terms of you know scaling and investing in, let's say, a custom agentic solution that does it really well? So you're not reliant on that person. And so it's it's two categories. So you have this you have this idea of deploying it for the strategy, and you have this idea of what the individuals are using. Now you can you can have connective tissue there, right? So back to that sandbox, you know, a good generative AI strategy is linked to an enterprise strategy or a business unit strategy. Sure. And within that, that's what sets the walls of the sandbox that you're playing in. Okay. And so then what often is is true is that you're the individuals that either in the center of excellence for AI, if it's a large enough company, or the leaders for any size organization, they often won't have a good vision into how that has to come to life, or like what needs to be true for that to work. You know, we're we're often prior, you see, clients on the receiving end of that, prior to generative AI, on the receiving end where it's like, they want us to do what? Like we can't do like that's not possible. Like we had they don't understand how we work. Now it's a little bit different of you want us to do that, you don't understand what it is we can do. Yeah, yeah, yeah. You know, but but the people sitting on the ground do as long as there's clear communication, right? Yeah.
SPEAKER_02So this gets tricky then because, you know, like if you think about it, well, we've got this one pain point in the in the company. We don't like doing this activity, or we've got a lot of people that spend manual time doing this. That's probably what a lot of people have heard. Find the find the manual, you know, where you're spending a lot of time manually doing stuff. But just because we solve that, if we haven't considered upstream or downstream from that fix, then like, okay, now we've just opened up the faucet, but it's flooding this other area of the business that wasn't that was that was built and designed to accommodate what we were able to do as far as throughput-output constraint. Interesting.
SPEAKER_00So I'd say one thing, maybe just like put one thing on. And that that challenge that you talked about of the downstream becomes harder and harder if you start crossing borders. Okay. Whether it's between business units or between functional parts of the business, that you'd you'd imagine that if it's just you working, like that bottleneck, you're very aware of that bottleneck and everything, and you're not stepping on anyone else's toes and you're not reliant on somebody else. And so one of the key tenants in the research that we put together, and this has you know been bearing out in client work, is that the ability for organizations to collaborate cross-functionally and not work in silos, yes, is a big driver of it isn't necessarily like a single use case, if it's a constrained use case to like a small group, but anything that spans business units, spans functional units, that if if you're a siloed company and you have an ownership culture, not in a good way, but like this is mine, not yours kind of ownership culture, you're gonna see a lot of problems that start to come up uh when when you do that. And and it'll it's not gonna break the silo down, it'll actually fortify the silo because people will start interesting, will start feeling more pain because of each other. So it's in some well, what we've seen in some cases is there's an assumption that if the other team, let's say, starts to see, if the salesperson starts to see more leads come, they're gonna then want to see, they're gonna want a solution. Right. But what happens in some cases is, well, you're giving me all these leads, but I want to talk to my existing relationships, and you're putting pressure on me to talk to new leads, like now I'm upset. Like I'm not just not more efficient, I'm actively upset by this thing that changed and is now messing with the way that I sell work.
SPEAKER_02Interesting. Man, we're covering process, change management, we're covering all the stuff here. But these are all important because I think that, you know, uh maybe the listener, you're the listener and you're checking this out, and you're like, hey, we're ready to do some stuff. And you kind of think it's going to be this fun process of, ooh, look, AI magic. I was able to, you know, write the email or the very basic things that people get started with. And you you don't know what you don't know. So as a result, you kind of get started with those things, but you expose the company to risk because there's no use policy in place. There's no hasn't been really any training. The training came from somebody that watched a TikTok video and is now doing the thing in their role. And you start doing that. And then there's this, well, the they feel like my their toes are being stepped on because all of a sudden you're forcing me to do something that wasn't part of my job description. Or what like this is really getting tricky here. So how do we make sure that when we're getting started, that we are thinking, we're playing some chess moves ahead so that we're not just getting caught up in the magic and the the oh that's cool, but we're actually doing this considering the downstream impacts.
SPEAKER_00Yeah, and and I'll I'll do a quick sidebar. Yeah, one of the things we work with clients on at the beginning of whether we're talking with them in a training session or we're speaking more generally about what has to be true in some of these operating model designs, there's a mindset that goes into it. And it's important that the leaders and individuals throughout the business have an exploratory, have a curious, have an excited mindset about doing this because if they don't, you you you you just you aren't gonna get the same level of buy-in and the same it could because it is fun when when you have the freedom to do it. So just setting setting the idea that we're gonna give you some space to explore. And we worked with a client that they had this culture in place a bit already with prior technologies, they just applied it to generative AI, where they're mandated, it's a loose mandate, but they at least give them the space to deploy 20% of their time to integrate generative AI solutions in their existing projects. And so they they don't have to do it, but it's there. And they so again, that's about the mindset. But but to your question of yeah, a bit more specifically about well, what do you do? There really are two paths that that we see are useful to take. If if you're an organization or in an organization that likes to understand a little bit more, you know, more of like a like a diagnostic activity and think about, well, where are we going to see some of the challenges? What types of use cases are we more likely to be able to deploy based on the way we work and everything like that? Start with a diagnostic activity to assess your generative AI readiness. So we we do a simple one that goes through five pillars. The first one is um alignment with your strategic vision. So generative AI vision aligned with strategic vision, or you could say just aligned with a business problem if you want to make it simple. Okay. Then it goes down and gets into end user proficiency, then looking at cross-functional collaboration, like we talked about. There's scalability and adaptability, which is you know, are we willing to make some decisions and change some things if we see evidence? And then governance and and you know, regulation or compliance. So some companies like to start there, and that's it's a nice place for anyone to start, but it is it's it's an academic exercise in a lot of ways because it's still a then what. Well, what what we what we recommend and what we work with clients on is even within a business unit, take it from this macro thing where everybody's involved, and let's say the the VP of that business unit is in charge of that or whomever, and shrink the problem down into a small group or even just an individual or maybe two individuals, and go through a process where, like we talked about before, pick one workflow, pick one part of that workflow, define one KPI that isn't ROI. Do some exploration and then see if that KPI is sufficient to understand if it's working, but it's maybe created another problem. So, like in that example before, if you just track number of leads generated, you're gonna see an effect, but obviously there's a problem downstream if they're not if they're not converting. So pick one, maybe two KPIs that aren't ROI.
SPEAKER_02Like it. Yeah.
SPEAKER_00Again, yeah, like it expenses and so in the case of the sales, it would be if you saw if you were tracking the number of qualified leads plus the number of new accounts being sold. And maybe even you added it in like the time spent with existing accounts, you would probably see this thing where the time with existing accounts might be going down, the lead flow is going up, and there's no change in the sales volume. And so you don't want to overcomplicate it, but but you know, some sort of simple set of KPIs, in the this is where that shrinking comes in. Have a relatively simple discussion about what has to be true within the way you work. So let's just say that that was a very small company and the sales team is one or two people, right? That's a very simple conversation of just saying, well, okay, we need to talk to leadership and we need to change the way we're incentivized to be able to sell new business. And so let's have that discussion. So make the operating model changes that need to be done and can be done, right? Oftentimes at this stage, what you want to be, you want to be have the teams be flexible on is be willing to say, look, we just can't do this right now. We're not they're not gonna change the incentive structure. Like we just need to go another path. But yeah, get to that point and then create, it sounds old fashioned, you know, it's an old business, business word that probably everybody, everybody hates, but put together a nice simple charter that just says, here's the experiment, here are the KPIs, here's the owner, here's the decision maker, this is what success looks like. And then run, you know, a seven, 10, 20, 30-day experiment. And then have there also be a clear path of, well, if success is met, what does that mean? What does that what does that look like? Is there a commitment to invest in the solution? So I want to tie this back to the question that you had. No, I like that. Yeah. Well, what's good about it is it it makes it to where you can have all these little micro investments and micro decisions. And they may or may not be aligned with this broader corporate strategy, but they're certainly aligned with the general principle of you know trying to make things more efficient, yeah. Maybe improve you know ROI or functionality of a given of a given unit. So you can you can have a lot of these micro pockets, but the key is start small, keep it simple, find solutions that work. What happens then is the individuals start to see it improve their own work, the more terminal leaders start to see it work, and that energy starts to propagate upward and outward in an organization.
SPEAKER_02I like this idea a lot about like a document about, okay, we're gonna do this. What are we looking for? What exactly are we doing? So there's not scope creep and ooh, let's do that, right? Um, and then how do we know that we've won? How do we know that this has been a successful effort? I like that idea a lot.
SPEAKER_00As a matter of fact, we're gonna start introducing that into all the work we do because it just seems Well, I I challenge your listeners just to think, yeah, well, think think for a moment, and and maybe for your listeners, you know, ask yourself, or are you someone that feels like AI is improving the work that you're doing or making yourself faster? And okay, if they if you say yes, say, well, what are you basing that on? Great. Yep. Now it might be someone's very, you know, a very conscientious person and they have it, they have it all tracked and so forth, but it's at least a coin flip that when you ask yourself that question, you're like, well, I I think it I think it makes things better. Gut, yeah. I'm pretty sure it does, you know, but but does it? And so you know, having that, and but but the other element of it is it makes it to another pitfall that we see that can can really derail enthusiasm for for generative AI, is you have people doing experiments and they just die on the vine. Yep. And that person can keep doing it and perhaps they're okay with that. But often you get a different type of burnout from doing generative AI over and over and over again on the same thing. You know, you can start to become very frustrated when you know that there are solutions that you could, you know, bring in a vendor and they could build, let's say, a custom agency solution that puts all that together and really streamlines the workflow. So keeping that vision and excitement. And the last part that it's a bit of a tricky conversation because it's it's a different way of thinking about work. You know, how how do you incentivize this type of behavior beyond just telling your people well, you're preparing yourself to be uh a leader of the future? It's like, okay, well, okay, okay, that might be enough, but it might not for your people to to encourage that type of enthusiasm and exploration.
SPEAKER_02And this is um opening up a lot of questions because we do a lot of like working with clients, and just when you think you've you're like, oh yeah, we we gotta we gotta figure it out. You bring up a couple of things here that have certainly um exposed some obvious uh gaps in what we have been doing that are simple fixes but will have big big leverage. In particular, I like this. Um like I realize one of the things that we're not doing, probably to the degree that we should, is starting out the relationship with that that strategy evaluation. So what does that look like for you guys? Do you say let's like bring out everything and let's take a look at it, or what does that strategy conversation look like for you, that that initial combo?
SPEAKER_00Yeah, and uh you know, I think anyone that's been in any sort of client services work knows that you can have a central theme of what you're trying to do, but it takes on different shapes and sizes with different clients, right? It yeah, it it might well so but the so the general theme of it is a way to look internally and explore each of those five areas. Yeah, and so what yeah, what we have set up is a formal diagnostic survey that ideally you would deploy it across the organization. Obviously, you probably wouldn't get everybody, and but but the more people you get the better, because you get a better view. But it it's basically exploring, you know, what would be maybe red lights, yellow lights, and green lights. Yep. And it and what we see with organizations, and I have a white paper that maps this out a bit, is that different there are different generative AI readiness personas or phenotypes or whoever however your audience thinks about that stuff. And based on that persona, there are different types of use cases that are going to be more or less accessible. Now, the you yeah, the utility in that is, and this is more so with the how do we realize our enterprise strategy more effective? Because you can say, okay, well, where can we win today? Right? What are the things that we won't have as many problems with because of how we how we work?
SPEAKER_03Yeah.
SPEAKER_00And then you set up a plan for where what you have to do or what you have to change to win tomorrow. Right. So those are that's getting into that transformation element, yeah, that operating model element, which is what no leader in any business wants to hear is that the problem's in the operating model, because that's the hardest thing to change because it's transformation. But then, you know, once once you figured that out, this other process, that second process we talked about, shrinking it down, you can overcome a lot of those barriers just by making an experiment smaller.
SPEAKER_03Right.
SPEAKER_00So that type of exploration could those, yeah, that's it's not gonna have the same type of impact. But if you think about what the alternative is, though, yeah, do you want to invest a million, five million, ten million dollars in an enterprise uh use case and uh and uh an application or a tool, and your organization just isn't ready for the implication of what that of what that means. So you know that that survey is great. Oftentimes it ends up being a bit it can be like a 10-minute discussion with the CEO of a of a company. It just it depends on the company. Yeah, right. You know, sometimes they're like, look, we're okay, we're okay here, or like, yeah, we knew it need to improve X, and you know, then you run with that. But yeah, it's starting with looking internally, looking in the mirror first, and then going from there.
SPEAKER_02What do you do in situations where because my my personal position is that yeah, you you've got that pain point here and you've got that pain point here. We can come in, we can have an automation or an agent address that. But that doesn't prepare your company for this new landscape that's happening, right? Like that's not AI. That's one small sliver of it. To me, it's your people need to be trained, they need to be doing what we call thinking in AI to where it's just the default for them, right? They're gonna like if there's a problem in the business, they're like, how can the models help me? Or is there an AI tool, right? And if you don't do that, you can have all the tools, but your people aren't ready for a a competitor who does have AI fluent people who are doing incredible things simply with just a copilot license, right? Yeah. So how do you how do you I mean you give them what they want or do you give them what they need?
SPEAKER_00Well, I I think I'd say in and we were talking about this a bit before, is you know, training does have to be thought of differently than what we typically do. And you know, it it could be there could be modules internally and so forth, but to to get the ball rolling, but it has to be ingrained in what they're doing. They have to get their hands dirty, yeah. It has to be they have to be fully immersed in using AI in their own work. Yes. And and what the yeah, what the target is, is if you if you think about for your listeners that maybe are more fluent in in AI or and and for those that that aren't, we could talk about another way, but if you feel like you can problem solve in an LLM, then you're kind of where you need to be.
SPEAKER_02Great. Right?
SPEAKER_00But if you feel like you're sitting, yeah, if you feel like you're sitting down and you don't know know enough to, let's say, think to ask the LLM well where within this workflow I just defined is a good generative AI um application, yeah, then you're probably not, you're not, you can't critically think in in LLMs yet. I like that. So you'd need you'd need those boots on the ground experiences. Yeah. Yeah. And you need to kind of organically explore it. And so this is where I've I've heard it said in a lot of different uh contexts, so I can't attribute it to anyone in particular, but you know, how important now is curiosity over pure intellectual horsepower? Yeah. You know, I would say I don't know, you know, with with absolute certainty how much more important curiosity is, but having people that are curious and want to like get in there and not just explore, but their mind naturally goes, you know, like associative thinkers or abstract thinkers, you know, they they often are individuals that it seems like are adopting these solutions. Uh makes sense. At least at least they're they're doing it a lot, you know, and they seem to be doing well. But that's that's supposition. I don't I don't have any data to back that up.
SPEAKER_02You know, I like it because the individual of high intellect may have bias, right? And the curious individual is like, well, what if we did this? What if we did that? And that's where the the true like aha's come from using the models is the curveball stuff, not the the standard, you know, write me an email for a subcontractor who has missed their deadline, like helpful, but that's not the what we call thinking in AI, right? It's fantastic.
SPEAKER_00Well, let me just just just share this. I know we probably have to wrap up here in a minute, but they there's I'll just share an anecdote with myself. I mean, uh I think of a of a specific moment where I was up late at night trying to work on something, and it got to the point where the LLM was started instead of answering my question. was giving me tips for for coping with with disappointment because it was it because it wasn't working and I was getting like noticeably agitated with the with the LLM so going through those motions yeah you know that's how you start to learn and if if you're if if you're not having those types of experiences you're not learning you know the the paths and the roadblocks to to using a tool like that.
SPEAKER_02Yep no that's perfect. Wow there's so much more that we could talk about but this was good and I've I've pulled a few ideas I'm gonna bring back to my team and say why aren't we doing it this way? That's pretty good Justin. So you mentioned that you've got some white papers and things like that. For those who want to dig in a little bit more with with how you're approaching this stuff and you know everything from like you said from SMB all the way up to enterprise where should they go to get more of this insight from you?
SPEAKER_00Well I don't want to sentence anyone to have to read my white paper. But they can read my white paper. It's it's it's on our website um I'll perhaps share it here for the show notes. Yeah yeah if you want to get into more of that question of you know what are these five pillars of readiness?
SPEAKER_03Yeah.
SPEAKER_00How should you think about different use cases that can be deployed how do you think about that when today win tomorrow roadmap it's all in in in the white paper there. But probably the funnest way to engage with with us is on our our website too and I'll provide this link, we have like a five, six, seven question uh miniature version of a diagnostic survey and and so you can go in it doesn't I believe the full ones like 25 questions or so but it it it pulled out the ones that had the highest relationship to to to readiness and crunched it down. But yeah just go and explore just it isn't even really about scoring it. It's just seeing the types of questions that that are posed and and and thinking about those for yourself and then you know you can submit your information and and we're of course happy to to have conversations with any of your listeners.
SPEAKER_02Awesome. Well man thank you so much I know that um if you're in the AI space as a professional it's extremely busy times for all of us right now. So I appreciate you making the the time to come in and talk to our guests today. We're gonna have links to all this stuff in the show notes and uh I would encourage you if and where are you based? Uh in in the Charlotte North Carolina and obviously I mean you can work wherever but if you're in that that uh region it would be my preference is always to work with with clients locally just because there's something that's the Zoom is one thing but sitting in the the room with them on the whiteboard and the the laptop open is always a good thing. So um well awesome Justin thank you so much again for the time and uh for all of our listeners my final bit of advice every single time is just go use the tools go use AI. So with that we'll wrap it up and we will uh see everybody next week with another amazing episode of using AI at work. Thanks Justin thanks everybody sounds great thanks thanks for tuning in to using AI at work. Don't forget to subscribe for more conversations about how to use AI at work. And a special thank you to our sponsor Chief AI Officer for empowering businesses with AI education and training. Visit their website for a free AI readiness assessment and AI strategy guide to help you get started using AI at work. That's www.chiefaiofficer dot com. Follow us on Twitter at the handle usingAI at work and visit www.usingai at work dot com for free resources to help you harness AI in your role