Why AI Adoption Fails at Work (And How to Fix It) with Bianca Baumann

Yesterday in AI  |  17 August 2026

Why AI Adoption Fails at Work (And How to Fix It) with Bianca Baumann

According to a McKinsey survey, 78% of organizations are now using AI, but buying enterprise licenses is the only easy part. The real challenge? Getting humans to actually change the way they work.

In this special extended episode of Yesterday in AI, Mike sits down with Bianca Baumann, VP of Learning Solutions & Innovation at Ardent Learning and a veteran workforce transformation executive. They skip the standard "what is AI" discussion and dig deep into the exact reasons why AI deployments stall out after the initial launch and how to fix the human side of the equation.

In this episode, we cover:

• Transformation vs. Theater: How to identify if your organization is genuinely evolving or just performing "innovation theater."
• The Manager Bottleneck: Why middle managers are the critical make-or-break point for everyday AI adoption.
• The Missing ROI: Why companies struggle to see a quantifiable return on their AI investments.
• Marketing to Your Team: How leaders can use "learner personas" and internal campaigns to get hesitant, fearful employees on board.

Links & Resources:

• Learn more about Bianca Baumann and Ardent Learning: https://www.ardentlearning.com/
• Read Think Like a Marketer, Train Like an L&D Pro: https://a.co/d/03AGKcU6
• Follow Bianca on LinkedIn: https://www.linkedin.com/in/biancabaumann/

Feedback? Email mike@yesterdayinai.news or connect on LinkedIn, X, or Bluesky. If you like the show, please take a minute to rate and review it so others can find it!

Transcript

SPEAKER_02 0:03

Yesterday in AI.

SPEAKER_00 0:05

Hi folks, and welcome back to another edition of Yesterday in AI, your daily digest of everything happening in the world of AI in roughly 10 minutes. I'm Mike Robinson. It's Monday, August 17th, and today we're focused on the thorny nature of workplace adoption of AI. This is uncharacteristically a longer episode because I wanted to have a discussion with someone who is very familiar with the topic rather than just report facts and figures to you. My special guest today is Bianca Baumann, Vice President of Learning Solutions and Innovation at Ardent Learning. Bianca is an enterprise workforce transformation executive, published author, and international keynote speaker, and has spent more than 15 years helping executive teams build workforce strategies that translate into lasting behavior change and quantifiable business results. Most importantly, for this discussion, Bianca is an expert who helps organizations bridge the gap between AI investment and real-world adoption. Let's get into it. Bianca Bauman, welcome to Yesterday in AI.

SPEAKER_01 1:04

Thank you so much, Mike. Pleasure to be here.

SPEAKER_00 1:06

Bianca, I cover new AI tools regularly on this show. From your vantage point, when a company buys enterprise licenses for these tools, what's the very first thing that usually goes wrong?

SPEAKER_01 1:16

What I usually see is the auto license goes live and everyone exhales. Like great job is done. But that really is the first mistake because buying access to those tools and calling it an adoption or even worse, capability. So people know what to do and what to do. And you might see some that go quiet, barely touch it, right? Or again, others will just go rogue. But both, in my mind, are really the same problem. Um, and often leadership assumes that the rollout is the finish line, but it really is kind of the starting point. Of course, fast forward six months, everyone's confused about hmm, why is the usage numbers? They look fine, but you know, nothing's really changed. We haven't seen an impact. So yeah, that's usually where it starts.

SPEAKER_00 2:13

As a follow-on to that, do you see organizations actually knowing the usage numbers and having that kind of insight?

SPEAKER_01 2:20

It depends on the tool that organizations have. Uh, you know, as an expert yourself, you know, some of the tools have better data and reporting metrics than others. And even within the tool, it depends on the tier that you have, right? So you might actually not be able to really dig deep into it. So that's a whole other problem.

SPEAKER_00 2:38

The pitcher team sent over mentioned a statistic from the McKinsey State of AI survey showing that 78% of organizations use AI, but widespread access hasn't helped organizations integrate AI into the way they actually work. What does the gap between having a license and actually integrating AI look like on the ground from your perspective?

SPEAKER_01 2:57

Yeah, I'd I'd like to start talking about that harder problem first, and then I'll get to the gap if that's okay. So again, I just uh I mentioned it, right? It's not really about integrating AI into the everyday. It's uh I mean it is, but it's really more about the capability uh as well in the first place, because without capability, you can't actually integrate anything, right? So that needs to get solved first. You know, I like to think about it this way like, you know, if I hand you a car key, it doesn't make your driver, right? You still need to learn how to actually drive, read the traffic, uh, the traffic signs, merge, parallel park, all that good stuff before, you know, getting behind the wheel actually becomes automatic. So that's kind of how I think about it. And access to AI works in my mind, really kind of the same way. You have the tool, but it doesn't teach the skill. And the skill has to exist first, or the tool just sits in our driveway. And even once some uh some of those baseline capabilities are there, I find that most organizations still haven't figured out or haven't said what that good use looks like uh in the specific roles. Everyone has a seat, a license, but nobody told them what to do. And so what's missing is that moment-to-moment stuff. So people have a lot of questions. For example, is this the right time to use AI? Will my manager back me up? Will I get in trouble if I use it? What happens if the output is wrong? Like there's a lot going on. And people aren't asking these questions in a vacuum eater. They spend months, right, hearing about ooh, AI is coming and it's taking my job. And so there's a lot of fear. And so nobody answers these questions for them. So what people do is they default to some of the safe options, and we talked about it before, either they freeze or they freelance, right?

SPEAKER_02 4:30

Right.

SPEAKER_01 4:30

And so in my mind, the real gap sits really in both places. So the capability to use AI at all and the clarity to know what good views look like once they have it. And having the tool, again, doesn't answer either one of those questions. And I really see the same patterns, you know, across all industries too.

SPEAKER_00 4:47

I'm not surprised. I definitely saw a lot of that at the very beginning, um, especially as people questioned exactly what are they supposed to be doing with the tools and what can they do with the tools.

SPEAKER_01 4:56

Yeah. And you know what? Often leadership doesn't know either. They're just like they just hear, oh, we got to go AI. And so, you know, they purchase the tools, and that's like, oh, now what? Right.

SPEAKER_00 5:05

Well, and it's also, it seems like it's also specific to what group you're working in, right? Like I can tell you generally what you could be doing with these tools, but at the end of the day, it matters what your group, your team is responsible for and what they can figure out.

SPEAKER_01 5:20

Yeah, role specific, context specific, 100%. Yeah.

SPEAKER_00 5:24

You've spoken about transformation versus theater previously. How can an IT leader or an executive tell if their organization is undergoing a genuine AI transformation or just performing innovation theater? In other words, what should they be looking for?

SPEAKER_01 5:37

The way I like to think about it is that a transformation that's theater just looks busy. So, you know, there might be town halls and again a tool rollout, a course on how to, you know, use AI and you're an expert after 15 minutes, and it's kind of like a slide in a deck and it's like AI first, right? It's just it's just busy, right? Transformation, on the other hand, kind of looks boring from the outside. So it really shows up in how we do our jobs differently on a Tuesday afternoon. And what's really important for transformations and those answers now to questions of, you know, what that looks like on that front is first you want to align on the impact an organization actually wants to see from AI. And I'm talking business results, I'm talking strategic objectives, KPIs, right? So not usage data doesn't really tell me anything apart from they, you know, used AI to plan their vacation. What do I know, right? Um, but once our stakeholders are aligned and they agreed, then next step is you really want to go into an AI readiness evaluation or assessment to kind of see across the organization where we're at. You want to define like different dimensions, such as strategy and leadership or workforce readiness, um, confidence. And then you score these dimensions as merging, developing, scaling, or embedded. So you really get a feel for where you're at and create a quick heat map, actually. You know, you can kind of look at it as like, hey, we're doing well. Where do we need to improve? Right. Now you really have a starting point on where you're at. And also, obviously, you don't just look at your whole organization. We just talked about it, Mike, also is like which roles, right? And which processes within those roles and find the most crucial processes within those roles. You can't do it all at once. If someone tells you you can, you should run the other way, right? But you've got to start small. You really got to figure out what this looks like more on a pilot scale, get an AI adoption blueprint, and then design the AI enablement from there, bring the people along. And as you can tell, this takes time, right? Like that's the biggest difference is really theater is quick and transformation. They're multi-months, maybe even years, right? Until it's all really, really ready to go. But again, start small, pick one workflow, one process, go from there, see that it runs smoothly, and then scale it, right? And the last thing I say on this one is theater really optimizes for looking innovative. Yeah. And transformation is optimized for changing behavior.

SPEAKER_00 7:57

Yeah, I agree. I think from my perspective, I don't know that we've had necessarily theater, but transformation has been the harder struggle, right? We've been intentional about what we're trying to do. But at the same time, when you look at it from, I guess, where I'm at, what we're doing potentially corporately versus what we're doing in other areas of the business. I'm trying to keep an eye on each thing, right? Make sure we make progress. But that's a very hard road because you can see some transformation happening. But then other places you're like, okay, well, now I need to spend more time over here or we need to do something to help this group move along, right?

SPEAKER_01 8:31

Oh, absolutely. It's hard because yeah, you have the day to day, you still got to run your companies, you still gotta pay the bills while you know you do all of this. And then within this over here, you still, as you just said, have all these different areas. It is, it is a lot. That's why it takes time, right?

SPEAKER_00 8:45

Yep, agreed. So why do you think so many companies struggle to see a quantifiable return on investment from their AI investments right now? Are they measuring the wrong things or just failing to change behaviors?

SPEAKER_01 8:58

Yeah, in my mind, companies aren't really struggling to measure ROI. They're struggling because they never defined what they were actually solving for before anything got rolled out, right? And so that that's that's the crux of it. And I mentioned before, it really goes back to aligning and agreeing on KPIs from the get-go. So everyone is going in the right direction, North Star, right?

SPEAKER_02 9:18

Yep.

SPEAKER_01 9:18

And you can't prove impact against a goal that nobody wrote down. Like that's that's that's really what it is. So yeah, most companies really measure the wrong thing in my mind. They're tracking usage, if even you know, possible, uh, as we chatted about already. But even if they can, now you're tracking activity and not tracking impact. And they're not really tracking behavior changes. So, you know, to answer that question, yes, they're also not tracking that because again, if we just turn on the tool, we're not measuring if they can actually use it well and how they use it and how things change for them. And behavior doesn't change on its own, even when people or companies want it to. Workflow underneath, underneath, so it really stays exactly the same. It's like, well, how can we expect different impact? But um, what I found interesting, you know, top performing adopters are nearly three times, I think, as likely to have actually redesigned how the work gets done and not just bolting AI on top. So if you skip that step, you know, you you don't see real impact. So I think, you know, the real question here is this has really two parts. Like, what are we trying to change uh about how people work? And can we can we show how it changed? I think those are two really, really important questions. So if you fix that at the start, I think ROI stops being that black box, that mystery.

SPEAKER_00 10:34

So you mentioned KPIs a few times. Uh so do you have specific ideas on what those KPIs should be for a company?

SPEAKER_01 10:43

You know what? Um, I wish I could just have a black and white answer because then I would probably be, you know, a millionaire. Um, but it's really it's really context and company specific, right? So for me, I always like to tie these KPIs to an organization's overall strategic goals and and business results. But often what we would see, you know, especially with AI, is certainly something around uh efficiency, effectiveness, you know, improved call resolution times, for example, like things like that, right? Like just productivity KPIs in general is what I what I find we'll see the most uh as it relates to AI.

SPEAKER_02 11:19

Okay.

SPEAKER_01 11:20

And I think curiosity is really, really crucial in the beginning of any AI transformation. Because if people are not curious about something, just they don't want to go there. And having some champions is definitely a wonderful starting point because then from there, they can help you spread the word, right? One thing I do with my team is every Friday we do an AI show and tell. And one week it's instructional designers, and the next week it's the tech team, and then we have our creative team and they get first dips on, hey, here's how I used AI this week. And then usually it ends up being like a wild mix of here's what we used AI for. But it's just fascinating how many ideas it sparked. It's like, oh great, I want to try this, right? And so just creating these spaces where people can try things and there is no punishment if something doesn't work and right, like just a safe environment. I think, yeah, that's just crucial. And I would not be able to get there with my team if I had said, please sit through an hour-long training.

unknown 12:19

Right.

SPEAKER_00 12:20

Well, and and encouraging them to come forward with different projects that they've worked on and showing it off, right? That's that's where the rubber meets the road, so to speak, right? Like oh, you got your hands dirty and now you've created something, right? Versus just trying to learn online.

SPEAKER_01 12:34

So and it doesn't have to be a big thing. Sometimes it's just, hey, I used it to work on this task and it took me 10 minutes instead of half an hour. Wonderful, right?

SPEAKER_00 12:43

Yeah, yeah, I agreed. So we often talk about you know, companies or the C-suite buying tech and the frontline workers using it, but you've argued that middle managers are actually the key to successful implementation. Why is that?

SPEAKER_01 12:55

Because they're the one translating what leadership says into what actually happens on a team, ideally at least, right? Uh that should be their job. And you know, I mentioned as I think early on in one of the first questions, will my manager back me up if I use this? Right? What happens um if I use this? And in general, that does not get answered in a company-wide email. It gets answered by one person during their team meetings or one-on-ones, right? And in my experience, IT owns the tool, HR and learning and development own the training and the enablement, and the business owns the outcome, but none of them own what happens in between, right? And there's a gap there, and that's the gap that managers can actually uh close for us. So for me, they're connective tissue. That's you know, that's who they are. And yeah, they're the only ones really positioned to close that gap because they're in the one-on-ones, they're in the rooms, they see how things are happening, you know, they support decisions, they create safe environments. And there is data out there that supports that too. So when you know leaders visibly support uh the use of AI, employees feel much more positive about uh using AI themselves. And it actually jumps from 15 to 55% on how people feel about the use of AI. It was more positive. So really not just a small bump, it's actually, you know, what almost a fourfold increase. And uh only about one in four frontline employees say they're actually currently getting that level of support. So, you know, it's needed, but we're not getting it. So hence we don't see the adoption we would like to see because people still have all these questions, right?

SPEAKER_00 14:22

I guess it poses a kind of a dual-edged sword situation, right? Which is that the managers can be definitely kind of cheerleaders for moving the group along. But at the same time, if the managers are harboring the same kind of fears or just not really sure what to do with this stuff, right? Have you seen anything where groups can really overcome the the manager level, not necessarily being a cheerleader and really trying to work with them to make this happen?

SPEAKER_01 14:49

Yeah, it's a really good point, right? Because it goes both ways. If the manager to your point is a cheerleader, it works. And if they're not, then the team would be scared too. It's definitely much harder. What I find works well then is if you have cross-functional teams that learn from one another. So you don't just have like the, you know, your direct manager, but you know, maybe there is regional huddles where people get together or something like that, where, you know, then another manager or another person in general doesn't have to be an exact same level manager, could even be one up, but just another person who's in a leadership position that they could follow as kind of a role model. So, you know, that helps a little bit. But yeah, it's it's hard. It's hard. And I mean, we probably also all have been there. We all left jobs because of our managers, right? It's just the things. So obviously they can make or break a lot of this. So yeah, if you don't have a supportive manager, it can be overcome, but it's much, much harder because you need more people. It takes a village then, right? To bring it up.

SPEAKER_00 15:43

So more of an uphill climb. Yes. Yeah, got it.

SPEAKER_01 15:46

Absolutely.

SPEAKER_00 15:47

So if I'm a manager right now listening to this, uh, what exactly do you think I would need to do on any given day to ensure my team is using AI effectively rather than just treating it as a distraction or worse yet, something to ignore?

SPEAKER_01 16:01

Yeah. I mean, I shared one example of what I do, right? Like uh find a way to do like an AI show and tell. But you know, it could also be as simple as pick one task on your team that uh they could use AI for whatever type. It doesn't have to be big, it can be something small, right? And then sit down with a couple of people and walk them through how you would use AI. Like really show them. It's like here's what I would do, you know, talk out loud about um what's going on, your train of thoughts, right? So they actually see what's okay to do, tell them explicitly, I think that's really important. Um, and then, you know, build in five minutes at your next team meeting to talk about uh what people tried, right? It's like, hey, we just sat down with Mike and he told us to do this, and we tried to use it over the last week, and here's what happened, right? Again, it goes also into just uh AI show and tell. But if you want it to be more formalized, right? Identify a specific task. And that's it. That's the whole move. And you could also, you know, go back to what we talked about earlier, like theater and transformation. If nothing about how your team works looks different in a month, you probably didn't pick the right task to use AI for, right?

SPEAKER_00 17:05

So Yeah, yeah, right. You're not making progress. So how should managers handle workforce confidence issues within their teams? Specifically, the biggest fear I think that all of them have right at this point is using these tools will eventually train something that will automate my job. What do you think?

SPEAKER_01 17:24

I mean, most important is to not skip past it. And now we also obviously go back to, you know, do we have a good manager or one that has room for improvement? So I mean, if you're not confident as a manager, this is really hard, right? To not skip past it. But if people are showing up scared and you pretend it's not there, it's not helping anyone, right? So the fear, again, often isn't really about that tool because people are smart. They can figure out how to use it somehow, right? But it's really is AI being used for them or against them, right? And I don't think this fear will go away as adoption grows either. I think it'll always be there one way or the other, especially because technology does evolve so much, right? I might feel confident and safe today, but next week it's already a different story. So it is interesting. There is that research out there that when organizations that are further along in the AI adoption, a lot of people actually worry more about job security, which if you think about it logically makes sense, right? Because now we're really good at using the tool, which may means we get really the most out of it. And now we're really like, do we really need Mike over there? Right. But uh I think again, part of all that fear is again that trust gap as well. Because I think what half of employees believe that AI benefits employers more than employees, and that's scary, right? And yeah, just reassuring them, working with them, as I just said, you know, you sit down, pick a task, actually naming clearly what stays human, you know, what is the machine. I think for me, that part is really crucial, that human-machine relationship. And it's more than just, yeah, we're checking the output, right? Especially as we talk more about AI agents now, you know, where where does the human end and the machine start and the other way around? Like having these conversations and showing people that AI actually still needs them. I think that is really, really, really important.

SPEAKER_00 19:20

I was listening to something recently that was talking about we've also now hit the point where individuals are not really checking what's coming out and seeing certain cases where there's ramifications of that choice, especially in the legal arena where you could be slapped with thousands of dollars of fines, right, for bringing something like that. I just read a story just before you joined today that was talking about a particular individual that thought that they could slip AI into the white space and thought that something was going to read it right in order to influence the outcome of the case. And as a result, now this dealing with a bad decision.

SPEAKER_01 19:55

That is so interesting and scary, right? So yeah, yeah, absolutely. I I watched a TV series the other day, I forgot what it was, but someone was really uh nervous to give a speech. And so someone wrote the speech for them, and then they actually said, wait for applause, right? And it was like, oops. But this is what happens when you see people copy and paste like the instructions from you know the LLM into their output and they don't realize like that's the equivalent now. Yes, right. Yeah, right.

SPEAKER_00 20:21

Yeah. Well, I found what you had to say about the 50% statistic you gave around it benefiting employers. I guess at the end of the day, I feel like that's always going to be the case because if you have employees who are more productive by using these tools, it's always going to benefit the employer and in the bottom line, right? More revenue. So as a follow-up to that, what do you think is necessary to move past that for the employee and recognize the employee in their efforts, right? If they're staying in place, there's kind of a workforce stabilization happening. If they're staying in place, they're just doing more with the same number of people instead of less people, right? Um, what do you do to kind of balance that out?

SPEAKER_01 21:03

It's a good question. I really think it goes back to the need to redesign processes, but also jobs. And as we get more efficient, I might not have a need for a Pima over here anymore, but a Pima has this amazing skill set that now, if I couple that with AI, she can do a whole new job. Right. Right. And I think that's where organizations have to start looking at. It's that internal mobility. So, you know, moving more towards a skills-based organization. So we understand who has what skills and where can I use them for what projects, and then couple that with AI and yeah, create new roles, jobs, whatever you want to call it, or just, you know, I know if we want to move away from roles. You look at projects, right? You know, for this project, here are the five people plus this agent and this these AI tools that I want to have on here. Right. So I think we have to get a little bit more comfortable with not having traditional jobs anymore. Yeah. But you know, we still have work, but it just looks Looks very differently. That would be my take on that.

SPEAKER_00 22:03

That's a good tick. I've always been fascinated by marketing strategies. And you just happen to have co-authored Think Like a Marketer, Train Like an LD Pro. So how can IT leaders and managers use marketing strategies to sell AI adoption to their own hesitant employees?

SPEAKER_01 22:19

Yeah, I know. That's a million dollar question. But you know, first off, I always like to think of employees as consumers, right? And uh, you know, sometimes people say, you can't call in that, but that's who we are. And they we choose to engage or tune out, right? Just like when I see ads uh anywhere, uh, you know, same concept. And what I find with most AI rollouts is that they just get treated like a single product launch, right? Announce it once, assume the memo did the job, you know, you're done, you can go home. But when you think about it from a marketer's perspective, they never build a campaign just around one announcement, right? There is campaigns that means multi-step, multi-touch. So you really kind of got to think about what is that entire journey? What are the different touch points? How do they enter, how do they exit? That's where segments come in as well. So you kind of really want to meet people where they are and have different messaging for different people, depending on where they are. You know, like a finance analyst and a machine operator, they aren't hearing AI is a priority the same, the same way. So you should stop messaging them the same way. And right, yeah, identifying that specific value to the specific person doing a specific job. I think that's, you know, where you'll be successful. Obviously, we all have already busy jobs, so now we need to create a campaign on top of that, right? So it is it is a full-time job. But I think it's well worth it, especially when you couple it with some change management best practices as well.

SPEAKER_00 23:39

You're putting marketing campaigns out there around adoption tools, helpful hints, things along those lines, right? To kind of juice the whole process along. And you don't feel like the entire company is actually reading this stuff, right? So, and and there's definitely evidence of that as as we go through, right? And so we're kind of getting down to even lower levels and saying, you know, have you tried this? And you know, how about this and so on and so forth? So have you seen any real good strategies that kind of help with that from the very beginning?

SPEAKER_01 24:09

Yeah, I think what you need like what, five to eight touch points for someone to read one message. So that's just something you need to understand, right? So exactly, oof, right? It it is a lot, but I think bringing personality into everything that you're doing. If it's just like, oh yeah, we have this tool, we want you to test it, okay, that can work. But you know, I don't know about our listeners' organizations, obviously, but hopefully, again, you can put in some humor too, knowing about all the stats that are out there, like, hey, yes, it benefits uh, you know, companies more than employees. Play with that. Like, you know, I mentioned like talk about the fear, talk about the issues that people, you know, already think that are there, and uh, you know, just do something like novelty is huge, right? So if you can do something different, I think that uh in my mind helps a lot. Also, and you're going to laugh about this, you know, if you're still in an office, hang a flyer in the bathroom. Everyone goes to the bathroom. I know it might sound really weird, but everyone is there, you know, like wherever they wash their hands, put a little flyer there about something, right? Exactly. You can miss it. Well, hopefully, if everyone washes their hands. So, you know, and then we mentioned champions and super users, power users before. Huge, right? Because you already have people that are super excited about this and they want to tell other people about it. So bring them into your marketing efforts as well. Yeah. And, you know, recognize them, give them a little, hey, if you do this, you get the next tier on the LLM, right? Like really recognize their efforts and give them a little something for it. And they love it because now, you know, they'll do even more and talk even more about it. So, right, it's kind of like a win-win. So, yeah, those are a couple of quick thoughts that uh I have to.

SPEAKER_00 25:51

Those are great ideas. Yeah, I especially enjoyed the bathroom. It's very practical.

SPEAKER_02 25:57

It is, right?

SPEAKER_00 25:59

Um, one of your concepts is learner personas. Uh, how should an organization logically segment its workhorse when rolling out an AI tool rather than doing a one size fits all training?

SPEAKER_01 26:10

Yeah. I just mentioned it, right, with the marketing techniques there already as well, because one size fits all training just doesn't work, right? Because we're assuming everyone starts in the same place, but nobody does. So segments is really a way to group your audience. And, you know, it could be what is this particular group afraid of? What's already broken in their day today that AI could fix? How much hand holding do they actually want, right? So it's it's really again, a segment is nothing else than a group of people that share a common character trait, right? To keep it simple. Uh so it could be skeptical employees, it could be more senior employees that are also skeptical, could be the same. But each of them just needs a different on-ramp to someone who's already using AI, right? So you really start at a different level. Someone who hasn't really used AI, it's like, yeah, here's the button to do XYZ, here's the you know, chat interface. Like it could be as you know, granular as that to, oh, here's how you create a skill in Clot if you're already more uh active. So these are really the different segments you want to look at. And I would probably also look at where AI can make the biggest difference to employees and then kind of pilot it from there. So yes, we said it benefits companies more, but I mean you're kind of gonna think that way too, right? Like, where can I get the biggest bang for my buck? And yeah, the persona work really helps you dial that in, and especially if you tie it back to your KPIs. And I apologize, I probably should have briefly mentioned for those listeners who don't know what a persona is, but it's like a fictitious character based on research and uh real data that you develop that represents your typical learner. And I think what's really important is it's not extra effort up front. It's not wasting effort on something generic, like it is part of your design and it can really help your transformation efforts.

SPEAKER_00 27:56

As a follow-up to that, what are some of the ways to identify those personas? Because obviously finding skeptics versus non-skeptics is really difficult, right? When you go back to the thing that I mentioned earlier where you're not really sure everybody's reading everything, right? And and the people who are reading it and who are excited are responding, right? And then there's a whole group that you're just that not sure. And I'm sure there's that messy middle as well. So always, yeah. So what's a what's a good way of kind of identifying what those personas are?

SPEAKER_01 28:24

So I usually like to start with the bigger segments and just think about with smart people in the room what those segments could be. And then I write down what we know and what we don't know about these segments, and then from there within a segment, so a segment could even just still be uh like regional or geographics or again just a role, like sales consultant or whatnot. Um, but then and this might come as a shock, you actually got to talk to people. Uh what? Right. I know it's a crazy thought, but uh and and I know we're laughing about this, but you'd be surprised how often I see companies not talking to their employees and just making assumptions, and that only gets you so far. So in the end, you know, look at whatever data you have available to you, but yeah, talking to people. And if you can't interview people, you know, send out a survey to, you know, get some feedback. I'm a big fan of interviews because surveys you can't read in between the lines, you don't see the body language, right? I can't really ask follow-up questions. And I mean, you'd be surprised how open employees are. It depends on who does the interviewing for sure, but you learn a lot, right? And just from there you can really define which personas makes sense.

SPEAKER_00 29:34

Okay, okay, that makes sense. And I also like the the suggestion that it depends on the interviewer because definitely makes a difference.

SPEAKER_01 29:41

No, absolutely. And you know, working for an agency, we have the advantage when we go into organizations who are third parties, so people they lay it on, they tell us all, right? So which is yeah, which is really finally someone will listen.

SPEAKER_00 29:57

Uh well, as an expert in learning and development, what's the one skill you think every employee needs to focus on for the next 12 months to stay relevant in this AI-heavy workplace?

SPEAKER_01 30:07

Kind of talked about it already, but judgment would be my top pick here. Again, knowing when to trust the output, when to push back on it. And if the output doesn't look right, you know, what do I do next? Do I try to prompt again? Do I go to my manager to talk about this? Do I go to peer, right? And I think everyone will hit that moment at some point. And the skill is knowing how to handle it. And, you know, I just mentioned prompting. Prompting in my mind is not really, I mean, it kind of is a skill, but I think it becomes more of a tactic now in my mind.

SPEAKER_02 30:39

Yeah.

SPEAKER_01 30:39

As it also changes so much, right? Uh every every week, it seems like. But judgment doesn't really expire, right? Like that's just something you have to hone and get really good at. And that's where, you know, going back to human-machine relationship, that's what we're really good at. The machine will get better and better and better and better. So do we in that human-machine relationship. And it's less a training topic. I can't train you to, you know, have good judgment. So that's, you know, that's that's a habit. And yeah, I feel like kind of summarizes the conversation we had today, you know, as well. It's really that relationship. And yeah, people bring the judgment and AI does its part, and that's that's how it works. And I actually saw this quote that I wanted to make sure I share with you today, and actually said, AI adoption isn't just about training people, it's about building the ability to use AI, the willingness to use it, and the conditions that make it stick. Like I saw that on LinkedIn. Unfortunately, there was like no quote like where it came from. I'm not 100% sure it was a picture, but I thought it was really it summarizes well what we talked about.

SPEAKER_00 31:40

Yeah, absolutely. Absolutely. Great way to go out on. So if anyone listening wants to find your book or follow your work at Ardent Learning or book you for speaking engagements, where should they go?

SPEAKER_01 31:51

Yeah, so you can find my book on uh Amazon uh as well as you know major book retailers. And following me is the easiest way to find me is on LinkedIn and uh also obviously ardentlearning.com uh for anything specific to Ardent Learning.

SPEAKER_00 32:07

You're not you're not out there on X.

SPEAKER_01 32:09

You know what? I do have an account, but I used it when it was still Twitter and fun. And you know, now I'm just kind of like, which is a shame. I kind of miss it, but it just doesn't work for me.

SPEAKER_00 32:20

Yeah, no, I get that. You get that. So totally get that. Well, thank you so much. I really enjoyed our conversation, Bianca, and I'm sure our listeners really appreciate the insights that you've provided today. So thank you very much.

SPEAKER_01 32:32

Thanks for having me, Mike. It was great.

SPEAKER_00 32:34

Absolutely. What a thoughtful conversation with some real solutions. I love that. If you enjoyed this interview and would like to hear more episodes like this, just let me know in the comments or the normal channels, and I'll see what I can do. And that's the show. If you have feedback from me, email Mike at yesterdaynaai.news or connect with me on LinkedIn, X or Blue Sky. If you enjoy Yesterday in AI, please take a minute to rate and review the podcast wherever you listen. Or share it with a friend. Thanks for tuning in today. Stay curious, and I'll see you tomorrow.