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Dawn Ely.

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I'm the Director of Enterprise Services at Cirrus.

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And so this is kind of my wheelhouse.

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I have executive strategy, workforce, operational excellence and

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chain in my team.

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So very familiar with some of these topics today and super excited to talk about it.

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We will go ahead and get started with the intros.

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Actually, if you don't mind giving me a second, I'm going to shut these doors because I'm getting just a little bit of feedback off the crowd in the room monitor.

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We get lots of jobs today.

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And so that's one of them.

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There we go.

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All right.

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So to begin our day, we're pleased to welcome Casey Webster and Dave Mahofsky.

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Casey is the Chief Executive Officer of Profit Quiver, a business transformation strategist and a certified AI consultant who helps leaders navigate change with a people-first approach.

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With more than 25 years of executive leadership experience, Casey has guided organization through complex transformation across education, workforce development, and enterprise environments.

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Her work focuses on helping leaders align culture, leadership, and strategy to achieve meaningful results with AI.

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Joining her is Dave Mahusky.

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Chief Executive Officer and Founder of Mindset Innovations Consulting and Co-Founder of the Precision X System.

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Dave brings deep experience in manufacturing leadership and frontline operations, along with a strong track record of helping organizations build the execution and accountability needed to sustain transformation.

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In their session, is your business AI ready?

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The human-centered domains that determine success or failure, they will explore the human factors that often determine whether AI initiatives succeed or fail.

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You will gain a practical framework to assess readiness, identify gaps, and lead AI adoption with greater clarity and alignment.

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Please join me in welcoming Casey and Dave.

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Great.

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Thanks, John.

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This sounded coming through OK for those in the back.

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So I thought we would just kind of start with just a quick.

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I'm awake now.

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So I'd start with a little bit of information.

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So why and how Dave and I kind of met.

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We were at an event similar to this, a manufacturing summit, and I was presenting on paradigms and mindset shifts.

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And obviously the name of his company being Mindset Innovations Consulting came up to me and we just kind of hit it off from his perspective of what work he was doing with his clients, the work that I was doing with my clients.

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And we knew that there was a gap.

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And so we're excited to share information with you on the work that we have come together to do.

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And we have a lot of information to cover.

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We want to make sure that you do stop by and see our booth at the end.

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I'm going to give you all an assignment to come to our booth to pick up something, because instead of handing it out here, I want you to come to our booth and engage with us.

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So we'll go ahead and get started.

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Stay true to time.

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Don is going to help us not go over on time because sometimes I have a tendency to do that and we've got lots to cover.

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So let's just dive right in.

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Probably not a surprise.

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The pressure is real right now in the market.

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And just a show of hands, how many of you are business owners or in a leadership capacity?

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Okay, great.

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You have come to the right place.

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So that AI is accelerating quite rapidly, and you also know that your competitors are moving.

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You're all in the same push.

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And we know that the gap between AI-oriented or enabled organizations and those and everyone else that aren't necessarily enabling is getting wider and wider.

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So AI is no longer optional.

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And honestly, it's going to kind of become table stakes, right?

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It's going to be kind of like Wi-Fi and iPhones here eventually.

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But the productivity gaps are also widening and your competitors probably are not waiting.

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So when we think about the idea of this paradox, AI is really not

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a technical problem anymore.

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There are great tools out there.

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We all can go out and purchase them.

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We can see what's available to us.

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So the technology is here.

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And it works.

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It's cheap.

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Some of it's free, right?

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We get what we pay for.

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And it's getting better every week.

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We obviously heard JC talk about that.

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But what it is, it's a leadership challenge right now and it's a culture problem.

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And I think for those of you who reflect and look in the mirror, we also know that 95% of AI initiatives, this is, you know, being cited in McKinsey, cited in Deloitte and Gartner, they're all talking about this idea that AI initiatives are actually failing in many organizations.

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And so we know that it isn't a tech problem, it is a people problem.

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So here is how, and this is an interesting slide, because here is how it looks in terms of data.

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Probably most of you would agree, as 95% of executives are telling us when we're visiting with them, that AI is a strategic priority, especially in 2026.

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If you haven't been focused on it up to this point, you are more than likely going to need to face that in 2026.

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Yet 95% of organizations are struggling with how to go about doing that and the AI integration.

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What's the strategy?

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What's the culture?

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What's the trust?

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They're using it poorly.

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Maybe they are using it secretively, right?

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How many team members are maybe using it secretively?

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Or they're maybe some are pretending that they are.

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And it's fascinating when we think about the reality underneath

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the executive confidence.

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So when we explore this further, it really, it isn't necessarily clear.

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There's a lot of ambiguity and there's a lot of uncertainty in the market.

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So it's next slide.

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So.

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So.

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OK, so leaders, consultants, they keep looking for an answer in strategy, they look for an answer in tools, and really implementation, right?

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So, none of these are actually the are actual the problems that we see, right?

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So, the problem is not strategy, the problem is in those tools.

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It's just really that, you know, the problem is our people, right?

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So, like, that's right to the center up there, and really, you know, we're not criticizing that.

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It's just really, it's really the honest diagnostic of it is

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AI adoption fails when human readiness fails.

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So you can have all the greatest AI tools, but until you have your people understand those tools and then leveraging them and using them, that's when it really takes off, right?

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So the rest of the session is really about how we see that readiness clearly before you invest in that.

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So we want to make sure that we're investing in the right tools.

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And then investing in a way that's going to be profitable for our companies and then also for our human beings too, because we need to have our tools enjoyable for the teams that are actually going to be using them, because that's when they actually use them.

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Okay.

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So next one is when we diagnose the organizations, we see three gaps over and over.

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So they are not unique to AI.

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They actually show up in every major transformation.

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Okay.

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So the alignment gap.

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So the leadership wants it, right?

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And

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The teams, they just don't trust it right now.

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So have you heard that where the leadership team says we're going to do something and all of a sudden the teams are like, our leadership team is just so stupid.

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We hear those, right?

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I mean, it happens.

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It definitely happens, right?

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The teams just don't trust it.

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And then the top and the bottom are out of sync because the top is saying we're doing this, the people don't trust us, so it's just out of sync, right?

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So the capability gap.

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People don't know how AI applies to them.

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They see the headlines like, okay, use ChatGPT at your home, right?

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But they cannot connect it to their actual job, or they're scared to as well.

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So using ChatGPT for your job, even though your job or your corporation didn't implement AI yet, so can be kind of scary there.

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And then the identity gap.

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So the last one there, they do not know

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who they are after AI.

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So this is the deepest one, really.

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And this one is the one that determines whether adoption sticks or it stalls, is what we like to say.

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So the next slide here.

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So here's what one of those gaps looks like in the real world.

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So as you're looking at the slide here, we're going to walk you through the five patterns that we see.

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So if you see your organization in any one of these, you're definitely not alone.

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You're actually in the majority, okay?

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So if we look at that, so the founder wants it, right?

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So me as a founder, I'm going to say, I want AI.

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And then the culture just shuts it down.

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So, they criticize and all those different things that happen, right?

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The pilot works, but then it never scales, or the tool's installed and it's barely used.

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So under 5% adoption on these tools,

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you paid 6 figures for.

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So, that can be tough, right?

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So, and then teams say we love it, but then nobody integrates it into the world, real workflow of their organization.

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And then middle managers, right?

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There's.

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managers.

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They stall it quietly.

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Leadership never sees it happening.

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And then, you know, does that sound familiar to you guys?

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Like, see how dysfunctional that is, especially in this day and age?

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Yeah.

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Correct.

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Yep.

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Do you see this scenario more than you see just pure ignorance to it?

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Yeah.

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I mean, I'm a business owner.

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I'm a manufacturing business.

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My thing is, what is this?

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How do I implement it?

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How does it help me?

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Am I behind the eight ball here?

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I mean, I'm behind the curb.

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Well, luckily you're not behind because there's so many CEOs and business owners and business leaders that are saying the exact same thing.

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I can't say that the culture of my people won't accept it or won't do it because you don't know how to begin.

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Right.

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And there's companies that are implementing it, but then she'll get into this more, but you'll see more scenarios.

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Have you guys heard the term shadow AI?

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So people are using it in their organizations, but that they don't have a policy.

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There's nothing written.

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So we'll kind of get into that too, but you're exactly right.

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You're not alone on that.

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Across the board.

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Because I did very much, I can see how this transpired because it's a dramatic change and we as humans fear change.

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So yeah, I can see that coming.

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I'm not there yet because I'm not even there.

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Yeah, I'm not that far into it.

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Yeah, which is good news.

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Right, which I would say is really good news because you're here and you're hearing this conversation.

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And what we see is there's this spectrum.

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And part of what we do is asking lots of questions to try to get a sense, where are the leaders first?

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Because

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And sometimes this is a little harsh, and Dave says, that was really direct.

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But what I'll say to leaders is the last thing that you want to do as a CEO or a leader in your organization without having some understanding of where your culture is at, and we've got diagnostics and assessment to do that, is to stand up in front of your entire workforce and to get excited.

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overly excited and passionate and say, we are going to make an AI transformation and wait.

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Because what's going to happen is your organization and your culture, you don't know where everybody is in terms of your population.

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And some people, that is going to shut them down.

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That's going to send some people to update their resume.

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That's going to send some people to be sabotagers for what you want later.

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So, it's great that you're here and having this conversation now, because the timing really matters.

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Good question.

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Yeah, I'll let Casey do this one.

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So I love this one.

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I love this slide.

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And you can see that we're modeling AI really well with our slides.

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And this is kind of what your question relates to.

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Inside your organizations, you may have some that if, let's say, a manager or a leader says to them, you know, what do you think about AI?

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Are you using AI?

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And they'll be like, oh, yeah, I love AI, I know AI.

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But do they really?

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Are they really using AI?

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Are they pretending?

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Are they pretending because they think you expect them to know it already?

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And it's that fear, that identity that's kind of rearing its head.

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The other thing is some are excited, which is great.

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And then you want some excitement.

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Some are terrified.

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Some may not necessarily trust.

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AI.

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So understanding where your people at and the whole point of this is, this is an emotional time within the market.

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And again, it's the people challenge that it's existing.

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And when AI is failing,

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It's not, again, because the technology, it's we want to empower people to be enabled with the technology to increase productivity and to help you reach your targets and your goals.

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But if there's an emotional friction or an emotional fragmentation and you have not set that foundation in place, that's where you're going to make some extra work for yourself and maybe minimize your impact.

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So I'll let Dave talk about this slide.

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This is one of my favorites too.

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You guys are priority reading a little bit there, but every organization is somewhere on the spectrum right now.

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So you're going to appreciate this slide for sure.

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So first one there is we see is denial, anxiety, skepticism, cautious curiosity, collaboration, and then advocacy.

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Okay, so leadership must know where, and here's the hard truth.

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So most leaders think they are further to the right than the organization actually is, right?

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So that gap between where leadership thinks they are and where the organization actually is, really actually a readiness failure, we like to say.

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So without saying it out loud,

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I'm going to ask you guys this.

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So where do you think your organization sits on the spectrum?

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Don't say anything, but just hold that answer, okay?

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So where do you guys think you guys are at in that spectrum?

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Just think about that.

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Okay, let's hold that answer.

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So before we show you the framework, we're going to look at a couple of different things here, right?

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So one more foundational shift, we'll say.

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So the old leadership, right?

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So there's the old leadership there.

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So was really built on control, right?

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Control, expertise, certainty, and then approval-based culture.

228
00:16:20,863 --> 00:16:22,943
Then the model gets us to where we're at today.

229
00:16:22,943 --> 00:16:32,143
So AI era leadership is built on, you know, it says orchestration, curiosity, experimentation, and then psychological safety, right?

230
00:16:32,863 --> 00:16:36,263
So that model is what wins the next decade.

231
00:16:36,263 --> 00:16:37,583
So think about 10 years from now.

232
00:16:38,543 --> 00:16:39,983
So this really isn't a tech upgrade.

233
00:16:39,983 --> 00:16:42,623
It's just a leadership identity shift.

234
00:16:43,103 --> 00:16:52,303
If you are trying to lead with AI and then the old leadership, as you see over there, you know, trying to come where we're at from the old leadership to where we're at now.

235
00:16:52,703 --> 00:16:55,823
And you're going to feel everything that we just showed you here in a second here.

236
00:16:55,823 --> 00:17:03,263
But it's, have you seen the leadership where it's, they go to, they get their master's degree, they're in leadership, organizational development, all that.

237
00:17:03,263 --> 00:17:05,503
And then, and then since they have that schooling,

238
00:17:06,223 --> 00:17:07,343
They're the authority, right?

239
00:17:07,823 --> 00:17:11,383
Well, now you have that AI at your fingertips and your cell phones and all that.

240
00:17:11,383 --> 00:17:14,423
And so now you can have leadership models with an open AI.

241
00:17:14,423 --> 00:17:20,223
And now that leader's going, oh, I spent all that money in schooling and now you can have this at your fingertips.

242
00:17:20,943 --> 00:17:22,943
And then now that's the new authority, right?

243
00:17:23,743 --> 00:17:24,383
Oh, man.

244
00:17:24,383 --> 00:17:29,023
So these senior leaders are going, okay, where's my identity at now?

245
00:17:29,183 --> 00:17:29,503
Right?

246
00:17:30,703 --> 00:17:31,583
So go to the next one.

247
00:17:31,903 --> 00:17:32,063
Yeah.

248
00:17:32,063 --> 00:17:35,663
And I think that I'm going to I'm going to go back.

249
00:17:35,943 --> 00:17:38,983
for a minute, because I think you did a really good job about explaining that.

250
00:17:38,983 --> 00:17:48,943
And so I would just say the reason why old leadership models exist was because we were living in a world of scarcity, right?

251
00:17:48,943 --> 00:17:50,623
Scarcity of intelligence.

252
00:17:50,623 --> 00:17:53,583
And now we're living in a world of abundance of intelligence.

253
00:17:54,303 --> 00:18:01,343
So how many of you, maybe there's not going to be anybody that says this, but how many of you have like, you know, large team meetings?

254
00:18:02,063 --> 00:18:07,663
and you're saying, we're going to do X, Y, Z, we're going to be working on this.

255
00:18:08,223 --> 00:18:16,783
Nine times out of 10, your people are taking what you say as knowledge, decisions, information.

256
00:18:17,263 --> 00:18:21,703
In today's world, how many of those people are running back to their desk and they're going to go ask OpenAI?

257
00:18:21,743 --> 00:18:25,343
I wonder if my boss was right on that, right?

258
00:18:25,623 --> 00:18:26,303
That could happen.

259
00:18:26,943 --> 00:18:31,583
And so it isn't just our teams, your teams and your managers identity.

260
00:18:31,983 --> 00:18:33,503
It's yours too.

261
00:18:34,383 --> 00:18:38,303
Because you've got to think about what got me here.

262
00:18:40,303 --> 00:18:41,183
I'm a little direct.

263
00:18:41,183 --> 00:18:45,183
What got you here is not going to get you where you're going to probably be going.

264
00:18:46,143 --> 00:18:48,303
Don't tell Casey, but I check her all the time.

265
00:18:49,583 --> 00:18:50,383
Sorry, I'm just kidding.

266
00:18:50,863 --> 00:18:51,103
Yes.

267
00:18:51,743 --> 00:18:54,783
And we, you know, we've been doing some YouTube.

268
00:18:54,783 --> 00:18:56,703
We've got a studio and we've been doing some YouTube videos.

269
00:18:56,703 --> 00:19:00,383
So go out and subscribe to our stuff because we'll be handing out some value that way.

270
00:19:00,783 --> 00:19:03,663
And in one of the, no, that's not good.

271
00:19:03,663 --> 00:19:05,143
You keep talking.

272
00:19:05,143 --> 00:19:05,983
OK, I'll keep talking.

273
00:19:06,303 --> 00:19:10,543
And one of the thumbnails that we created was your leadership is expiring.

274
00:19:11,823 --> 00:19:13,663
And that's I hate to be that direct.

275
00:19:14,623 --> 00:19:16,943
But everything's changing and it's okay.

276
00:19:17,183 --> 00:19:20,463
But we need to make sure that we're helping leaders navigate that.

277
00:19:20,783 --> 00:19:28,823
And that brings us to why we felt it was really important to create a system and to have some frameworks to cut through the noise.

278
00:19:28,823 --> 00:19:31,023
Because I don't know about you, I'm in AI.

279
00:19:31,583 --> 00:19:33,743
I'm annoyed by the noise about AI.

280
00:19:34,143 --> 00:19:35,423
I get out on LinkedIn.

281
00:19:35,423 --> 00:19:39,423
I'm so tired of having people try to sell me AI with their AI.

282
00:19:40,303 --> 00:19:41,423
And I'm an AI company.

283
00:19:42,223 --> 00:19:42,863
It's annoying.

284
00:19:43,183 --> 00:19:44,823
And so we have to cut through that noise.

285
00:19:44,823 --> 00:19:48,223
And for you as business leaders, this is a time of uncertainty.

286
00:19:48,223 --> 00:19:51,903
And then the sad part is it's going to continue to be a time of uncertainty.

287
00:19:52,143 --> 00:20:01,743
But we need to make sure that we can calm, take a box breath and be like, OK, what do we need to do for our enterprise and how are we going to navigate this?

288
00:20:02,623 --> 00:20:08,303
So this is what we call our 12 domains of human centered AI culture adoption.

289
00:20:08,623 --> 00:20:10,703
Now notice the bottom.

290
00:20:11,103 --> 00:20:22,063
really important because we want to make sure within our PrecisionX system we have in every domain we have assessment.

291
00:20:22,583 --> 00:20:38,783
diagnostics, we have workshops, we have assessments around, like for example, if we look at shadow AI, we can come in and we can help you determine what's really happening within your organization.

292
00:20:39,103 --> 00:20:49,023
And the nice thing about working with us as in businesses tell us that is that we can kind of be the messenger, we can kind of be the person who's like, hey, let's really talk about what you're doing on a day-to-day basis.

293
00:20:49,423 --> 00:20:53,343
The framework is built on this archery metaphor.

294
00:20:53,903 --> 00:20:55,183
And why?

295
00:20:55,343 --> 00:21:01,183
Because we believe that AI adoption is not about moving fast.

296
00:21:02,383 --> 00:21:03,903
Doesn't that sound wrong?

297
00:21:04,303 --> 00:21:07,823
It's not about moving fast, but it's aiming with precision.

298
00:21:08,223 --> 00:21:12,583
And it's making sure that much like a bow with any archers in the room, a bow

299
00:21:12,783 --> 00:21:18,863
with a weak, you know, limb is not going to release a strong arrow.

300
00:21:19,423 --> 00:21:27,343
We want to make sure that we're strong and an organization with a weak domain cannot release a strong AI strategy.

301
00:21:28,063 --> 00:21:39,423
So we have to make sure and we have to make sure that your people and you as leaders have an updated quiver for a new world, new mindsets, new skills, new capabilities and new technologies.

302
00:21:39,903 --> 00:21:53,903
And again, notice the foundation of this is starting with human readiness and psychological safety, because we're human and we don't want to lose the human aspect, but we have a lot of people that are just fearful.

303
00:21:55,183 --> 00:21:56,143
and concerned.

304
00:21:56,223 --> 00:21:59,663
And we're not gonna walk through all twelve of these today.

305
00:21:59,663 --> 00:22:00,943
We just do not have the time.

306
00:22:02,223 --> 00:22:15,663
And that's a whole longer conversation, but we do host lots of sessions on all of our Precision X system domains and we offer, you know, solutions and we work, everything that we do is customized and personalized.

307
00:22:15,663 --> 00:22:20,783
But what we are gonna do today is we've picked 4 domains that we believe

308
00:22:21,903 --> 00:22:25,343
It's where organizations sometimes break most often.

309
00:22:25,823 --> 00:22:30,223
So we're kind of took a guess that we think that we would like to share these with you.

310
00:22:30,703 --> 00:22:36,943
And we can kind of dive into the first, the first one, which is that bottom.

311
00:22:37,263 --> 00:22:38,423
Dave, were you taking this side?

312
00:22:39,503 --> 00:22:40,383
Yeah, I'll take this one.

313
00:22:42,143 --> 00:22:47,503
So this one here, so these first three domains, they're really the foundation for everything, okay?

314
00:22:47,983 --> 00:22:51,103
And so without them, AI becomes really fear-based, right?

315
00:22:51,663 --> 00:22:54,943
And so teams freeze, leaders over control.

316
00:22:55,663 --> 00:22:59,983
So this is really the whole framework, either stands up or it collapses.

317
00:23:00,103 --> 00:23:04,143
And really, we're domain 3, the leadership evolution lives.

318
00:23:04,543 --> 00:23:06,383
And so I want to go there first.

319
00:23:06,383 --> 00:23:07,823
So let's go to that one.

320
00:23:09,983 --> 00:23:15,023
So domain 3, it says there, leadership evolution, are your leaders ready to lead differently?

321
00:23:15,103 --> 00:23:21,503
So the first domain here, so I'm going to deep dive on this one a little bit.

322
00:23:23,183 --> 00:23:25,983
And leaders really never want to look at this themselves.

323
00:23:25,983 --> 00:23:30,863
But so question, are your leaders ready to lead differently?

324
00:23:33,103 --> 00:23:35,543
Some of you kind of nod, some kind of smile, some are saying no.

325
00:23:35,543 --> 00:23:36,623
All right.

326
00:23:37,703 --> 00:23:38,143
Maybe.

327
00:23:38,703 --> 00:23:39,703
So look at these signals.

328
00:23:39,703 --> 00:23:42,583
So executives delegate AI to IT, right?

329
00:23:43,183 --> 00:23:45,023
And then they disengage.

330
00:23:45,183 --> 00:23:51,023
Or leadership talks about AI strategy, but has not changed how they make decisions.

331
00:23:51,183 --> 00:23:52,143
That happens, right?

332
00:23:53,103 --> 00:23:56,863
Leaders cannot articulate what AI means for their own role.

333
00:23:58,303 --> 00:23:59,903
So let me give you a little story here.

334
00:24:02,063 --> 00:24:04,223
We worked with a mid-sized company just recently.

335
00:24:04,583 --> 00:24:06,383
There was about 1,800 employees.

336
00:24:07,183 --> 00:24:10,303
And basically the CEO announced an AI initiative, right?

337
00:24:10,823 --> 00:24:15,103
And then IT was tasked with that execution because it's, technology, so IT.

338
00:24:16,063 --> 00:24:24,703
And was it six months later, we were in the room with the executive team and they were asking for updates on the AI strategy.

339
00:24:25,263 --> 00:24:32,063
And so dashboards, slides, adoption numbers, you know, basically when we looked at those numbers, they were flat.

340
00:24:32,463 --> 00:24:34,783
And, you know, teams were not using the tools.

341
00:24:35,183 --> 00:24:40,143
Department heads were just asking IT basic questions like, weren't we supposed to do something with this?

342
00:24:40,943 --> 00:24:42,463
And so really delegating, right?

343
00:24:43,823 --> 00:24:44,943
Here's what stuck.

344
00:24:45,743 --> 00:24:52,383
So the executives had not changed a single thing about how they led, right, and what they prioritized or the decisions that they made.

345
00:24:52,783 --> 00:24:58,543
They had really just delegated AI the same way they would just delegate like an HR system.

346
00:25:00,783 --> 00:25:00,903
Okay.

347
00:25:01,663 --> 00:25:02,423
Yeah, absolutely.

348
00:25:02,423 --> 00:25:15,903
Yeah, because I think it's important to note that oftentimes we get contacted by companies that fortunately kind of say, oh, I think we need some help because they've invested.

349
00:25:15,903 --> 00:25:23,023
And if you follow the money, they spend money on things and then they realize that, oh, we need to go back and figure out how are we going to make this sticky.

350
00:25:23,583 --> 00:25:25,423
And so then we get called in.

351
00:25:25,983 --> 00:25:27,823
to be like, well, can you help us?

352
00:25:27,823 --> 00:25:28,823
Can you talk with our team?

353
00:25:28,823 --> 00:25:33,503
And then we ask the executive, well, who's heading, you're heading it up, right?

354
00:25:34,823 --> 00:25:36,343
And they're like, oh.

355
00:25:36,343 --> 00:25:39,023
It's just always a delegate to IT.

356
00:25:39,503 --> 00:25:41,343
Yeah, and to IT, yeah.

357
00:25:41,663 --> 00:25:43,103
So then we try to fix the problem.

358
00:25:43,103 --> 00:25:47,983
So we go backwards, which doesn't save you time in terms of your strategy execution.

359
00:25:48,863 --> 00:25:50,743
Yep, I'll let you take this next one.

360
00:25:58,503 --> 00:25:59,743
Dangerous today.

361
00:26:00,223 --> 00:26:00,863
Holy cow.

362
00:26:02,463 --> 00:26:05,663
So domain 7, I want to talk about that.

363
00:26:05,663 --> 00:26:08,063
That's identity and role reinvention.

364
00:26:08,703 --> 00:26:14,543
And, you know, through, I like to describe this as it's like identity coherence.

365
00:26:14,783 --> 00:26:16,623
So I want to think about this.

366
00:26:16,623 --> 00:26:19,423
We as individuals need to be self-aware.

367
00:26:21,263 --> 00:26:23,423
We need to understand, you know,

368
00:26:23,863 --> 00:26:30,783
what brings value to those people that I get to share my life with in the workplace?

369
00:26:31,063 --> 00:26:34,223
What are the things that I'm proud of?

370
00:26:34,943 --> 00:26:44,543
My job description says that I need to accomplish these tasks, but now AI might be able to kind of come in and complete these tasks.

371
00:26:44,543 --> 00:26:51,823
But it's hard because we feel a little threatened because our identity is derived by what we do.

372
00:26:51,983 --> 00:26:52,943
oftentimes.

373
00:26:53,143 --> 00:26:59,183
And we've worked hard to get to, especially in leadership roles, we've worked hard for that promotion and that title.

374
00:26:59,663 --> 00:27:06,583
And we hear it often that job replacement conversation that's in the media that's coming up.

375
00:27:06,583 --> 00:27:09,423
We heard it this morning, right, in the opening keynote.

376
00:27:09,743 --> 00:27:12,463
And it's creating this emotional distress in the market.

377
00:27:13,343 --> 00:27:22,423
And honestly, it's the identity coherence factor that you as leaders need to just acknowledge because it's going unnamed.

378
00:27:23,823 --> 00:27:29,983
And it's really less about what job title people have.

379
00:27:29,983 --> 00:27:32,303
It's about, well, I was proud of that.

380
00:27:34,063 --> 00:27:40,143
And fear leads them to believe, well, am I losing myself?

381
00:27:40,663 --> 00:27:42,863
And people resist losing themselves.

382
00:27:42,863 --> 00:27:44,383
They lose that identity.

383
00:27:45,103 --> 00:27:53,543
And it isn't necessarily that they're, they're not resistant to transformation, but they're resisting losing themselves.

384
00:27:53,543 --> 00:27:55,183
And it really isn't about the change.

385
00:27:55,183 --> 00:27:56,943
It's about the instability.

386
00:27:58,743 --> 00:28:07,183
And when you have instability, you start questioning, you know, we hear things like, I'm just not sure where I fit in anymore.

387
00:28:08,623 --> 00:28:11,343
or I just don't feel appreciated anymore.

388
00:28:13,263 --> 00:28:16,623
And those are the types of comments that people are making.

389
00:28:16,943 --> 00:28:20,903
Or somebody will say, everything is just changing so fast.

390
00:28:20,943 --> 00:28:27,023
I feel like I don't know what's expected, and I just can't put my thumb on it, but it just feels like...

391
00:28:27,263 --> 00:28:34,383
And those are the reactions to that uncertainty and that instability that they're feeling.

392
00:28:34,943 --> 00:28:36,703
And they just want clarity.

393
00:28:37,023 --> 00:28:39,983
They just, I'm not clear anymore about what I'm supposed to do.

394
00:28:41,263 --> 00:28:53,743
Maybe if your organization hasn't started AI, they may say something like, well, I just don't know what leadership's going to do, and I'm not quite sure if I'm even going to have a job.

395
00:28:54,343 --> 00:28:57,183
And if I do have a job, am I going to want that job?

396
00:28:57,223 --> 00:28:58,543
Am I going to enjoy that job?

397
00:28:59,023 --> 00:29:08,943
And so where most change management is going to fail, it's going to come from that identity coherence issue.

398
00:29:09,343 --> 00:29:21,183
Now, for those of you who have your agenda and you're looking this afternoon, Selena Pearman is doing a session that's going to talk about adaptation and change management, and she's excellent.

399
00:29:22,983 --> 00:29:29,183
What we're adding here is a little bit of a different layer underneath what you'll hear about if you attend her session.

400
00:29:29,743 --> 00:29:39,423
And it's really about before you can adapt, you have to know who you are becoming in a new environment.

401
00:29:40,223 --> 00:29:47,903
And which brings us, you know, as humans, it brings us peace to know, who am I becoming?

402
00:29:47,903 --> 00:29:50,623
Because we're always aspiring to what's next.

403
00:29:50,623 --> 00:29:52,383
As humans, we're always thinking,

404
00:29:52,743 --> 00:30:04,063
So hopefully we're thinking in ahead and thinking from a forward lens, which brings us to any any thoughts or questions on that before I kind of move on.

405
00:30:05,103 --> 00:30:05,983
Show of hands.

406
00:30:05,983 --> 00:30:07,183
Does that make sense?

407
00:30:08,703 --> 00:30:09,943
Does that resonate with you?

408
00:30:09,943 --> 00:30:12,863
Does OK, so that's what's happening.

409
00:30:12,863 --> 00:30:13,823
That's what we're hearing.

410
00:30:14,023 --> 00:30:15,623
We're seeing that time and time and time again.

411
00:30:15,623 --> 00:30:18,543
It is a re reoccurring theme.

412
00:30:20,103 --> 00:30:23,583
We're going to talk about domain 11 because this one is equally as important.

413
00:30:24,623 --> 00:30:29,263
Every organization has stakeholders, right?

414
00:30:29,263 --> 00:30:32,623
Internal stakeholders, external stakeholders, the market.

415
00:30:32,623 --> 00:30:35,903
Right now, the market may want to know how does your company approach AI.

416
00:30:36,303 --> 00:30:48,303
And if I ask each executive separately, let's say you have a C-suite and we have a conversation with the CFO and we say, you know, what does AI mean for your business?

417
00:30:49,383 --> 00:30:55,983
And then I go over to the chief operations officer and I say, tell me, well, what does AI mean for your business?

418
00:30:56,423 --> 00:30:57,743
And I have that same question.

419
00:30:59,263 --> 00:31:03,583
Time and time again, the answers aren't matching.

420
00:31:03,903 --> 00:31:05,183
So think about that question.

421
00:31:05,183 --> 00:31:14,223
If you were to have somebody on the outside come into your executive leadership team and do that, do you think that everybody would say the same thing?

422
00:31:15,903 --> 00:31:17,183
I see shaking heads.

423
00:31:17,343 --> 00:31:17,623
No.

424
00:31:18,383 --> 00:31:19,983
So that's a challenge, that's a problem.

425
00:31:20,783 --> 00:31:29,023
So there are these signals that are clear that AI strategy means different things to different executives.

426
00:31:29,503 --> 00:31:36,463
And if you have in your executive C-suite different meanings of AI,

427
00:31:37,023 --> 00:31:42,943
Your functional leaders in their departments and their teams, that's just compounded.

428
00:31:43,263 --> 00:31:51,743
I often say that with AI, if you didn't have alignment before AI and now you're bringing in AI, it's like putting a jet pack on and hitting that wall.

429
00:31:52,223 --> 00:31:54,223
It's just going to speed up your dysfunction.

430
00:31:55,263 --> 00:31:59,983
You're just going to get to your dysfunction a whole lot faster and you're going to hit the wall a whole lot harder, right?

431
00:32:01,663 --> 00:32:09,263
Maybe you're bored, and I would imagine, as an executive, your board is saying, What are we doing with AI?

432
00:32:09,503 --> 00:32:10,703
What are we doing with AI?

433
00:32:11,503 --> 00:32:11,903
Right?

434
00:32:13,103 --> 00:32:13,823
What are we doing?

435
00:32:14,223 --> 00:32:15,143
We need to get AI.

436
00:32:15,143 --> 00:32:18,383
And you're sitting back like, whoa, I don't know what to do with AI.

437
00:32:19,423 --> 00:32:20,703
I know we need to do something.

438
00:32:20,863 --> 00:32:23,743
And then your team, your team is looking for you.

439
00:32:24,063 --> 00:32:32,543
They're looking for you to not stand up without any other context or conversation and get excited because then that's going to just amplify their fear, right?

440
00:32:32,623 --> 00:32:33,503
You heard me say that.

441
00:32:33,903 --> 00:32:41,823
But the CFO, an operations manager, you know, all of your key leaders need to really be

442
00:32:42,303 --> 00:32:49,583
coming together and having that alignment, or you're going to make things challenging.

443
00:32:49,663 --> 00:32:51,503
So I'll give you an example of this.

444
00:32:52,303 --> 00:32:55,223
And Dave gave an example of a company.

445
00:32:55,223 --> 00:32:57,983
This one had about $150 million in revenue.

446
00:32:59,463 --> 00:33:03,423
A CEO had announced AI transformation is going to be happening.

447
00:33:04,143 --> 00:33:12,543
Basically, in a leadership meeting, told their executives, and this is before we got involved, but told their executives, here's what we're going to do.

448
00:33:13,343 --> 00:33:16,183
I want each department to go back to your area.

449
00:33:16,223 --> 00:33:21,023
I want you to talk to your teams and I want you to brainstorm, figure out use cases.

450
00:33:22,143 --> 00:33:24,063
So, right, I see nodding.

451
00:33:24,783 --> 00:33:33,183
So your chief marketing officer or your COO goes back and the team comes up with, here's some use cases, here's how we think we can use that.

452
00:33:34,303 --> 00:33:44,143
The one person that really had a problem with that, once things kind of got going six months in, any guesses what role was concerned the most?

453
00:33:45,263 --> 00:33:46,383
The CFO.

454
00:33:46,543 --> 00:33:46,943
Why?

455
00:33:49,063 --> 00:33:50,863
Hola, money, money, money, money.

456
00:33:51,263 --> 00:34:00,623
The CFO was frustrated about the costs because each of those departments were going in a silo doing some

457
00:34:02,783 --> 00:34:04,223
In and out some cash, right?

458
00:34:04,223 --> 00:34:05,663
They're one of the best tools.

459
00:34:05,663 --> 00:34:10,783
And as the CEO and the leader, what do you think the board's reaction was?

460
00:34:12,463 --> 00:34:13,983
And who do you think had the answer to that?

461
00:34:16,223 --> 00:34:24,223
So we're not going to see the return on investment if you don't have the structure to be able to benefit.

462
00:34:24,223 --> 00:34:25,503
And here's the test.

463
00:34:26,223 --> 00:34:29,263
We run this test with executive teams often.

464
00:34:29,423 --> 00:34:31,823
We ask each member that same question.

465
00:34:32,063 --> 00:34:33,663
We kind of get them back together.

466
00:34:33,663 --> 00:34:37,183
When the answers don't match, we focus there.

467
00:34:39,783 --> 00:34:41,983
So I can't stress this enough.

468
00:34:41,983 --> 00:34:48,623
And then if your teams internally, they live life and they represent your brand, right?

469
00:34:49,783 --> 00:34:53,743
And we know the markets, there's a lot of people in the market that are like, pooey on AI.

470
00:34:55,143 --> 00:34:58,783
Well, what happens when they're representing your company in their day-to-day life?

471
00:34:59,343 --> 00:35:00,863
So you have to have a governance.

472
00:35:01,343 --> 00:35:03,903
Your teams need to know what are your principles?

473
00:35:04,143 --> 00:35:09,183
What do you believe about AI and how is your company going to do AI for good?

474
00:35:10,383 --> 00:35:12,303
Because that will also make a difference too.

475
00:35:13,583 --> 00:35:14,623
OK, I'm going to.

476
00:35:14,783 --> 00:35:15,423
I went off script.

477
00:35:15,423 --> 00:35:15,783
Sorry, Dave.

478
00:35:15,783 --> 00:35:16,463
Don't be mad at me.

479
00:35:16,543 --> 00:35:17,023
Oh, you're good.

480
00:35:17,223 --> 00:35:18,063
I'm just making sure.

481
00:35:18,143 --> 00:35:18,623
OK, we got it.

482
00:35:18,743 --> 00:35:19,343
OK, we got it.

483
00:35:19,663 --> 00:35:20,383
We're good on time.

484
00:35:20,863 --> 00:35:21,983
OK, so this next one.

485
00:35:23,343 --> 00:35:28,383
So here is where, when we talked about those 12 domains earlier, this is where it all comes together, right?

486
00:35:28,383 --> 00:35:29,863
So all those 12 domains come together.

487
00:35:30,223 --> 00:35:36,943
So AI must move from experiment to really building that system to culture and then to habit, okay?

488
00:35:37,583 --> 00:35:43,023
And so that is really your sustainability engine when we think about AI, right?

489
00:35:43,743 --> 00:35:47,343
So most organizations get stuck at the experiment stage.

490
00:35:47,663 --> 00:35:50,783
We saw that too, all that dysfunction that we just talked about with that organization.

491
00:35:51,463 --> 00:35:59,023
they wanted AI, then people wanted the best AI, thinking that the best AI or the best AI is the most expensive AI, but then now you're just confusing everybody, right?

492
00:35:59,023 --> 00:36:03,743
So they run pilots forever and then never integrate.

493
00:36:04,063 --> 00:36:10,863
The 12 domains that we talk about, the framework that we have specifically to Precision EcSystem, is designed to move you through every stage.

494
00:36:11,343 --> 00:36:16,783
Okay, so notice on 10, 11, and 12 on this visual, okay, so 10, 11, 12.

495
00:36:18,143 --> 00:36:23,663
So regulatory and risk alignment, and then stakeholder alignment, and then there's engagement.

496
00:36:23,783 --> 00:36:24,143
Okay.

497
00:36:25,103 --> 00:36:27,823
So basically it's measurement and momentum.

498
00:36:28,143 --> 00:36:36,303
And then these are the domains that turn AI from just basically a project or a talk into really organizational capability.

499
00:36:36,983 --> 00:36:37,143
Okay.

500
00:36:39,183 --> 00:36:39,663
So

501
00:36:40,463 --> 00:36:43,543
Next one here, it might be hard to, you can see that.

502
00:36:43,543 --> 00:36:43,823
Okay.

503
00:36:44,703 --> 00:36:48,023
Here is why most leaders cannot solve this from inside their own organization.

504
00:36:48,023 --> 00:36:48,303
Okay.

505
00:36:48,303 --> 00:36:51,023
So leaders are inside the system, right?

506
00:36:51,023 --> 00:36:51,983
They're in that system.

507
00:36:52,703 --> 00:36:56,223
And then resistance is really invisible from within.

508
00:36:56,223 --> 00:37:00,783
So when we think about identity fear, it's the unspoken, right?

509
00:37:00,863 --> 00:37:05,423
And then again, middle managers, they're going to self-protect and then that culture defends it.

510
00:37:05,423 --> 00:37:09,103
So you really need someone who understands the psychology

511
00:37:09,503 --> 00:37:13,103
And Selena is going to bring that later on this afternoon, is that psychology to that.

512
00:37:13,583 --> 00:37:15,823
You need systems, kind of like the Precision X system.

513
00:37:16,023 --> 00:37:16,943
You need that change.

514
00:37:17,063 --> 00:37:18,703
And then also you need the AI, right?

515
00:37:19,183 --> 00:37:21,023
And then let's bridge them together.

516
00:37:21,503 --> 00:37:22,943
And truly, that's what we do there.

517
00:37:22,943 --> 00:37:31,263
So that's exactly why we made that Precision X system, you know, with Casey and I, you know, market president of a university, and then our mindset innovations that comes to that.

518
00:37:31,983 --> 00:37:36,223
So really brings that psychological human side with AI systems.

519
00:37:36,783 --> 00:37:37,583
And then solutions.

520
00:37:37,583 --> 00:37:41,423
So, businesses ensuring that we're addressing the critical gaps.

521
00:37:42,623 --> 00:37:47,143
We like to say sticky adoption that many are faced with.

522
00:37:47,143 --> 00:37:47,263
Okay.

523
00:37:48,463 --> 00:37:48,943
Next.

524
00:37:49,343 --> 00:37:51,743
So we're going to leave this room shortly.

525
00:37:51,743 --> 00:37:55,303
She gave me the 10-minute warning a little bit ago, so maybe 8 minutes now.

526
00:37:55,303 --> 00:37:56,383
Not yet.

527
00:37:56,383 --> 00:37:57,543
So about 8, 7 minutes.

528
00:37:57,543 --> 00:38:00,223
So we're going to leave this room shortly, right?

529
00:38:00,703 --> 00:38:03,023
And when you get back to your organization tomorrow,

530
00:38:03,583 --> 00:38:06,303
These are the five questions we want you asking.

531
00:38:06,623 --> 00:38:06,743
Okay.

532
00:38:06,783 --> 00:38:09,743
So I'm going to ask you guys some questions.

533
00:38:10,383 --> 00:38:10,623
Okay.

534
00:38:10,623 --> 00:38:15,023
One, how many of your leaders use AI daily?

535
00:38:19,663 --> 00:38:20,863
You don't have to raise your hand at once.

536
00:38:20,863 --> 00:38:22,463
Are your leaders doing it?

537
00:38:23,263 --> 00:38:23,983
Shadow AI?

538
00:38:24,303 --> 00:38:25,103
I see nodding.

539
00:38:25,263 --> 00:38:25,863
There's some nodding.

540
00:38:25,863 --> 00:38:26,223
Okay.

541
00:38:27,903 --> 00:38:32,143
And then how many of your teams feel safe experimenting?

542
00:38:34,863 --> 00:38:35,343
That's good.

543
00:38:35,823 --> 00:38:36,463
Some, yeah.

544
00:38:38,623 --> 00:38:42,783
Is AI embedded in workflows or is it just kind of floating outside of them?

545
00:38:44,623 --> 00:38:47,103
Kind of embedded a little bit.

546
00:38:48,543 --> 00:38:48,663
Okay.

547
00:38:48,703 --> 00:38:53,103
Do your people see AI as amplification or do they see it as a threat?

548
00:38:55,103 --> 00:38:56,623
Probably mixed bag is my guess.

549
00:38:56,623 --> 00:38:57,663
Yeah, a little mixed bag.

550
00:38:57,743 --> 00:38:59,183
Yeah, absolutely.

551
00:38:59,743 --> 00:39:05,183
And then if your top AI champion left tomorrow, would adoption survive?

552
00:39:06,223 --> 00:39:07,423
Couple head nods, that's good.

553
00:39:10,703 --> 00:39:17,103
So yeah, each one of these maps to one or more of the 12 domains that we talked about, and your answers will tell you there.

554
00:39:17,183 --> 00:39:19,983
So I'll give you this slide, Casey.

555
00:39:19,983 --> 00:39:22,543
You got 5 minutes.

556
00:39:22,623 --> 00:39:23,503
Yep, very quick.

557
00:39:24,143 --> 00:39:33,663
So this is why it's important to get this right, because we want to make sure that we are seeing return on investment and that we're creating velocity and results.

558
00:39:34,143 --> 00:39:47,263
And with that archery metaphor, it's making sure that you are hitting your targets and that your strategy is going to have an upside, because nobody wants to waste money, right?

559
00:39:47,983 --> 00:39:52,463
So making sure that you're going to see the compounding advantage and what does that look like.

560
00:39:53,263 --> 00:39:56,863
If you forget anything else that we said today, I want you to remember this.

561
00:39:57,343 --> 00:40:05,663
Because the companies that are going to win the next decade are going to be the ones that aren't necessarily just all about the best tools.

562
00:40:05,903 --> 00:40:10,783
It's going to be about those that have the strongest AI leadership culture, and that means governance.

563
00:40:11,263 --> 00:40:15,663
And I often like to say, you wouldn't kill a fly with a bazooka.

564
00:40:16,623 --> 00:40:21,103
You don't necessarily need the biggest AI solution.

565
00:40:21,423 --> 00:40:25,823
You just need to have a solution that's going to get the job done and done well.

566
00:40:27,343 --> 00:40:27,983
Let's see.

567
00:40:27,983 --> 00:40:29,023
And this one's you.

568
00:40:30,223 --> 00:40:31,903
Okay, so really quick here.

569
00:40:31,903 --> 00:40:34,223
So you heard from 4 domains today, right?

570
00:40:34,783 --> 00:40:36,863
And so let's go deeper on one fifth one.

571
00:40:36,863 --> 00:40:43,983
So on May 28th, we're going to be hosting a 90 minute leader briefing focused on the, focused on domain 10, really.

572
00:40:44,223 --> 00:40:47,183
So that's regulatory and risk, risk alignment.

573
00:40:47,183 --> 00:40:49,263
So there's some risk to it too, right?

574
00:40:50,143 --> 00:40:51,023
And so

575
00:40:52,343 --> 00:41:01,503
Aaron Warner, Doug, I think you'll be talking about some, you're going to cover some AI, shadow AI and governance, and you know, I think you're talking about that later today, I feel like.

576
00:41:02,543 --> 00:41:06,623
So if that resonates with you, go to their sessions as well as this one.

577
00:41:06,623 --> 00:41:10,943
But we connected to the full Precision X Readiness Framework.

578
00:41:11,783 --> 00:41:13,663
And so this QR code, it's virtual.

579
00:41:13,663 --> 00:41:14,863
So if you want to take a look at it,

580
00:41:16,383 --> 00:41:19,103
it's a, I think it's what, $97 or something like that.

581
00:41:19,103 --> 00:41:20,943
But it's like, we've got a sign at the booth too.

582
00:41:20,943 --> 00:41:22,943
So you can give it a check out.

583
00:41:25,303 --> 00:41:25,423
Yeah.

584
00:41:25,423 --> 00:41:27,583
So if you guys want to scan that, but it's at the booth too.

585
00:41:28,183 --> 00:41:28,463
Yeah.

586
00:41:29,583 --> 00:41:29,743
OK.

587
00:41:29,743 --> 00:41:35,663
And so of the four domains we covered today, which domain resonated as a barrier in your organization?

588
00:41:36,503 --> 00:41:43,983
Anybody want to share which one kind of jumped out at you?

589
00:41:44,143 --> 00:41:44,783
that's a good one.

590
00:41:45,463 --> 00:41:46,463
That's good, but not good, right?

591
00:41:47,823 --> 00:41:47,943
Yeah.

592
00:41:47,943 --> 00:42:02,143
And I think the, I would just add to that, we have helped companies pick who their champion is, and it would probably, it surprises a lot of people that you're, what you may think would be your best champion necessarily isn't.

593
00:42:02,543 --> 00:42:05,023
So it's how you select that is really important.

594
00:42:05,503 --> 00:42:10,623
But so that barrier in your organization, think about that today and then bring it to our booth.

595
00:42:10,623 --> 00:42:11,663
We want to hear about it.

596
00:42:12,303 --> 00:42:14,783
Not to sell you something, we just want to hear about your barrier because

597
00:42:15,343 --> 00:42:17,263
As we know with AI, we're all learning together, right?

598
00:42:17,263 --> 00:42:20,703
So we want to hear your barriers to it and what you guys experience in your organization.

599
00:42:23,183 --> 00:42:29,623
And so this one here kind of talks about if you're serious about it, AI companies transform companies, leaders do.

600
00:42:29,623 --> 00:42:32,703
So it just kind of talks about that transformation.

601
00:42:33,783 --> 00:42:35,823
And again, just come to our booth, talk to us.

602
00:42:36,063 --> 00:42:36,983
We'd love to hear from you.

603
00:42:38,223 --> 00:42:41,743
And really, we just want to aim with intelligence and use that metaphor.

604
00:42:42,183 --> 00:42:44,063
And I just want to add one last thing.

605
00:42:44,543 --> 00:42:54,183
is that when, I'll put this slide here too, but our archery, so if you go to our booth, we have a little bow and arrow set up and you can actually shoot at the target.

606
00:42:54,183 --> 00:42:54,783
It's a lot of fun.

607
00:42:54,943 --> 00:43:11,663
But what our aim for was, pun intended, aim on that was to really just, when we implement AI and we have to go through the people side of it first, once we get into the implementation stage, now who's ever using the bow and arrow is gonna hit that bullseye every time they use it.

608
00:43:12,143 --> 00:43:12,543
Okay, that's

609
00:43:12,663 --> 00:43:13,583
So that's the metaphor to it.

610
00:43:13,583 --> 00:43:15,503
So it doesn't matter who's using it in the organization.

611
00:43:15,503 --> 00:43:23,823
We're going to make sure you hit your targets, your, you know, if it's financials you want to hit, whatever it is inside that AI model.

612
00:43:23,823 --> 00:43:24,063
So.

613
00:43:24,703 --> 00:43:28,703
And I think that it's because everything's personalized and customized to your organization.

614
00:43:28,703 --> 00:43:30,703
We don't do one-size-fits-all.

615
00:43:30,943 --> 00:43:33,343
We start with any consulting relationship.

616
00:43:33,343 --> 00:43:37,103
It's about making sure that we understand your business and the friction points.

617
00:43:37,463 --> 00:43:39,743
So thank you for listening to us.

618
00:43:39,743 --> 00:43:41,503
We hope that you stop by our booth.

619
00:43:41,663 --> 00:43:45,263
And thank you to Cirrus for the collaboration and partnership.

620
00:43:45,263 --> 00:43:46,063
It's been great.

621
00:43:46,703 --> 00:43:47,503
And good luck.

622
00:43:47,503 --> 00:43:51,583
May the force be with you out there in the uncertainty land.

623
00:43:51,743 --> 00:43:52,863
We feel for you.

624
00:43:53,183 --> 00:43:54,383
We'd love to be able to help.

625
00:43:55,623 --> 00:43:56,383
Thank you so much.

626
00:43:59,943 --> 00:44:03,183
And we are out of time for questions, so I'll just suggest that if you want to