How to Become an AI-Native Organization

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Hey everyone, welcome to another episode of the Leadership Growth Podcast.

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I'm your host, Daniel Stewart, along with my brother, Peter Stewart.

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And we have a fantastic guest today.

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Melissa Reeve, welcome to the Leadership Growth Podcast.

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Thanks so much for having me.

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I'm just so... I'm jazzed for our conversation today.

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I'm excited to be here.

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And jazzed is such a good word because the topic...

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I mean, I guess a part of us all might be...

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you know, sick of talking about AI.

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However...

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However...

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it ain't going anywhere,

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and figuring out how to embrace it,

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how to understand it, how to manage it,

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especially from an organizational level.

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How to help our organizations become more AI-native,

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as we transition into what this might mean,

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and to wade through the noise.

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So, Melissa's going to be here to help us wade through this noise,

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understanding how to help us do this.

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So, let me, uh, let me share a little bit of a background of Melissa,

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and then we're going to jump in to understanding more AI

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as it relates to our organizations.

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Melissa Reeve is the creator

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of the Hyperadaptive Model

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and author of Hyperadaptive: Rewiring the Enterprise to Become AI-Native,

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and yes, we'll no doubt dive into what hyperadaptive means.

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So keep that and we'll dive into this.

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Prior to leaning into AI,

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Melissa spent 25 years as an executive

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and agile thought leader,

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which led to pioneering work in agile marketing,

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and her role as the first VP of marketing at Scaled Agile,

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and co-founding the Agile Marketing Alliance.

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She lives in Boulder, Colorado, lovely Boulder, Colorado,

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with her husband, dogs, and chickens.

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I don't know if we'll get into the chickens, but that might be a fun question later,

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where she also enjoys hiking and gardening, no doubt, in those beautiful Flatirons.

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So, Melissa, welcome again to Leadership Growth Podcast.

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-Thanks so much.

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Pleasure to be here.

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-So, let's start off with this question that

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many of us are feeling because there is such potential

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with AI and it's so, so frequently around us.

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We might be experiencing a bit of FOMO,

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the fear of missing out, the fear of being behind,

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perpetually behind.

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Give us a sense of what are you seeing in the marketplace.

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What are ways that people are kind of trying to

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reconcile these expectations

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around what AI could be doing, what we're seeing now,

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and let's use that as kind of a jumping off point for our conversation here.

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-Yeah, I appreciate the question, Daniel,

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and I'd start by saying, I think just about everybody

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has FOMO, including the people on the front lines.

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I remember being at a conference earlier or later last year,

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and there was somebody who was

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literally a programmer at OpenAI,

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and she felt like she couldn't keep up with AI.

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So it's not just executives.

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It's not just your average individual.

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It's even the people who are writing the code.

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And I don't know if it's because AI can generate the code so fast.

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We've got all these features coming in.

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And quite honestly, our systems and humans

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we're not equipped to absorb these changes so quickly.

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And even Dario Amodei, the CEO of Anthropic at another conference,

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was saying that, um, if we stopped releasing features today,

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it will take enterprises 10 years to integrate what we already have.

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And that was 6 months ago.

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And I know he's released a ton of stuff since then.

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So I think, I think it's just our normal state

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and to a certain, certain amount don't worry about,

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or a certain extent don't worry about it.

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And then I think the other thing is just acknowledging

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that before we see the returns on our AI investments,

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we have to allocate time for what I call sense-making.

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So we have to wrap our heads around

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what the capabilities are, how we can use them.

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And I feel like so many organizations are trying to rush to that ROI state,

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without really recognizing or acknowledging that sense-making,

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that sense-making period.

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-It's... you're bringing up a lot of good points to help kind of level set

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uh, leaders, organizations,

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because this wave, this flow, I mean, it is here,

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it is moving, you know, it feels more like we're, we're on that river.

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We're in the ocean.

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The current's taking us.

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And we just need to make sure we're steering in the right direction.

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Uh, so we're we're going with that.
-Absolutely.

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So appreciate that, that grounding.

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So let's dig into this, this topic of AI a little bit more.

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And we may kind of come back to

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how we can alleviate the FOMO, by some of the things we're doing.

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So we may bookend this with tying that back.

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So what, let's let's kind of get the foundation.

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When we talk about an AI-native organization,

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like, what does that really mean?

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It's a term that's thrown around all the time.

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Like...

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-Yeah.

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-What's your definition of it?

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-Well, I appreciate the question because it's true.

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It's like agentic.

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What is agentic AI?

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You know, there's these words out there that people are tossing around

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and everybody's got a different definition.

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So my definition is

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that it's an organization that's able to use AI

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to sense and respond

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and react in real time through the power of AI.

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And so then that begs the question,

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how do we get there?

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If we aren't, if we aren't AI-native from the get go.

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How do we how do we transform there?

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And I think the biggest difference for me

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is existing organizations, they're what I call linear organizations.

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So you have strategy to execution.

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I'm like, with my hands drawing an X and Y axis, right?

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So you have, you have strategy to execution and you have concept to delivery.

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And you have handoffs and delays through both of those axes.

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And what I believe AI will do is compress both of those axes.

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You know, we can deliver things faster.

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We can get decision support so that we won't

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have to go through so many layers of hierarchy.

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When you think about that linear organization,

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that's an organizational structure

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that is from the 20th century.

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And I like to say you can't expect 21st century results

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with a 20th century operating model.

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And so, an AI-native operating model

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is one that's born out of today's reality, that...

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those, the compression of both of those dimensions,

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rather than, we're gonna, we're a brand new company

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and we're just from the get go, gonna spin up a, a sales silo and a finance silo.

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And I feel like that is such a key differentiation.

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-And you're building on so many themes

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as we think of the more traditional,

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often militaristic hierarchical based structure of decision making rights at the top.

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They decide what needs to happen.

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It flows down.

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It's in this lovely pyramid shaped thing,

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and each level has certain responsibilities,

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and at the end of each month, or each quarter, or each milestone

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they produce X amount of something, and then it's sent out.

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It's this very nice linear understanding.

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It's easy to conceptualize.

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It's easy.

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However, over the past 100 years,

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that model has consistently been challenged.

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And with AI,

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it seems to be like one of the greatest challenges

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to this hierarchical structure.

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And it really reinforces this participative,

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this democratic, this more empowering kind of notion,

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even as we structure the organization.

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Because everybody has access to so much knowledge.

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-That's right.

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Yeah.

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With the internet, we started to democratize knowledge, right?

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Anybody could access knowledge on the internet.

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And I feel like AI starts to democratize skills.

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And it really changes who can do what in the organization.

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And so even this thought of

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separating the organizations into functions starts to blur.

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You know, we have an HR person who might be able to

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develop their own recruiting video.

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We have a traditional marketing person

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who can code analytics and apps for the website.

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And so in the book, I start to advocate

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for a reorganization around value streams,

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and the value that we're delivering in the organization.

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And that's, that's a pretty different...

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I know there's organizations out there who certainly are doing it

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and have been chipping away at that for a while.

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But to wholesale

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reorganize in that way, it's going to take us a hot minute.

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And so that, that's really the basis of the model is,

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is how do we get from these linear structures into

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a hyperadaptive organization,

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and that organizational model is orchestrated value streams powered by AI.

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-This key point that you're really illustrating well

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that I think is a powerful take home for listeners...

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It's recognizing AI-native.

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This terminology is not about the adoption of a new technology,

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in and of itself.

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It is about a mindset shift

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in how we do business,

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in how we interact with each other,

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in how we organize our organizations, businesses, agencies,

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whatever you might want to call it.

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And just right there, unpacking that

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is a powerful statement for leaders to sit and think about.

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We're not just integrating a new technology.

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We are completely altering the fundamentals of how we are interacting.

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-That's right.

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We're rewiring the organization.

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And what I start to cover in the book is all the layers of that.

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So I like to say money follows, culture follows the money.

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And so that implies that we need to

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rewire our funding structures as well.

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And in AI-native organizations,

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I advocate for three.

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I call it the Hyperadaptive Funding Model,

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but it essentially has three layers, right?

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So you have an innovation layer.

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So that's your company's internal VC firm,

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because good ideas can come from anywhere.

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-Mm hmm.

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-We know this.

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Let's enable it.

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Then let's have our value streams,

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which can form and they're fully funded value streams.

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That becomes the mainstay of the organization, not functional areas.

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And then we have our stable foundation.

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And that's keeping the lights on.

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That's making sure we have the infrastructure that we need.

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And when you think about how those three layers start to get funded,

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you fund,

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you fund ventures and innovation very differently

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than you fund your stable layer.

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And I believe that we haven't been able to

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suss that out in organizations because it's too much overhead.

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It would be too much overhead to fund our our organizations that way,

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although again, I know there's certain organizations

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that are leaders and have this down.

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But I do believe that AI

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can start to change that equation in terms of

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how organizations are funded, how the money flows,

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and then we'll finally be able to change the culture

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and change the way we operate.

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-As we imagine that as a future scenario,

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as a future end state, in whatever form that might shape,

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for each different industry or organization,

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what are the phases or steps

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to get there?

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Where does, you know, what's that kind of maturity model,

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those, those process steps?

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Where does an organization start?

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What does that look like?

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-Yeah, I appreciate the question because,

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you know, for a listener, they're like, hey, Melissa, that sounds great.

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Like, what do I do tomorrow?

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-Exactly.

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(laughing)

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And, and so that, I really took that to heart because,

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because I do have this background in lean and agile and transformation,

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I really purposely built the model

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to iteratively and incrementally start rewiring the organization

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toward that hyperadaptive, hyperadaptive future.

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And, um, and how I did it

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was I, this is a research backed model.

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This wasn't just Melissa sitting in a vacuum and, you know, pontificating.

255
00:13:23,708 --> 00:13:25,291
So I used AI.

256
00:13:25,291 --> 00:13:29,958
I used deep research to start surfacing the patterns

257
00:13:29,958 --> 00:13:31,708
of those organizations.

258
00:13:32,083 --> 00:13:32,916
Unilever.

259
00:13:32,916 --> 00:13:36,875
There's a great organization out of China called Ping An Insurance.

260
00:13:37,125 --> 00:13:39,250
They started their AI journey in 2008.

261
00:13:39,750 --> 00:13:43,208
And there are some really leading organizations.

262
00:13:43,666 --> 00:13:45,291
And I used DeepAI,

263
00:13:45,291 --> 00:13:47,750
or deep research, not just to surface them,

264
00:13:47,750 --> 00:13:50,000
but to help synthesize those patterns,

265
00:13:50,375 --> 00:13:53,708
to say, how do we get from here into that future.

266
00:13:54,375 --> 00:13:58,166
And so the hyperadaptive model is a five stage journey.

267
00:13:58,541 --> 00:14:02,833
And of course journeys, like all of our journeys, they're not necessarily linear, right?

268
00:14:02,833 --> 00:14:06,833
Sometimes we go off the path and different people are moving at different speeds.

269
00:14:07,541 --> 00:14:09,875
But you have to start with your foundation

270
00:14:10,083 --> 00:14:13,041
and get some foundational structures in...

271
00:14:13,291 --> 00:14:18,375
in place before you start to augment your tasks with AI,

272
00:14:18,666 --> 00:14:22,791
before you then move into agentic AI

273
00:14:22,791 --> 00:14:24,916
where the roles really start to change.

274
00:14:25,583 --> 00:14:26,958
And in the model,

275
00:14:26,958 --> 00:14:31,041
I feel like the piece that most organizations are missing

276
00:14:31,333 --> 00:14:34,041
is what I call the layer of support structures.

277
00:14:35,083 --> 00:14:37,500
So this is not only your AI councils,

278
00:14:37,500 --> 00:14:41,000
I advocate for a network of dynamic AI councils,

279
00:14:41,500 --> 00:14:44,166
but support systems for your AI leads.

280
00:14:44,500 --> 00:14:47,833
Do you have a network of what I call AI Activation Hubs,

281
00:14:48,208 --> 00:14:52,458
uh, which are the ones that will continually update your organization

282
00:14:52,458 --> 00:14:53,916
as AI changes.

283
00:14:54,833 --> 00:14:57,916
When you think back to the 1990s

284
00:14:57,916 --> 00:15:00,500
and the rollout of the PC,

285
00:15:01,125 --> 00:15:04,541
we didn't just like hand people a PC and say, well, good luck with that.

286
00:15:04,541 --> 00:15:05,500
(laughing)

287
00:15:06,125 --> 00:15:08,333
You know, we built IT help desks.

288
00:15:08,333 --> 00:15:11,833
We built these support systems to help people

289
00:15:11,833 --> 00:15:15,000
learn this new technology, this powerful new technology.

290
00:15:15,541 --> 00:15:20,375
And I feel like organizations haven't quite clicked into that yet.

291
00:15:20,375 --> 00:15:22,583
They're handing out the licenses.

292
00:15:23,041 --> 00:15:26,458
They're providing access to video libraries,

293
00:15:26,833 --> 00:15:28,458
and they're saying, “good luck with that.”

294
00:15:28,875 --> 00:15:34,125
And then they're disappointed when they're not having orchestrated agentic AI.

295
00:15:34,583 --> 00:15:37,541
So, you know, we'll get to the one takeaway,

296
00:15:37,541 --> 00:15:41,500
but uh, it may be related to those support structures.

297
00:15:43,083 --> 00:15:45,625
-Yeah, those support structures are key.

298
00:15:45,625 --> 00:15:48,750
I think, to help with that adoption and to look at it.

299
00:15:48,750 --> 00:15:51,500
So I'm, this may be a bit of a tangent.

300
00:15:52,041 --> 00:15:55,291
But here's where we get the human side of these reactions to

301
00:15:56,083 --> 00:15:57,875
great ideas, this, you know,

302
00:15:57,875 --> 00:16:00,833
complete paradigm shift of how we're moving.

303
00:16:01,541 --> 00:16:06,083
But when you start to shatter, the functional structure,

304
00:16:07,291 --> 00:16:10,458
our human nature is, “You're taking away my role.

305
00:16:10,833 --> 00:16:12,750
You're taking away my my value.

306
00:16:12,750 --> 00:16:14,750
This is my area.

307
00:16:14,750 --> 00:16:15,666
What am I supposed to do?

308
00:16:15,666 --> 00:16:17,750
Like, how, how, wait a minute!

309
00:16:17,750 --> 00:16:20,666
So HR team is producing videos

310
00:16:20,916 --> 00:16:23,125
based on, you know, producing comms,

311
00:16:23,416 --> 00:16:25,458
but yet the comms department isn't involved.

312
00:16:25,458 --> 00:16:28,541
Like, what, what, no, this isn't supposed to happen!”

313
00:16:29,041 --> 00:16:31,916
Like, what advice do you give to those leaders

314
00:16:31,916 --> 00:16:35,333
who are feeling like this, you're moving my cheese,

315
00:16:35,333 --> 00:16:38,541
you're taking away my responsibility, like, there's no order anymore.

316
00:16:39,333 --> 00:16:39,625
-Yeah.

317
00:16:40,000 --> 00:16:42,708
So the good news is, is we have precedent, right?

318
00:16:42,708 --> 00:16:45,166
And that was really the impetus for the book,

319
00:16:45,500 --> 00:16:48,708
is we have precedent in terms of factory automation.

320
00:16:49,041 --> 00:16:52,041
We have precedent in terms of something called DevOps,

321
00:16:52,041 --> 00:16:54,791
which is the automation of the software delivery pipeline.

322
00:16:54,875 --> 00:16:55,291
-Mm hmm.

323
00:16:55,375 --> 00:16:57,166
And what we saw...

324
00:16:57,166 --> 00:17:00,625
one of the first things I did was I reread this book called The DevOps Handbook.

325
00:17:01,125 --> 00:17:05,000
And I said, what lessons can we learn from

326
00:17:05,250 --> 00:17:09,041
that automation that we can now apply as

327
00:17:09,333 --> 00:17:14,541
other parts of the organization start to automate their activities.

328
00:17:14,875 --> 00:17:17,166
And to summarize it generally,

329
00:17:17,166 --> 00:17:22,000
what we see is we see people shift from doing the task

330
00:17:22,375 --> 00:17:26,583
to building, monitoring and maintaining the thing that does the task.

331
00:17:27,208 --> 00:17:29,791
And so what I like to say

332
00:17:29,791 --> 00:17:33,958
is that jobs are made up of tasks,

333
00:17:33,958 --> 00:17:36,833
processes, decisions and human interactions.

334
00:17:37,375 --> 00:17:40,166
And our real work

335
00:17:40,166 --> 00:17:45,375
is going to be deconstructing roles and figuring out...

336
00:17:45,375 --> 00:17:49,000
because we know AI isn't going to be able to take over a job.

337
00:17:49,291 --> 00:17:50,958
I mean, we're treating them like monoliths.

338
00:17:51,208 --> 00:17:51,541
(laughing)

339
00:17:51,541 --> 00:17:54,291
And we know this from factory automation,

340
00:17:54,291 --> 00:17:58,083
where only 1% of the world's factories are fully automated,

341
00:17:58,458 --> 00:18:02,708
and 34% of factory activities are non-automatable.

342
00:18:03,541 --> 00:18:07,250
And so I, when I hear this, like all jobs are going away,

343
00:18:07,625 --> 00:18:09,416
you know, it kind of rubs me the wrong way.

344
00:18:09,416 --> 00:18:12,250
And I can see both of you are like, yeah, that's not quite right.

345
00:18:12,833 --> 00:18:15,083
Um, so that's part of the rewiring too,

346
00:18:15,458 --> 00:18:18,375
is how are the jobs shifting,

347
00:18:18,708 --> 00:18:21,333
and how do we go from doing the thing

348
00:18:21,625 --> 00:18:23,916
to building, monitoring, and maintaining?

349
00:18:24,416 --> 00:18:28,750
And in stage three of the model, we do that on a pretty small scale.

350
00:18:29,208 --> 00:18:31,916
Like we inject agentic AI

351
00:18:31,916 --> 00:18:36,291
into really select roles so that we can monitor.

352
00:18:36,291 --> 00:18:38,333
I spin up something called the AI Impact Hub,

353
00:18:38,791 --> 00:18:41,125
so that we can see what does upskilling look like?

354
00:18:41,666 --> 00:18:43,375
What does reskilling look like?

355
00:18:43,375 --> 00:18:45,291
We know we hired good people,

356
00:18:45,708 --> 00:18:49,125
and we want to retain that institutional knowledge.

357
00:18:49,500 --> 00:18:53,041
So let's do that on a small scale, kind of figure out what we're dealing with

358
00:18:53,416 --> 00:18:57,833
before we start rolling out agentic AI to the whole organization.

359
00:18:58,250 --> 00:19:01,666
And in that way, we can we can really scale this

360
00:19:01,666 --> 00:19:03,625
because scale is is a thing.

361
00:19:03,916 --> 00:19:05,500
You know, how do you do this at scale?

362
00:19:05,791 --> 00:19:08,916
-As we keep imagining different scenarios

363
00:19:09,375 --> 00:19:11,416
through this phasing process,

364
00:19:12,000 --> 00:19:14,500
we're, we're talk— our listeners right now

365
00:19:14,500 --> 00:19:19,833
are going to come from a 100 different industries and perspectives and sizes.

366
00:19:20,333 --> 00:19:26,583
And those that may be in a high tech or a technology focused organization

367
00:19:26,583 --> 00:19:28,333
might be viewing this in one way.

368
00:19:28,916 --> 00:19:31,208
And then others who are attorneys

369
00:19:31,208 --> 00:19:35,625
or run janitorial services or run restaurants

370
00:19:35,625 --> 00:19:38,416
or run manufacturing.

371
00:19:38,416 --> 00:19:41,541
I mean, there's also... or insurance organizations

372
00:19:41,541 --> 00:19:42,291
or healthcare.

373
00:19:42,833 --> 00:19:44,958
All of them might be, well,

374
00:19:44,958 --> 00:19:48,458
well, would be coming from very different perspectives wondering,

375
00:19:48,833 --> 00:19:52,375
how does this fit for us?

376
00:19:52,833 --> 00:19:53,291
-Mm hmm.

377
00:19:53,291 --> 00:19:54,375
-Because in some ways,

378
00:19:55,583 --> 00:19:58,125
I'll say easier, conceptually,

379
00:19:58,125 --> 00:20:01,583
to then imagine if we're talking about a technology group,

380
00:20:02,000 --> 00:20:04,250
in terms of even coding,

381
00:20:04,541 --> 00:20:07,541
that might be a more straightforward approach

382
00:20:07,541 --> 00:20:11,541
because it follows some of the, even the language that has been used.

383
00:20:12,166 --> 00:20:14,458
What can we do to help apply this

384
00:20:14,958 --> 00:20:19,625
to non-tech related entities and companies?

385
00:20:20,375 --> 00:20:20,791
-Yeah.

386
00:20:21,041 --> 00:20:24,375
So I profile so many interesting use cases in the book,

387
00:20:24,375 --> 00:20:26,041
and Unilever comes to mind.

388
00:20:26,041 --> 00:20:27,166
They're a soap company.

389
00:20:27,500 --> 00:20:30,625
Uh, McDonald's comes to mind, they're fast food.

390
00:20:31,000 --> 00:20:34,208
And one of the use cases I profile

391
00:20:34,583 --> 00:20:37,375
is the stress of working in the store.

392
00:20:37,875 --> 00:20:39,916
And they with their AI,

393
00:20:39,916 --> 00:20:44,541
they're talking about their employees and how stressful it is when a rush hits.

394
00:20:44,833 --> 00:20:48,541
And they, um, they're trying to manage the drive-through.

395
00:20:48,791 --> 00:20:50,750
They're trying to manage the front counter.

396
00:20:51,000 --> 00:20:54,291
They're trying to deal with any customer service issues.

397
00:20:54,708 --> 00:20:57,416
And so McDonald's is asking themselves,

398
00:20:57,416 --> 00:21:01,375
how do we inject AI into this flow

399
00:21:01,375 --> 00:21:04,166
so that we can alleviate the stress

400
00:21:04,166 --> 00:21:06,916
of our frontline workers and our managers.

401
00:21:07,541 --> 00:21:10,416
And there's so many interesting angles here.

402
00:21:10,416 --> 00:21:13,333
One is, of course, mundane things like scheduling

403
00:21:13,333 --> 00:21:17,125
and keeping track of just some in-store logistics.

404
00:21:18,041 --> 00:21:20,708
Imagine if you're on the hottest day of the year

405
00:21:21,125 --> 00:21:23,416
and your McFlurry machine breaks.

406
00:21:23,833 --> 00:21:28,458
That was happening at a regular basis in McDonald's all over the world.

407
00:21:29,000 --> 00:21:30,708
What they were able to do

408
00:21:30,708 --> 00:21:35,208
is they were able to use AI to inject predictive maintenance

409
00:21:35,208 --> 00:21:37,208
into their McFlurry machines

410
00:21:37,208 --> 00:21:41,708
so that they had more reliable service on those hot days.

411
00:21:41,958 --> 00:21:45,958
And I wouldn't be surprised, because other organizations are doing this,

412
00:21:46,208 --> 00:21:48,833
if they also are monitoring the weather

413
00:21:49,125 --> 00:21:54,333
so that they can more proactively deliver more McFlurry mix

414
00:21:54,625 --> 00:21:57,583
uh to the stores that need it ahead of that heat wave.

415
00:21:58,125 --> 00:22:01,333
So those are just some of the innovative ways

416
00:22:01,333 --> 00:22:04,875
that non-technology companies are using AI

417
00:22:04,875 --> 00:22:07,541
and integrating it into their organizations.

418
00:22:08,208 --> 00:22:09,375
-That's a great example.

419
00:22:09,666 --> 00:22:12,000
Because all these examples, they give,

420
00:22:12,000 --> 00:22:14,833
well they give me and I assume they're giving our listeners

421
00:22:15,125 --> 00:22:22,041
just different ways to look at the adoption and the integration of the way AI is.

422
00:22:22,500 --> 00:22:26,750
So there's a premise I've heard you repeat a few times now

423
00:22:27,083 --> 00:22:30,208
of it's the shift from just doing

424
00:22:30,666 --> 00:22:35,000
to the kind of building and monitoring and maintaining idea.

425
00:22:35,000 --> 00:22:36,916
And it sounds... okay, that's where we wanted to go.

426
00:22:37,291 --> 00:22:40,916
So if I'm a listener and I'm saying, okay, great.

427
00:22:40,916 --> 00:22:44,166
I'm in a fairly traditionally structured organization.

428
00:22:44,916 --> 00:22:48,166
AI is coming in, whether it's Claude or ChatGPT or,

429
00:22:48,416 --> 00:22:50,958
you know, AI incubators that are in-house,

430
00:22:50,958 --> 00:22:51,916
whatever it might be.

431
00:22:52,750 --> 00:22:54,416
We want to become AI-native.

432
00:22:54,875 --> 00:22:55,333
-Yeah.

433
00:22:55,333 --> 00:22:57,333
-What, what are appropriate

434
00:22:58,041 --> 00:23:03,791
kind of horizon one, horizon two, horizon three benchmarks to try and get to?

435
00:23:04,583 --> 00:23:05,000
-Yeah.

436
00:23:05,333 --> 00:23:09,041
I mean, I, I think the first thing is, is again, this recognition of,

437
00:23:09,041 --> 00:23:12,791
of support, and I, it's a totally different way of learning.

438
00:23:12,791 --> 00:23:15,583
I like to say that AI learning is social learning.

439
00:23:16,041 --> 00:23:18,708
And part of this is because

440
00:23:18,708 --> 00:23:21,291
the use cases for AI are endless.

441
00:23:21,750 --> 00:23:23,458
And I think that's part of the other,

442
00:23:23,458 --> 00:23:24,833
the other part of the churn,

443
00:23:25,250 --> 00:23:28,666
is that people are, they're, they're “using AI,”

444
00:23:29,041 --> 00:23:33,666
but not in a way that really moves the ball forward for organizations.

445
00:23:34,291 --> 00:23:38,583
And so, um, the question becomes

446
00:23:38,583 --> 00:23:41,750
is how do you activate AI in a meaningful way?

447
00:23:42,250 --> 00:23:45,500
And I'll give you the example of Moderna.

448
00:23:45,833 --> 00:23:48,416
I love Moderna for so many reasons.

449
00:23:48,666 --> 00:23:51,875
One is, and I know we're going to talk more about AI North Stars,

450
00:23:51,875 --> 00:23:52,875
but they have a great one.

451
00:23:53,333 --> 00:23:57,041
And theirs is to deliver 15 drugs to market

452
00:23:57,041 --> 00:23:59,583
in five years with the help of AI.

453
00:24:00,375 --> 00:24:02,208
And if you know anything about pharmaceutical,

454
00:24:02,208 --> 00:24:06,208
you know that it typically takes 10 years to deliver one drug.

455
00:24:07,083 --> 00:24:09,875
So that is like, Daniel's shaking his head, like, that's amazing.

456
00:24:09,875 --> 00:24:11,041
-That's shooting high.

457
00:24:11,166 --> 00:24:11,541
Yes.

458
00:24:11,541 --> 00:24:11,875
-Yeah.

459
00:24:11,875 --> 00:24:12,750
-It's pretty high.

460
00:24:12,750 --> 00:24:13,500
-Yeah, that's the moonshot.

461
00:24:13,500 --> 00:24:14,291
Definitely.

462
00:24:14,291 --> 00:24:14,916
-Right.

463
00:24:15,000 --> 00:24:17,416
But all of a sudden what you get is you get focus.

464
00:24:18,083 --> 00:24:20,083
And when people are thinking about

465
00:24:20,541 --> 00:24:23,291
all... of all the things I could be doing AI,

466
00:24:23,625 --> 00:24:27,750
is this going to move the needle on our North Star?

467
00:24:28,625 --> 00:24:32,500
And we start to align around business outcomes

468
00:24:32,875 --> 00:24:35,625
rather than what I call random acts of AI.

469
00:24:36,333 --> 00:24:38,875
So that's my clarion call to leaders,

470
00:24:39,166 --> 00:24:41,125
figure out why you're using AI,

471
00:24:41,500 --> 00:24:44,250
and figure out what your philosophical stance is.

472
00:24:44,250 --> 00:24:46,916
Are you really using it to reduce headcount?

473
00:24:47,208 --> 00:24:49,000
Are you, are you looking to grow?

474
00:24:49,250 --> 00:24:51,541
Are you looking to improve customer service?

475
00:24:51,875 --> 00:24:53,708
Like, what is, what is that?

476
00:24:53,708 --> 00:24:56,458
And then communicate that out to your organization.

477
00:24:56,750 --> 00:24:58,541
Because that starts to bring the temperature down.

478
00:24:59,125 --> 00:25:02,416
Like, like people understand why they're using AI,

479
00:25:02,750 --> 00:25:04,958
what it's for, what we're trying to do with it,

480
00:25:05,291 --> 00:25:07,416
then start activating your leads.

481
00:25:07,750 --> 00:25:10,708
So Moderna had a prompting contest

482
00:25:10,708 --> 00:25:15,416
where they wanted to surface their best prompters in their organization,

483
00:25:15,791 --> 00:25:19,875
and they identified the 100 best prompters,

484
00:25:20,166 --> 00:25:21,125
which is a great start,

485
00:25:21,541 --> 00:25:23,583
but then you need to activate those people

486
00:25:24,000 --> 00:25:28,666
so that they can become your frontline change leaders in the organization.

487
00:25:29,041 --> 00:25:33,416
Because what's happening is we're seeing this bifurcation in organizations

488
00:25:33,416 --> 00:25:35,916
between the power users and everybody else.

489
00:25:36,875 --> 00:25:41,208
And we're not unlocking the value of those power users

490
00:25:41,208 --> 00:25:45,208
and creating learning contagion and social contagion.

491
00:25:45,666 --> 00:25:47,666
So then the other compounding issue...

492
00:25:47,916 --> 00:25:50,666
You got me talking here, Peter, so I'm going to keep going.

493
00:25:50,666 --> 00:25:53,083
-Well, let me pause you, Melissa, before you dive in.

494
00:25:53,083 --> 00:25:55,458
I just want to make sure when you said “prompters,”

495
00:25:56,000 --> 00:25:57,291
I just want to make sure everybody's following,

496
00:25:57,291 --> 00:26:01,958
I assume, you're saying prompters is those who are drafting and engineering

497
00:26:01,958 --> 00:26:06,000
the best prompts to enter into AI to then get the outcomes, correct?

498
00:26:06,333 --> 00:26:06,958
-That's right.

499
00:26:06,958 --> 00:26:07,291
-OK.

500
00:26:07,458 --> 00:26:07,833
-Yeah.

501
00:26:08,291 --> 00:26:10,166
Like, what cool thing did you do with AI?

502
00:26:10,166 --> 00:26:10,708
-Yeah.

503
00:26:11,333 --> 00:26:11,791
-Yeah.

504
00:26:12,250 --> 00:26:13,583
And then you've got to create...
-And that's half the battle.

505
00:26:14,416 --> 00:26:15,708
-Yeah, it's half the battle.

506
00:26:16,166 --> 00:26:19,541
And then the other, the other two parts of the battle are one,

507
00:26:19,541 --> 00:26:20,625
how do you share that?

508
00:26:20,625 --> 00:26:24,500
And then two, well, those are the two part, two additional parts.

509
00:26:24,750 --> 00:26:26,416
Two is how do you keep up with that?

510
00:26:27,000 --> 00:26:28,833
Because AI is changing so quickly.

511
00:26:29,416 --> 00:26:32,166
And so that's where this layer of

512
00:26:32,166 --> 00:26:34,708
layers of the organization are so critical.

513
00:26:35,083 --> 00:26:37,041
You have the AI Activation Hub

514
00:26:37,041 --> 00:26:41,125
and their role is to really be monitoring those changes.

515
00:26:41,958 --> 00:26:44,208
What, what's new that's dropped this year,

516
00:26:44,500 --> 00:26:46,083
or this year, gosh, this week?

517
00:26:46,375 --> 00:26:51,000
And then how do I, how do I send that through the systems we've built

518
00:26:51,000 --> 00:26:53,291
to the leads to the practitioners

519
00:26:53,666 --> 00:26:56,416
that's just in time, just enough, just for me?

520
00:26:57,000 --> 00:26:59,750
And it shifts education from this, like,

521
00:26:59,750 --> 00:27:01,625
let me go to this 2-day course

522
00:27:02,041 --> 00:27:03,416
to I understand,

523
00:27:03,416 --> 00:27:06,583
I'm going to have to learn 15 to 20 minutes every single day,

524
00:27:07,583 --> 00:27:09,500
in order to keep on top of this.

525
00:27:10,125 --> 00:27:12,083
But we need to invest in these, these

526
00:27:12,625 --> 00:27:14,708
parts and pieces of the organization

527
00:27:15,000 --> 00:27:16,583
that can empower this fly—

528
00:27:16,583 --> 00:27:18,875
what I call the AI learning flywheel to happen.

529
00:27:19,416 --> 00:27:22,791
-It's almost like it's reminding me of different centers of excellence

530
00:27:22,791 --> 00:27:25,958
or expertise that have been built up over the years

531
00:27:26,291 --> 00:27:30,541
where whether it's in change management or talent acquisition

532
00:27:30,541 --> 00:27:32,125
or specific DevOps

533
00:27:32,125 --> 00:27:34,583
or a specific methodology,

534
00:27:34,875 --> 00:27:38,583
there is, there is this group of kind of expertise

535
00:27:39,083 --> 00:27:43,583
that they can then be utilized for higher level education training.

536
00:27:43,875 --> 00:27:47,083
And then there's like the more help desk version of it,

537
00:27:47,083 --> 00:27:49,833
or the self-service approach even.

538
00:27:50,333 --> 00:27:54,500
But all of this needs to be applied toward an AI mindset

539
00:27:55,458 --> 00:27:58,833
versus what I think is happening more often today,

540
00:27:58,833 --> 00:28:01,000
which is simply, hey,

541
00:28:01,291 --> 00:28:04,208
AI itself should be inherently easy,

542
00:28:04,791 --> 00:28:06,708
or just go and figure it out

543
00:28:06,916 --> 00:28:10,916
because nobody else has expertise enough to then guide and direct.

544
00:28:11,333 --> 00:28:12,750
And yet the challenges,

545
00:28:13,000 --> 00:28:16,083
there's only about a billion different combinations and options

546
00:28:16,083 --> 00:28:18,583
in which you can then utilize AI for.

547
00:28:19,250 --> 00:28:19,500
So...

548
00:28:19,500 --> 00:28:20,291
-That's right.

549
00:28:20,291 --> 00:28:23,958
-Back to this point around this North Star idea.

550
00:28:24,250 --> 00:28:27,416
I like this idea of this rallying cry

551
00:28:27,750 --> 00:28:29,541
or something to be able to help

552
00:28:30,041 --> 00:28:33,250
connect business issues and a business problem,

553
00:28:34,083 --> 00:28:35,625
to utilize AI.

554
00:28:35,875 --> 00:28:37,375
And we're using AI

555
00:28:37,791 --> 00:28:41,041
because that's the current technology vernacular.

556
00:28:41,875 --> 00:28:44,125
But otherwise, it's process improvement.

557
00:28:44,125 --> 00:28:45,791
It's value stream improvement.

558
00:28:45,791 --> 00:28:50,333
It's being able to add increased value to the client.

559
00:28:50,666 --> 00:28:55,166
And right now we're utilizing AI as the placeholder to describe that.

560
00:28:56,041 --> 00:29:00,166
So this, this intersection though between the value

561
00:29:00,583 --> 00:29:03,791
for the business problem and the value add for AI,

562
00:29:04,291 --> 00:29:05,875
what are good ways

563
00:29:06,333 --> 00:29:09,083
that leaders can go about crafting

564
00:29:09,708 --> 00:29:12,958
that North Star for their organization,

565
00:29:12,958 --> 00:29:17,166
without that becoming demoralizing in and of itself?

566
00:29:17,166 --> 00:29:19,958
There's kind of that, that happy medium there.

567
00:29:20,916 --> 00:29:21,875
-Yeah, I think so.

568
00:29:21,875 --> 00:29:23,916
I mean, I think you're absolutely right that,

569
00:29:24,458 --> 00:29:28,708
that the leaders need, the leaders also need support.

570
00:29:29,166 --> 00:29:32,750
And what I see happening in many ways is,

571
00:29:33,500 --> 00:29:35,000
is just kind of a handoff.

572
00:29:35,416 --> 00:29:40,291
The board puts pressure on the C-suite to “do something with AI,”

573
00:29:40,666 --> 00:29:43,416
the C-suite then goes to their lieutenants and says,

574
00:29:43,416 --> 00:29:45,916
“Hey, we got to do something with AI.”

575
00:29:45,916 --> 00:29:47,416
I mean, it's so abstract.

576
00:29:47,791 --> 00:29:50,666
And then, you know, and then, and then you've got the messy middle

577
00:29:50,666 --> 00:29:54,041
which is like, “Okay, I get I'm supposed to do something.

578
00:29:54,375 --> 00:29:58,500
Now I'm the one who's supposed to translate that into, into frontline reality.”

579
00:29:59,125 --> 00:30:03,083
And so I actually think one of the best things that leaders can do

580
00:30:03,416 --> 00:30:08,083
is really focus their efforts on the capabilities of AI.

581
00:30:08,416 --> 00:30:10,125
Not necessarily...

582
00:30:10,500 --> 00:30:14,916
the, um, the detail on how to build an agent,

583
00:30:15,333 --> 00:30:18,291
but really have a few trusted sources

584
00:30:18,583 --> 00:30:20,791
where they can turn to,

585
00:30:21,125 --> 00:30:23,416
to understand not only its capabilities,

586
00:30:23,833 --> 00:30:26,666
but it's limitations in a much more nuanced way.

587
00:30:27,333 --> 00:30:29,583
So, for example, I uh...

588
00:30:29,583 --> 00:30:33,125
I talked to an executive, he was a CEO.

589
00:30:33,125 --> 00:30:35,041
It was a much smaller organization.

590
00:30:35,500 --> 00:30:38,458
But he went straight to orchestration,

591
00:30:38,750 --> 00:30:45,000
and he wanted to orchestrate some very complex metrics for his organization.

592
00:30:45,416 --> 00:30:47,833
But honestly, the organization wasn't ready.

593
00:30:48,375 --> 00:30:52,875
And so he brought in some outside help to help with this orchestration

594
00:30:52,875 --> 00:30:54,875
because they wanted to do something with AI.

595
00:30:55,333 --> 00:30:58,750
They burned a lot of human capital, a lot of dollars.

596
00:30:59,125 --> 00:31:02,416
They, they got the thing working to about 70%.

597
00:31:03,000 --> 00:31:03,625
Not good enough.

598
00:31:04,208 --> 00:31:07,000
You know, it wasn't good enough for the clients.

599
00:31:07,000 --> 00:31:08,625
It wasn't good enough internally.

600
00:31:09,083 --> 00:31:12,291
And he now had a staff

601
00:31:13,250 --> 00:31:16,750
that was a little bit disillusioned with AI's capability.

602
00:31:16,750 --> 00:31:19,666
He felt like he'd spent a lot of time and money on this thing

603
00:31:20,125 --> 00:31:21,333
that, that didn't work.

604
00:31:21,333 --> 00:31:22,166
And there you go.

605
00:31:22,166 --> 00:31:25,166
There's, you know, there's another pilot in the AI graveyard.

606
00:31:25,708 --> 00:31:28,750
And so, regarding that North Star,

607
00:31:28,750 --> 00:31:32,875
really, if you, if you understand the limitations of AI and capabilities,

608
00:31:32,875 --> 00:31:35,125
and then understand it's a journey,

609
00:31:35,625 --> 00:31:39,166
you can set a North Star that makes sense for your organization.

610
00:31:39,375 --> 00:31:43,250
-I think that will also then as circle back to the FOMO.

611
00:31:43,875 --> 00:31:46,833
As you set that plan, set that North Star,

612
00:31:47,375 --> 00:31:49,708
it's sure, we may not be doing absolutely everything

613
00:31:49,708 --> 00:31:51,250
as we look to our right and our left,

614
00:31:51,583 --> 00:31:52,666
that others might be,

615
00:31:52,916 --> 00:31:55,041
but we have clear focus.

616
00:31:55,416 --> 00:31:56,541
We know where we're heading.

617
00:31:56,875 --> 00:31:58,958
And we can feel each day like we're making progress

618
00:31:59,583 --> 00:32:02,833
towards somewhere as opposed to just floundering out in the middle of the ocean

619
00:32:03,708 --> 00:32:06,166
without, uh, you know, without that clear direction.

620
00:32:06,416 --> 00:32:07,875
-There's so much low hanging fruit.

621
00:32:07,875 --> 00:32:08,750
That's my other thing.

622
00:32:08,750 --> 00:32:13,958
It's like, focus on those 15 minutes that you can save a 1000 times over.

623
00:32:14,416 --> 00:32:16,791
And that starts moving the needle.

624
00:32:17,125 --> 00:32:19,208
Like if you don't have your North Star in place

625
00:32:19,208 --> 00:32:21,791
and if you don't, you know, you're just getting started...

626
00:32:22,125 --> 00:32:22,750
start there.

627
00:32:23,333 --> 00:32:26,000
And you'll start to, you'll start to see progress.

628
00:32:26,000 --> 00:32:26,458
-Yeah.

629
00:32:26,666 --> 00:32:29,041
Well, this has been tremendous to hear these thoughts

630
00:32:29,041 --> 00:32:31,083
and to get us thinking about so many ways.

631
00:32:31,291 --> 00:32:35,875
So just to remind our listeners, your most recent book, Hyperadaptive,

632
00:32:36,250 --> 00:32:37,875
where can people get a hold of that?

633
00:32:38,125 --> 00:32:39,541
-Yeah, so it's out on Amazon.

634
00:32:39,541 --> 00:32:41,416
It's available for pre-order.

635
00:32:41,708 --> 00:32:43,541
It'll be coming later this spring.

636
00:32:43,541 --> 00:32:46,541
And so there are, there are select few individuals

637
00:32:46,541 --> 00:32:48,708
who are getting advanced copies,

638
00:32:48,708 --> 00:32:51,708
but I would encourage you to get your pre-order in

639
00:32:51,708 --> 00:32:54,916
because you will get, uh, some bonus materials

640
00:32:54,916 --> 00:32:57,500
that'll, that'll guide you in the right direction

641
00:32:57,500 --> 00:32:59,583
until you can have the physical book in your hand.

642
00:33:00,125 --> 00:33:01,750
-Yeah, that's fantastic.

643
00:33:02,083 --> 00:33:05,208
And we can, we can mention this in our show notes as well.

644
00:33:05,208 --> 00:33:07,708
So listeners, please go and check that out.

645
00:33:08,041 --> 00:33:10,875
And so as we wrap this up, Melissa,

646
00:33:11,375 --> 00:33:15,708
what's the one thing that leaders can keep in mind

647
00:33:16,125 --> 00:33:18,666
to be able to help shift their organization

648
00:33:18,666 --> 00:33:21,833
into a more native AI organization?

649
00:33:22,291 --> 00:33:25,833
-Well, I think the awareness that the rewiring is available,

650
00:33:26,208 --> 00:33:27,083
that it's doable,

651
00:33:27,708 --> 00:33:31,041
and that it's going to require investment

652
00:33:31,333 --> 00:33:37,500
in the people, the processes, the support structure beyond technology.

653
00:33:37,916 --> 00:33:40,500
And AI isn't a point in time.

654
00:33:40,708 --> 00:33:42,166
It's in constant motion.

655
00:33:42,416 --> 00:33:44,458
So this is a line item that will be

656
00:33:44,791 --> 00:33:49,458
on your, on your balance sheets and on your budgets for years to come.

657
00:33:50,083 --> 00:33:50,500
-Yeah.

658
00:33:50,791 --> 00:33:51,458
Well put.

659
00:33:51,666 --> 00:33:52,125
Well put.

660
00:33:52,125 --> 00:33:54,708
Melissa Reeve, thank you so much for being a guest

661
00:33:54,708 --> 00:33:56,666
on the Leadership Growth Podcast today.

662
00:33:56,666 --> 00:33:57,791
-Such a great conversation.

663
00:33:57,791 --> 00:33:58,625
Thanks for having me.

664
00:33:59,666 --> 00:34:01,958
-And to all of our listeners, thank you for joining us.

665
00:34:01,958 --> 00:34:03,541
Please like and subscribe.

666
00:34:03,541 --> 00:34:07,666
And we look forward to having you join us in future episodes.

667
00:34:07,666 --> 00:34:10,708
All the best along your leadership journey.

668
00:34:10,708 --> 00:34:11,416
Take care everyone.

669
00:34:12,083 --> 00:34:15,125
If you like this episode, please share it with a friend or colleague.

670
00:34:15,375 --> 00:34:19,166
Or better yet, leave a review to help other listeners find our show.

671
00:34:19,833 --> 00:34:22,666
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672
00:34:23,416 --> 00:34:26,583
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673
00:34:26,583 --> 00:34:28,958
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674
00:34:29,500 --> 00:34:31,791
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