June 1, 2026 • 59 mins
Most organizations are trying to skip straight to AI orchestration — and it's costing them. Melissa Reeve, founder of HyperAdaptive Solutions and author of HyperAdaptive: Rewiring the Enterprise to Become AI Native, joins Chad Harvey to break down why the support structures that would actually make AI work are being skipped, what a real AI transformation roadmap looks like, and why giants will fall if they don't rewire now.
Melissa spent 25 years as a marketing exec and agile thought leader — including a run as the first VP of Marketing at Scaled Agile, where she helped scale from 60,000 to over a million people trained in their framework. Now she's applied that operating model lens to AI, building her five-stage HyperAdaptive model from 18 months of research into how organizations like Toyota, FedEx, and JP Morgan are actually becoming AI native.
In this episode: 🎹 Why AI is like a piano — easy to touch, hard to master 🏗️ The support structures most mid-market companies are missing 📡 What the AI Activation Hub is and why your org needs one 🔄 The AI Learning Flywheel — how knowledge flows up and down your org 🎯 Why every leader needs an AI North Star (and what Moderna's looks like) ⚠️ The bifurcation problem: AI power users vs. everybody else 🧱 Why legacy operating models rooted in Taylorism won't survive AI 📊 How AI is breaking the annual budget cycle — and why that's a good thing 🤝 Why middle management isn't obsolete — it's your secret alignment weapon 🏆 The 1% Club: the organizations that will actually win the AI race
Whether you're a CFO at a $300M manufacturer or a COO at an 80M SaaS company, this episode gives you a clear-eyed, stage-by-stage framework for building an AI-native organization without burning through capital or burning out your people.
📖 HyperAdaptive by Melissa Reeve — available on Amazon and major retailers
🌐 hyperadaptive.solutions
🔗 Connect with Melissa on LinkedIn: melissa.m.reeve
🎙️ AI for the C-Suite is the show for senior leaders who know AI matters and need to figure out what to do about it. Subscribe wherever you get your podcasts and follow us on LinkedIn.
🌐 aiforthecsuite.com
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:02):
I'm Chad Harvey and this is AI for the C-suite, the show for senior leaders who know AImatters and need to figure out what to do about it.
Each episode, we dig into what it all actually means for middle market orbs, how it'schanging decisions, strategy, leadership, and the nature of work.
Let's get into it.
(00:22):
If you have spent any time in the AI conversation lately, you've probably noticed thateveryone wants to talk about agents, orchestration, and what's next.
My guest today wants to slow that down, not because the future doesn't matter, but becauseorganizations haven't built the support structures that would let them actually get there.
(00:43):
Melissa Reeve is the founder of HyperAdaptive Solutions and the author of HyperAdaptive,rewiring the enterprise to become AI native.
She spent 25 years as a marketing exec and agile thought leader.
including a run as the first VP of marketing at Scaled Agile, where she helped scale thecompany from 60,000 to over a million people trained in their framework.
(01:05):
She co-founded the Agile Marketing Alliance along the way.
Now she's turned that operating model lens on AI.
Her five-stage hyper-adaptive model is the result of 18 months of research into how orgslike Toyota, FedEx, and JP Morgan are actually rewiring themselves to become AI native.
Melissa?
(01:26):
Welcome to AI for the C-suite.
What a great introduction.
Thank you so much and it's a pleasure to be here.
I am excited to hear what you bring to the table today and I know our listeners will aswell.
Let's start off by jumping in the way back machine.
You have made the point that when companies put a PC on every desk in the 90s, they didn'tjust tell folks to play with it.
(01:48):
They actually stood up IT help desks and entire IT departments.
Yet with AI, you've observed that leaders are skipping that step.
What is actually missing in mid-market companies that would be
the equivalent of that 90s help desk for AI.
Yeah, it's a great question and an uh apt uh analogy.
I feel like the PC is such a powerful piece of technology and we didn't expect everybodyto absorb it overnight.
(02:15):
And I think part of the disconnect is that AI is easy to use.
I have this whole analogy of a piano that you can walk up to a piano and start startdinking around on the keys, but it's actually not that easy to learn.
You have to
You have to put in your reps, just like your piano practice, to really get a sense of it.
(02:37):
And I think the organizations are missing that we need to fire up specific supportstructures to help people essentially play the piano.
And in the hyper adaptive model that starts to look like at the foundation level, AIcouncils and AI leads.
And I might hear your listeners, I can almost feel them saying, yep, yep, we got that.
(03:00):
And so my question to them is, okay, you've got an AI council, is it cross-functional andis it dynamic?
Or is it a council that meets every month or once a quarter, it establishes a document andputs it on a shelf uh out on the internet, hoping that people will understand it or firing
(03:23):
up e-learning one time and quizzing people.
Because we know that AI is much more dynamic.
And so we've got to put these support systems in place.
And I don't know that we'll want to go into dynamic governance right now, but just put apin in that one.
AI leads, how are you programmatically supporting your AI leads?
(03:44):
Do you have something that I call the AI activation hub?
And that's the organization that keeps track of what's going on with AI and
granularizes it for the right people in the right moment at the right time.
And that's just like a small sampling of the support structures that any organizationneeds to fire up to support their humans.
(04:10):
And I think what's so cool about the hyper adaptive model is it's fractal.
And so if you're a smaller organization, you might just need one AI activation hub.
If you are 200 or 500 or 5,000 people,
You might need an interconnected network of these hubs, but the model really holds nomatter what size you are.
(04:34):
So who owns this?
Where does it sit?
Is it an IT?
Is it an HR?
Is it a new role?
Like you've talked about this being fractal, so I want to understand and help ourlisteners understand what's your vision for where this resides?
Does it reside in one place or multiples?
How's this look?
Yeah, isn't that an interesting question?
(04:54):
And it's an interesting question because behind it is this presupposition that it belongsin a silo.
And AI is like peanut butter.
It cuts across the whole organization.
And so we need, at the top, we need a cross-functional group of people who are driving it.
(05:15):
And then we need, so people who are owning the firing up of this infrastructure.
and then the infrastructure should be embedded throughout the organization.
And I write about this in the book how these baby steps of cross-functional ownership ofAI really is the prelude to what's coming in stages three, four, and five, as we start to
(05:41):
dismantle the functional silos and start to organize around value streams.
boy, that sounds like a call to revolution.
uh Let's go back briefly to something you touched on.
You started to explore this analogy of using a piano.
And I think that really lands because anybody can sit down and start hitting keys, butbecoming a concert pianist takes years, decades.
(06:05):
And I feel like what you're saying is that a lot of leaders are asking their folks tostart playing uh full-blown concertos and sonatos right out of the gate.
So if we can torture this musical analogy a little bit further, what does it look likethough, um when you're being deliberate and what's it look like in practice for a CEO that
feels real pressure to show AI progress this quarter?
(06:27):
Mm-hmm.
Yeah, and I think it's managing.
in the book, I talk about Satya Nadella of Microsoft as he was trying to essentiallyrewire Microsoft to be a different type of company.
He knew the culture was needed to change.
so he he went to his shareholders and he said, yes, I'm going to give you some short termwins.
(06:53):
And yes, it's going to take us a minute to get to that that.
concert pianist level uh in that orchestration.
And I would encourage anybody who's listening to also give your organization some grace.
Like it's great that you're aiming for the orchestration.
It's great.
And let's start setting the foundation.
(07:14):
And how that starts to look on the front lines is like in my world, I have somethingcalled the Applied AI Workshop.
And what we do is we get everybody together looking at their processes.
And you want to analyze one process together.
And I have something called the focus framework, which is just a way of surfacing the mostimportant use case in that process.
(07:40):
And what you're doing there is one, you're teaching everybody how to look at theirprocesses and inject AI into those processes.
And we need to do that because
processes are going to have to reinvent themselves over and over and over again.
The second thing we're teaching people to do is how to prioritize those use cases.
(08:01):
Again, we're going to be prioritizing new use cases for a long time to come.
The third thing we're doing is we're establishing AI as social learning.
And because there are so many use cases, there's not really a static curriculum.
that you can roll out to the organization to say, well, here's your AI training, Bing,check the box.
(08:23):
And so we're also embedding that and we're creating psychological safety.
So there's a lot in this one activity.
And if I were a CEO and I was starting to fire this up and I had a board of directors thatI was answering to, I might be saying, we held X number.
of these applied AI workshops.
(08:43):
We held X number of these learning experiences and we were able to hone in on these highvalue use cases.
And at the end of the workshop, what I do is we talk about how to measure success.
And so if you're doing this in the right way, you should come out of these learningexperiences being able to articulate hours saved or times, you know, improved time to
(09:10):
market.
really quantifiable metrics that start to roll up into meaningful progress.
You outlined, I think, five points by my count there, if not more, that we could probablyspend the rest of our time talking about.
So I want to be judicious and I want to pluck one or two of those.
So let me go a little bit deeper on something you said, because I want you to explain whatyou meant a little bit more to the audience.
(09:36):
You talked about AI as social learning.
What do you mean when you say that?
Yeah, so I have this construct.
say like, there's static curriculum, and then there's dynamic curriculum.
And that's a real mental mindset shift, because we're so used to receiving staticlearning.
(09:58):
Like, we sit and we get.
We've done that for many years in school, in training.
And that works when things aren't moving so quickly.
But because things are moving so quickly, I think I saw that
Anthropic had released something like 36 big new features just January through April ofthis year.
(10:19):
When it's moving at that speed, what we need to do is to create infrastructure to teacheach other and to monitor this.
And so I started to outline that structure of the AI activation hubs going into AI leads,going into practitioners.
So I want you to imagine
(10:40):
Quad 4.7 just gets released.
Your AI activation hub is monitoring that and saying, OK, here's what it means for legal.
Here's what it means for marketing.
Here's what it means for operations.
We're going to atomize the learning, and we're going to send it to the AI leads, who arethen going to get it into the hands of the practitioners.
(11:02):
And in that way, we've not only created this infrastructure for always-on learning,
But we've created this network where social learning can happen.
And people then can take that nugget of learning and exchange information with each otherto say, does this really apply to my work and what cool things are we doing?
(11:27):
OK, that was very helpful.
I've got more questions.
uh So one of the suppositions out there in uh futurist land right now about AI models isthat we will soon within, I don't know what time frame, but we will soon in the near
future start to see more tailored, more targeted, more specifically trained models comingout as opposed to general purpose models.
(11:50):
And with what you just framed here in terms of the activation hubs and the ongoinglearning, I'm wondering
How does that change what you're laying out here for folks that are building theiractivation hubs around these general purpose models and knowledge bases and context bases
that are specific to their organizations versus more narrowly tailored models?
(12:11):
Does that make sense?
Yeah, does.
It does.
And I think what I'm trying to advocate for here, Chad, um is a new set of pipes throughwhich learning can be deployed.
So whether we're talking about atomizing the learning for Claude 4.7 or a new uhspecialized LLM just got released, we have the infrastructure in place to deploy
(12:41):
that and we have the infrastructure in place to manage what I call integrated learningloops.
So we've talked about the one direction that learning can go through this infrastructureand I have a name for this.
This is the AI learning flywheel.
And so we talked about new information getting atomized going down through the activationhub to the practitioner.
(13:07):
But there's another direction, right?
Which is the practitioner comes up with this amazing new thing, and then it goes up to anAI lead.
It gets codified in the AI activation hub.
And if you have a network of these hubs, they're sharing with each other.
And that type of integrated learning, in my opinion, is not going to slow down with AI, nomatter what model we're talking about.
(13:35):
Okay, that's fair.
I think the other thing that you called out that I'm very interested in exploring is thisidea of continual reinvention.
You touched on the fact that we are going to be reiterating, reinventing processes.
And I would imagine some of that is going to be driven by the humans.
Some of it is going to be driven by AI analysis.
(13:56):
Some of it's going to be a hybrid.
At what point, maybe that's the wrong question.
I was going to ask something, but...
I'm interested in the fatigue uh piece of this uh from a human perspective, because we'vebeen throwing digital change at people now for 30 plus years.
And I feel like so many folks just want to show up and do their jobs as opposed tocontinually reinvent how the work gets done.
(14:22):
So I'm wondering, um without asking anything specific, just generally, what are yourthoughts on this issue of continual reinvention?
Because I agree with you and I think it is here to stay for at least the next three toseven years.
Mm So I think it's in part.
I don't have any good answers, right?
But the the yeah, the image that comes to mind as you were talking was somebody on abicycle.
(14:45):
And if you're riding a bicycle and somebody like you, you just get going and you get yourrhythm and somebody is like, oh, no, can you stop and go to the right?
And then you start going to the right and you're just about to get your rhythm.
And so no, can you stop and can you go to the left now?
Like that's change fatigue.
(15:06):
But if, if somebody says, look, we're going to be like changing directions every 10minutes and you just got to be ready for it.
And that's the new normal.
And you're on your bike and everyone's like, okay, now left.
And you're like, okay, now left.
And now somebody's like, look, something changed.
Not right.
Okay.
Not right.
And I think it's because the conditioning is that we will get to a frozen solid state.
(15:34):
And if the expectation is, the game's changed, like, it just is change, and that's part ofthe fun, that's a different framing.
But we've made it so difficult to change that it feels painful to get anything reallygoing.
What do you think the implications of this are for larger organizations, especiallypublicly traded ones, whose entire business model is predicated upon process optimization
(16:03):
and ringing efficiency out of pennies.
And now we are talking about wholesale change um at a scale and a speed with which I don'tthink anybody has ever experienced before.
What does that do to business models that are based on that?
Well, you're exactly right.
And that's the entire premise of the book, right?
So I start off the book and I talk about these linear organizations.
(16:26):
So you have strategy to execution, you have concept to delivery.
That's what I call a linear organization.
And AI is going to smush both of those dimensions.
And so I call it out and I say it requires a new operating model.
And the operating models of enterprises today are built out of Taylorism.
(16:48):
They're built out of the silos that emerged after World War II, and they won't survive AI.
And so the premise of the book is really if you are one of these larger organizations, howdo you rewire your people, your processes, your roles, iteratively and incrementally, to
rewire while in flight?
(17:08):
Because I say giants will fall.
There will be enterprises that won't be able to rewire themselves.
and get to this place where you're constantly, that's why I call it hyper adaptive, right?
You're sensing and you're responding in near real time.
Giants will fall is a much softer phrase than there will be blood.
I like it.
(17:29):
All right.
Let's, let's bounce back to something that you touched on.
And I want to go a little bit deeper on, in your opening comments, you talked about yourmodel having five stages.
And I think you, you said, um, something to the effect that most organizations justhaven't even moved past stage one at this point.
So let's put ourselves in the shoes of a mid-market exec.
(17:50):
Let's say I'm a.
CFO at a $300 million manufacturer or a COO at a 80 million SaaS company, whatever.
What are the specific signs that they are stuck at stage one, even if they're brilliantlycrafted slide decks claim otherwise?
Yeah.
And I do see a lot of that.
You we all want to be ahead of where we are.
I think if you start to see what I call the bifurcation problem, and I have this journeymap that starts at confusion, just figuring out what is AI, you know, what model should we
(18:22):
be using?
Where should we be implementing?
Who owns it?
And then you start to see a few early wins.
And the early wins are isolated use cases that really shine.
You start to point your AI leads.
You start to get your AI councils.
then what generally happens is you get this bifurcation.
(18:46):
You get your power users of AI, and then you get everybody else.
And the everybody else is usually people who are using it for email and people who areresisting.
So then the question comes.
becomes, how do you start to cross that?
And that's where you start to really activate your AI leads.
(19:06):
You start to create that social learning, start to engage that middle layer.
The next stage after that starts to look like you're automating good chunks of workflows,but maybe they're not connected.
So Legal has a really great workflow, but they aren't able to share it with marketing.
(19:27):
marketing can't really share what they're doing with sales.
And then what we start to see is we start to see those stitched together and we start tosee a little bit of movement and momentum.
I've yet to really see many organizations hit that stage where they're starting toorchestrate across functions.
(19:49):
I've had a lot of folks with, and I've had a lot of conversations with folks ratherrecently about something that touches on what you just laid out there with respect to the
permissions level and the governance level uh necessary for AI to orchestrate some ofthat.
And so I'm interested in your take on that without getting too far down in the technicalweeds.
(20:12):
What does that look like when we're talking about?
connecting the different workflows.
I'll go back to the example that you talked about a minute ago with legal and withmarketing.
What does that look like when we're starting to knit those together and remove um specificelements of touch that a human would be involved with and automating some of that?
What does that look like from a governance perspective and how does this all kind of cometogether?
(20:36):
Well, let me first sketch out dynamic governance, and then we'll dive into how it appliesto what you're talking about.
So we teased dynamic governance, and I like to think of it as levels.
So you might have a cross-functional group who's figuring out your general guardrails.
Here's our ethical stance on it.
Here's what we can and can't do with data.
(20:59):
uh Some general rules of the road, if you will.
And then depending on the size of the organization, you might have another level ofgovernance at the functional level, where people in marketing are saying, okay, here's how
we're interpreting this, and here's what we think it means for marketing or for finance orfor legal.
(21:20):
And then this is where your activation hubs come into play again.
So now they're taking that and they're empowering your AI leads who are your frontlineguardians.
And those are the ones, if I have a
question and I'm building uh out an automation and I can go to my AI lead and say, this iswhat I want to do.
(21:41):
Where do you think the guardrails are?
But I think to augment that, you probably want some sort of custom GPT or some sort ofchat based bot where you can just ask questions.
Here's what I'm thinking about doing.
Is this a red light, a yellow light, a green light?
Now, when you're talking about
(22:03):
what you just mentioned, is bridging the gap between marketing and legal.
That's where I think we're back into that motion we talked about, where we're bringingeverybody together who works on a workflow.
We're analyzing that workflow because there's more than just figuring out where AI plugsin, right?
(22:26):
There's decisions that get made in a workflow.
And so we really need to not only map out
what happens with the work, but what decisions are being made along the way.
How does AI affect those decisions?
And then I would have somebody in the room, depending on the size of that workflow, whocan directly speak to governance and say, okay, we have these generic policies, let's be
(22:52):
field testing them against the real world and making some calls.
really struck me was the way that you talked about the AI leads.
You called them frontline guardians.
And I find that fascinating in a number of respects.
One of which is there's a lot of ongoing conversation in leadership development circlesright now about the death of tenured expertise uh and the flattening of org charts in the
(23:20):
age of AI.
And when you said frontline guardians, it kind of reached right up and smacked me acrossthe face that
That's what we're talking about here.
We're talking about people that understand the work.
Maybe they've been there 20 years, maybe they've been there, you know, two months.
Um, and their expertise is no longer tied or married to their tenure or their position onthe org chart.
(23:41):
They are the frontline guardians.
And so I'm wondering, what are your thoughts about the disruptive nature of what we aretalking about as a whole here on an org structure?
Mm hmm.
Yeah.
And in the book, I dive into this because there is a flattening of the hierarchy in my inmy world, though, that doesn't mean you're laying off half your half your workforce.
(24:04):
Yeah, it's a it's a redeployment.
And I like to cite the World Economic Forum.
And I need to memorize these exact numbers.
But it's something like 72 million people are are 72 million jobs are going away and 98million jobs are going to be created by AI.
And when you think about that, that represents massive displacement.
(24:28):
So if you are an organization, sure, I guess you could say, well, I'm going to lay offthose 72 million jobs that don't exist, let them upskill or reskill, and then hire them
back when I need the 98 million.
But I think a much smarter move is to recognize that that shift is going to happen.
Keep your institutional knowledge, because what
(24:50):
generally happens in these shifts.
I looked back to factory automation, I looked to DevOps, which is the automation of thesoftware delivery pipeline.
And generally what happens is the jobs go from doing the task to building, monitoring, andmaintaining the automations that do the task.
And so really what you're looking for is you're looking for those individuals who have theattitude and the aptitude
(25:18):
to move from doing the task to building, monitoring, and maintaining, and seeing how youcould upskill or reskill.
And I like to, I know I'm talking a lot here, but I like to give the example of Toyota.
And it will actually let me start with Mercedes-Benz.
So uh they found people on the factory front line who had a natural aptitude for data.
(25:43):
And they trained them in machine learning and data science.
and then they redeployed them to bring that knowledge and skill to the front line of thefactory floor.
And Toyota has equally powerful examples.
So that's just a small taste of how that redeployment can happen.
(26:05):
really uh you're not talking too much.
I had you on here so that you could talk.
ah And I really appreciate you going deep with the Toyota and the Mercedes example becausethat build, monitor and maintain transition is what's really missing in a lot of the
conversations right now about what the nature of work in the future is going to look like.
And we often are getting stuck on the fact that, well, we've asked people to do a task andnow those tasks are going away and we don't know what comes next.
(26:32):
So we're just going to lay them off, right?
That seems to be, you know, the,
We start here and we go to, you know, point Z without stops in between.
And I love that framing of build, monitor, and maintain.
And I'm wondering outside of the activation hubs and outside of a commitment on behalf ofan organization to reskill their folks so that they don't have that brain drain and that
(26:55):
they can get to that build, monitor, and maintain.
What do you think that path looks like?
How long do you think that takes as we make transitions like that?
Again, with an organization that is committed to its people.
Yeah, so a couple of things are coming to mind.
One is, as we move into stage three of the model, what we start to do.
(27:16):
So now for our listeners, we started at stage one, our AI leads, our AI councils.
Stage two looks like let's start integrating AI into our existing workflows and modifyingthem.
And then stage three, we start to get this more of an end-to-end automation in a slice ofa workflow.
(27:41):
But the other thing that's happening in stage three is we're starting to experiment withvalue stream orientation.
And so if there's any listeners who like a value stream, what's that?
It's a concept from lean manufacturing where we organize around customer value.
(28:01):
And I like to use the example of a bank because most people understand a bank and we'veinteracted with one.
And so if you think about a bank, they might have a value stream around new graduates.
They might have uh special products for retirees.
They might have another set of products for ultra high net worth individuals.
(28:22):
All three of those are what we would call value streams.
And we start to organize putting everybody
who can deliver that value on a value stream.
And the reason why this makes so much more sense in the world of AI is really two reasons.
One is we're all picking up what I call adjacent competencies.
(28:42):
So we can do things that we haven't been able to do before.
And then the second reason is that uh it doesn't make sense to organize functionally.
But we don't want to do this all at once because it's very disruptive.
So in stage three, we're one, we're starting to experiment with value streams on a limitedbasis, and we fire up what I call the AI Impact Hub.
(29:08):
And their role is to do exactly what you articulated, which is to say when people movefrom this functional orientation into the value streams, what impact does it have on their
roles, on upskilling, what does training look like, how disruptive is it to ourorganization?
(29:28):
And we want to do that on a small scale before we really blow that up in stage four.
pull on one of the threads that you threw out there while you were spinning.
uh You used the phrase adjacent competencies.
And I want to make sure that we're on the same page here as I asked my question.
One of the things that I am thinking of when you say adjacent competencies is I amthinking about things that maybe I had a tiny little bit of knowledge about before and now
(29:56):
AI is amplifying my ability to uh do more in that area.
Is that essentially what you mean?
Okay, so.
Here's the million dollar question.
ah I think we have the potential to really get out over our skis ah within organizationland ah as we continue to stretch our adjacent competencies.
(30:17):
So how do we know where the line is if I'm an employee that is pushing myself into thoseadjacent competency spaces?
Yeah, so I'm going to rely, I'm going to turn back to our pipes that we've laid downbecause these pipes not only service the organization in terms of delivering that
learning, but they're also measuring progress and they're also answering questions like,hey, know, the AI leads comes to them and says, you know what, George is vibe coding apps
(30:46):
like No Tomorrow over there.
And I'm a little bit concerned about the security.
And so now you've got your AI activation hub that can go
knock on George's door and say, you Hey George, can you show us what you've got?
Cause a, it might be amazing.
And the rest of the organization might want to know about it or B, you know what?
I think, I think we need to pull back on that a little bit.
(31:09):
And so we've got a coordination layer that we've built in to help address whether it'sthings like learning or the scenario that you just put forth, which is like, whoops,
George went a little too far in the vibe coding.
that's right.
Or marketing shouldn't be writing the contracts for our POs, right?
Yeah.
(31:29):
Okay.
No, that's very helpful.
And I love that phrase.
um One of the exciting things about living in this age right now with a general purposetechnology like AI is we get to invent all kinds of new concepts and language.
And I love that uh phrase, adjacent competencies.
I don't know if you coined that or you plucked that from somewhere else, but I think thatperfectly describes that.
(31:49):
skill set stretch that AI augmentation enables right now.
Yeah, I'm pretty sure I plucked it, but I can't remember where.
All right.
Well, in lieu of attribution, we're going to give it to you today.
So well done.
uh Okay.
Let's go back to something else that you talked about uh earlier.
You talked about process mapping and we kind of touched on that a little bit more.
(32:10):
And my understanding here of your model is that one of the things you propose that insteadof hiring consulting firm to map your processes, frontline workers are going to map their
own work to figure out where the AI fits in.
Is that correct?
Yeah, so one of the things we learned from business process re-engineering is that itdidn't really work.
(32:31):
So this is going back to the land of ERPs and things like that.
Great, we hired a bunch of consultants to come in to do all the process mapping.
It was big, it was expensive, it was arduous.
They laid down new process maps.
They left.
And then a lot of times there was tissue rejection.
Like nobody wants the outside coming in and being like thou shalt and you will and youdon't really understand our process and you don't know about Betty who won't let you know
(32:58):
any decision go without her watching it.
And so what we really want to do is we want people to get ownership.
And that doesn't mean that you can't rely on outside people.
mean, obviously my livelihood is partially based on that, but it does mean that you don'twant over-reliance on it.
(33:19):
you want people to own, literally own the process.
And when you think about people owning the process with AI that has an awful lot ofbaggage associated with it, which is, okay, if I map my processes, does that mean I'm
mapping myself out of a job?
(33:40):
You have got to create the psychological safety.
We haven't talked about AI nor stars or like how to create that psychological safety, butyou have to create the psychological safety for people to want to reinvent their own
processes.
um, and I think, I think if you do that, you start to see the worlds open up.
(34:04):
And I also want to toss out there that process mapping today does not look anything likeit used to.
where it took days and it was painful.
There's so much AI software out there right now that'll help you get that process down.
And in fact, it'll take it for swag at that flow chart for you.
ah So don't be afraid of it.
(34:27):
Couldn't agree more.
um I'm a big fan of the conversational process mapping approach.
em Let's dig in, because you said we haven't talked about it.
So let's talk about it.
Let's talk about that North Star.
Let's talk about what psychological safety looks like.
What does that uh entail right now as we within the context of our larger conversation?
(34:47):
Mm-hmm.
So if you are a leader listening to this, I would ask you, do you have an AI North Star?
Like, why are you implementing AI outside of you should?
Like, you should is great to an extent.
You should probably put a PC on everybody's desk, but really, are you hoping to achieve?
(35:10):
And the North Star I point to the most often is Moderna.
And they set the North Star of releasing 15 new drugs in five years with the help of AI.
And if you're familiar with the pharmaceutical industry, just to get one drug out in 10years would be quite an accomplishment.
(35:33):
So I feel like what that does is it unlocks motivation.
All of a sudden, people understand why we're implementing AI.
what that means to me, whether you're in marketing or sales, you're like, yeah, let's,let's do this.
Let's achieve this big hairy audacious goal that we've never been able to do beforebecause of AI.
(35:56):
So set that and then set your AI philosophical stance.
Like this is, they're very intertwined and related, but are you looking to just improveproductivity and harvest those gains?
Are you looking to reimagine the company?
Are you looking to create amazing customer experiences?
(36:20):
You need to be able to articulate that throughout the organization for people's minds toreally open up and for them to feel safe in terms of leaning in.
And I think that that's where a lot of organizations outside of AI even really fall down.
ah It's mission creep.
(36:41):
It's the idea of not understanding what business you're actually in.
And if you can't answer some of those fundamental questions, I don't see how you're evergoing to get to that North Star point that you just articulated.
Yeah, yeah, I mean, I have these cringe moments where like, Jack Dorsey and and block isone and I get that he's trying to reimagine his organization, but I, I just feel like, you
(37:05):
know, you're just doing it in a very uh blunt manner.
You know, I, I get that some people like to throw things off cliffs and watch him smash.
But I just think about when he wants to reimagine the company or grow the company, andmaybe he doesn't.
Maybe they're just like, hey, I want to improve the bottom line and sell it and be done.
But if you truly want a durable company and you just harvest the gains of AI and lay offhalf your workforce, what happens when the displacement comes and you need to hire back
(37:37):
for new roles?
What kind of reputation does your organization have?
Is it somewhere that somebody's gonna wanna work?
I feel like we didn't as deeply explore as we should have yet.
What you articulated earlier regarding the bifurcation problem, um, that, that gap betweenthe AI power users and everybody else, because I see this all the time and we're three
(38:07):
plus three and a half years now into the age of, uh, generative, agentic AI.
And I don't think many organizations at this point are starting from scratch.
They've got, uh, right.
So there's a whole grab bag of.
where they are on the spectrum now.
And let's just say I'm an organization where I've got that bifurcation problem.
I've got a handful of power users.
(38:28):
Maybe we've been power using for a year.
Maybe it's three years.
I don't know.
And then we've got everybody else.
How do we go back in and hit the reset button here?
What does that look like as opposed to a de novo?
We've got greenfield.
We're starting from scratch with this.
Does it change the processes that you've outlined?
think so.
Yeah.
mean, think work with what you have, start with where you are.
(38:50):
You know, again, you probably have some AI leads.
You probably, you know, so if you have, let's just say that you have some AI leads andyou, and usually what happens is you, you have these people who have naturally leaned in
and then you start anointing them or appointing them.
You know, Hey, you're part of the AI council.
(39:12):
Hey, I'm going to call you an AI lead, but it's, it's in name only.
oh You're not really changing the culture of your organization.
So have you as a leader, have you articulated that North Star?
Like what have you done to create safety within your organization, to create that AI NorthStar to say, it's okay for you to lean into AI.
(39:37):
And sometimes it's a time problem or a capacity problem, which is many organizations arerunning so lean.
that you think about the bulk of the people.
You have those power users who they're probably learning it on their own dime and theirown time.
So for everybody else who isn't willing to work nights and weekends to learn AI, you gotto create the space.
(40:03):
And so you create the space by having these social learning experiences.
And I like to share PricewaterhouseCoopers, they have something called the promptingparty.
And like it sounds fun, right?
It sounds like there's going to be pizza on Friday and we're all going to get together andwe're going to learn.
That's how you start to bridge the gap between your power users and everybody else.
(40:29):
You just, I don't know that I would expect you to have an answer to this question, but youjust said something there that raised another question in my mind.
I feel like if we're talking about power users, we're talking about early adopters.
And if we're talking about early adopters, that's a very distinct personality type.
And it doesn't matter if we're talking about mapping it using DICS, or predictive index,or Colby or whatever.
(40:52):
How well does an early adopter personality set
Translate into a steady hand as an AI lead I want
Yeah, well, it's very, very true.
um so that's why we have to give it, in my opinion, this programmatic support.
You know, can't just name somebody an AI lead and say, go spread the good news, becausenot everybody's a preacher.
(41:16):
yeah, that's right.
That's right.
Yeah.
But if you, so I have something I call the AI lead accelerator, and it is thatprogrammatic way.
to take those power users and say, here's how you overcome resistance.
Many people don't know that naturally.
Here's some techniques you can use like empathy mapping.
(41:39):
And here are some ways that you can share knowledge.
We're going to spin up these communities of practice or these other forums for you to dothe knowledge exchange.
But we've got to give them the tools that they need.
in order to spread the good word.
And when I put people through the AI lead accelerator, some of what I'm doing isevaluating them for the fit.
(42:09):
you naturally an AI lead who can evangelize or do you just kind of help those within yoursphere?
And usually people bubble up to the top, like these guys are natural trainers, these guysare the natural support systems.
I'm going to cut.
put these guys on Slack over here to spur conversation.
And then these guys, they're AI lead and name only, and that's cool too.
(42:36):
All right.
We've been going like this all over the place here, but I've been deliberately trying tomake sure that we spend at least a little bit of time on each phase.
And I know we touched on kind of stage three or phase three in your model earlier, but Iwant to go a little deeper on that because my understanding is that's where the real
rewiring occurs.
And you touched on this, you know, I don't think you talked yet about strike teams orinnovation circles.
(43:01):
I'm throwing this out there because I recognize almost nobody's there yet.
Right.
Uh, and if they are, they're certainly not talking about it.
It's secret sauce.
So most of our listeners with that in mind, you're not there yet, but you, you need toknow what you're building towards and what does stage three look like and feel like?
What's it mean for the C suite, especially.
And I guess I'm also interested.
(43:24):
Does the job of being an executive change when you're in a stage three org?
Yeah, so I feel like there's stage three, which is where we start to experiment with thosevalue streams we talked about.
When you think about a value stream, you might have, that's where leadership really startsto change, right?
(43:44):
Because we go from leading a function to leading a value stream.
So now you're leading cross-functional teams.
And
There's a couple of things that are happening there.
One is I want to talk about middle management because there's so many people who thinkthat that just evaporates.
(44:05):
And I think middle managers, their durable skills, besides just organizing work, they'reextremely adept at alignment, building alignment around a diverse set of stakeholders.
And what do we need?
(44:25):
as AI gets more and more powerful.
We need to figure out what the heck we're building for and toward.
And I feel like these are the people who can help us align around that.
When you think about, ah not everybody, I'm not saying it's a one-to-one.
I think some of those people will drop into the build, monitor, maintain.
(44:47):
Some of them might migrate into leading value streams or parts of value streams.
ah But I also think some of them will navigate politics.
There's always going to be politics.
Navigate alignment.
There's always going to be alignment challenges.
ah So how does that feel?
It should start to feel in stage three that the organizational model is starting to shift,but not in a quiet way.
(45:15):
People might be hearing about it here and there, like, oh, there's a pocket over therethat's experimenting.
It's stage four that it really starts to feel like, this is going to be in my backyard.
and we're doing that on purpose.
(45:36):
You just said, we could have a three hour episode here today.
This is fantastic.
um So you just kind of laid out a position and you alluded to the fact that some peoplebelieve that middle management is going to evaporate.
ah There's research papers out there, the headless firm, there's others that touch onthis.
And basically for our listeners, if you can envision it, it's kind of like an hourglassthat continues to get narrower and narrower at the waist with the center section being
(46:01):
middle management.
And you kind of pushed back against that just now.
ah saying I don't think we're going to see the disappearance of that middle layer.
What?
I'm so many questions.
I'm very interested in this because one of the theories out there, and again, we're at theintersection of theory and application as we all build this ship in real time.
(46:23):
One of the theories out there is that middle management is also an interpolation and aninterpretation layer between the C-suite and the work that actually gets done.
One of the ideas is that
uh in that headless firm idea, the middle gets squeezed.
And so the C-suite is going to have to actually interpret more directly what's going on atthe frontline, whether it's humans or AI or things like that.
(46:45):
But your thesis here is you think that middle management is going to continue to beresolute as an alignment builder.
So maybe a shifting uh from their responsibilities of interpretation to more alignment.
Like, let's talk a little bit more about this because it's interesting.
yeah, I don't, I do think it's going to go away.
So I, I'm not, I'm not saying it's.
(47:08):
Yeah.
I, I, I think it'll change.
And I, I'm, I'm saying that those people are redeployable is what I'm saying.
And I totally agree with you.
Some of the, executive level change, they need to stop being handed PowerPoints and breakthat habit.
Yeah.
You know, it's like you are now responsible for getting your own information.
(47:31):
It's always on.
You can just ask the question.
Like, let's break that PowerPoint addiction.
And I would love to see executives, and I talk about this in the book, like mostexecutives that I know and myself included when I was one, overwhelmed by the number of
meetings, by the amount of information.
(47:51):
And maybe with AI, you can reinvent your own workflow.
so that you can spend more time on strategy, so that we can make better decisions.
We can do more scenario modeling.
And I feel like it's going to require behavior modification, I think.
(48:11):
I don't, yeah, like I don't mean, I don't mean that like a, like a, a, a, a, a, a, a, a,a, a, a, like a, like a, like
is, and this is going to be a deal breaker, I talk about how budgeting changes and I talkabout how incentives change and need to change in order to move in this direction because
(48:38):
ego and to a certain extent survival in the modern world, if you are an executive, howmany people do I manage?
How much budget do I manage?
And that oftentimes equates to self-worth.
And so that is one of the biggest shifts I think we need to make.
(48:59):
That is a point that I have not heard anybody make in three years on this podcast.
uh The idea of a revised definition of self-worth ah and job satisfaction in terms of theC-suite.
That is a really interesting point.
And I also want to thank you for course correcting me on my slight misinterpretation ofwhat you said earlier about middle management.
(49:23):
this is very interesting.
Do you think...
Well, let me ask you this is somebody that's been studying organizations, AI aside for along time, been in them, been studying them.
Why do you think we've gotten so wedded at the C-suite level to how much budget we overseeand how many people are, you know, underneath us on the org chart?
Yeah, because it's equated to money.
(49:46):
Same with quarterly bonuses, right?
Like it's, I mean, I've lived this.
It's a little intoxicating and it's a little addicting to hit your number, get yourquarterly bonus, especially if the company is doing well and then your lifestyle goes up
and now you're trying to maintain a lifestyle and the thought that any part of that wouldgo away.
(50:11):
it quite frankly can feel very threatening.
Yeah.
So how do we recalibrate that sense of self-worth and redefine what this means asbudgetary priorities shift from human capital, uh not shift, but rebalance, as it were,
from human capital to technical capital?
Yeah, and I don't talk about it a lot on podcasts because so many people are just, we'reall like, ah, how do I get my processes and my processes?
(50:38):
But I really feel strongly and I articulate how this looks, how the budgeting processstarts to change over these five stages.
We no longer have to make it a yearly event.
What if it's quarterly event?
What if it's monthly event, AI?
allows us to recalibrate and run the data so much more cleanly.
(51:03):
We get feedback around what's working and not working.
And we let the data decide rather than, the budget meetings I tend to sit in is like abunch of half data and mostly opinion.
And so it's really recalibrating the culture around budgets and how they're donegradually.
(51:25):
so that by the time we're hitting stages three, four and five, the culture's alreadystarting to change.
People are feeling relief.
Budgeting doesn't feel quite as onerous as it used to.
And that starts to break down some of the rigidness around how things are done.
And then I think the other thing is that executives aren't monoliths.
(51:46):
There's all kinds of executives out there.
And it's the ones that are willing to move into a new space.
that'll lead the way for everyone else.
It's funny, as you were talking, I was thinking about a conversation I had earlier thisweek with an exec of a mid-market firm that's private equity backed.
(52:07):
And they were talking about how they are getting ready for their quarterly review with theleadership committee and how the 57 slides have to be this, but then they've got to insert
this over here.
um so yes, your comments and your analysis, they're spot on.
It's uh just made me laugh.
All right.
(52:27):
I know from uh our prior conversation that your mentality or your phrasing or your word ofthe year, if you will, for 2026 is be deliberate.
So let's say that I'm a mid-market exec.
I'm listening to this podcast on my Monday morning commute.
I've got an hour on my calendar this week to work on AI or think about it.
(52:48):
What should I actually do with that hour to make sure that I am being deliberate versusjust busy?
Mm-hmm.
I think most executives are spending time thinking about that future state, theorchestration.
(53:11):
They feel this pressure to get to that stage.
And I think it's because there's so much hype.
There's the FOMO.
If I don't get to this stage of orchestration, then
I will get beat out by my competition that does.
(53:32):
I am here to tell you that
Like I don't have a crystal ball, but I'm here to tell you that we're all in the sameboat.
Like we're all feeling it.
And what I see is it's actually the organizations that try and skip ahead to thatorchestration that fall behind because you're burning through capital and political
(53:54):
capital and your people get cynical because it didn't work versus the 1 % club who says,we're just going to try and get 1 % better with AI every day.
And we're going to put the structures and the people in place to help make that happen.
And we're going to keep marching toward toward orchestration without trying to leap therebefore we're ready.
(54:19):
So if you have an hour, I want you to just sit with that.
What what can we do given everything we've talked about?
There's tons of ideas and goodness in this podcast.
What can we implement today?
that looks more like 1 % or 5 % rather than 50%.
love it.
And if you don't be reminded that giants will fall.
(54:45):
All right, Melissa, our time here is is drawing to a close and our always goes veryquickly on this show.
What else do you want our listeners to know about the work that you're doing or the modelthat you've thrown out there?
I know I said we were going to try and hit all the different stages and phases.
I don't know that we were entirely successful, but we got through a lot of it.
(55:06):
What?
What maybe did I leave as a stone that's unturned that we should throw out here for ourlisteners?
Well, I just want to paint a picture and I do call stages four and five the emergingfrontier because it's still being shaped.
But in stage five, I just want to paint a picture of what the organization of the futuremight look like.
(55:27):
And I like to articulate it in three layers.
So the top layer, you might have what I call innovation circles.
So this is your internal venture capitalists.
This is your R &D.
You're funding it in a different manner.
than the rest of the business.
The middle layer might be cross-functional value streams where people are delivering valueto your customer long set, whether it's product lines or whatever that value looks like in
(55:55):
your organization.
Those are funded.
You still might have pieces of functional experts, but they start to look more like striketeams going into the value streams that can help them out when there's a really
tricky legal issue or really tricky marketing issue.
And then the bottom is like your stable layer.
(56:16):
You know, these are the people who are keeping the lights on and infrastructure.
And you think about how that's funded.
That funding looks a little different too.
And so when you're thinking about what we're heading from these linear organizations, whatwe're heading to orchestrated value streams that can sense and respond in near real time.
(56:39):
That's the journey.
I love the picture that you just painted there.
And one of the questions that pops into my head here is, again, talking about this futuretheoretical state of an organization and those three substrates or those three layers
there.
That probably looks radically different in terms of head count and capability uh two orthree or four years from now than it would have even a year or two ago.
(57:08):
So fascinating stuff.
All right.
Melissa, what can our listeners do to connect with you?
Where can they go to find you?
Where can they buy your book?
Are you engaged in any signings anytime soon?
uh Where do we connect?
I appreciate that.
So the book comes out May 12th, so depending on when this drops, but you can certainlyorder it already on Amazon, major retailers.
(57:32):
I am available at hyperadaptive.solutions.
LinkedIn is another great place to find me, melissa.m.reeve.
And yeah, thanks for having me on the show today, Chad.
No, thank you.
I enjoy these conversations with folks that are helping us simultaneously understand thepresent and invent the future.
ah You definitely are one of those.
(57:53):
So thank you for your time today.
I appreciate it.
It's been a pleasure.
And for our listeners, thank you for once again listening to AI for the C-suite.
I hope this episode was useful for you.
If it was, subscribe wherever you get your podcasts, follow us on LinkedIn and check outaiforthecsuite.com.
And until next time, keep your algorithms running, your leadership evolving and your AI incheck.
(58:17):
Take care everybody.
I'm Chad Harvey and this is AI for the C-suite, the show for senior leaders who know AImatters and need to figure out what to do about it.
Each episode, we dig into what it all actually means for middle market orbs, how it'schanging decisions, strategy, leadership, and the nature of work.
Let's get into it.
(00:22):
If you have spent any time in the AI conversation lately, you've probably noticed thateveryone wants to talk about agents, orchestration, and what's next.
My guest today wants to slow that down, not because the future doesn't matter, but becauseorganizations haven't built the support structures that would let them actually get there.
(00:43):
Melissa Reeve is the founder of HyperAdaptive Solutions and the author of HyperAdaptive,rewiring the enterprise to become AI native.
She spent 25 years as a marketing exec and agile thought leader.
including a run as the first VP of marketing at Scaled Agile, where she helped scale thecompany from 60,000 to over a million people trained in their framework.
(01:05):
She co-founded the Agile Marketing Alliance along the way.
Now she's turned that operating model lens on AI.
Her five-stage hyper-adaptive model is the result of 18 months of research into how orgslike Toyota, FedEx, and JP Morgan are actually rewiring themselves to become AI native.
Melissa?
(01:26):
Welcome to AI for the C-suite.
What a great introduction.
Thank you so much and it's a pleasure to be here.
I am excited to hear what you bring to the table today and I know our listeners will aswell.
Let's start off by jumping in the way back machine.
You have made the point that when companies put a PC on every desk in the 90s, they didn'tjust tell folks to play with it.
(01:48):
They actually stood up IT help desks and entire IT departments.
Yet with AI, you've observed that leaders are skipping that step.
What is actually missing in mid-market companies that would be
the equivalent of that 90s help desk for AI.
Yeah, it's a great question and an uh apt uh analogy.
I feel like the PC is such a powerful piece of technology and we didn't expect everybodyto absorb it overnight.
(02:15):
And I think part of the disconnect is that AI is easy to use.
I have this whole analogy of a piano that you can walk up to a piano and start startdinking around on the keys, but it's actually not that easy to learn.
You have to
You have to put in your reps, just like your piano practice, to really get a sense of it.
(02:37):
And I think the organizations are missing that we need to fire up specific supportstructures to help people essentially play the piano.
And in the hyper adaptive model that starts to look like at the foundation level, AIcouncils and AI leads.
And I might hear your listeners, I can almost feel them saying, yep, yep, we got that.
(03:00):
And so my question to them is, okay, you've got an AI council, is it cross-functional andis it dynamic?
Or is it a council that meets every month or once a quarter, it establishes a document andputs it on a shelf uh out on the internet, hoping that people will understand it or firing
(03:23):
up e-learning one time and quizzing people.
Because we know that AI is much more dynamic.
And so we've got to put these support systems in place.
And I don't know that we'll want to go into dynamic governance right now, but just put apin in that one.
AI leads, how are you programmatically supporting your AI leads?
(03:44):
Do you have something that I call the AI activation hub?
And that's the organization that keeps track of what's going on with AI and
granularizes it for the right people in the right moment at the right time.
And that's just like a small sampling of the support structures that any organizationneeds to fire up to support their humans.
(04:10):
And I think what's so cool about the hyper adaptive model is it's fractal.
And so if you're a smaller organization, you might just need one AI activation hub.
If you are 200 or 500 or 5,000 people,
You might need an interconnected network of these hubs, but the model really holds nomatter what size you are.
(04:34):
So who owns this?
Where does it sit?
Is it an IT?
Is it an HR?
Is it a new role?
Like you've talked about this being fractal, so I want to understand and help ourlisteners understand what's your vision for where this resides?
Does it reside in one place or multiples?
How's this look?
Yeah, isn't that an interesting question?
(04:54):
And it's an interesting question because behind it is this presupposition that it belongsin a silo.
And AI is like peanut butter.
It cuts across the whole organization.
And so we need, at the top, we need a cross-functional group of people who are driving it.
(05:15):
And then we need, so people who are owning the firing up of this infrastructure.
and then the infrastructure should be embedded throughout the organization.
And I write about this in the book how these baby steps of cross-functional ownership ofAI really is the prelude to what's coming in stages three, four, and five, as we start to
(05:41):
dismantle the functional silos and start to organize around value streams.
boy, that sounds like a call to revolution.
uh Let's go back briefly to something you touched on.
You started to explore this analogy of using a piano.
And I think that really lands because anybody can sit down and start hitting keys, butbecoming a concert pianist takes years, decades.
(06:05):
And I feel like what you're saying is that a lot of leaders are asking their folks tostart playing uh full-blown concertos and sonatos right out of the gate.
So if we can torture this musical analogy a little bit further, what does it look likethough, um when you're being deliberate and what's it look like in practice for a CEO that
feels real pressure to show AI progress this quarter?
(06:27):
Mm-hmm.
Yeah, and I think it's managing.
in the book, I talk about Satya Nadella of Microsoft as he was trying to essentiallyrewire Microsoft to be a different type of company.
He knew the culture was needed to change.
so he he went to his shareholders and he said, yes, I'm going to give you some short termwins.
(06:53):
And yes, it's going to take us a minute to get to that that.
concert pianist level uh in that orchestration.
And I would encourage anybody who's listening to also give your organization some grace.
Like it's great that you're aiming for the orchestration.
It's great.
And let's start setting the foundation.
(07:14):
And how that starts to look on the front lines is like in my world, I have somethingcalled the Applied AI Workshop.
And what we do is we get everybody together looking at their processes.
And you want to analyze one process together.
And I have something called the focus framework, which is just a way of surfacing the mostimportant use case in that process.
(07:40):
And what you're doing there is one, you're teaching everybody how to look at theirprocesses and inject AI into those processes.
And we need to do that because
processes are going to have to reinvent themselves over and over and over again.
The second thing we're teaching people to do is how to prioritize those use cases.
(08:01):
Again, we're going to be prioritizing new use cases for a long time to come.
The third thing we're doing is we're establishing AI as social learning.
And because there are so many use cases, there's not really a static curriculum.
that you can roll out to the organization to say, well, here's your AI training, Bing,check the box.
(08:23):
And so we're also embedding that and we're creating psychological safety.
So there's a lot in this one activity.
And if I were a CEO and I was starting to fire this up and I had a board of directors thatI was answering to, I might be saying, we held X number.
of these applied AI workshops.
(08:43):
We held X number of these learning experiences and we were able to hone in on these highvalue use cases.
And at the end of the workshop, what I do is we talk about how to measure success.
And so if you're doing this in the right way, you should come out of these learningexperiences being able to articulate hours saved or times, you know, improved time to
(09:10):
market.
really quantifiable metrics that start to roll up into meaningful progress.
You outlined, I think, five points by my count there, if not more, that we could probablyspend the rest of our time talking about.
So I want to be judicious and I want to pluck one or two of those.
So let me go a little bit deeper on something you said, because I want you to explain whatyou meant a little bit more to the audience.
(09:36):
You talked about AI as social learning.
What do you mean when you say that?
Yeah, so I have this construct.
say like, there's static curriculum, and then there's dynamic curriculum.
And that's a real mental mindset shift, because we're so used to receiving staticlearning.
(09:58):
Like, we sit and we get.
We've done that for many years in school, in training.
And that works when things aren't moving so quickly.
But because things are moving so quickly, I think I saw that
Anthropic had released something like 36 big new features just January through April ofthis year.
(10:19):
When it's moving at that speed, what we need to do is to create infrastructure to teacheach other and to monitor this.
And so I started to outline that structure of the AI activation hubs going into AI leads,going into practitioners.
So I want you to imagine
(10:40):
Quad 4.7 just gets released.
Your AI activation hub is monitoring that and saying, OK, here's what it means for legal.
Here's what it means for marketing.
Here's what it means for operations.
We're going to atomize the learning, and we're going to send it to the AI leads, who arethen going to get it into the hands of the practitioners.
(11:02):
And in that way, we've not only created this infrastructure for always-on learning,
But we've created this network where social learning can happen.
And people then can take that nugget of learning and exchange information with each otherto say, does this really apply to my work and what cool things are we doing?
(11:27):
OK, that was very helpful.
I've got more questions.
uh So one of the suppositions out there in uh futurist land right now about AI models isthat we will soon within, I don't know what time frame, but we will soon in the near
future start to see more tailored, more targeted, more specifically trained models comingout as opposed to general purpose models.
(11:50):
And with what you just framed here in terms of the activation hubs and the ongoinglearning, I'm wondering
How does that change what you're laying out here for folks that are building theiractivation hubs around these general purpose models and knowledge bases and context bases
that are specific to their organizations versus more narrowly tailored models?
(12:11):
Does that make sense?
Yeah, does.
It does.
And I think what I'm trying to advocate for here, Chad, um is a new set of pipes throughwhich learning can be deployed.
So whether we're talking about atomizing the learning for Claude 4.7 or a new uhspecialized LLM just got released, we have the infrastructure in place to deploy
(12:41):
that and we have the infrastructure in place to manage what I call integrated learningloops.
So we've talked about the one direction that learning can go through this infrastructureand I have a name for this.
This is the AI learning flywheel.
And so we talked about new information getting atomized going down through the activationhub to the practitioner.
(13:07):
But there's another direction, right?
Which is the practitioner comes up with this amazing new thing, and then it goes up to anAI lead.
It gets codified in the AI activation hub.
And if you have a network of these hubs, they're sharing with each other.
And that type of integrated learning, in my opinion, is not going to slow down with AI, nomatter what model we're talking about.
(13:35):
Okay, that's fair.
I think the other thing that you called out that I'm very interested in exploring is thisidea of continual reinvention.
You touched on the fact that we are going to be reiterating, reinventing processes.
And I would imagine some of that is going to be driven by the humans.
Some of it is going to be driven by AI analysis.
(13:56):
Some of it's going to be a hybrid.
At what point, maybe that's the wrong question.
I was going to ask something, but...
I'm interested in the fatigue uh piece of this uh from a human perspective, because we'vebeen throwing digital change at people now for 30 plus years.
And I feel like so many folks just want to show up and do their jobs as opposed tocontinually reinvent how the work gets done.
(14:22):
So I'm wondering, um without asking anything specific, just generally, what are yourthoughts on this issue of continual reinvention?
Because I agree with you and I think it is here to stay for at least the next three toseven years.
Mm So I think it's in part.
I don't have any good answers, right?
But the the yeah, the image that comes to mind as you were talking was somebody on abicycle.
(14:45):
And if you're riding a bicycle and somebody like you, you just get going and you get yourrhythm and somebody is like, oh, no, can you stop and go to the right?
And then you start going to the right and you're just about to get your rhythm.
And so no, can you stop and can you go to the left now?
Like that's change fatigue.
(15:06):
But if, if somebody says, look, we're going to be like changing directions every 10minutes and you just got to be ready for it.
And that's the new normal.
And you're on your bike and everyone's like, okay, now left.
And you're like, okay, now left.
And now somebody's like, look, something changed.
Not right.
Okay.
Not right.
And I think it's because the conditioning is that we will get to a frozen solid state.
(15:34):
And if the expectation is, the game's changed, like, it just is change, and that's part ofthe fun, that's a different framing.
But we've made it so difficult to change that it feels painful to get anything reallygoing.
What do you think the implications of this are for larger organizations, especiallypublicly traded ones, whose entire business model is predicated upon process optimization
(16:03):
and ringing efficiency out of pennies.
And now we are talking about wholesale change um at a scale and a speed with which I don'tthink anybody has ever experienced before.
What does that do to business models that are based on that?
Well, you're exactly right.
And that's the entire premise of the book, right?
So I start off the book and I talk about these linear organizations.
(16:26):
So you have strategy to execution, you have concept to delivery.
That's what I call a linear organization.
And AI is going to smush both of those dimensions.
And so I call it out and I say it requires a new operating model.
And the operating models of enterprises today are built out of Taylorism.
(16:48):
They're built out of the silos that emerged after World War II, and they won't survive AI.
And so the premise of the book is really if you are one of these larger organizations, howdo you rewire your people, your processes, your roles, iteratively and incrementally, to
rewire while in flight?
(17:08):
Because I say giants will fall.
There will be enterprises that won't be able to rewire themselves.
and get to this place where you're constantly, that's why I call it hyper adaptive, right?
You're sensing and you're responding in near real time.
Giants will fall is a much softer phrase than there will be blood.
I like it.
(17:29):
All right.
Let's, let's bounce back to something that you touched on.
And I want to go a little bit deeper on, in your opening comments, you talked about yourmodel having five stages.
And I think you, you said, um, something to the effect that most organizations justhaven't even moved past stage one at this point.
So let's put ourselves in the shoes of a mid-market exec.
(17:50):
Let's say I'm a.
CFO at a $300 million manufacturer or a COO at a 80 million SaaS company, whatever.
What are the specific signs that they are stuck at stage one, even if they're brilliantlycrafted slide decks claim otherwise?
Yeah.
And I do see a lot of that.
You we all want to be ahead of where we are.
I think if you start to see what I call the bifurcation problem, and I have this journeymap that starts at confusion, just figuring out what is AI, you know, what model should we
(18:22):
be using?
Where should we be implementing?
Who owns it?
And then you start to see a few early wins.
And the early wins are isolated use cases that really shine.
You start to point your AI leads.
You start to get your AI councils.
then what generally happens is you get this bifurcation.
(18:46):
You get your power users of AI, and then you get everybody else.
And the everybody else is usually people who are using it for email and people who areresisting.
So then the question comes.
becomes, how do you start to cross that?
And that's where you start to really activate your AI leads.
(19:06):
You start to create that social learning, start to engage that middle layer.
The next stage after that starts to look like you're automating good chunks of workflows,but maybe they're not connected.
So Legal has a really great workflow, but they aren't able to share it with marketing.
(19:27):
marketing can't really share what they're doing with sales.
And then what we start to see is we start to see those stitched together and we start tosee a little bit of movement and momentum.
I've yet to really see many organizations hit that stage where they're starting toorchestrate across functions.
(19:49):
I've had a lot of folks with, and I've had a lot of conversations with folks ratherrecently about something that touches on what you just laid out there with respect to the
permissions level and the governance level uh necessary for AI to orchestrate some ofthat.
And so I'm interested in your take on that without getting too far down in the technicalweeds.
(20:12):
What does that look like when we're talking about?
connecting the different workflows.
I'll go back to the example that you talked about a minute ago with legal and withmarketing.
What does that look like when we're starting to knit those together and remove um specificelements of touch that a human would be involved with and automating some of that?
What does that look like from a governance perspective and how does this all kind of cometogether?
(20:36):
Well, let me first sketch out dynamic governance, and then we'll dive into how it appliesto what you're talking about.
So we teased dynamic governance, and I like to think of it as levels.
So you might have a cross-functional group who's figuring out your general guardrails.
Here's our ethical stance on it.
Here's what we can and can't do with data.
(20:59):
uh Some general rules of the road, if you will.
And then depending on the size of the organization, you might have another level ofgovernance at the functional level, where people in marketing are saying, okay, here's how
we're interpreting this, and here's what we think it means for marketing or for finance orfor legal.
(21:20):
And then this is where your activation hubs come into play again.
So now they're taking that and they're empowering your AI leads who are your frontlineguardians.
And those are the ones, if I have a
question and I'm building uh out an automation and I can go to my AI lead and say, this iswhat I want to do.
(21:41):
Where do you think the guardrails are?
But I think to augment that, you probably want some sort of custom GPT or some sort ofchat based bot where you can just ask questions.
Here's what I'm thinking about doing.
Is this a red light, a yellow light, a green light?
Now, when you're talking about
(22:03):
what you just mentioned, is bridging the gap between marketing and legal.
That's where I think we're back into that motion we talked about, where we're bringingeverybody together who works on a workflow.
We're analyzing that workflow because there's more than just figuring out where AI plugsin, right?
(22:26):
There's decisions that get made in a workflow.
And so we really need to not only map out
what happens with the work, but what decisions are being made along the way.
How does AI affect those decisions?
And then I would have somebody in the room, depending on the size of that workflow, whocan directly speak to governance and say, okay, we have these generic policies, let's be
(22:52):
field testing them against the real world and making some calls.
really struck me was the way that you talked about the AI leads.
You called them frontline guardians.
And I find that fascinating in a number of respects.
One of which is there's a lot of ongoing conversation in leadership development circlesright now about the death of tenured expertise uh and the flattening of org charts in the
(23:20):
age of AI.
And when you said frontline guardians, it kind of reached right up and smacked me acrossthe face that
That's what we're talking about here.
We're talking about people that understand the work.
Maybe they've been there 20 years, maybe they've been there, you know, two months.
Um, and their expertise is no longer tied or married to their tenure or their position onthe org chart.
(23:41):
They are the frontline guardians.
And so I'm wondering, what are your thoughts about the disruptive nature of what we aretalking about as a whole here on an org structure?
Mm hmm.
Yeah.
And in the book, I dive into this because there is a flattening of the hierarchy in my inmy world, though, that doesn't mean you're laying off half your half your workforce.
(24:04):
Yeah, it's a it's a redeployment.
And I like to cite the World Economic Forum.
And I need to memorize these exact numbers.
But it's something like 72 million people are are 72 million jobs are going away and 98million jobs are going to be created by AI.
And when you think about that, that represents massive displacement.
(24:28):
So if you are an organization, sure, I guess you could say, well, I'm going to lay offthose 72 million jobs that don't exist, let them upskill or reskill, and then hire them
back when I need the 98 million.
But I think a much smarter move is to recognize that that shift is going to happen.
Keep your institutional knowledge, because what
(24:50):
generally happens in these shifts.
I looked back to factory automation, I looked to DevOps, which is the automation of thesoftware delivery pipeline.
And generally what happens is the jobs go from doing the task to building, monitoring, andmaintaining the automations that do the task.
And so really what you're looking for is you're looking for those individuals who have theattitude and the aptitude
(25:18):
to move from doing the task to building, monitoring, and maintaining, and seeing how youcould upskill or reskill.
And I like to, I know I'm talking a lot here, but I like to give the example of Toyota.
And it will actually let me start with Mercedes-Benz.
So uh they found people on the factory front line who had a natural aptitude for data.
(25:43):
And they trained them in machine learning and data science.
and then they redeployed them to bring that knowledge and skill to the front line of thefactory floor.
And Toyota has equally powerful examples.
So that's just a small taste of how that redeployment can happen.
(26:05):
really uh you're not talking too much.
I had you on here so that you could talk.
ah And I really appreciate you going deep with the Toyota and the Mercedes example becausethat build, monitor and maintain transition is what's really missing in a lot of the
conversations right now about what the nature of work in the future is going to look like.
And we often are getting stuck on the fact that, well, we've asked people to do a task andnow those tasks are going away and we don't know what comes next.
(26:32):
So we're just going to lay them off, right?
That seems to be, you know, the,
We start here and we go to, you know, point Z without stops in between.
And I love that framing of build, monitor, and maintain.
And I'm wondering outside of the activation hubs and outside of a commitment on behalf ofan organization to reskill their folks so that they don't have that brain drain and that
(26:55):
they can get to that build, monitor, and maintain.
What do you think that path looks like?
How long do you think that takes as we make transitions like that?
Again, with an organization that is committed to its people.
Yeah, so a couple of things are coming to mind.
One is, as we move into stage three of the model, what we start to do.
(27:16):
So now for our listeners, we started at stage one, our AI leads, our AI councils.
Stage two looks like let's start integrating AI into our existing workflows and modifyingthem.
And then stage three, we start to get this more of an end-to-end automation in a slice ofa workflow.
(27:41):
But the other thing that's happening in stage three is we're starting to experiment withvalue stream orientation.
And so if there's any listeners who like a value stream, what's that?
It's a concept from lean manufacturing where we organize around customer value.
(28:01):
And I like to use the example of a bank because most people understand a bank and we'veinteracted with one.
And so if you think about a bank, they might have a value stream around new graduates.
They might have uh special products for retirees.
They might have another set of products for ultra high net worth individuals.
(28:22):
All three of those are what we would call value streams.
And we start to organize putting everybody
who can deliver that value on a value stream.
And the reason why this makes so much more sense in the world of AI is really two reasons.
One is we're all picking up what I call adjacent competencies.
(28:42):
So we can do things that we haven't been able to do before.
And then the second reason is that uh it doesn't make sense to organize functionally.
But we don't want to do this all at once because it's very disruptive.
So in stage three, we're one, we're starting to experiment with value streams on a limitedbasis, and we fire up what I call the AI Impact Hub.
(29:08):
And their role is to do exactly what you articulated, which is to say when people movefrom this functional orientation into the value streams, what impact does it have on their
roles, on upskilling, what does training look like, how disruptive is it to ourorganization?
(29:28):
And we want to do that on a small scale before we really blow that up in stage four.
pull on one of the threads that you threw out there while you were spinning.
uh You used the phrase adjacent competencies.
And I want to make sure that we're on the same page here as I asked my question.
One of the things that I am thinking of when you say adjacent competencies is I amthinking about things that maybe I had a tiny little bit of knowledge about before and now
(29:56):
AI is amplifying my ability to uh do more in that area.
Is that essentially what you mean?
Okay, so.
Here's the million dollar question.
ah I think we have the potential to really get out over our skis ah within organizationland ah as we continue to stretch our adjacent competencies.
(30:17):
So how do we know where the line is if I'm an employee that is pushing myself into thoseadjacent competency spaces?
Yeah, so I'm going to rely, I'm going to turn back to our pipes that we've laid downbecause these pipes not only service the organization in terms of delivering that
learning, but they're also measuring progress and they're also answering questions like,hey, know, the AI leads comes to them and says, you know what, George is vibe coding apps
(30:46):
like No Tomorrow over there.
And I'm a little bit concerned about the security.
And so now you've got your AI activation hub that can go
knock on George's door and say, you Hey George, can you show us what you've got?
Cause a, it might be amazing.
And the rest of the organization might want to know about it or B, you know what?
I think, I think we need to pull back on that a little bit.
(31:09):
And so we've got a coordination layer that we've built in to help address whether it'sthings like learning or the scenario that you just put forth, which is like, whoops,
George went a little too far in the vibe coding.
that's right.
Or marketing shouldn't be writing the contracts for our POs, right?
Yeah.
(31:29):
Okay.
No, that's very helpful.
And I love that phrase.
um One of the exciting things about living in this age right now with a general purposetechnology like AI is we get to invent all kinds of new concepts and language.
And I love that uh phrase, adjacent competencies.
I don't know if you coined that or you plucked that from somewhere else, but I think thatperfectly describes that.
(31:49):
skill set stretch that AI augmentation enables right now.
Yeah, I'm pretty sure I plucked it, but I can't remember where.
All right.
Well, in lieu of attribution, we're going to give it to you today.
So well done.
uh Okay.
Let's go back to something else that you talked about uh earlier.
You talked about process mapping and we kind of touched on that a little bit more.
(32:10):
And my understanding here of your model is that one of the things you propose that insteadof hiring consulting firm to map your processes, frontline workers are going to map their
own work to figure out where the AI fits in.
Is that correct?
Yeah, so one of the things we learned from business process re-engineering is that itdidn't really work.
(32:31):
So this is going back to the land of ERPs and things like that.
Great, we hired a bunch of consultants to come in to do all the process mapping.
It was big, it was expensive, it was arduous.
They laid down new process maps.
They left.
And then a lot of times there was tissue rejection.
Like nobody wants the outside coming in and being like thou shalt and you will and youdon't really understand our process and you don't know about Betty who won't let you know
(32:58):
any decision go without her watching it.
And so what we really want to do is we want people to get ownership.
And that doesn't mean that you can't rely on outside people.
mean, obviously my livelihood is partially based on that, but it does mean that you don'twant over-reliance on it.
(33:19):
you want people to own, literally own the process.
And when you think about people owning the process with AI that has an awful lot ofbaggage associated with it, which is, okay, if I map my processes, does that mean I'm
mapping myself out of a job?
(33:40):
You have got to create the psychological safety.
We haven't talked about AI nor stars or like how to create that psychological safety, butyou have to create the psychological safety for people to want to reinvent their own
processes.
um, and I think, I think if you do that, you start to see the worlds open up.
(34:04):
And I also want to toss out there that process mapping today does not look anything likeit used to.
where it took days and it was painful.
There's so much AI software out there right now that'll help you get that process down.
And in fact, it'll take it for swag at that flow chart for you.
ah So don't be afraid of it.
(34:27):
Couldn't agree more.
um I'm a big fan of the conversational process mapping approach.
em Let's dig in, because you said we haven't talked about it.
So let's talk about it.
Let's talk about that North Star.
Let's talk about what psychological safety looks like.
What does that uh entail right now as we within the context of our larger conversation?
(34:47):
Mm-hmm.
So if you are a leader listening to this, I would ask you, do you have an AI North Star?
Like, why are you implementing AI outside of you should?
Like, you should is great to an extent.
You should probably put a PC on everybody's desk, but really, are you hoping to achieve?
(35:10):
And the North Star I point to the most often is Moderna.
And they set the North Star of releasing 15 new drugs in five years with the help of AI.
And if you're familiar with the pharmaceutical industry, just to get one drug out in 10years would be quite an accomplishment.
(35:33):
So I feel like what that does is it unlocks motivation.
All of a sudden, people understand why we're implementing AI.
what that means to me, whether you're in marketing or sales, you're like, yeah, let's,let's do this.
Let's achieve this big hairy audacious goal that we've never been able to do beforebecause of AI.
(35:56):
So set that and then set your AI philosophical stance.
Like this is, they're very intertwined and related, but are you looking to just improveproductivity and harvest those gains?
Are you looking to reimagine the company?
Are you looking to create amazing customer experiences?
(36:20):
You need to be able to articulate that throughout the organization for people's minds toreally open up and for them to feel safe in terms of leaning in.
And I think that that's where a lot of organizations outside of AI even really fall down.
ah It's mission creep.
(36:41):
It's the idea of not understanding what business you're actually in.
And if you can't answer some of those fundamental questions, I don't see how you're evergoing to get to that North Star point that you just articulated.
Yeah, yeah, I mean, I have these cringe moments where like, Jack Dorsey and and block isone and I get that he's trying to reimagine his organization, but I, I just feel like, you
(37:05):
know, you're just doing it in a very uh blunt manner.
You know, I, I get that some people like to throw things off cliffs and watch him smash.
But I just think about when he wants to reimagine the company or grow the company, andmaybe he doesn't.
Maybe they're just like, hey, I want to improve the bottom line and sell it and be done.
But if you truly want a durable company and you just harvest the gains of AI and lay offhalf your workforce, what happens when the displacement comes and you need to hire back
(37:37):
for new roles?
What kind of reputation does your organization have?
Is it somewhere that somebody's gonna wanna work?
I feel like we didn't as deeply explore as we should have yet.
What you articulated earlier regarding the bifurcation problem, um, that, that gap betweenthe AI power users and everybody else, because I see this all the time and we're three
(38:07):
plus three and a half years now into the age of, uh, generative, agentic AI.
And I don't think many organizations at this point are starting from scratch.
They've got, uh, right.
So there's a whole grab bag of.
where they are on the spectrum now.
And let's just say I'm an organization where I've got that bifurcation problem.
I've got a handful of power users.
(38:28):
Maybe we've been power using for a year.
Maybe it's three years.
I don't know.
And then we've got everybody else.
How do we go back in and hit the reset button here?
What does that look like as opposed to a de novo?
We've got greenfield.
We're starting from scratch with this.
Does it change the processes that you've outlined?
think so.
Yeah.
mean, think work with what you have, start with where you are.
(38:50):
You know, again, you probably have some AI leads.
You probably, you know, so if you have, let's just say that you have some AI leads andyou, and usually what happens is you, you have these people who have naturally leaned in
and then you start anointing them or appointing them.
You know, Hey, you're part of the AI council.
(39:12):
Hey, I'm going to call you an AI lead, but it's, it's in name only.
oh You're not really changing the culture of your organization.
So have you as a leader, have you articulated that North Star?
Like what have you done to create safety within your organization, to create that AI NorthStar to say, it's okay for you to lean into AI.
(39:37):
And sometimes it's a time problem or a capacity problem, which is many organizations arerunning so lean.
that you think about the bulk of the people.
You have those power users who they're probably learning it on their own dime and theirown time.
So for everybody else who isn't willing to work nights and weekends to learn AI, you gotto create the space.
(40:03):
And so you create the space by having these social learning experiences.
And I like to share PricewaterhouseCoopers, they have something called the promptingparty.
And like it sounds fun, right?
It sounds like there's going to be pizza on Friday and we're all going to get together andwe're going to learn.
That's how you start to bridge the gap between your power users and everybody else.
(40:29):
You just, I don't know that I would expect you to have an answer to this question, but youjust said something there that raised another question in my mind.
I feel like if we're talking about power users, we're talking about early adopters.
And if we're talking about early adopters, that's a very distinct personality type.
And it doesn't matter if we're talking about mapping it using DICS, or predictive index,or Colby or whatever.
(40:52):
How well does an early adopter personality set
Translate into a steady hand as an AI lead I want
Yeah, well, it's very, very true.
um so that's why we have to give it, in my opinion, this programmatic support.
You know, can't just name somebody an AI lead and say, go spread the good news, becausenot everybody's a preacher.
(41:16):
yeah, that's right.
That's right.
Yeah.
But if you, so I have something I call the AI lead accelerator, and it is thatprogrammatic way.
to take those power users and say, here's how you overcome resistance.
Many people don't know that naturally.
Here's some techniques you can use like empathy mapping.
(41:39):
And here are some ways that you can share knowledge.
We're going to spin up these communities of practice or these other forums for you to dothe knowledge exchange.
But we've got to give them the tools that they need.
in order to spread the good word.
And when I put people through the AI lead accelerator, some of what I'm doing isevaluating them for the fit.
(42:09):
you naturally an AI lead who can evangelize or do you just kind of help those within yoursphere?
And usually people bubble up to the top, like these guys are natural trainers, these guysare the natural support systems.
I'm going to cut.
put these guys on Slack over here to spur conversation.
And then these guys, they're AI lead and name only, and that's cool too.
(42:36):
All right.
We've been going like this all over the place here, but I've been deliberately trying tomake sure that we spend at least a little bit of time on each phase.
And I know we touched on kind of stage three or phase three in your model earlier, but Iwant to go a little deeper on that because my understanding is that's where the real
rewiring occurs.
And you touched on this, you know, I don't think you talked yet about strike teams orinnovation circles.
(43:01):
I'm throwing this out there because I recognize almost nobody's there yet.
Right.
Uh, and if they are, they're certainly not talking about it.
It's secret sauce.
So most of our listeners with that in mind, you're not there yet, but you, you need toknow what you're building towards and what does stage three look like and feel like?
What's it mean for the C suite, especially.
And I guess I'm also interested.
(43:24):
Does the job of being an executive change when you're in a stage three org?
Yeah, so I feel like there's stage three, which is where we start to experiment with thosevalue streams we talked about.
When you think about a value stream, you might have, that's where leadership really startsto change, right?
(43:44):
Because we go from leading a function to leading a value stream.
So now you're leading cross-functional teams.
And
There's a couple of things that are happening there.
One is I want to talk about middle management because there's so many people who thinkthat that just evaporates.
(44:05):
And I think middle managers, their durable skills, besides just organizing work, they'reextremely adept at alignment, building alignment around a diverse set of stakeholders.
And what do we need?
(44:25):
as AI gets more and more powerful.
We need to figure out what the heck we're building for and toward.
And I feel like these are the people who can help us align around that.
When you think about, ah not everybody, I'm not saying it's a one-to-one.
I think some of those people will drop into the build, monitor, maintain.
(44:47):
Some of them might migrate into leading value streams or parts of value streams.
ah But I also think some of them will navigate politics.
There's always going to be politics.
Navigate alignment.
There's always going to be alignment challenges.
ah So how does that feel?
It should start to feel in stage three that the organizational model is starting to shift,but not in a quiet way.
(45:15):
People might be hearing about it here and there, like, oh, there's a pocket over therethat's experimenting.
It's stage four that it really starts to feel like, this is going to be in my backyard.
and we're doing that on purpose.
(45:36):
You just said, we could have a three hour episode here today.
This is fantastic.
um So you just kind of laid out a position and you alluded to the fact that some peoplebelieve that middle management is going to evaporate.
ah There's research papers out there, the headless firm, there's others that touch onthis.
And basically for our listeners, if you can envision it, it's kind of like an hourglassthat continues to get narrower and narrower at the waist with the center section being
(46:01):
middle management.
And you kind of pushed back against that just now.
ah saying I don't think we're going to see the disappearance of that middle layer.
What?
I'm so many questions.
I'm very interested in this because one of the theories out there, and again, we're at theintersection of theory and application as we all build this ship in real time.
(46:23):
One of the theories out there is that middle management is also an interpolation and aninterpretation layer between the C-suite and the work that actually gets done.
One of the ideas is that
uh in that headless firm idea, the middle gets squeezed.
And so the C-suite is going to have to actually interpret more directly what's going on atthe frontline, whether it's humans or AI or things like that.
(46:45):
But your thesis here is you think that middle management is going to continue to beresolute as an alignment builder.
So maybe a shifting uh from their responsibilities of interpretation to more alignment.
Like, let's talk a little bit more about this because it's interesting.
yeah, I don't, I do think it's going to go away.
So I, I'm not, I'm not saying it's.
(47:08):
Yeah.
I, I, I think it'll change.
And I, I'm, I'm saying that those people are redeployable is what I'm saying.
And I totally agree with you.
Some of the, executive level change, they need to stop being handed PowerPoints and breakthat habit.
Yeah.
You know, it's like you are now responsible for getting your own information.
(47:31):
It's always on.
You can just ask the question.
Like, let's break that PowerPoint addiction.
And I would love to see executives, and I talk about this in the book, like mostexecutives that I know and myself included when I was one, overwhelmed by the number of
meetings, by the amount of information.
(47:51):
And maybe with AI, you can reinvent your own workflow.
so that you can spend more time on strategy, so that we can make better decisions.
We can do more scenario modeling.
And I feel like it's going to require behavior modification, I think.
(48:11):
I don't, yeah, like I don't mean, I don't mean that like a, like a, a, a, a, a, a, a, a,a, a, a, like a, like a, like
is, and this is going to be a deal breaker, I talk about how budgeting changes and I talkabout how incentives change and need to change in order to move in this direction because
(48:38):
ego and to a certain extent survival in the modern world, if you are an executive, howmany people do I manage?
How much budget do I manage?
And that oftentimes equates to self-worth.
And so that is one of the biggest shifts I think we need to make.
(48:59):
That is a point that I have not heard anybody make in three years on this podcast.
uh The idea of a revised definition of self-worth ah and job satisfaction in terms of theC-suite.
That is a really interesting point.
And I also want to thank you for course correcting me on my slight misinterpretation ofwhat you said earlier about middle management.
(49:23):
this is very interesting.
Do you think...
Well, let me ask you this is somebody that's been studying organizations, AI aside for along time, been in them, been studying them.
Why do you think we've gotten so wedded at the C-suite level to how much budget we overseeand how many people are, you know, underneath us on the org chart?
Yeah, because it's equated to money.
(49:46):
Same with quarterly bonuses, right?
Like it's, I mean, I've lived this.
It's a little intoxicating and it's a little addicting to hit your number, get yourquarterly bonus, especially if the company is doing well and then your lifestyle goes up
and now you're trying to maintain a lifestyle and the thought that any part of that wouldgo away.
(50:11):
it quite frankly can feel very threatening.
Yeah.
So how do we recalibrate that sense of self-worth and redefine what this means asbudgetary priorities shift from human capital, uh not shift, but rebalance, as it were,
from human capital to technical capital?
Yeah, and I don't talk about it a lot on podcasts because so many people are just, we'reall like, ah, how do I get my processes and my processes?
(50:38):
But I really feel strongly and I articulate how this looks, how the budgeting processstarts to change over these five stages.
We no longer have to make it a yearly event.
What if it's quarterly event?
What if it's monthly event, AI?
allows us to recalibrate and run the data so much more cleanly.
(51:03):
We get feedback around what's working and not working.
And we let the data decide rather than, the budget meetings I tend to sit in is like abunch of half data and mostly opinion.
And so it's really recalibrating the culture around budgets and how they're donegradually.
(51:25):
so that by the time we're hitting stages three, four and five, the culture's alreadystarting to change.
People are feeling relief.
Budgeting doesn't feel quite as onerous as it used to.
And that starts to break down some of the rigidness around how things are done.
And then I think the other thing is that executives aren't monoliths.
(51:46):
There's all kinds of executives out there.
And it's the ones that are willing to move into a new space.
that'll lead the way for everyone else.
It's funny, as you were talking, I was thinking about a conversation I had earlier thisweek with an exec of a mid-market firm that's private equity backed.
(52:07):
And they were talking about how they are getting ready for their quarterly review with theleadership committee and how the 57 slides have to be this, but then they've got to insert
this over here.
um so yes, your comments and your analysis, they're spot on.
It's uh just made me laugh.
All right.
(52:27):
I know from uh our prior conversation that your mentality or your phrasing or your word ofthe year, if you will, for 2026 is be deliberate.
So let's say that I'm a mid-market exec.
I'm listening to this podcast on my Monday morning commute.
I've got an hour on my calendar this week to work on AI or think about it.
(52:48):
What should I actually do with that hour to make sure that I am being deliberate versusjust busy?
Mm-hmm.
I think most executives are spending time thinking about that future state, theorchestration.
(53:11):
They feel this pressure to get to that stage.
And I think it's because there's so much hype.
There's the FOMO.
If I don't get to this stage of orchestration, then
I will get beat out by my competition that does.
(53:32):
I am here to tell you that
Like I don't have a crystal ball, but I'm here to tell you that we're all in the sameboat.
Like we're all feeling it.
And what I see is it's actually the organizations that try and skip ahead to thatorchestration that fall behind because you're burning through capital and political
(53:54):
capital and your people get cynical because it didn't work versus the 1 % club who says,we're just going to try and get 1 % better with AI every day.
And we're going to put the structures and the people in place to help make that happen.
And we're going to keep marching toward toward orchestration without trying to leap therebefore we're ready.
(54:19):
So if you have an hour, I want you to just sit with that.
What what can we do given everything we've talked about?
There's tons of ideas and goodness in this podcast.
What can we implement today?
that looks more like 1 % or 5 % rather than 50%.
love it.
And if you don't be reminded that giants will fall.
(54:45):
All right, Melissa, our time here is is drawing to a close and our always goes veryquickly on this show.
What else do you want our listeners to know about the work that you're doing or the modelthat you've thrown out there?
I know I said we were going to try and hit all the different stages and phases.
I don't know that we were entirely successful, but we got through a lot of it.
(55:06):
What?
What maybe did I leave as a stone that's unturned that we should throw out here for ourlisteners?
Well, I just want to paint a picture and I do call stages four and five the emergingfrontier because it's still being shaped.
But in stage five, I just want to paint a picture of what the organization of the futuremight look like.
(55:27):
And I like to articulate it in three layers.
So the top layer, you might have what I call innovation circles.
So this is your internal venture capitalists.
This is your R &D.
You're funding it in a different manner.
than the rest of the business.
The middle layer might be cross-functional value streams where people are delivering valueto your customer long set, whether it's product lines or whatever that value looks like in
(55:55):
your organization.
Those are funded.
You still might have pieces of functional experts, but they start to look more like striketeams going into the value streams that can help them out when there's a really
tricky legal issue or really tricky marketing issue.
And then the bottom is like your stable layer.
(56:16):
You know, these are the people who are keeping the lights on and infrastructure.
And you think about how that's funded.
That funding looks a little different too.
And so when you're thinking about what we're heading from these linear organizations, whatwe're heading to orchestrated value streams that can sense and respond in near real time.
(56:39):
That's the journey.
I love the picture that you just painted there.
And one of the questions that pops into my head here is, again, talking about this futuretheoretical state of an organization and those three substrates or those three layers
there.
That probably looks radically different in terms of head count and capability uh two orthree or four years from now than it would have even a year or two ago.
(57:08):
So fascinating stuff.
All right.
Melissa, what can our listeners do to connect with you?
Where can they go to find you?
Where can they buy your book?
Are you engaged in any signings anytime soon?
uh Where do we connect?
I appreciate that.
So the book comes out May 12th, so depending on when this drops, but you can certainlyorder it already on Amazon, major retailers.
(57:32):
I am available at hyperadaptive.solutions.
LinkedIn is another great place to find me, melissa.m.reeve.
And yeah, thanks for having me on the show today, Chad.
No, thank you.
I enjoy these conversations with folks that are helping us simultaneously understand thepresent and invent the future.
ah You definitely are one of those.
(57:53):
So thank you for your time today.
I appreciate it.
It's been a pleasure.
And for our listeners, thank you for once again listening to AI for the C-suite.
I hope this episode was useful for you.
If it was, subscribe wherever you get your podcasts, follow us on LinkedIn and check outaiforthecsuite.com.
And until next time, keep your algorithms running, your leadership evolving and your AI incheck.
(58:17):
Take care everybody.
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