Ep 760: AI Change Management That Works: 5 Moves The Top 5% Make (Start Here Series Vol 21)

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AI Change Management: Five Precise Moves That Top Companies Execute for Real Results

For organizations investing heavily in artificial intelligence, the gap between implementation and genuine business results remains wide. Data reveals that while 97% of executives see personal wins from AI, just 29% observe tangible enterprise-level ROI. This disconnect is not the result of poor technical execution—it stems primarily from a failure in change management strategies specifically adapted for AI.

The following blueprint extracted from recent, real-world practices used by the top performing 5% of companies addresses this gap directly. Each section zeros in on the concrete moves leaders are making—without relying on traditional, outmoded methods—and connects these actions to measurable business gains.

AI Change Management: Reallocate Budgets to People and Processes

Boston Consulting Group reports that 70% of AI's value derives directly from investments in people and processes. Yet, conventional budget allocations heavily favor tools (70%), assign 20% to data infrastructure, and a scant 10% to the humans using AI.

In contrast, leading companies spend up to 5–10 times more on AI training and process redesign than on technology licenses. Despite the fact that AI tool costs are low—often $30 to $100 per employee per month—while average employee hourly rates are far higher, thorough training and process investments are rare. This allocation ensures that the technical capacity of AI is matched by comprehensive readiness and skill among the workforce, yielding actual business impact rather than just technical activity.

AI Native Workflows: Complete SOP Overhauls Instead of Add-Ons

A recent McKinsey study demonstrates unequivocally that workflow redesign delivers the most substantial EBIT impact among 25 attributes tested. Incrementally inserting AI into legacy workflows simply accelerates broken processes.

Best-in-class organizations reengineer standard operating procedures (SOPs) from scratch so that processes are modular and built for frequent, even monthly, obsolescence. Old models of “future-proofing” SOPs for years are a liability; agility and the willingness to discard antiquated steps is essential. Packing AI on top of multi-step workflows is abandoned in favor of creating entirely new processes that take full advantage of automation and human-AI collaboration. For instance, a process that would once have eight steps may now be reducible to two—with most of the remainder automated.

Unlearning for AI: Discarding Old Roles Instead of Simple Upskilling

Prevailing upskilling strategies are hazardous in the AI context. The assumption that legacy expertise forms a stable foundation is fundamentally flawed; baseline skills themselves are being systematically disrupted by AI. Harvard Business School research suggests every role now requires a 30% “digital and AI mindset,” not a sprinkling of chatbot prompts on existing skills.

Approaches that simply add AI training onto long-standing routines misjudge the scope of change. Instead, the old job itself must be unlearned and rebuilt. Organizations achieve this by empowering teams to rebuild roles, responsibilities, and internal expertise with domain knowledge increasingly delegated to AI models. Change management here is not piecemeal—it is foundational.

Ritualized Weekly Enablement: Moving Beyond Annual Training

Annual or even quarterly AI training fails to match the pace and demands of practical transformation. Gallup findings indicate that employees with direct, ongoing managerial support are nine times more likely to say AI meaningfully transformed their work.

Leading organizations operationalize this by institutionalizing weekly AI enablement meetings. These forums—often scheduled on Mondays and/or Fridays—are used to discuss successful experiments, address challenges, and share changes in AI systems that affect day-to-day work. This structure ensures team-wide alignment, constant skills refresh, and immediate knowledge sharing about evolving AI capabilities and resultant workflow adaptations.

Specific Metrics: Grading Behavior Change, Not Just Tool Utilization

Most organizations continue to measure AI adoption by tool login counts or license utilization rates. However, high performers refocus metrics on actual behavioral shifts. Success is tracked by reviewing job descriptions, auditing redesigned workflows, and directly assessing whether teams are changing their methods and delivering greater value.

Prominent tech organizations are embedding AI evaluation into performance reviews, requiring not only individual prompt use but demonstrable changes in team outputs and workflow adaptability. Grading is not about how often tools are accessed, but about the observable evolution in how work is accomplished.

Enterprise Case Studies: Scaling Adoption with People-First Practices

• Moderna achieved 80% internal chat tool adoption by hosting weekly AI forums with 2,000 participants.
• BBVA started with 250 senior leaders, then achieved 83% weekly AI activity across the bank.
• JPMorgan kept headcount flat but shifted internal staff to client-facing roles by automating manual knowledge work.

These cases offer concrete proof: overhauling SOPs, ritualizing enablement, and focusing on measurable behaviors produces sustainable AI integration and ROI.

Next Steps for Effective AI Change Management

Three essential actions translate this blueprint into practice:

1. Select and Rebuild: Identify one old SOP and reimagine it as AI native, engaging whole teams and focusing on pain points.
2. Empower AI Champions: Find individuals seeing ROI from AI, support them in tearing down and reconstructing entrenched workflows.
3. Provide Ongoing Support: Acknowledge the identity challenges inherent in these transitions, and structure HR and management interventions to support employees through the process.

The path to tangible AI ROI does not start with technology but with purposeful, people-centered change management, relentless process redesign, and direct measurement of new behaviors. Only these sustained, specific actions have shown to bridge the gap between AI hype and business outcome.

Topics Covered in This Episode:

1. AI Change Management vs. Technical Problem
2. AI Adoption’s People and Process Gap
3. Top 5% Change Management Playbook Steps
4. Budget Split: Funding People Over Tools
5. Dangers of AI Upskilling and Reskilling
6. Rebuilding AI-Native SOPs from Scratch
7. Weekly AI Enablement Rituals for Teams
8. Grading AI Behavioral Change, Not Tool Use
9. Enterprise ROI Gap in AI Adoption
10. Case Studies: Moderna, BBVA, JPMorgan AI Transformation

Episode Transcript 

 Jordan Wilson [00:00:16]:
Most companies in 2026 think that AI is a run of the mill technical problem to solve. They think if they just pick the right model, buy the right licenses, and get IT to roll it out, then the results will surely follow. So they spend 70% of their budget on tools, 20% on data plumbing, and maybe 10% on the actual humans who have to use this stuff every day. Then they're shocked when absolutely nothing changes. Here's the truth. AI is not a technical problem, not even close. It's a change management problem dressed up as a technical one. The Boston Consulting Group has been publishing the data for a while.

Jordan Wilson [00:00:58]:
They say that 70% of AI's core value comes from people and processes, not from the technical side or from the model in the context engineering. So the gap, it's not a tool gap. It's a people gap. And today I'm going to walk you through the five moves that the top 5% of companies are making to close that gap and to actually get measurable returns on AI. Alright. Let's start here, and that's what this start here series is. If you're new, my name is Jordan Wilson, and this is the start here series. But before we get into that, I wanna tell you the big picture.

Jordan Wilson [00:01:40]:
Change management decides who wins with AI this year. Here's why. I think that at this point, it's no longer a model or the harnessing that a company puts around that model that's gonna be the differentiator. Because if we're being honest, you know, maybe that might give you a couple of weeks, head start on the others, but the gap is so small now. So, you know, there's a recent study from a writer that said 54% of c suite executives say AI adoption is actually starting to tear their company apart because it is not in human nature to give up agency to an AI model. And that's exactly the big people problem that is causing this change management crisis that is AI. And most executives feel that they can see the personal AI wins, yet very few are seeing enterprise level ROI. And the gap between those individual wins and enterprise wins, it's not the technology.

Jordan Wilson [00:02:44]:
It's not do we use GPT five four or Opus four seven? That's not what this is. It's redefining traditional management and people management. And the 5% who have already cracked it are running a deliberate five move change management playbook. So that's what we're gonna be diving into on today's show. We're gonna talk about that budget split that separates the 5% winners from everyone else this year. I'm gonna tell you why upskilling is absolutely going to set your enterprise up for failure, so don't do the whole AI upskilling thing. I'm gonna show you how Moderna, BBVA, and JPMorgan run the playbook that the 5% use every week and give you three moves that you can run this week without waiting for budget or approval. Alright.

Jordan Wilson [00:03:34]:
Let's get into it. Welcome to Everyday AI. This is the start here series. After literally 750 podcast episodes, I hear from new listeners all the time saying, Jordan, where do I start? And until the start here series, I didn't have an answer, but now I do. Well, you start here with the start here series. Alright. Now we're on volume 21 of the start here series. I think it's best when you go in order, but this is the essential podcast series to both learn the AI basics and to double down on your AI knowledge.

Jordan Wilson [00:04:04]:
So go to starthereseries.com. That's going to give you free access to our exclusive inner circle community. Right now, there's no other way that you can get access, I think, aside from that. And in the start here series space there, that you'll have access to, you can go read, write, write about it, listen. Right? We have an entire playlist of every single episode in order, so you don't gotta go looking around. Alright. If you missed our last volume, a lot of people said this is one of their favorite episodes ever. So, I guess it was a good one.

Jordan Wilson [00:04:39]:
So go listen to volume 20 of the start here series. It was episode seven fifty seven. It was the seven silent sins of doing AI rights, how to spot and overcome the invisible AI work traps, which leads really well into this episode. This is the AI change management that works. So here's why this is such an issue because most companies assume that they could take their usual digital transformation playbook, which included the traditional change management piece. How they, you know, got teams to adapt to the Internet, to adapt to the cloud, whatever it is. Right? But change management, if you aren't familiar, right, is just the discipline of turning new tools into new behaviors at scale. And like I said, you know, usually, there's the people, the process, and the technology.

Jordan Wilson [00:05:31]:
So change management really, works with Fronto, the people in the processes. So, you know, why traditional change management doesn't work in an AI native workplace anymore is because all those other, big shifts in how work worked. Right? You could talk about ERP, CRM, cloud mobile, whatever. All of those things were additive. Right? The core job, the core responsibility of the human stayed the same. Right? The people in the process were relatively the same. It's just the technology slightly changed. But now the entire role of the people in the actual process is completely different because AI is not additive.

Jordan Wilson [00:06:16]:
Whereas those other kind of shifts, they were additive. Whereas AI, it changes the actual playbook. And I think, you know, this really goes to agency. Right? And human choice. And I think this is probably maybe harder for people who are mid career or have been out in the workforce for at least, you know, ten, fifteen years. I think this is maybe that group that is hit hardest, because they've probably been rewarded maybe multiple times with promotions, new jobs, pay raises for your agency, for your ability as a human to synthesize and personalize information and to create new business value for your company. Right? That's my definition of a knowledge worker. And agency, that is those choices that we humans make, and we leverage that domain experience.

Jordan Wilson [00:07:14]:
We leverage that subject matter expertise of the last five, ten, fifteen, twenty, twenty five years. Right? And it's extremely difficult for successful humans to give away that agency. And that's why the people in the process side of this change management in the AI native workforce is absolutely bonkers because it's hard as a human being to say, Hey, you know what? You know, someone, I don't know, in their in their forties, in their fifties of of a VP, someone that has absolutely been through it. Right? They they've been through the grinder. They've they've risen to the top. And then you're like, hey. Now, actually, you're just gonna be orchestrating this agent now. Right? You no longer have to lead this team of 50.

Jordan Wilson [00:08:01]:
Right? Or maybe this is now a team of 20 and, well, everyone's just using agents, and most of your job is gonna be feeding all of, you know, your team's internal IP, into these, reasoning models and building these agentic systems. And you're like, wait. I'm I'm having to give away all of my domain expertise to an AI. Right? Because the job itself is changing. And most AI rollouts rollouts are quietly tearing companies apart because of that. So studies say that 97, ninety seven percent of executives feel those personal AI wins while only 29% see enterprise level ROI. And you think why is that? Right? Because internally, right, when one person sits down, they're still leveraging that domain expertise, to work one on one, whether they're whatever they're using. Right? Copilot, using whatever it is.

Jordan Wilson [00:08:57]:
And for the most part, it's very easy for people to personally see those gains using AI. But it's usually when you are starting to work with others because that's where you would normally, kind of flex those domain expertise muscles, right, or or those domain expert muscles. It's when you're working with others because it's the natural human inclination, to justify your position, to justify your title. Right? And when that's gone, and it starts to, you know, AI starts to take over, that's when you get that gap. Right? And that's when that's what leads to the gap between those individual wins and the enterprise wins because the people and the processes are completely different. And in that same writer study that I talked about earlier, and that's where we see that, the company, 54% of c suite executives saying that AI adoption is tearing the company apart, because it completely shifts how the workplace is working in 2026 and beyond. So there are those that are getting it right. Right.

Jordan Wilson [00:10:05]:
This is the, the 5% here, that I say are writing the change management playbook. And these aren't the ones that are picking the right tools. Right? Again, this has nothing to do with the technical side. They're just rewriting and changing the change management playbook. Right? I literally think does does anyone remember? I don't know. Maybe this was just me. Maybe this was the the DARE program, or or whatever from the from the nineties. Right? So nineties babies.

Jordan Wilson [00:10:37]:
Do you, like, do you remember the, you know, someone coming in in ripping, like, a phone book? I think it was, you know, something to do with the drug education awareness program or whatever it was. Right? But I literally think that's what needs to happen in your organization. Think of that big fat 200 page, manual, right, that your company has been running off of for the last five, ten, fifteen, twenty years. If you're still using some version of that or it's just been slightly updated, you gotta be that person that's just ripping that thing apart. So you need to do it this way. It is five steps and we're going to walk through them. One, fund. Two, rebuild.

Jordan Wilson [00:11:19]:
Three, unlearn. Four, ritualize. And five, grade. Alright. So you need to fund the 70%. That's the people in the processes. You need to rebuild AI native. You need to unlearn the old job.

Jordan Wilson [00:11:32]:
You need to ritualize being AI native weekly, and then you need to grade and reinforce the behavior in each move in order compounds on the last. So, yeah, you can't just skip somewhere in the middle or start at the end and work your way backwards. It doesn't work like that. So let's talk about move one. You need to fund the 70% that actually drives behavioral change. So again, this is from the Boston Consulting Group. Right? They're, 7030, but essentially saying that 70% of the value of AI comes from actually investing in the people and processes. Only 20% is the data and 10% is the actual tooling or the AI algorithms that you're using.

Jordan Wilson [00:12:17]:
Right? The model choice, you know, makes only a minimal impact on the overall value that organizations derive from AI. And most companies flip it the other way. Most companies are spending the majority of their time in attention on the tools. Right? And don't get me wrong. Obviously, as someone that follows this every single day for three plus plus years, I get that. Right? And that's what I talk about here on this show, but I don't talk about it here on this show so that you can spend it 70% of the time. No. The opposite.

Jordan Wilson [00:12:51]:
I talk about it here almost every single day, so you don't have to spend any of the time. Right? The the point isn't for me to, you know, come on with our, you know, Wednesday deep dives and our Friday, you know, AI features, and then for you and your team to go talk about it for, you know, three, four, five hours. No. Get your team around, listen to the podcast together, talk about it for ten minutes, and that's it. Leave it there. Right? And then start spending more of the time on the actual people in the processes. Alright? And then the 5% fund, the the the 5% of the organizations that fund change management like the main event are the ones that are winning. Right? Those are the 5% that are spending the time, resources, and money on the people and the processes.

Jordan Wilson [00:13:42]:
Right? When was the last time? And I, if I'm being honest, I don't know if I've ever met a single company that has spent more on AI training, right? Then they are spending on the actual AI tool stack. Right. Which is actually so freaking sad to think about because, I mean, the AI tools for the most part are very cheap. Right? Yeah. You might be paying, you know, $60 a month for, you you know, an enterprise seat or, you know, now since some recent price changing, you know, maybe there's some API usage on top of that. But let's say you're paying a $100 a month for an employee, right, which is probably less than that. You're probably more in the 30 to 50 range. But still, on the high side, a $100 a month for an employee to have access to a tool that is absolutely revolutionary.

Jordan Wilson [00:14:40]:
Let's just say for easy math, very easy math, Right? Let's say that employee makes, $50 an hour. Easy. Right? We'll say Mid Mid America. Right? Someone middle of the, of the corporate ladder. That's two hours. Two hours of monthly training. No one's, no one's doing that. Right? It should outspend that you should be probably investing at least a five to one ratio, whatever you're spending.

Jordan Wilson [00:15:11]:
You know, different studies say different things, but you know, an easy rule of thumb is five to 10 x. Right? So whatever you're spending on AI tooling across your organization, yeah, you should be five x ing or 10 x ing that, on your people in the processes. So on your people to train them and then on also those people or maybe outside organizations to help you rebuild your processes to become AI native. I don't know if I've literally, and I've met hundreds of companies. I don't know if a single company has done that. Right. Obviously their spend for AI is very high, right? Because they're building things on the AI side, you know, implementing, you know, I don't know, OpenAI's models, Gemini's models, anthropics models into their own products and services so the AI spend goes high. So they're like, well, of course, we can't spend, you know, five x to 10 x on people.

Jordan Wilson [00:16:05]:
Well, you absolutely can. If you wanna be in the 5% who are getting it right, that's how you get it done. Alright. So sorry. Went on a tangent there. So move one was fund the 70% that actually drives the behavioral change. Move number two is rebuild AI native from scratch. Don't sprinkle AI on top.

Jordan Wilson [00:16:23]:
I've talked about this so much before. A McKinsey study last year, show that workflow redesign had the biggest, EBIT impact of the 25 attributes tested. So the biggest workflow redesign, right? That had the biggest impact. Notice, notice it wasn't using the best model or anything like that. When you're talking about earnings before interest and tax, when you're talking about the bottom line across the 25 different attributes that McKinsey looked at, blowing it up. Right? Blowing up your SOPs is the thing that leads to success. So just bolting AI onto legacy standard operating procedures just makes broken processes run faster, not better. Right? AI is not some magical band aid that you write.

Jordan Wilson [00:17:18]:
You wouldn't put a a a a band aid on a bullet wound. Right? It's it's not how you heal it. Right? So many people think that AI is going to fix broken processes. So they stick it on top. They stick it in the middle. They know that, oh, and this you know, let's just say this eight step workflow, steps three and four aren't the best. So let's see, you know, use chat g p t for that. No.

Jordan Wilson [00:17:44]:
Absolutely not. Because there's a good chance if you just, break it down and rebuild it, that eight step workflow becomes maybe a one or two step workflow where the other good majority of it can be automated. And the 5% who get it right rip up that SOP and rebuild it from the ground up. Build it up modularly. That's the biggest thing. Right? I think when we talk about building standard work processes, we build it with five years in mind. Right. Which was the right thing to do in 1990s and early two thousands and twenty fifteen and 2020.

Jordan Wilson [00:18:25]:
That was the right thing to do. You would bill your processes to be as future proof as possible. That was the goal. That is a recipe for disaster. You have to build your AI native SOPs to be obsolete in a couple of months. You have to assume that however you're building out this process is going to feel very antiquated in a year. Right? Or they're building it out for a future role that they're gonna hire, and that person's gonna be in the position for five years, and then it's never gonna change again. Right? We're gonna talk a little bit, more on the people side and job descriptions and things like that.

Jordan Wilson [00:19:06]:
And I think employees aren't gonna wanna hear this, but it's the truth. Alright? Which leads us into well, steps, moves three and four address this. But move three is unlearn the new job, not just the old tool. Alright? I'm not gonna go on my normal, unlearn rant if you listen to the podcast at all. You know, I hate upskilling. Reskilling is okay. Right? But everyone talks about upskilling the same thing, how you can't apply AI on the top of an old process as an organization. You can't do the same thing to a an employee that's been in the job for ten, fifteen, twenty years.

Jordan Wilson [00:19:44]:
You can't just expect to say, hey, let's take your current skill set and let's see in your individual skill set, in your individual work processes where AI can make it better. No. You rebuild it for everyone, from the ground up. Upskilling is legit dangerous. It assumes that your baseline expertise that you're building on top of won't be disrupted, but it a 100% is going to be disrupted. Right? If you're gonna build a new expensive roof, you probably wanna check the foundation. Well, here's the reality in this, alliteration and this metaphor. Right? The foundation is gonna change.

Jordan Wilson [00:20:18]:
Right? Every year. It's gonna be wiped out. So you have to understand that your foundational baseline skills are also going to be wiped out if you haven't come to that realization already. So upskilling on top of your current skill set is a useless endeavor. Right? There was a Harvard business school study that said that saw that every employee or said that every employee needs at least 30 a 30% digital and AI mindset. In other words, what that study means is that about a third of your work should be augmented. A third of your work should be interacting and talking with AI. Right? To me, that's like, oh, gosh.

Jordan Wilson [00:20:59]:
That's a lot of wasted time. Right? I'm 95% of my time, is is chatting with AI, but, that's I think that's an easy baseline to follow. Right? And I'm loosely interpreting, what they mean there by a digital and AI mindset, but that's essentially what it means. If right now you're not spending a third of your time, augmented by AI, right, that doesn't just mean chatting with chatbots, but if a third of your time isn't, being augmented with or collaborating with AI, you're gonna fall behind. And then those 5% that are getting it right, they let the large hangout they let the large language models hold the domain expertise, and they practice the new identity weekly. Alright. Move four, ritualize the weekly enablement instead of the quarterly training. Alright.

Jordan Wilson [00:21:49]:
Here's what that means. A Gallup, study said that employees with manager support are more than nine times likely to say that AI transformed their work. Okay? I look at that study, two different ways. Number one, I'm like, well, of course, they're gonna be more successful, with their AI enabled in AI enablement if they, number one, aren't using shadow AI, right, which so many companies are. So when you have your managers, explicit, support and training and resources, of course, they're more likely to say that AI transformed the work. But talk about more than nine times, that's a huge jump. That's not a small, like, yes. You know, I'm more likely to say, you know, twice as likely to say AI transformed my work.

Jordan Wilson [00:22:40]:
No. When you have buy in from management from all layers and all levels, that's when you can actually get to the new AI transformation. But here's here's what I wanna start to differentiate here. I think a lot of organizations are still looking at AI as a marathon and it is technically a marathon, but it's also a series of sprints. So this isn't something that you can look at over the year. Right? I think most organizations are past the concept of yearly pilots. My gosh. If you're still thinking yearly AI pilots in 2026, you're smoked.

Jordan Wilson [00:23:23]:
I don't care how big you are, because, yeah, that's that's that's how fast the space is moving. And if you are sitting there arguing with me, just just wait. You know, I'll say 9090% of of companies will fall on, on the brunt side of that, so to speak. Right? If you're still doing year long AI pilots, it's gonna be bad. You you know, quarterly kickoffs are are okay. You know, annual seminars, you you know, those can build new habit habits, but you need weekly rituals. Right? Here's what I mean by that. The best and most successful AI teams that I've talked to are those and a lot of them pick either Mondays or Fridays, which I think is really cool.

Jordan Wilson [00:24:05]:
Right? It's it's kind of anchoring anchoring your week, in AI. And I've even talked to some organizations that do a Monday and a Friday. Right? It's opening up the week and closing the week, which I think is smart as well. But that's where you come, you share what's working, what's not, challenges for next week. Right? Here's our our our transparency report, our observability reports, our challenges, what we're scoping next, our use cases. Here's the new you you know, in in in these systems, right, we use Gemini for this and we use chat g b t for this. Here's what changed in those models and how it impacts our work. Right? You need to be having those conversations at least once a week, right, as a team, as department, as an entire organization.

Jordan Wilson [00:24:57]:
And if you're not, you need to start that. Alright? And then here we go. Move five. You need to grade the behavior change, not the license logins or seed counts. Right. So many organizations, just right. It's it's like, how are you measuring AI? Right. And it's like, okay, utilization rates.

Jordan Wilson [00:25:14]:
Right. Which is easy. Right? It's quantifiable. So I get it. I understand. Right? But in those instances, sometimes I ask this, sometimes they don't because they don't always wanna ruffle feathers. Right? Especially for, you know, clients that hire us with AI implementation, but maybe I should start doing this. Just be a little more, you know, brash and say, hey.

Jordan Wilson [00:25:34]:
It's not worth working with your organization or even trying to implement AI until you absolutely rewrite every single job description. Right? And a lot of people would think, oh my gosh. That'll take years. Well, no. It doesn't. With AI, it should take not very long at all. Take your old job description, you know, have a conversation with your direct reports, you know, HR, whoever. Talk over the job description.

Jordan Wilson [00:26:00]:
Record it. Say, here's what it looks like with AI. You know, put that into Claude, Chachibuty, Gemini, or whatever. Spit it out and get it approved. Make sure that they're, consistent across the entire organization. You you know, team first, done. It's not a difficult thing, but it's the expectations. And and and and step five is is the hardest.

Jordan Wilson [00:26:24]:
Right? Because behavioral behavior change is the root of change management, and that's why this thing is a big problem. Because what employee wants to sit down and say, yeah. I haven't had a new job description in eight years. Right? I don't wanna come. Right? I don't wanna sit down and rework how my job works because, hey, the reality is I've talked about this a lot of times. You wanna talk about the number one reason that your company is not getting ROI on generative AI. Well, it's because you don't know how to measure it, and there's a good chance that your employees are either using it or they're pocketing, that time savings from using AI. Right? I've said this example before.

Jordan Wilson [00:27:07]:
I know many smart people that have essentially automated at least 80 to 90% of their job. They're working remotely. They're, you know, hybrid, and, yeah, they just pocket that time spending a lot more time on the golf course or doing things around the house. Right? That is the problem. That is one of the problems, but it's also not that employee's fault. It's your fault, manager. It's your fault, CEO. It's your fault, department head, because they're still working off an antiquated job description.

Jordan Wilson [00:27:39]:
Right? So, yeah, we always talk about, you know, grading behavior changes. Well, how often are people using large language models, and that's not it. I think you have to first redefine your actual job descriptions, which means redefining what your department is probably working on and redefining the expectations and the expected outputs. That's a big one. Alright. So look at the tech giants. That's what they're doing now. Right? For better or worse, whether you agree with it or not, and if you're a listener at one of those, you know, organizations, maybe you're not enjoying this and, well, that's part of change management.

Jordan Wilson [00:28:13]:
Right? So Microsoft, Meta, Google, and Amazon are all now grading AI usage directly in performance reviews. Alright? So the 5% that are getting it right, they're not just grading prompts per employee, and they're looking at session depth in shipped workflow redesigns quarterly. Right? They're looking, are you changing how you're actually working? Not just are you saving time? Are you producing more? Are you creating more value? But are you constantly changing how you or your team works? All right. So here's what I want you to focus on. Actually let me go over some quick stats here. I wasn't gonna go over these, but, you know, maybe they're important enough. So, couple of use cases that kind of prove the five move playbook works. So Moderna, they hit 80% internal and the chat adaption for their internal tool, and they run a 2,000 person weekly AI forum.

Jordan Wilson [00:29:13]:
BBVA trained 250 senior leaders first, then scaled to 83% weekly active AI usage bank wide in JPMorgan held headcount flat while reshaping roles through operation shrank and client facing roles grew. So essentially they were able to operationalize with AI, some of the, more manual knowledge work that is done internally, kept headcount flat, and then sent their people out in the world more for more client client facing interactions. Alright. So here's what I want you to do now that you know the five steps, and let me just re say them. Move one is to fund the 70% that actually drives behavior change. Move two is rebuild AI native from scratch. Don't just bolt it on the top. Move three is unlearn unlearn the old job, not just the old tool.

Jordan Wilson [00:30:07]:
Move four is to ritualize the weekly enablement instead of quarterly training. And move five is to grade the behavior change, not license logins or seed counts. So now that you know this, here's your three moves I want you to do, right, whether this Friday, this Monday, doesn't matter. So pick one old SOP the way that you used to do it and ask what it looks like to be rebuilt AI native from scratch. Could everyone together see who's using what tool, what success are they finding, what is an old dumb SOP. Here's the thing. Probably most people hate it. Rebuild it from scratch.

Jordan Wilson [00:30:41]:
Alright. Step two, find someone who's already, right, identifying your AI champions, who's finding that ROI individually, and then find that old SOP and have them tear it down and rebuild it. Right? Tear down, rebuild. Alright. So unlearn, relearn, train, repeat. That is step two. And that is really rebuilding the model of digital transformation. And then step three gets into that change management.

Jordan Wilson [00:31:11]:
And maybe I'll end on a different note here for step three, because giving, if you do step one to step two, that's going to ruffle feathers, right? Maybe not so much for those employees in their twenties that are maybe a little more agile, or maybe a little more AI native. I don't know. But for everyone else, going through this process is tough. Because what it might look like is, you you know, Bill over there in IT has hung his career on a certain skill set, a certain domain expertise that now you may give to a large language model and Bill is going to feel entirely threatened. And Bill is probably not gonna feel good about this. That's the reality. This isn't easy, right? People think it's, it's all fun and games, but a lot of people, which we talked about in our last episode, in these seven silent sins, a lot of people hang their identity, not just their corporate identity, but even their personal identity on what they do in work. And if you, all of a sudden start handing that off to agents, which y'all it is the right thing to do.

Jordan Wilson [00:32:24]:
It's not easy. Right. It's like I had to go through a certain grieving process many years ago when I realized that large language models with proper prompting were better writers than me. Right. I previously won ACP story of the year. I was a journalist for a long time. I was a Pulitzer fellow, all those things. And when I realized that AI was a better writer than me, it's hard.

Jordan Wilson [00:32:48]:
Right? You're like, oh, frick. I've been getting paid to do this thing for, you know, a decade and a half. And well, now what? Right. That uncomfortable period is where your future strength grows in an AI native workplace. But step three is important because it involves what comes after giving away that human agency. And that's where the next phase of change management, which is people management comes into play. In your organization, you need to employ human support systems via human resources and management for what that looks like. Right? How do you make sure that Bill, after he gives away the the the the true skill set, right, that he's hung his career on for twenty years? Yes.

Jordan Wilson [00:33:35]:
He's probably gonna be managing an agent, but what do you do with his time then? What do you do with his extra time? Right? Yes. You can, you you know, he can become part of your AI champion team, and he can go help build, you know, new SOPs, new ways of work, create new lines of revenue, but it's easier said than done, right? When you rip up the job descriptions, when you rip up the old SOPs and when you deploy agentic AI that works. It's not always easy. So step three is you have to employ that human support system. All right. I hope this one was helpful as we tackled AI management that works AI change management that works in the five moves, the 5% make. Alright. Do me a favor.

Jordan Wilson [00:34:21]:
Number one, go to starthereseries.com. Alright. We're gonna be recapping today's episode in our newsletter. But in the start here series space in our inner circle community, you can go listen to, read about, and collaborate and network with others who are going through this start here series journey together. So, there's a playlist inside there, in our, on a Spotify playlist with every single episode, so it's a little easier to track. Alright. So number one, do that. If you're listening on the podcast, appreciate your support.

Jordan Wilson [00:34:50]:
Please make sure you subscribe to the Everyday AI podcast. Leave us a rating if you can. So that's a wrap for today. Thank you for tuning in. Hope to see you back tomorrow and everyday for more Everyday AI. Thanks, y'all.