Where are you on the AI transformation journey?

Depending on who in their organization is talking, they’re somewhere on what has become a predictable curve. Maybe their developers are flying with AI, but their operations teams are still figuring out what Copilot does. Maybe they’ve got a governance council, but zero coordination between their business units. They’re making progress in some areas, but spinning their wheels in other areas. Both things are true simultaneously.

What I’ve noticed is that most the uncertainty follows a predictable pattern, but some people are confused around the terrain. They don’t have a map.

So I built one.

The AI Adoption Terrain

Repeated conversation reveal organizations reaching one of these waypoints. The Hyperadaptive Model extends beyond the last waypoint, but I’ve found most organizations wrestling with these first six landmarks:

This is a visualization of the organizational AI transformation journey, mapped to the Hyperadaptive Model at the bottom. It isn’t a linear checklist, but more like actual terrain. With elevation changes. With traps. With a few places where the ground looks solid but isn’t.

Most organizations, can identify we’re there. Sometimes two people from the same organization point to different spots, which is useful information in itself.

Find Your Waypoint

Here’s how each zone feels like from the inside.

Confusion Zone. This is where most organizations started in November 2022, when ChatGPT hit. They didn’t know who owned AI, where it should sit in the organization, what models to use. Most organizations have moved on from this stage.

Early Wins. Licenses deployed, training videos assigned, box checked. A few curious people figured things out. Real wins, real enthusiasm, but the wins are trapped in pockets. AI Leads may have been named. An AI Council may exist. Neither is well-supported. The terrain here is deceptively flat. It feels like progress, because there is progress. But it tends to be isolated, what I call Random Acts of AI.

AI Bifurcation. This is the place where the majority of organizations are stuck right now. Power users have pulled significantly ahead. Everyone else is standing still, or going through the motions of using tools they don’t yet trust. The gap between the people who get it and the people who don’t is widening and visible. Organizations here often diagnose this as a training problem or a culture problem. It’s usually neither. It’s a systems and infrastructure problem.

Localized Progress. Things are working, but in isolated pockets. A team, a department, a function has genuinely figured something out. The temptation here is to scale before the foundation is solid: to reach for orchestrated agents before the organization has learned how to spread simpler wins. The ambition is real and right. The support systems to spread, scale, and sustain need to be put in place.

Coordinated Progress. This is where things start to feel different. The flywheel is turning. Activation Hubs are forming. Learning is beginning to move across the organization instead of staying in the team that generated it. The terrain is rising and the footing is solid. Organizations here are building the systems that makes progress compound.

Job Redesign. As automation takes root, roles begin to shift. People start moving from doing the work to building, monitoring, and maintaining the systems that do the work. This transition is genuinely challenging and genuinely important. The unrecognized part is that it goes much better when organizations do it deliberately on a small scale before they try to do it everywhere at once.

The Hyperadaptive Future. Orchestrated agents. Value stream orientation. A new operating model that the organization is capable of continuously updating because it has built the infrastructure to support it. This isn’t a finish line. The horizon keeps moving. A Hyperadaptive organization doesn’t ‘arrive.’ It gets better at traveling. While the book Hyperadaptive: Rewiring the Enterprise to Become AI-Native speaks to this emerging frontier, most organizations have yet to pass into this space.

There Are Specific Moves for Each Stage

Here’s what I want you to take away from this map: wherever you are on this terrain, there are proven moves for getting to the next zone. Specific, research-backed executable moves that organizations have made, and that the Hyperadaptive model is built to support. I’ve distilled lessons from leading organizations so you don’t have to.

Over the coming weeks, I’ll be publishing a series of field reports on what those moves actually look like in practice, complete with the politics, the resource constraints, and the 90-day markers you’d recognize if your organization tried it.

We’ll start with the AI Leads move, because it’s where most Stage 1 organizations could unlock real value. Then use case prioritization; how to go from a hundred ideas to three you’re actually starting with. Then the learning infrastructure that keeps what your teams figure out from disappearing into someone’s Slack archive.

But first:, find yourself on the map.

To get a clearer picture of which moves apply to your specific situation, explore the conversational Waypoint Survey.

Find Your Waypoint

Where do you land on this map? What feedback do you have? Tell me in the comments.

If you’d rather just tell me directly, hit reply.

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Then he said: look around. You just told us you believe this is transformational. So why are most of your organizations set up to capture none of it?

The room got quiet (in a good way).

What followed was a conversation that felt different from most of what I hear at tech conferences right now. We skipped the ‘wow’ moments of AI and talked about what happens after the productivity gains arrive. And based on the questions we got and the conversations afterward, that’s a topic people are genuinely hungry for.

So let me share the core of what we covered, because I think it applies to most of you reading this.

The Surplus Is Already Here. Have You Decided What to Do With It?

Vibe coding and its concomitant productivity gains are real. EY’s most recent AI Pulse Survey found that 96% of organizations investing in AI are experiencing some level of productivity improvement, with 57% describing those gains as significant. Developers are building faster, freeing up time (although I think the backlog is so deep, it may or may not feel like real progress).

In some organizations, headcount is being reduced (though the EY data is interesting here: only 17% of organizations seeing productivity gains actually used them for headcount cuts. The headlines rail otherwise… so much hype.

The gains are landing. So, the question becomes…what happens next?

In both the research behind Hyperadaptive and in the conversations I have with enterprise leaders, there are essentially three postures organizations take toward an AI-generated productivity surplus. Ryan and I presented these last week:

• The first is Harvesting. Every efficiency gain goes straight to the bottom line. AI becomes a cost-reduction tool. The CFO is briefly delighted. Headcount shrinks or hiring freezes. Fast track to irrelevance, because your competitors are doing something different with their surplus.

• The second is Experimenting. A few power users are doing genuinely cool things. There’s energy, there are demos, there are Slack channels full of prompts. But there’s no map, no milestones, and no organizational learning happening. Motion without progress. (I see this one the most, honestly.)

• The third is Building. The surplus gets deliberately reinvested in capacity. AI Champions get protected time. The organization starts to rewire itself, one stage at a time. Compounding advantage.

Be honest with yourself about which one describes your organization right now. When did your organization last have an explicit conversation about where AI productivity gains are actually going? Can those gains even be measured?

The Missing Piece Can’t Be Found in AI Tools

The 2025 DORA research found that top-performing organizations are seeing 20 to 60 percent productivity gains from AI. Most organizations sit at 5 to 10 percent. The tools are the same. The models are available to everyone. The gap reflects differences in human factors, not access to technology.

The conundrum faced by most organizations is that coding speed is up dramatically, but the value captured by most organizations has not moved at the same pace. Because capturing the value requires a deliberate decision about reinvestment, and most organizations have not made that decision explicitly.

There’s a useful frame from Eric Ries’ work called the Temptation to Harvest. When a new capability creates genuine value, leaders face a choice to extract that value as margin, or reinvest it to build something bigger. AI has handed every organization a surplus. And right now the default choice — the path of least resistance, as well as pressure from the board — is to harvest.

Ryan made a version of this argument from his own experience, describing an earlier fight with a CFO about reinvesting software licensing savings into corporate social responsibility rather than letting it evaporate into the P&L. The CFO wasn’t thrilled. The reinvestment kept the organization focused on purpose and impact. That’s the same choice in a different context.

Related Article: Why SaaS Companies Require A Bigger Mission

One Decision, Compounded Over Seventeen Years.

The clearest illustration I know for what deliberate reinvestment looks like over time is Ping An Insurance.

In 2008, Ping An made a quiet decision to make data the foundation of everything. Not AI specifically, as they didn’t have the AI yet. Just a commitment to getting their data house in order and organizing around the human being in front of them rather than the products they were trying to sell. They restructured around the customer. They established a dedicated technology division, not as a support function but as a strategic driver. By 2017, they had formalized AI as one of their five core technologies and committed roughly 1% of revenue to AI research and development annually.

Today, Ping An serves over 240 million retail customers. Customers inside their digital ecosystem hold an average of 2.9 contracts each, compared to 1.2 for customers outside it. Revenue per ecosystem user reaches $5,288, compared to $1,399 for non-users. Nearly 4x.

That’s the result of one deliberate decision made seventeen years ago about what kind of organization they wanted to be, compounded. The technology followed the answer.

Ryan put it well on stage: Ping An’s 2008 is your 2025.

Related Article: The Five Stages of Becoming AI-Native

The window for this kind of decision isn’t what it used to be. Unlike the decade-plus runway organizations had with digital transformation, research on AI competitive dynamics suggests the compounding advantage for early reinvesters is closing much faster. (I’ll hedge that slightly: the exact timeline is contested, but the directional pressure is real and consistent across multiple sources.)

Three Things to Carry Out of This

I closed our session with three asks that I’ll repeat here, because I mean them.

Invest the surplus intentionally. Before the next efficiency gain disappears into the P&L, have the explicit conversation about where it goes. In everyday budget conversations, with specifics.

Name one AI Champion. One person in your organization, protected time, actual mandate to spread what they learn. That’s where the 20 to 60 percent gains start, and it costs almost nothing compared to what you’re already spending on AI tooling.

Related Article: Why Appointing AI Leads Isn’t Enough (and What to Do Instead)

Find a way to amplify yourself, not just your teams. Ryan’s part of our session touched on the human journey. He talked about the path from curious to generative to amplifying yourself with AI. I’ll let Ryan tell that story on his Manifest Substack (mnfst.ai), because he tells it better than I can. But the organizational and individual journeys are connected. You can’t build an AI-native enterprise without leaders who have made that journey personally.

The tools are delivering. At some point, you will have to decide what happens to the surplus, or let the default decide for you.

What does your organization’s reinvestment decision actually look like right now? I’d genuinely like to know. Drop it in the comments.

p.s. If you’re leading an AI transformation and want a framework for moving from Harvesting or Experimenting to Building, the AI Lead Accelerator is built for exactly that: hyperadaptive.solutions/accelerate.

And if you want the full roadmap — five stages, case studies, and the frameworks we referenced above — Hyperadaptive: Rewiring the Enterprise to Become AI-Native releases May 12th. Pre-order at hyperadaptive.solutions/book.

Thanks to and the Red Thread Research team, I found myself at UNLEASH America, an event focused on reinventing work. UNLEASH put me squarely in the people side: CHROs, heads of talent, L&D leaders, HR technology practitioners. Brilliant people wrestling with questions I typically address from a different vantage point.

And what I found there clarified something I’ve been circling for months: Who owns AI transformation? Truly integrating AI is a whole-enterprise problem. And right now, nobody quite owns it. The ambiguity is stalling organizations out.

A State of Confusion on Who Is Driving What

Analyst provided a keynote that was equal parts data deluge and honest editorial (his words, not mine). He’s been traveling the world meeting CHROs and HR leaders, and his diagnosis of the moment didn’t hold back: confusion. Not ignorance. Not reluctance. Confusion. The kind that signals you’re standing in the middle of an epic shift from one state of the world to another, and it is unclear who should be in the driver’s seat.

IT spending on AI is up nearly 62%. Does that mean IT should own the transition? Some 40% of companies are now spending $10 million a year or more on AI. Some of this is training. Should HR be driving the bus? The average large enterprise runs more than 1,000 distinct technology systems (!), 97 of which are employee-facing, almost all of them integrating AI into their software. Should the vendors supply the knowledge?

In part, because of this confusion, most organizations are moving at a fraction of the speed the technology, Bersin noted. AI capability is advancing on a steep upward curve. Organizations, with their jobs, structures, cultures, legacy systems, and deeply human resistance to change, are moving on a much shallower one.

My take is that we haven’t figured out we need to ‘invest in the rest’ when it comes to AI. For years, I’ve said shopping is the easy part. Installation is where the friction appears. And when you look at the above list — jobs, structures, legacy systems, human resistance — it takes an deliberate amount of time and resources to rewire these parts of the business. We know from the past that ignoring the people part of technology leads to delays and frustration. AI is no different.

Where AI Diverges the Past

Everyone I talk to (or hear speak), from the front lines of those building the models to the small business owner is breathless is overwhelmed by the non-stop flood of features, advancements, and hype around AI

Previous waves followed predictable adoption life cycles, years long, with room to plan, pilot, and scale. AI is doing none of that. Model updates are dropping every six weeks. Employees are already using tools their employers haven’t sanctioned. Your competitor isn’t waiting for you to complete your governance review.

So, how do we keep up with it all? Bersin outlined a deliberate progression (one that maps remarkably well to the Hyperadaptive journey; see below).

He said organizations start with individuals using AI to do their current jobs better, writing faster, analyzing data more quickly, reading emails with more efficiency. That’s real, but it’s table stakes. The next move is automating repeatable tasks, building reusable workflows. Then comes the moment where those individual automations start talking to each other, multi-agent coordination. And finally, entire business processes get reimagined around super-agents that orchestrate end-to-end work no single system ever touched before.

His example: UKG has built a super-agent capable of handling a front-line worker’s request, such as “I need an extra $400 after tax before Christmas, can you find me the right shifts?” The agent simultaneously checks qualifications, identifies eligible shifts, optimizes for pay rate, calculates tax impact, and returns a complete recommendation. That’s the future. Coordinated intelligence operating across what used to be five separate tools and three human handoffs.

Addressing Real Fears Around AI

The people side of the house isn’t just confused about technology. They’re watching their workforce become frightened and trying to figure out what to do about it.

Bersin cited University of Michigan consumer confidence data showing that American workers are experiencing a 50-year low in how comfortable they feel about their economic futures. He was careful to note that this isn’t just about inflation or income inequality. There is a palpable fear factor layered on top. People are reading the headlines about jobs being eliminated by AI, and many of them don’t have an understanding what happens to them in that story.

For large swaths of the workforce, reinventing themselves professionally is terrifying. They don’t know how. They don’t know where to turn for support. They don’t have models for it. The AI video library isn’t cutting it. And they’re watching headlines that suggest they may have to, whether they want to or not.

This matters because the most sophisticated agent architecture in the world fails if the humans it’s meant to augment don’t trust it, don’t understand it, or have quietly checked out. We need to provide the missing infrastructure.

So, Who Owns This?

In the room at UNLEASH, it was clear that HR leaders see AI transformation as, at least partly, their problem. The organizational change, the reskilling, the talent architecture, the workforce sentiment. All of that is undeniably their domain. But many of them are waiting for IT to lead on the technology side. And many IT leaders are pushing back by pointing to the business process owners. And the business process owners are looking at the C-suite. And the C-suite is making announcements.

Everyone sees the problem. Nobody fully owns it.

I believe this isn’t an HR problem or an IT problem. It’s a systems problem. And systems problems require systems thinking, which doesn’t stop at functional boundaries. Frankly, we need a cross functional AI Transformation office, alongside dedicated support structures that allow the organization to continue to reinvent themselves (I outline suggested ones in the Hyperadaptive Model).

What Bersin described as “systemic HR, stitching together the fragmented specialties of the HR function around actual business problems rather than internal organizational charts,“ is actually a version of something every function needs to do right now.

The siloed approach to AI, where each function pilots its own tools and builds its own workflows and reports its own wins, is how you end up with 97 employee-facing applications that can’t talk to each other and a workforce that’s more confused than empowered.

I created the Hyperadaptive Model because I saw the limits of previous technology transformations. I saw them stall as IT transformed only to hit the wall of finance or HR. I envisioned a lightweight model that cut across functions, giving everyone a shared destination with enough wiggle room to move at their own pace. A model that could stay durable as people moved from individual AI augmentation through process optimization, early automation, scaled AI, to what I call the Hyperadaptive state: an organization that doesn’t just use AI but continuously evolves with it. The model requires governance structures that cut across functions (AI Councils). It requires human nodes in every part of the organization who can translate AI’s potential into local context (AI Leads). It requires the social infrastructure to spread learning faster than any formal training program can (Communities of Practice and AI Activation Hubs).

The blueprint, grounded in research extended for the age of AI and examples from leading company is outlined in the book Hyperadaptive. What we need to do now, is organize around the blueprint and fund it.

What You Can Do With This

If you’re reading this as an AI transformation leader, I’d offer you two challenges coming out of this conference.

First, go find your counterparts in functional areas. Not to hand them the problem, but to build a shared map of it. The architecture questions and the workforce questions are not separable. The leader who understands both will be the one who actually moves the needle.

Second, create your shared roadmap. The research is here. The models exist (I’m sure there are many). What’s missing in most organizations is the organizational will to treat AI transformation as a whole-enterprise priority that extends beyond the technology.

The gap between where the technology is going and where most organizations are standing is real. But it’s not inevitable. It’s a design problem. And design problems have solutions, if you can work together to find them.

Hyperadaptive: Rewiring the Enterprise to Become AI-Native releases May 12th and is available for pre-order now at hyperadaptive.solutions/book. If you order now, you get early access to the model and supporting materials. It is research-grounded, enterprise-focused, and built for exactly the moment we’re all standing in.

And if you’d rather explore these concepts live, I’m hosting a peer roundtable on April 16th, just two weeks away, where we’ll dig into organizational alignment as the AI transformation function nobody assigned. We have a nice group formed. Upgrade to paid to be part of it.

And then... nothing scales.

Six months later, the AI Slack channel has gone quiet, as everyone gets wrapped up in business as usual. The AI Lead is still carrying their full original workload. A few teams are experimenting, but the insights aren’t spreading. Leadership is frustrated. The AI Lead is exhausted. And somewhere in a conference room, someone is asking why the ROI isn’t materializing.

These organizations have confused naming AI leads with enabling AI leads. These are not the same thing.

See if These Symptoms Feel Familiar…

If you’ve been paying attention to how AI adoption is unfolding inside organizations, a few patterns are hard to miss.

• AI Leads were appointed, but their job descriptions didn’t change. Their original responsibilities didn’t shrink to make room for their new mandate. They’re being asked to drive transformation in the margins of an already-full role. Enthusiasm is not infinite. It runs out.

• There’s no shared language, curriculum, or training program behind the title. Each AI Lead is winging it in their own direction, which means every team gets a different version of AI adoption — and nothing coheres at the organizational level.

• AI Leads are operating as islands. They may be evangelizing inside their own function, but there’s no formal mechanism for the marketing AI Lead to share what they learned with finance, or for the operations AI Lead’s discovery to spark an idea in customer success. The organizational design doesn’t support knowledge flow across boundaries.

• Leadership assumed that enthusiasm equals capability and walked away. The implicit message sent to AI Leads everywhere: you’re passionate about this, so you’ll figure out how to help others with AI. This is like assuming a talented piano player now knows how to teach someone how to play piano.

If you recognize your organization in any of these patterns, you’re not alone. But you are leaving significant value on the table.

What Enabled AI Leads Accomplish

Before we talk about what’s missing, let’s be clear about what we need from our AI Leads (it’s a lot more than cheerleading).

An effective AI Lead plays three distinct roles simultaneously.

They are a domain-specific implementer: someone who can identify high-value AI opportunities within their functional area, leveraging both their AI fluency and their deep contextual knowledge of how work actually gets done there. No outside consultant has this combination. Your AI Lead does this naturally, but it may stop there without formal guidance.

Which leads us to our second point. They can be enabled to become peer educators. The person others feel comfortable approaching with questions, who can provide hands-on, contextual guidance rather than abstract theory. Research consistently shows that peer learning dramatically improves knowledge retention over formal training alone. Your AI Lead is the person who makes AI feel accessible rather than threatening.

Last, we need them to be cross-functional connectors. A node in a network that allows insights from one part of the organization to spark innovation in another. In the Hyperadaptive™ Model, this happens through communities of practice and AI Activation Hubs. Marketing’s discovery informs sales. Finance’s experiment improves operations. This cross-pollination is how AI knowledge spreads at the speed organizations actually need.

That’s a significant portfolio of responsibility. Now ask yourself, what has your organization done to enable its AI leads?

Most Organizations Fail at Programmatic Support

The biggest mistake I see (and, don’t worry…it’s a common one) is appointing AI Leads and then leaving them to figure it out. This is a structural gap. For some reason, we think that people should be able to ‘figure it out on their own,’ but this lack of support is why AI efforts stall after the pilot phase.

I believe that programmatic support for AI Leads has three key dimensions.

1. Formal Training That Transforms Enthusiasm Into Expertise

Natural curiosity about AI is a great starting point, but it isn’t a destination. AI Leads require formal training that builds three distinct capability sets: AI fluency, process optimization expertise, and the ability to lead change. All three of these matter.

AI fluency means understanding not just how to use AI tools, but how to evaluate them, when to apply them, and where they fall short. (Large language models, for context, still hallucinate. Your AI Leads need to know this, and so do the practitioners they’re guiding.) It also means understanding your organization’s AI governance policies and where the guardrails are.

Process optimization expertise is what separates an AI enthusiast from an AI Lead who can actually create organizational change. It’s the ability to map workflows, identify where AI augmentation adds real value, establish feedback mechanisms, and measure outcomes, not just implement tools.

When Moderna introduced generative AI, they didn’t hand employees access and hope for the best. They prioritized upskilling their workforce and building a foundation of AI literacy that could sustain change over time. That’s the difference between a tool rollout and a transformation.

Last, we need to empower our AI leads to work with their peers; even those who may feel resistance to AI. We can arm them with skills to create ah-ha moments with AI and ways to empathetically relate to those who are struggling with moral or philosophical resistance to AI.

The training also needs to address something that often gets overlooked: what’s in it for the AI Lead personally. Will this role streamline their team’s operations? Create greater visibility with leadership? Open new career pathways? AI Leads who understand both the organizational value and the personal benefit of their role show up with a sense of ownership.

2. A Channel for Knowledge to Actually Flow

Your AI Leads are not just implementers. They are part of the nervous system of your AI adoption effort. Information flows through them to keep the organization updated as AI changes. They become the channel for signals to actually move through them.

This is where another piece of the Hyperadaptive System comes in: AI Activation Hubs. These are dedicated centers where AI expertise resides, AI advancements are tracked, and knowledge gets atomized into digestible, actionable learning. The Hub’s job is to stay current with AI capabilities and then distribute that knowledge through AI Leads to the practitioners who need it.

Think about what that flow looks like in practice.

Imagine it’s a Tuesday morning. Your AI Activation Hub has just identified a new capability in your Microsoft CoPilot that’s directly relevant to contract review in the legal department. They don’t send a company-wide email that gets buried. Instead, they brief the four legal AI Leads, who speak both the language of AI and the language of legal operations, and equip them with the context, talking points, and a simple use case to share with their team. By Thursday, three lawyers are experimenting with it. By the following week, someone has a workflow that cut document review time in half. That story goes back up through the AI Lead, through the Hub, and into the organization’s knowledge base, where it becomes the basis for the next wave of adoption.

This is what an AI learning flywheel looks like in motion. It’s not a training event. It’s a system. And once you get it spinning, it becomes self-sustaining.

3. A Community That Cuts Across the Organization

Motor Oil Group’s AI Garage initiative offers a useful model here. Their AI champions don’t work in isolation. They participate in weekly show-and-tell sessions, share what they’ve learned across functions, and translate AI concepts into their specific business context. The result is what researchers call learning contagion, or the organic spread of effective AI practices across traditional organizational boundaries.

This is what happens when you formalize the community infrastructure around your AI Leads. They stop being isolated enthusiasts and start becoming a strategic network. Regular cross-functional gatherings create forums where the AI Lead from supply chain and the AI Lead from customer experience can discover that they’re solving related problems and accelerate each other’s progress rather than duplicating effort.

Informally, these networks begin to emerge on their own in organizations where AI adoption has gained any traction. Formalizing them by giving them structure, rhythm, and dedicated platforms for knowledge sharing, converts organic momentum into sustained capability.

What This Looks Like When It Works

Unilever didn’t operationalize AI by purchasing licenses and hoping adoption would follow. They trained 23,000 employees in AI usage and simultaneously built the agile capabilities to focus those new skills on the highest-priority organizational challenges. That type of commitment moves AI from a technology rollout to an organizational infrastructure investment.

Wells Fargo went further, developing systematic upskilling programs, establishing academic partnerships with institutions like Stanford, and creating structured career pathways for AI talent. The message to employees was clear: this is a long-term commitment, and your growth inside it is real.

These aren’t one-time events. They’re ongoing systems. And they happen because someone decided that naming AI Leads wasn’t enough.

A Question Worth Sitting With

If you have AI Leads in your organization, take a moment to answer these honestly: Do they have formal training? Not a one-time workshop, but an ongoing program? Do they have protected time to actually do the work, or are they being asked to transform your organization in the spaces between their existing responsibilities? Are they connected to each other and to a central hub that keeps them current? And do they know what’s in this role for them?

If the answer to most of those is not really, you haven’t failed. You’ve just identified the next most important thing to build.

Empower Your AI Leads

At Hyperadaptive Solutions, we built the AI Lead Accelerator specifically to address this gap. It provides a structured program that gives AI Leads the shared language, practical frameworks, peer community, and ongoing support they need to move the needle. If you’re realizing your AI Leads need more than a title, we’d love to talk.

And if you want the full picture of how AI Leads fit into a comprehensive organizational transformation model (including the support structures, stage-by-stage blueprint, and research behind it) my upcoming book Hyperadaptive: Rewiring the Enterprise to Become AI-Native (IT Revolution Press, 2026) lays it all out. Pre-orders are open now at hyperadaptive.solutions/book.

The organizations that scale AI aren’t the ones the ones that built the infrastructure to spread what they know.

It’s the right question. And most enterprises I talk to aren’t asking it.

The dominant model for AI learning in large organizations right now looks something like this: leadership identifies a skill gap, L&D builds a curriculum, a platform gets licensed, cohorts get scheduled, modules get completed, and certificates get issued. The box is checked. The program is declared a success.

The speed of AI advancements turns this training model on its head.

Six months after traditional AI training, the capabilities that felt cutting-edge in Q1 are table stakes in Q3. The workflows that employees just learned have already shifted. And the organization’s response simply can’t be to build another training program.

This is the training trap. And understanding why it fails is the first step toward building something that actually works.

The Structural Mismatch of Learning AI

There’s a structural mismatch at the heart of enterprise AI learning that most organizations haven’t fully confronted: the tools are evolving faster than the training cycles.

The expiration date on technical training used to be measured in years. A PowerPoint certification from 1997 was still largely valid in 2002. An Excel course from 2008 didn’t require a major overhaul by 2010. The tools were stable enough that a discrete learning event such as a workshop, a certification, or a cohort could remain relevant long enough to justify the investment.

With AI, that expiration window has compressed to months, if not weeks. GPT-4 to o3. Claude 2 to Claude 4.6. Each release changes what’s possible, what’s expected, and what employees need to understand about their own work. A training program built on last season’s capabilities teaches people to think about AI’s possibilities in terms that no longer describe what the tool can actually do.

The World Economic Forum projects that 59% of the global workforce will need reskilling by 2030. But the implication buried in that statistic is that reskilling is not a project with a finish line. It is a permanent operating condition. The moment you complete a comprehensive training rollout, the clock on its obsolescence has already started.

And yet, organizations keep reaching for the training program because it is the most familiar shape for learning. It has a sponsor, a budget, a timeline, and a completion metric. It looks like progress. It produces certificates.

What it doesn’t produce is an organization that knows how to keep learning as the landscape shifts.

So… How Can We Keep Up with AI?

The real question becomes whose job is it to keep up with AI?

In most organizations, the honest answer is everyone’s, which means no one’s. Keeping up with AI is a near full-time job. And everyone in the organization is full up. The CAIO has a strategy to manage. The business unit leaders have quarterly targets to hit. The managers have teams to run. Staying current on AI model developments and their implications for our workflows is on everyone’s list…and nobody’s actual job.

The result is predictable. The organization’s working knowledge of AI capabilities drifts behind the actual state of the tools. People use yesterday’s mental models to make today’s decisions. Competitive advantage erodes from a slow accumulation of not-quite-current understanding.

PwC recognized this problem early and built something different. Rather than rolling out uniform AI training to their entire workforce, they created a network of AI champions who were explicitly responsible for staying current on AI capabilities, translating that knowledge for their specific business context, and getting it into the hands of their peers. Prompting Parties, highly interactive, peer-led sessions where employees experiment with generative AI on real work problems, generated over 400 requests for additional sessions in the first few months and reached more than 22,000 employees.

PwC didn’t try to keep everyone equally current on everything. They built a distribution infrastructure for learning. A network of informed nodes who could translate new capability into relevant context for the people around them.

That’s a fundamentally different architecture than a training program.

The AI Learning Flywheel

In the Hyperadaptive Model, this architecture has a name: the AI Learning Flywheel. And it runs on three interconnected layers.

AI Activation Hubs are the sensing layer. A team (or network of teams in large organizations) whose job includes monitoring AI advancements, evaluating their implications for specific functional contexts, and atomizing that knowledge into micro-bites of learning that can actually travel through the organization. This is the group that absorbs the complexity so everyone else doesn’t have to. They are, in effect, the organization’s immune system against AI-obsolescence.

The AI Leads are the distribution layer. Embedded in business functions, AI Leads receive the contextualized knowledge from the Activation Hub and get it into the hands of their peers. They’re function-specific translators who understand both the capability and the workflow it affects. And they’ve been trained on the most effective way to spread knowledge. Legal AI Leads understand legal workflows. Finance AI Leads understand finance workflows. That context is what makes the learning land.

The Communities of Practice are the integration layer. Informal at first, increasingly structured over time, these communities are where practitioners share what’s working, surface what isn’t, share learnings with the Activation Hubs, and build the collective intelligence that no individual node could develop alone. During Stage 3 of the Hyperadaptive journey, some form around technical challenges, such as “how do we build effective learning loops into our customer service automations?” Others address human concerns: “what skills should we develop as our roles evolve?”

Together, these three layers create a flywheel: new capability enters through the Activation Hub, gets contextualized by AI Leads, gets applied and stress-tested by practitioners, and the resulting insights flow back up through the network. The flywheel is not a program with a start and end date. It is an operating rhythm.

I've written an Ebook on the Flywheel

What This Looks Like in Practice

Let me make this concrete. Anthropic releases it’s newest model, Claude 4.6. For most organizations, the response is: someone reads the release notes, maybe a Slack message goes out, and 95% of employees continue using AI the way they were using it last quarter.

In an organization with a functioning Learning Flywheel, it plays out differently. The AI Activation Hub in, say, the legal function assesses the specific implications of the 4.6 release for how legal currently uses AI, including what changed, what’s now possible that wasn’t, what previous workarounds are now unnecessary. They spin up a brief, contextualized update, including a short video, a one-pager, a practical example. It goes to the Legal AI Leads, who demo it to their peers.

With this approach, the legal team’s working mental model of their AI tools updates in days, not quarters. No enterprise-wide training program required. No cohort scheduling. No completion metrics that have nothing to do with whether anyone actually changed how they work.

What ‘always on’ learning requires is investment in the learning arena. The good news? You can upskill and redeploy existing people to make this happen.

Creating dedicated ways for learning to flow is what it means to have a learning infrastructure rather than a learning program. The infrastructure is always on. The knowledge flows continuously. The organization doesn’t fall behind between training cycles because there are no training cycles. Rather, there is the ongoing rhythm of sense, translate, distribute, apply, and feed back.

What Does Your Organization Look Like?

Take a moment to map how your organization currently handles a major AI model release. Who finds out first? How long does it take to reach the practitioners actually using the tool? What determines whether they update how they work, or whether they keep doing what they were doing? Who is responsible for that answer?

In most organizations, that map reveals a learning infrastructure that stops at the awareness layer. People know something has changed, but the knowledge doesn’t reliably travel to the people who need to act on it, in the context that would make it actionable.

The WEF’s Future of Jobs Report 2025 found that 52% of leaders now rank job redesign as their top workforce priority. That tells you the work itself is the moving target. An organization where the learning infrastructure can’t keep pace with tool evolution is already losing ground on the larger challenge of work redesign.

Building the flywheel is not a technology project. It requires designated people (Activation Hubs, AI Leads), a communication rhythm, and the cultural commitment to treat learning as infrastructure rather than event. It requires, at minimum, asking: who in this organization is it actually someone’s job to keep up with AI? And then building the distribution system that lets their knowledge travel.

Final Thoughts

The organizations winning with AI are the ones where new capability travels fastest from the people who understand it to the people who can act on it.

That’s a distribution problem. And distribution problems require infrastructure.

The training program made sense in a world where the tools were stable and the gaps were bounded. In a world where the models evolve every six months, where the workflows are continuously redesigning, and where the humans responsible for judgment calls on AI-generated output need to be getting sharper continuously, the training program is a coping mechanism dressed up as a strategy.

That’s not to say we don’t need our L&D professionals. We do. We just need them in a different way. Embedded in Activation Hubs. Continuously atomizing the learning.

Build the flywheel. Designate the sensing layer. Enable the translators. Create the communities. And then let learning flow through the organization the way AI moves through your processes: continuously, contextually, and without a finish line.

Melissa Reeve is the founder of Hyperadaptive Solutions and author of the forthcoming Hyperadaptive: Rewiring the Enterprise to Become AI-Native (IT Revolution Press / Simon & Schuster, May 2026). The Hyperadaptive Model helps Fortune 500 enterprises build the infrastructure for AI-native operations. Pre-order your copy at hyperadaptive.solutions/book.

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The projector shone overhead. The HVAC system hummed. The laptop in front of me glowed. The conference room booking system that someone, somewhere, configured. The fiber optic cable feeding the Wi-Fi. The building management software keeping the lights at exactly the right brightness.

So many things in that room existed because electricity was invented as a general purpose technology. Not just the lights. But so many physical items. And I thought about the jobs required to build and support them. The software engineer who built the booking app. The network security analyst protecting the Wi-Fi. The UX designer who made the laptop feel intuitive. None of those jobs existed before electricity reorganized what work meant.

And I thought to myself…in twenty years, someone will sit in a room full of things that don’t exist today, created by people working jobs that don’t have names yet. The same way I couldn’t have predicted cloud architect in 1985, we can’t fully predict what’s coming.

But, based on history, we know it is coming. So, let’s take a moment to suspend the ‘jobs are going away.’ narrative to jump into what the future might hold.

First, an Honest Look at the Data

Before we get to the future, let’s be clear about the present. The World Economic Forum’s Future of Jobs Report 2025 surveyed over 1,000 global employers representing more than 14 million workers. Their headline finding: the WEF estimates a net new change in jobs, not a vanishing of them. By 2030, 170 million new jobs are projected to be created while 92 million are displaced, resulting in a net employment increase of 78 million jobs (roughly 7% of today’s total workforce).

That net positive number is real, and it matters. But two data points in the WEF report deserve a harder look before we move on.

First, the WEF lists Software and Applications Developers and Light Truck Delivery Drivers among the top fastest-growing roles through 2030. I can feel you squinting. If you follow the tech industry, you know junior developers are being laid off right now as AI coding tools eliminate what was once entry-level work. And if you’re watching the transportation sector, autonomous vehicle investment seems to point directly away from more drivers, not toward them.

Here’s how to hold both truths at once. With software, AI automates existing software AND enables the creation of entirely new categories of software products and services that weren’t economically viable before. The market expands. The nature of the work transforms dramatically (less writing boilerplate, more architecting and auditing what AI generates), but net employment grows because there is simply more software in the world to build and maintain. The current junior developer layoff wave is real disruption at the leading edge, not the final destination.

With drivers, the WEF’s 2030 timeframe falls before full autonomous vehicle deployment at scale for complex last-mile delivery. E-commerce growth (itself partly AI-enabled) is currently creating demand that outpaces what technology can absorb in that window. Both things are happening simultaneously, and the 5-year projection reflects that specific window. The 10-year picture may look different.

The broader lesson here is that the labor market transition is nonlinear and full of tensions. Which is exactly why the second WEF data point matters most.

If the world’s workforce was made up of 100 people, 59 would need training by 2030. Of these, employers foresee that 29 could be upskilled in their current roles and 19 could be upskilled and redeployed elsewhere within their organization. However, 11 would be unlikely to receive the reskilling needed, leaving their employment prospects increasingly at risk.

That is the number that should focus every AI transformation leader. Not the net gain of 78 million jobs, but the 11 people in every 100 who fall through the cracks if we don’t act deliberately. (If you want to dig into the specific failure patterns to avoid, I wrote about them in an earlier piece: The AI Digital Rust Belt Is Optional.)

What History Tells Us About Job Evolution

Across every wave of automation in the last century, we’ve seen that when machines take over the doing, humans shift to building and maintaining the machines that do it. When the washing machine took over the laundry, we built and maintained washing machines. When we automated the switchboard, we shifted from manually connecting calls to building and maintaining switchboards.

At FedEx, when their advanced automated sorting system came online, they trained five hundred team members to operate it, including eighty-five new hires whose entire role was to maintain the system. Then they went further. Their BOT-it program turned frontline workers into citizen coders. Employees with no prior coding background developed fifty-six automation products that saved tens of thousands of manual hours. The people who once executed the processes became the people who built the next generation of processes.

This is the through-line. And as AI becomes more capable, the jobs on the building, monitoring, and maintaining side of that equation get more interesting, and more numerous. Let’s get specific about what that actually looks like.

Imagining the Jobs That Come With AI

Here’s the exercise I want you to try. AI is already enabling the design and manufacture of physical products that didn’t exist five years ago. Each of those products requires humans to build, monitor, and maintain the AI systems that create them. Those humans will have job titles we haven’t invented yet.

Consider what’s already emerging from engineering and materials labs:

AI is now designing structural components, including aerospace brackets, architectural beams, car chassis, that look almost biological. Organic curves that eliminate every unnecessary millimeter of material. The shapes are optimized for strength in ways human engineers wouldn’t manually draft.

But someone has to sign off before those parts go into an aircraft. Enter the AI Validation Engineer: an engineer who takes the AI’s design output and stress-tests it against physical reality, because simulation and the real world diverge in ways that experience catches first. They also ensure the part can actually be manufactured by the equipment that exists. We need a host of new jobs to:

• Build the plants to manufacture these new aircraft parts

• Source the materials that go into building the ultra-light parts

• Maintain the robots that assemble next-generation parts

AI is discovering new battery chemistries by simulating millions of atomic combinations. The Crystal Structure Validation Engineer is the person who takes those AI-discovered compounds and tests them across real-world temperature ranges, charge cycles, and edge conditions that the model never saw. The AI finds the candidate. This person determines whether physics cooperates.

AI is enabling responsive meta-materials, including fabrics and building materials engineered at the microscopic level to respond to environmental stimuli such as changing their thermal insulation, absorbing specific sound frequencies, adapting to conditions. Someone has to define those stimulus-response parameters. Along with this new technology comes new roles in research, manufacturing, and maintenance.

As we explore drone delivery services we see the emergence of Drone Mission Controllers responsible for coordinating the intersection of drones between services and other flying objects operating in an unscripted environment.

AI is enabling screenless environmental sensors: earpieces, glasses, pendants with outward-facing AI chips that continuously parse your physical surroundings. Someone has to define what that AI pays attention to and what it ignores. The Contextual Awareness Designer is responsible for that model (as well as for the moment when the system misclassifies something it shouldn’t). They’re part product designer, part cognitive scientist, part safety engineer.

Not one of these job titles appears in a standard HR system today. Every single one maps directly to the build-monitor-maintain pattern. And every single one requires a human who deeply understands both the technology and the domain it’s operating in.

The Organizations That Can See What’s Coming

The above roles don’t emerge by accident. They are invented by organizations that have built the capability to sense what’s needed next, form teams to define it, and develop people to fill it faster than a competitor can recognize the need exists.

The Hyperadaptive Model sets the stage for this exact outcome. By integrating learning loops and changing the operating model, organizations move their people off career ladders and into a network that evolves roles continuously and intentionally, rather than scrambling when the disruption has already arrived.

As Hyperadaptive notes, the goal in the most advanced stages of organizational AI maturity isn’t to fill today’s roles better. It’s to build an organization that can sense the need for a new role, define it, and empower people to fill it faster than anyone else.

The new jobs are coming. The question is whether your organization will be inventing them, or scrambling to staff them after you’ve laid off all of your best workers or before someone else does.

One Last Thought

I left that AI session thinking about all of it. The room full of things that wouldn’t exist without electricity. The jobs that electricity created. The things that are being built right now in labs and foundries that will require people to build them, watch over them, calibrate them, and course-correct them when they stray.

The piece of this I keep coming back to is imagination. Because the organizations that will define this decade aren’t the ones slashing headcount. They’re the ones actively inventing what comes next.

That’s a different kind of leadership. And it’s available to anyone willing to start imagining.

If you’re building the organizational capability to navigate this transformation, Hyperadaptive by Melissa Reeve (IT Revolution Press, 2026) offers a research-grounded roadmap — from the first AI experiments to the fully AI-native enterprise. Pre-order now.

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That’s not a rounding error. That’s a genuine transformation story, and Dorsey deserves credit for executing it with unusual clarity and conviction.

But here’s my question leaders like Dorsey:

Are you building a more efficient company, or you building a more significant one, that will last the test of time?

Because those are not the same thing. And the difference between them may be the most important leadership question of our time.

Let’s Talk About Railroads

In 1960, Harvard Business School professor Theodore Levitt published what would become one of the most cited business essays in history: Marketing Myopia. His central argument was deceptively simple. The American railroad industry didn’t decline because people stopped needing transportation. It declined because railroad companies believed they were in the railroad business — not the transportation business.

They optimized their trains. They cut operational waste. They were ruthlessly efficient at doing exactly what they had always done. Meanwhile, automobiles and airlines ate their market, their customers, and eventually their future. The railroads were so busy getting better at their existing mission that they never stopped to ask whether that mission was still the right one.

Here’s the parallel: Block is, at its core, a financial software company. Square processes payments. Cash App moves money. The AI agent “goose” drafts emails and automates workflows. These are genuinely impressive capabilities. But if Block’s highest ambition is to run those capabilities with fewer people and higher margins, then it is, in a very real sense, in the railroad business.

The efficiency is real. The myopia might be too.

What Business Is Block Actually In?

Let’s think about who Block actually serves. Square is the financial infrastructure for millions of small and micro-businesses — the food truck owner, the independent nail salon, the pop-up boutique at a weekend market. Cash App has become a primary banking interface for millions of Americans who are underserved or entirely excluded by traditional financial institutions.

That’s not a payments company. That’s potentially a financial inclusion platform at civilizational scale.

The question Dorsey and his team have answered is: How do we do more with less? That’s a fine question. But the question they haven’t answered loudly enough is: What does ‘more’ actually mean for the people who depend on us?

Imagine a Block that used its AI capabilities not just to improve margins, but to actively reshape the financial lives of underserved communities. Imagine AI-powered financial coaching embedded in Cash App that helps a first-generation college graduate understand their credit score. Imagine Square evolving from a payment terminal into an AI-powered small business operating system that does for the owner of a neighborhood restaurant what a $200,000 CFO does for a Fortune 500 company.

That’s a much bigger company. A harder company to build. And one that would create a far more durable moat than operational efficiency ever could.

The IBM Lesson Nobody Remembers Correctly

People love to tell the IBM turnaround story as a technology story. It’s actually a mission story.

When Lou Gerstner arrived at IBM in 1993, the company was collapsing under the weight of its own hardware legacy. Conventional wisdom at the time was to break IBM apart and sell its divisions. IBM had been an extraordinarily efficient manufacturer of mainframes for decades.

Gerstner refused. Not because he was sentimentally attached to the hardware. But because he looked at IBM’s customer relationships, including deep, decades-long partnerships with the world’s largest enterprises, and asked a different question than his predecessors had been asking. He didn’t ask, How do we sell more hardware? He asked, What do our customers actually need?

The answer was integration. They needed someone to help them make sense of their increasingly complex technology environments. IBM’s mission shifted from making computers to solving the world’s hardest technology problems. IBM Global Services was born. And a company that was weeks from irrelevance became, again, one of the most important enterprises on the planet.

The lesson isn’t that you pivot away from your core. It’s that your core capability, including your people, your relationships, your institutional knowledge, is the raw material for a much grander mission, if you’re willing to ask the bigger question.

Ping An Didn’t Just Get More Efficient. It Got More Ambitious.

The most instructive counterexample to Block’s current trajectory isn’t a tech company at all. It’s Ping An Insurance, profiled in my upcoming book, Hyperadaptive: Rewiring the Enterprise to Become AI-Native. Headquartered in Shenzhen, China, it has used AI to expand its definition of what it is.

Ping An started with a clear and humble goal: better insurance underwriting. That’s about as unglamorous as it gets. But instead of using AI to simply process policies faster and cut underwriters, the company asked a different question: What could we become if AI let us understand our customers more completely?

The answer transformed the entire organization. Data from connected vehicles informed insurance pricing. Medical diagnostics enhanced risk assessment. Customer conversations with AI-powered voice robots continuously refined service delivery across all channels. A person checking their health metrics through Ping An’s app might now receive tailored financial planning suggestions. Scenario-based marketing reaches customers at precisely the moment a product would be most relevant.

What began as an insurance company became, in the words of Hyperadaptive, an orchestrator of customer life journeys.

The business results reflect the ambition. Customers using Ping An’s digital ecosystems hold an average of 2.9 contracts, compared to 1.2 for non-ecosystem users. Average revenue per ecosystem user is $5,288 — nearly four times the $1,399 generated by non-users. When your AI systems work together this seamlessly, customers don’t just buy products. They buy into an entire ecosystem that genuinely improves their lives.

Ping An didn’t shrink its way to significance. It used AI as a launchpad to become something no one had imagined an insurance company could be.

The Danger Hiding in the Headcount Numbers

There is a quieter risk in what Block is doing, and it deserves naming directly.

In Hyperadaptive, I describe what happens when organizations over-rotate on head-cutting: “Institutional knowledge walks out the door. You won the technical battle, but lost the cultural war.”

Block is cutting nearly half of its workforce. That is not a reorganization. That is a wholesale renegotiation of the organization’s relationship with its own accumulated knowledge. The people leaving don’t just hold job descriptions. They hold customer relationships, institutional memory, hard-won context about what went wrong in 2021 and why, and the quiet, undocumented understanding of how things actually get done.

AI tools are remarkable. But they learn from people. The people who lived the past, who understand the nuances of why Block’s lending product works in one market and struggles in another, who know which enterprise client needs a phone call and which one wants to be left alone. Those people are not replaceable by a language model, at least not yet, and not without significant loss.

The CFO’s productivity data is compelling. But productivity metrics measure what we can count. Institutional knowledge is precisely the thing that resists counting until the day it’s gone.

The Leadership Mindset We Need Today

What separates the railroad operators from the Ping Ans of the world isn’t intelligence or resources. It’s the question they start with.

Efficiency-oriented leaders ask: How do we do what we do with less?

Mission-oriented leaders ask: Given everything AI now makes possible, what should we become?

In Hyperadaptive, I describe this as the difference between purpose and process.

You can revolutionize every process in your organization and still watch momentum stall, because without a clear and elevating purpose, you’ll just end up doing the wrong things more efficiently.

The leaders in your organizations who are currently framing their AI transformation as a headcount exercise are, in the most generous interpretation, solving for the wrong variable. Efficiency is a byproduct of great strategy, not a substitute for it. Margin expansion is a result of serving customers better, not of serving fewer of them.

The question for every AI transformation leader reading this is whether you are using AI to become a smaller, cheaper version of what you already are, or to become something genuinely more important to the people and communities you serve.

I sincerely hope it is the latter.

Before You Open That Org Chart

If you are an leader in charge of AI (and if you’re reading this, you likely are) here is the exercise I’d ask you to do before your next workforce planning conversation:

Write down what your best customers would genuinely grieve if your company disappeared tomorrow. Not the features. Not the integrations. The actual gap in their lives. The thing they’d have to figure out on their own.

Now ask: Is your AI strategy making that thing better, bigger, and more central to everything you do? Or is it making your cost structure leaner while that core value quietly erodes?

Dorsey isn’t wrong that AI changes the economics of running a company. He’s making a rational, data-driven decision by the numbers he has in front of him. But the most transformative companies of the next decade won’t be the ones that used AI to cut the most efficiently. They’ll be the ones that used it to see further, serve more deeply, and become indispensable in ways their competitors can’t easily replicate.

Ping An didn’t start with a plan to orchestrate customer life journeys. It started with a commitment to understanding its customers better than anyone else, and it let that commitment compound.

That’s the leadership mindset that changes everything. The efficiency will follow. It always does.

— Melissa

P.S. — Something new is happening on the paid side of this Substack. I’m launching monthly peer roundtables. These small, candid, off-the-record conversations with enterprise leaders who are deep in the AI transformation weeds are where we can all learn together. Plus short ‘conversation roundup’ videos where I share what I’m actually hearing from the front lines. The first roundtable is April 16th on leadershp alignment. If you’ve been on the fence, things are about to get a lot more interesting over here.

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Melissa M. Reeve is the author of Hyperadaptive, a research-backed guide for leaders building AI-native organizations. The Hyperadaptive Model provides a five-stage pathway from AI pilots to full organizational transformation. Learn more and take the free Hyperadaptive Discovery Survey at hyperadaptive.solutions/discover.

Hi Everyone!

When I launched the paid community, I promised you real conversations with peers who are navigating the same AI transformation chaos you are.

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🗓 First Peer Roundtable: Thursday, April 16th “Getting Your Organization to Agree on What 'Adopting AI' Means?"

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The students were terrified. They are watching the rapid automation of the exact foundational tasks they are currently learning, and they are assuming their future value is being systematically erased before they even receive their diplomas.

The pre-submitted questions weren’t about how to use deep research or develop prompts. They were about how to survive in the ‘real world’ with AI. One student asked what she should be learning right now to get a job after graduation. Another asked about entry-level jobs being eliminated. The questions reflected the vulnerability they felt every day. And their anxiety is entirely validated by the data. Polling from early 2025 reveals that 56 percent of college seniors are pessimistic about their career prospects, with 62 percent citing direct concern over how AI will impact their intended professions.

While many of us are looking at AI as a tool for enterprise acceleration, the emerging workforce is looking at it as an executioner of the entry-level job. The reality is somewhere in-between. When we zoom out and look at the macroeconomic data, we see that the entry-level job is not dead. It is, however, demanding a fundamentally different set of skills.

AI Moves Fast. Labor Markets…slower.

Part of the panic stems from the sheer velocity of this technological shift. It feels overwhelming because it is historically unprecedented. Survey data from Harvard University shows that generative AI reached an adoption rate of nearly 40 percent among working-age adults in the U.S. in less than two years. To put that in perspective, the internet took over two years just to reach a 20 percent adoption rate, and the personal computer required three full years to hit that same 20 percent threshold.

We are experiencing consumer adoption at a breakneck pace, which creates the illusion that the entire labor market is collapsing overnight. But labor markets do not move at the speed of software updates. Macroeconomic labor shifts historically unfold over decades.

Research from the Yale Budget Lab indicates that thirty-three months post-ChatGPT, the aggregate occupational mix of our broader economy is only marginally different from its baseline. In fact, the occupational mix is tracking only about one percentage point higher than it was during the adoption of the internet at the exact same point in its lifecycle. The daily nature of how we execute individual tasks is changing rapidly, but broad economic displacement and the macroeconomic reallocation of labor remain incredibly sluggish.

It feels like a tsunami, but economically, it is a slow tide. To understand where this tide is taking us, let’s take a look at the past.

Educational Systems Struggle to Keep Up

The societal anxiety surrounding the obsolescence of human labor is a historical constant, yet macroeconomic data consistently demonstrates that general-purpose technologies act as catalysts for skill reallocation rather than outright labor elimination.

Let’s look at the integration of the computer to understand our current reality.

When early supercomputers (and eventually personal computers) arrived in university labs and corporate offices, they introduced capabilities that completely eclipsed human processing power. Yet, we did not stop using our brains, nor did we abandon teaching the fundamental principles of mathematics, science, and engineering. Instead, these machines became instruments of profound augmentation. They accelerated research, unlocked new frontiers of discovery, and allowed us to model previously impossible scenarios. The technology simply elevated our ambitions.

But there is a critical difference between the computer revolution and our current moment: time. The integration of the computer played out over decades. Our systems had the luxury of a long, forgiving adoption curve. Educational institutions and corporate training programs evolved organically alongside the technology, slowly pivoting toward the analytical skills required to operate these new digital tools. Because the transition was gradual, we did not acutely feel the disruption in our daily lives.

Generative AI offers a radically different timeline. The sheer speed of its advancement is completely overwhelming our societal infrastructure, robbing the system of the time required to adjust in real time. Because the leap in capability is happening almost instantaneously, it creates the visceral illusion that human skills and entry-level jobs are simply evaporating overnight.

The reality is much more nuanced. What we teach and how we teach it will inevitably evolve. The focus will naturally shift toward critical thinking, problem framing, and the rigorous verification of AI outputs, exactly what I discussed with the class. But massive systems like higher education do not pivot on a dime. They cannot keep up with a technology that reinvents itself every six months.

And.. The Entry-Level Roles Just ‘Shifted Left’

What those students at CSU were experiencing was not the end of human usefulness. They were feeling the massive, grinding disconnect between AI’s exponential capabilities and the legacy skills still being prioritized in the traditional college curriculum.

For generations, recent college graduates cut their teeth on routine, low-stakes tasks like drafting standard marketing copy, creating literature reviews, or processing accounts payable. These foundational tasks safely simulated the trial-and-error required for future strategic decision-making. Today, these routine, rule-based cognitive tasks are precisely the functions that large language models execute with maximum efficiency and near-zero marginal cost.

The corporate response has been swift. United Kingdom technology companies cut their dedicated graduate roles by 46 percent year-over-year from 2023 to 2024, forecasting even further reductions by 2026. A Stanford University analysis corroborates this, indicating that early-career workers ages 22 to 25 in occupations heavily exposed to generative AI have already suffered a 13 percent relative decline in overall employment.

But, critically, leaders must understand that the broader labor market is not shedding jobs indiscriminately. The PwC 2025 Global AI Jobs Barometer notes AI makes people even more valuable. In highly AI-exposed industries, organizations are driving up revenue per employee by a factor of three.

Companies are still hiring, but they have ‘shifted left,’ fundamentally expecting a different set of skills from workers. The modern entry-level worker is no longer permitted to be a mere producer of raw material. We are now expecting 22-year-olds to act as AI auditors and human-AI teaming facilitators. We are asking them to step into roles that demand a level of critical reasoning, systemic oversight, empathy, and contextual judgment that traditional undergraduate degrees were simply not designed to cultivate.

Rearranging the Puzzle Pieces of Work

When I hear the phrase ‘jobs are going away,’ I cringe. Not because it is true. Or false. But because it treats jobs as monoliths. Things that can be turned on or off. In reality, jobs are fluid collections of tasks, processes, decisions, and human interactions.

If we view an entry-level marketing role as a single, unbreakable block of tasks, then yes, AI looks like a threat. But if we deconstruct that role into its modular components, the reality changes. AI becomes a tool to execute the routine cognitive pieces, freeing up the human worker to focus on problem framing, experiment design, and contextual communication.

When AI handles the routine data synthesis and the first-draft generation, entry-level humans handle escalations, exceptions, alignment, and AI evaluation skills. We are empowering faster learning and pushing advanced skill acquisition earlier in a career journey.

The Leader’s Mandate

As we navigate this Hyperadaptive landscape, our mandate as AI transformation leaders is clear. We our role is to ‘rewire’ the organization toward higher-order cognitive tasks.

The gap that exists in almost every enterprise today is the massive chasm between what the organization wants to accomplish and what the organization actually has the resources to execute. AI is the bridge that closes that gap. It is an expansion of capacity, not a deletion of human value.

As enterprises, we must overhaul our hiring metrics and our onboarding processes. We can no longer hire for baseline production speed. We must prioritize candidates who demonstrate strong critical reasoning, the ability to interrogate a system, and the capacity for systemic oversight.

As academic institutions, we must seamlessly weave AI into the curriculum, teaching students how to use this powerful new tool as a thought companion, how to evaluate the output, and how to super-charge their capabilities.

And for the students I met last week, keep the faith. The entry-level job is not gone, but the expectations for it have changed. Focus on integrating AI into your world, keeping up with AI capabilities as you can, and strengthening durable skills like critical thinking and problem framing. You’ve got this. 🙌

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Additional Reading:

In 2010, the world shipped 121 million dedicated cameras. By 2023, that number had cratered to 7.7 million. That’s a 94% collapse in just over a decade. The smartphone didn’t just disrupt the camera industry. It vaporized the economic moat that protected an entire professional class. When smart phones automated focus, exposure, and color grading, the ability to capture a technically correct image (once an economically viable skill) became a free feature of the hardware in your pocket.

And guess what? Generative AI is doing the exact same thing to code right now.

What Happened to the Photographers?

Let me be clear about what the smartphone didn’t do. It didn’t kill photography. The Bureau of Labor Statistics still tracks the profession, and high-end commercial photographers continue to earn more than $200,000 a year. Annie Leibovitz isn’t losing sleep over your iPhone 17.

What the smartphone killed was the middle of the profession.

The local studio photographer who made a solid middle-class living shooting high school seniors, small business headshots, and weekend weddings? That world largely evaporated. Those services are now either performed by clients themselves or outsourced to gig workers competing on price in a race to the bottom. The median hourly wage for photographers in 2024 was $20.44, below the total for all occupations.

The labor market didn’t shrink uniformly. It split. The top time, including visual strategists who direct complex commercial productions where the cost of failure is high, actually thrived. The bottom tier exploded with semi-professionals willing to work for portfolio building rates. And the middle hollowed out.

The Pie Grew, But The Slice Got Thinner.

Here’s where the economics get fascinating. When the smartphone made image capture essentially free, consumption exploded. Global daily image volume went from roughly 80 million in 2000 to over 6.3 billion in 2024, a 78-fold increase. Jevons Paradox (which keeps coming up, BTW), strikes again. When you dramatically increase the efficiency of producing something, total consumption increases rather than decreases.

The digital image creation pie grew enormously. Goldman Sachs estimates the Creator Economy, which didn’t even exist in the film era, is worth $250 billion and projected to reach $480 billion by 2027.⁷ Ad spend in the Creator Economy alone will hit $37 billion in 2025, growing four times faster than the general media market.

But the individual photographer’s slice of that massive pie got thinner. The total economy for photos and videos expanded 10x or 20x, yet professional photography services barely grew. The value migrated away from the person capturing the image and toward the platforms aggregating content.

Now Watch the Same Movie, Different Cast

Generative AI is the new iPhone, this time for code instead of cameras. Just as computational photography abstracted the physics of light into a touch interface, large language models are abstracting the syntax of programming into natural language.

The Jevons Paradox is already kicking in. When AI reduces the marginal cost of generating code to near zero, the threshold for what warrants a software solution drops dramatically. Apps built for a single meeting, a specific campaign, and a temporary workflow start to becomes economically viable.

And the bifurcation is already visible. Hiring for entry-level developers has flattened at around 7% growth, while demand for senior technical leads has spiked 22%.

Companies are opting for smaller, senior-heavy teams augmented by AI rather than traditional pyramids of juniors.

Gartner predicts that citizen developers such as business analysts, marketers, and designers using AI-powered low-code tools will soon outnumber professional developers four to one.

The ‘citizen photographer’ with an iPhone became the dominant producer of visual content. The ‘citizen developer’ with an AI coding assistant is about to become the dominant producer of software.

From Writer to Orchestrator

The photographers who survived the smartphone revolution moved up the stack from technician to creative director, from capturing images to directing visual strategy.

Software engineers will need to make the same leap. The future role isn’t about being a coder. The future is in orchestration. Someone who defines system architecture, selects appropriate AI models, verifies security and integrity of the output, and integrates components into cohesive business solutions.

And just as deepfakes created a premium for verified, trustworthy photography, AI-generated code will create a massive premium for security and trust. Industries with high consequences for failure, such as finance, healthcare, and aerospace, will pay premiums for human-verified code. The engineer’s primary task shifts from creation to audit. Finding the subtle hallucination in an AI-generated codebase becomes the new high-value work.

This is the Hyperadaptive model in action: people move from doing the task to building, monitoring, and maintaining the AI agents that do the task. It’s not a minor tweak to a job description. It’s a fundamental rewiring of roles, skills, and organizational structures.

The Opportunities We Can’t Yet Predict

I am an optimist, so here’s what gives me hope.

When the smartphone put a camera in every pocket, nobody predicted YouTube. Nobody predicted Instagram. Nobody predicted that “Influencer” would become a legitimate career (is it?), or that major brands like Dove and Gymshark would hire professionals to make content that looks amateur. 🤯 That they would pay creators to shoot on iPhones to achieve the authentic aesthetic that Gen Z actually trusts.

The entire Creator Economy represents a category of work that was unimaginable in the film era. It emerged because ubiquitous cameras changed not just how images were made, but how they were used. The photograph transformed from a physical object intended for preservation into a fleeting unit of communication.

Software is about to undergo the same transformation. When code becomes ubiquitous, entirely new categories of work will emerge that we can’t predict today. The pie grows and spawns entirely new types of bakeries.

What This Means for AI Transformation Leaders

If you’re leading an AI transformation, the photography parallel offers a clear playbook.

Technical proficiency is becoming a commodity. Just as mastery of f-stops stopped being a differentiator, proficiency in Python or Java syntax will cease to be one. The middle will compress, just as local studio photography did.

The shift from coder to orchestrator won’t happen organically. It requires intentional role redesign, new career pathways, and robust support structures. This is significant organizational rewiring.

The durable skills such as complex system architecture, security governance, data ethics, product strategy, and the ability to bridge human intent and machine execution remain. Those are also the skills most organizations aren’t investing in nearly enough.

As you rewire, be on the lookout for emergent opportunities. The most valuable roles five years from now probably don’t have job titles yet. Build an organization that can sense and respond to them as they emerge.

The photography revolution didn’t kill visual media. It made it ambient. It wove it into every human interaction. Software is about to become equally ambient. The question becomes how will your organization respond to the shift? Will it fire all of the coder only to realize these are the same people who are best-suited to orchestrate the agents? Will it hire only high-end engineers, only to discover there is no one to take their place when they leave?

Or will you take the leap that you still need people to build, monitor, and maintain an army of AI coding agents? And take the steps needed to shepherd your people through the change?

The choice is yours.

For a comprehensive framework on how to rewire your organization for this shift, from role redesign to support structures to measurement, pre-order Hyperadaptive: Rewiring the Enterprise to Become AI-Native, publishing May 2026 with IT Revolution Press.

Relevant Articles:

There is no clicking of a mouse. There is no frantic alt-tabbing between windows. There is no copy-paste fatigue in my wrists.

Instead, I am leaning back in my chair, watching the sun fade behind the mountains outside my window in Boulder, while the work of an entire department happens invisibly in the background.

For the last three years, we have been promised a revolution. We were told AI would change everything. But for most of 2023, 2024, and even 2025, that revolution felt a lot like work. We became prompt engineers (which is really just a fancy way of saying we became micromanagers of very smart, very literal interns). We spent our days coaxing, correcting, and formatting output.

Today feels different. Today feels like the promise of AI has finally arrived.

As I dictate this article, which my specialized Substack agent pulls into a cohesive article, I have three other distinct entities running parallel workflows that would have previously taken me a week to coordinate.

Claude Cowork is Finding Files I’m Too Lazy to Organize

Running locally on my desktop is an agent tasked with navigating the chaotic labyrinth of my hard drive. I am notoriously disorganized digitally. Files go into the void. But right now, this agent is contextually understanding my entire digital history. It found the lost manuscript drafts, sorted them by version history, and then (without me asking) paused to inquire: “I see a pattern here. Do you want me to archive the 2024 versions into a separate ‘Legacy’ folder to clean up your workspace?” Yes. Yes, I do!

SIMULTANEOUSLY… AI is Locating Independent Bookstores

While Claude Code is doing its thing, a browser agent is scouring the web for independent bookstores in the Denver-Boulder metro area. But it’s not just listing them, it is:

• Navigating their individual “About” pages

• Identifying which ones have “Local Author” programs

• Finding the specific email addresses of the buyers

• Drafting personalized outreach letters based on their specific submission guidelines.

It knows my book, Hyperadaptive: Rewiring the Enterprise to Become AI-Native, is about organizational change, so it is prioritizing stores that feature business and leadership sections.

And…AI is Reconfiguring My New CRM 🤯

This is the one that truly feels like magic. I am currently moving my entire marketing stack to a new CRM. In the old world, this was a frustration-filled exercise involving a steep learning curve, CSV exports, and hours of mind-numbing recreation. Today, I asked Gemini’s Browser Agent what I wanted to do: “I don’t want to learn this new system. Just look at my old contact form, build a replica in the new system, set up the landing page, and give me the embed code.” It took over the browser and made it happen, step-by-step, handing me the final embed code to drop into my website.

Hours saved. Sanity saved!

I am not coding. I am not managing projects. I am simply stating my intent, and the system is adapting around me.

This is Beyond CustomGPTs, Folks…

We need to pause and recognize the magnitude of this shift.

When Generative AI first debuted, the wow factor was high, but the friction was higher. We spent hours context-switching and cleaning up after AI. You’d have a conversation in ChatGPT, copy the text, paste it into a Word doc, realize the tone was off, go back to ChatGPT, refine the prompt, copy it again, paste it into an email, and then manually attach a file.

We were optimizing tasks, but we were breaking our flow.

What I experienced this afternoon is the shift from single task optimization to intent-based, multi-step workflow execution

I shared a note earlier this week showing a video of Gemini filling out a podcast submission form. It read my Google Drive for my bio, scanned my email for context, found my headshot, and filled out the fields. That was a huge timesaver and a move toward AI autonomy.

Why Your Current Processes are Liabilities

Now, I want you to imaging taking my personal epiphany this afternoon and multiplying it by 5,000 employees.

This is where the fear sets in.

In a traditional enterprise, we manage change through standardization. We build Standard Operating Procedures (SOPs). We buy a software platform (like Salesforce or SAP), we train everyone on the process, and we lock it down for three years to amortize the cost.

But look at what I just did. I swapped my entire marketing backend on a Thursday afternoon because I didn’t ‘feel’ like learning the old one. I am nimble. I re-invent processes on the fly. I am, to use the terminology from my book, Hyperadaptive.

But how do you scale that?

If you are a CIO or a CEO, this level of fluidity is terrifying.

• What happens when your finance team starts vibe coding their own API connections to bypass IT security?

• What happens when your marketing team deploys five different browser agents to scrape competitor data without legal review?

• What happens when the process you documented last month is rendered obsolete by a new agentic workflow that a junior employee discovered this morning?

The traditional approach of centralized control, rigid software governance, and waterfall change management cannot survive this. You cannot write a policy manual fast enough to keep up with an agent that updates itself every night. If you try to lock this down, your high performers will leave to work for companies that let them fly, and your competitors will lap you while you’re waiting for your AI Steering Committee to schedule their next quarterly meeting.

The Hyperadaptive Shift: Moving From Process to Intent

This is the core argument of Hyperadaptive: Rewiring the Enterprise to Become AI Native.

The breakthrough isn’t the technology. The technology is just the fuel. The breakthrough is a New Operating Model.

To survive the Agentic Era, we have to move from Process-Based Management to Intent-Based Leadership.

In the old world, I would manage a junior employee by checking their work: “Did you fill out the form correctly? Did you get the right approvals?” In the agentic world, I need to manage the intent: “Does this bookstore outreach align with our brand values? Is the tone of this landing page accurate to our mission?”

This requires a massive shift in how we structure our organizations. We need to shift to an outcome-based mentality. We need to install Integrated Learning Loops. We need to move the decision-making power to the edge of the organization, where the people doing the work can deploy these agents instantly.

As I wrote in the manuscript: “The only way to win is to build a learning organization that can learn faster than the world is changing.”

Right now, my home organization is learning at lightspeed. The friction is gone. The coordination cost (the tax we pay for trying to get things done) has vanished.

This is what it means to be AI-native: Limited coordination costs. The ability to sense and respond in near real-time. An organization that can reinvent itself continually.

I am not a software engineer. I am a strategist. But because of these agents, I am now capable of executing at the level of a full technical team. This is the future. It is messy, it is fast, and it is incredibly empowering.

But it demands a new kind of leader. A leader who isn’t afraid to let go of how it’s always been done and embrace the chaos of how it could be done.

And while it is one thing to have the vision, it is another thing to put it in action. That’s where the Hyperadaptive Model comes in, guiding you step-by-step from here...to there.

The Leader’s Prompt

I want you to close your eyes and imagine your department three years from now.

Imagine your teams have this power. Imagine they have agents that can reorganize their hard drives, code their own integrations, and scout new business opportunities while they sleep.

If your team had zero technical friction—if they could build any workflow they could imagine instantly—what is the first rigid process in your company that would shatter?

Is it your procurement cycle? Your hiring process? Your annual budgeting?

Let’s talk about it in the comments. Because the agents are already here, and they aren’t waiting for permission.

I recently looked back the $1,500 annual upskilling stipend I provided to my teams. On paper, it was a forward-thinking perk. But in reality, it was a failure.

Most of my team couldn’t (and didn’t) spend it. Not because they didn’t want to learn, but because the day-to-day reality of work wouldn’t let them. To use that money, they had to go offsite to a conference or sit through a three-day workshop. But the relentless pressure of meetings, deadlines, slack-fire-drills made leaving the office for three days feel like a dereliction of duty.

I’m feeling the struggle of setting daily work aside as I try to learn Claude Code. I’ll watch ten minutes of a tutorial, get a spark of insight, and then... it’s time to make dinner. Or a meeting starts. My learning is never complete because it is being squeezed into the margins of a work life built for a pre-AI tempo.

This is the friction almost every worker I know is feeling. We are getting access to AI tools and training while keeping the same 40-hour-a-week production mandates. We are telling people to innovate in the 15 minutes between Zoom calls.

Why Traditional Training Won’t Fix It

Traditional Learning & Development (L&D) is built on the concept of training events. We treat learning like a software update where you take humans offline, install the new knowledge, and reboot.

But AI training can’t operate this way. The technology is moving too fast. The pace of technology deployment is outstripping the investment in human skills.

The trap is believing that a one-time workshop or a library of AI videos will rewire years of established processes.

If you are still budgeting for learning as a moment in time, or trying to keep a library up-to-date, you are effectively funding AI failure.

Capacity Management. So Boring, Yet So Necessary.

To lead humans through AI change without chaos, we have to start talking about upskilling solutions alongside Capacity Management. This is a discipline with deep roots in Agile, but it has been largely a practice ignored by most of the organization.

The reality is that there will always be more to do than can be realistically done, and the magic of high-performing teams is the ability to raise the most important work to the top of the stack. They allocate their time judiciously. When we ignore the discipline of rigorous capacity management, allocating our time willy-nilly, learning goes to the wayside.

And we get stuck in our current ways of working.

In a Hyperadaptive organizations, learning isn’t an extra activity, but integrated into workflows. Moving to a culture where capacity is managed and learning integrated into the fabric of operations requires three fundamental shifts in how we lead:

1. Explicitly Fund the Learning Hour

If you don’t explicitly allocate hours for learning in your capacity planning, you are defaulting to failure. Leaders up-and-down the hierarchy must understand that there are real trade-offs to be made with important initiatives.

To prioritize AI literacy, some business-as-usual projects must be put on the back burner.

We must shift from event-based learning to an always-on approach, think 45 minutes, three times a week, baked into the normal job (“time for our AI Learning Power Hour!”). As Ryan Martens notes in the foreword to my upcoming book Hyperadaptive, you can’t strategize your way through a hurricane, and sometimes you have to ease off the course by twenty degrees to make actual headway.

2. Respect the J-Curve of ROI

Every leader wants immediate productivity gains from AI. Research shows that AI-augmented consultants can finish tasks 12.2% faster with 40% higher quality. But there is a J-Curve involved.

Before you get the spike in productivity, you will see a dip. People will be slower while they learn. If you measure ROI too early, you will kill the initiative. Instead, we must measure interim metrics, such as:

• AI Literacy Rates: How many people can effectively use agentic systems?

• Use Case Velocity: What is the quality and volume of AI use cases being surfaced from the frontline?

• Cognitive Load: Are we freeing human capacity for creativity, or just piling more on?

3. Build Psychological Safety into the Calendar

One of the most effective practices I’ve seen is Failure Friday, where teams share their AI failures (some fabulous, some frustrating). If your team is popcorning AI ideas but afraid to admit when an automation fails, you won’t build the institutional capabilities around experimentations needed to survive.

We need to create practice arenas, olaces like PwC’s Prompting Parties, where employees can experiment on real work items without the fear of a botched production result. This is how you move from knowing AI to being capable with it.

Creating AI Learning Flywheels

Because of the speed of changes with AI, we need to rethink the role of L&D in the enterprise. I posit that the future of L&D looks like learning professionals embedded into a self-sustaining system. They become the AI sense-makers, monitoring AI developments, atomizing the learning, and delivering updates to the right people and the right time. In the Hyperadaptive model, we use a four-step flywheel:

1. Spark: Use hands-on workshops to spark AI usage and new ways to solve real business.

2. Spread: Identify your natural AI Leads, the enthusiasts who will teach their peers, then provide them with systematic, ongoing support.

3. Scale: Establish an AI Activation Hub to house best practices, atomize the learning, measure success, and keep up with the pace of change.

4. Sustain: Embed these loops into the organization’s DNA so learning happens faster than the world changes.

I detail this flywheel in my AI Learning Ebook

Download the Ebook

Something to Consider

We often blame culture for slow AI adoption, but culture is just a reflection of where we spend our time and money.

My question for you this week: If I looked at your team’s calendar for next Tuesday, would I see AI Learning as a protected, funded line item, or is it a ghost that only appears if they finish their real work early? What is your reality? 🤔

Explore How to Integrate AI into Your Organization

The articles in my Substack are rooted in my upcoming book, Hyperadaptive: Rewiring the Enterprise to Become AI-Native. The book comes out in May 2026, but you can get early access to the model and frameworks by pre-ordering the book.

Order via amazon, then redeem your bonus materials at: hyperadaptive.solutions/book

PRE-ORDER ON AMAZON

Despite thirty years spent at the front lines of new approaches and technologies, AI felt different. The scope felt vast and amorphous, like I was trying to draw a line around a cloud that shape-shifted every time I looked up.

Economist refer to AI as a General Purpose Technology (GPT). To them, GPT is a technical classification. To a leader, it’s a nightmare.

For frame of reference, the steam engine was a GPT. The internal combustion engine was a GPT. The Internet was a GPT. General purpose technologies create breakthroughs on a dozen fronts simultaneously.

The sheer scale of AI results in friction. If you are a biology researcher, AI is an on-deck analyst for data sets, large and small. If you are an Art Director, it’s an infinite canvas for ideation. When a new technology can do anything, the human brain often reacts by doing nothing. The brain wants to wait for “things to settle” or for someone else to show parse the ever-changing landscape of AI.

Many leaders are paralyzed by the AI noise. Doing nothing seems easier. And boy-oh-boy, do I get it. I spent the first six months after the release of ChatGPT doing nothing.

Then, I entered a state I call AI sense-making.

Why I Dedicated Six Months to Finding AI’s Rhythm

AI sense-making is the deliberate effort understand the general shape of AI in order to constrain the infinite potential of AI into a context that makes sense for your business.

Imagine if someone dropped you in the middle of the ocean. Everything around you feels like…salt water. That’s how AI felt to me at first. It all felt the same. Only after months of following Google alerts set for “AI integration,” “AI automation,” and “AI tools,” did I start to get my bearings and get my bearings. (Just like you’d start to signpost around the sun, the currents, and the weather in the middle of the ocean).

Day after day, I watched the same terms, technologies, and patterns appear. Eventually, the AI noise became a tune. I established a baseline and started to see the norm. I could suddenly tell when a headline was hype and when it was a genuine signal of structural change.

Last week, I realized that ‘AI sense-making’ is an invisible, unnamed struggle that leaders face.

And it’s lonely.

How to Build Your Own Sense-Making System

The truth is that most leaders don’t have the luxury to invest six months of full-time headspace to dedicate to AI sense-making. You have a P&L to protect. But you can hack it by following these three steps:

1. Identify Your Sense-Makers. Stop reading generic AI News. Find the three people in your specific niche who are doing the deep research on your behalf.

2. Find Your Peers. AI learning is social learning. We learn from each other. Surface others in your industry, in your role, in your function using AI and connect.

3. Focus on AI Use Cases that Matter. To manage complexity, constrain. Don’t start with “what can AI do”? Start with a specific pain point that you think AI might be able to solve, and go from there.

Need a Safe Space for AI-Sense Making?

I’ve created this space, this community, so that we can help each other make sense of AI. The posts are my gift to you. To go further - connecting with your peers, accessing sense-making tools, exploring topics that matter - consider joining the paid. It is a safe place to have real conversations about things that matter, like:

• Our upcoming roundtable on Rewiring Roles with AI

• Top influencers in the AI Transformation space

• The results of your Hyperadaptive™ Discovery Survey

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Making Sense Together

Sense-making is a social act. We learn faster when we learn together.

I want to hear from you: How did you take AI—this massive, overwhelming thing—and finally make it “click” for your specific context? Was there a specific moment where the “amorphous” became “actionable”?

Let me know in the comments below.

Further Reading:

The Wreckage of the Factory Floor

In the late 1970s and early 1980s, the American economy churned. Global competition and the emergence of free trade zones shuttered manufacturing plants and decimated entire communities. The Iron Belt rusted over, and a generation of workers who had built their identities around the blast furnace and the assembly line we…

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The sugar high of Generative AI is over.

For the last 18 months, I’ve watched enterprises go into a frenzy of experimentation. I’ve sat in on conversations where leaders talk ab…

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Walk into many organizations today and you’ll see a bifurcation. At the top, the C-Suite is shouting for speed, innovation, and AI integration. At the bottom, individual contributors are quietly using ChatGPT t…

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Hyperadaptive Intelligence is for leaders navigating the frontier of AI adoption, where pilots succeed, tools proliferate, and progress still stalls. I write about the human systems AI puts under stress: organizational design, decision-making, governance, learning, and change. This is not a newsletter about tools or hype. It’s a thinking space for peopl…

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Remember the early 1990s? When the Personal Computer revolution hit the enterprise, we didn’t just drop a beige box on every desk, pat the employee on the shoulder, and wish them “good luck.”

We built an entire world to support that shift. We built IT departments. We hired Help Desk technicians. We trained installers and …

Read more

We have all been there. You hire an expert to come in for a day. They show your team the magic of Generative AI. Eyes light up. People are amazed by what they can create in seconds. You leave the room feeling like transformation is finally happening.

Then, everyone goes back to their desks, a few power users emerge and th…

Read more