Jacob Morgan

We tried to make work happier—and ended up making it hollow. Cultures softened. Leaders hesitated. Performance drifted. After 100+ CHRO interviews, I wrote The 8 Laws of Employee Experience—a blueprint to bring strength and humanity back to work. Learn more about it here: linkedin.com/pulse/my-new-b…

Truly flexible work means that employees have a voice in shaping how, where, when, and even why they work.

Work looks like it is constantly changing. But a lot of the time, it's just wearing different costumes. First, it was perks. Then it was flexibility. Now it is AI, productivity, and speed. The language, the tools, and the expectations evolve. But the bigger mistake often stays the same: we keep misunderstanding what employee experience is actually supposed to be. Employee experience was never meant to be a collection of perks, a bundle of accommodations, or a race to deploy more tools. At its core, it is about how leaders design work so people and organizations can succeed together. That means trust. It means clarity. It means growth. It means meaningful work. It means creating an environment where people can learn, contribute, adapt, and perform. This is where leadership matters. Perks can make work nicer. Flexibility can make work easier. AI can make work faster. But leadership determines whether any of those things actually make work better. That is the trap many organizations fall into. They keep optimizing around what is visible and easy to signal. Better offices. More benefits. More flexibility. More tools. More dashboards. More output. None of those things are inherently wrong. The problem starts when leaders confuse them for the employee experience itself. The real foundation is deeper than that. People want to trust leadership. They want to understand what is expected of them. They want managers who know how to lead human beings, not just enforce processes. They want work that helps them grow, connects them to purpose, and gives them a reason to care. Technology can support that. Flexibility can support that. Even perks can support that. But none of them can replace it. That is why this moment matters so much. Organizations do not have to blindly follow every trend just because it looks modern, progressive, or efficient. They can stop and ask a better question: are we designing work for appearances, or are we designing work that actually helps people thrive over the long term? AI raises the stakes. It can help organizations become smarter and better designed. But it can also push them into another shallow cycle where employee experience becomes nothing more than a productivity race dressed up as innovation. The organizations that get this right will be the ones led by people who keep work human while making it more adaptive. We have more signals now than ever before. We can see the pattern more clearly. So the question is no longer whether work is changing. The question is: will leaders keep chasing the costume, or will they finally redesign the core?

There’s been a lot of conversation lately around upskilling and reskilling for AI. And look, that makes sense. Of course companies need to help people learn new tools and build new capabilities. But the more I think about it, the more I feel like that’s not actually the hardest part. The harder part is movement. Can people actually move as the work changes? That’s the question I don’t think enough companies are asking yet. A lot of talent models were built for a much slower world. Jobs were more stable. Career paths were more linear. Learning sat off to the side. You took a course, went through a program, had a development conversation, and hopefully that turned into progress over time. That model kind of worked when change moved slowly enough for the organization to catch up. I’m not sure it works anymore. Because once work starts shifting faster than roles, the issue is no longer just whether people can learn something new. The issue is whether the company can actually make skills visible, connect people to adjacent opportunities, and create ways for movement to happen before the disruption becomes obvious. That feels like a very different challenge to me. Some organizations are going to use AI mostly to compress labor. That’s the obvious path. It feels measurable. It feels efficient. It gives leaders something clean to point to. Other organizations are going to use AI to make capability more fluid inside the business. Not just by training people, but by making it easier for them to move into new work as it emerges. That’s a much more interesting signal. Because if your answer to AI is mostly learning, but your internal system still makes movement hard, then the training only gets you so far. You’ve helped people build skills, but you haven’t really built a structure that knows what to do with them. That’s why I keep coming back to this idea that the real divide here is not just skills. It’s movement. The companies that handle this well won’t just be the ones that teach people faster. They’ll be the ones that make it easier for people to move faster. That feels like the real AI talent divide.

This is from today’s episode of Future Ready Today, where I break down California’s new AI workforce executive order, Microsoft and EY’s $1B enterprise AI push, and what these signals mean for leaders trying to prepare their workforce for what’s next. Listen here: podcasts.apple.com/us/podcast/two… Watch here: youtu.be/5vF06qcyUnk ♻️ Share this with a leader who needs to see it.

California just gave us a preview of the AI workforce problem every company is about to face. Governor @GavinNewsom signed a first-of-its-kind executive order focused on preparing workers, small businesses, and communities for AI-driven economic disruption. On the surface, that sounds like a major policy moment. And in some ways, it is. Government is finally acknowledging that AI is not just a technology issue. It is a labor issue, an economic issue, and a trust issue. But once you look at what the order actually does, the signal gets more interesting. It gives state agencies 180 days to study the issue, build recommendations, explore updates to California's WARN Act, evaluate severance standards, create dashboards, look at transition support, and consider ideas like worker ownership and universal basic capital. That may all be useful eventually. But companies are not waiting 180 days. AI pilots are already moving into full-scale enterprise deployments. @Microsoft and @EYnews just committed more than $1 billion to help large companies move AI from experimentation into core operations. EY says it has already deployed Copilot to 150,000 employees, measured a 15% productivity boost, and reduced some manual tax document processing work by up to 90%. That is the tension. Government time is measured in studies, frameworks, dashboards, and recommendations. Company time is measured in deployments, productivity gains, workflow redesign, cost savings, and restructuring. Worker time is measured in anxiety, uncertainty, skill pressure, and the question people rarely ask out loud: "Am I next?" That gap is where trust breaks. When employees see AI rollouts before they see a transition plan, they do not hear "innovation." They hear headcount risk. They hear leaders talking about the future without explaining what that future means for them. This is why leaders cannot outsource their AI workforce strategy to government, consultants, or technology vendors. Policy may eventually create guardrails. Consultants may help with deployment. Vendors may provide the tools. But the responsibility for trust still sits inside the company. Which roles are changing? Which tasks are disappearing? Which skills are becoming more valuable? Where will people be retrained? Where will work be redesigned? Where will headcount actually shrink? And how clearly are leaders willing to say any of this out loud? Because vague upskilling language is not a workforce strategy. Saying "AI will augment everyone" is not a transition plan. The companies that handle this well will not just be the companies with the most AI tools. They will be the companies that can move fast without making their people feel disposable. The real AI strategy question is not just: "How do we use AI?" It is: "What happens to our people when we do?" That is the leadership test.

This is from today’s episode of Future Ready Today, where I break down California’s new AI workforce executive order, Microsoft and EY’s $1B enterprise AI push, and what these signals mean for leaders trying to prepare their workforce for what’s next. Listen here: podcasts.apple.com/us/podcast/two… Watch here: youtu.be/5vF06qcyUnk ♻️ Share this with a leader who needs to see it.

Technology can no longer be viewed simply as a suite of productivity tools; it’s becoming the core infrastructure of the employee experience itself.

The hardest part of AI is not point B. It is getting people from point A to point B without breaking trust along the way. That was the thread running through today's Future Ready Today episode. @Meta is cutting thousands of jobs while redirecting enormous capital into AI infrastructure. @AnthropicAI is bringing in @karpathy to help build systems that use Claude to make Claude better. And a new @MilkenInstitute and @HarrisPoll found that 80% of Americans want government workforce transition programs now, while 68% say they are navigating the AI transition entirely alone. These are not separate stories. They are all signals of the same thing: AI is moving faster than the systems designed to help people move with it. Companies are redesigning work. AI labs are accelerating model development. Workers are being told to adapt. But the transition plan still feels thin, fragmented, or missing entirely. This is where the pain comes from. People are not just afraid of technology. In fact, 69% of workers believe AI can create more opportunity than it eliminates. This is not a technophobic workforce refusing to change. These are people who understand what is coming, but they want the infrastructure to help facilitate the transition before the disruption fully arrives. I do believe we will be better off in the long run. But the long run is not where people are living right now. They are living in the transition. And when upside is concentrated at the top while risk is distributed across the workforce, this stops being just an economic issue. It becomes a legitimacy issue. Too much of the AI message today sounds like doom, job loss, displacement, and "figure it out." Then leaders act surprised when employees push back, students boo commencement speakers, and workers distrust the vision. If you sell the end of the world, don't be surprised when people reject the pitch. The so what is this: AI adoption is not just a technology strategy. It is a workforce transition strategy. If leaders treat AI only as a tool rollout, they will miss the human system around it. If they move faster than people can understand and adapt, the backlash will not be because people hate innovation. It will be because they do not see a bridge between the disruption and the opportunity. The solution is to build that bridge before the disruption fully arrives. That means more conversations around programs, policies, infrastructure, opportunity, and potential. It means communicating the future in a way people can actually see themselves inside of, instead of only hearing what might be taken away. The companies that get this right will not just deploy AI faster. They will help people move through the transition in a way that preserves trust, builds capability, and avoids leaving workers behind. Because opportunity without a transition plan still feels like being pushed toward a cliff.

I got an email from @NBCNews asking me to come on the program to debate the topic of four-day work week. I said yes because I thought it would be a real discussion. A chance to talk honestly about the idea, the data behind it, and the questions leaders should actually be asking before treating it like the future of work. That's not what happened. The whole thing lasted maybe two minutes. They went to the other guest first. He answered. Then they came to me for maybe 20 or 30 seconds. Then they went back to him. And that was it. No chance to respond. No actual back and forth. No real discussion. But in some ways, that short segment captured the entire problem. The four-day work week is being sold like it's self-evidently good. It sounds progressive. It sounds humane. It sounds like the kind of idea that should belong to the future. And because it sounds so good, a lot of leaders stop interrogating it. They stop asking what happens to competitiveness, strategic work, responsiveness, labor costs, and the industries where time and presence still matter. That is where the conversation falls apart. The question is not whether people would like to work less for the same pay. Of course they would. The question is whether the model creates a stronger organization. Whether it holds up under pressure. Whether it improves the long-term health of a business instead of just making the short-term story sound better. Those are very different questions. What concerns me is how often this now happens in leadership conversations. The narrative gets ahead of the operating logic. A concept becomes attractive, and that attractiveness starts to substitute for evidence. By the time anyone slows down enough to test the assumptions, the idea has already been framed as enlightened and the skepticism gets treated like the problem. It isn't. Sometimes skepticism is the responsible move. Sometimes the harder question is the more important one. And sometimes the future of work gets misread because too many people are rewarding what sounds good instead of what actually works. The future won't be shaped by the ideas that get the loudest applause. It will be shaped by the leaders willing to ask: does this actually make the organization stronger?

You cannot run a 21st-century organization with 20th-century assumptions and frameworks about talent. - Dr. Cora Koppe-Stahrenberg, Chief people officer at @BATplc

We are making the AI and entry-level jobs conversation too simple. The easy debate is whether AI will wipe out junior roles. That is where the headlines go because it is dramatic and clean. But work rarely changes in clean lines. The more important issue is what happens to the development path underneath those jobs. Junior people do not become senior because a title changes. They become senior because they spend years building judgment. They sit with messy research. They rewrite drafts. They clean up data. They prepare reports. They fix the deck again. They watch what gets challenged in meetings. They learn which details matter and which ones are noise. A lot of that work gets dismissed as grunt work, but it was doing something important. It was the bottom rung. It gave people repetition, context, pattern recognition, and taste. It taught them what good work actually looks like before they were expected to lead it. Now AI can remove a lot of that. In many cases, that is a good thing. Nobody needs to protect low-value administrative work just because it used to be part of the job. But leaders need to be honest about the tradeoff. When the task disappears, the learning can disappear with it. That is what makes this moment so interesting. The companies using or exploring AI are not necessarily running away from junior talent. Many are moving toward it because AI gives early-career workers more leverage. The grunt work can go to the machine. More of the thinking can go to the person. That sounds like progress. But it also creates a new problem. A junior employee can move faster than ever and still not get better. If AI does the work before they understand the work, speed becomes a trap. The output improves on paper while judgment gets weaker underneath. That is why the Gen Z data matters. Younger workers are already saying they rely on AI too much. Some believe it is weakening their skills. I would not brush that aside as fear or resistance. That sounds like an early warning from the people closest to the change. There is a big difference between using AI well and becoming dependent on it. Using AI well means you can question the output, improve it, explain it, and still own the decision. Dependence means the tool becomes your first move, your shortcut, and eventually your substitute for thinking. Leaders do not need to preserve every old task. They need to preserve the capability those tasks used to build. Remove low-value work, but redesign how people learn. Keep humans close to judgment. Build AI fluency without letting it become dependency. The question is not just whether AI replaces people. The better question is what kind of people your system is building.

The first wave of AI at work was about access. Give employees the tools, let them experiment, encourage use cases, and celebrate teams moving faster. That made sense because most organizations needed to get past the fear stage. AI had to stop feeling like a side project and start becoming part of the operating system of work. But the next phase is different. Once AI moves from “people using tools” to “people creating agents,” the risk changes. A chatbot that helps someone write a summary is one thing. An agent that can access company data, pull information from systems, send outputs, make recommendations, interact with workflows, or trigger next steps is something else entirely. Now multiply that across departments. Marketing builds agents. HR builds agents. Finance builds agents. Engineering builds agents. Operations builds agents. Employees build their own agents to save time, avoid bottlenecks, and move faster. At first, this looks like exactly what leaders wanted: more experimentation, more productivity, more initiative, and more evidence that AI adoption is finally happening. But underneath that progress, a different problem starts to form. No one knows how many agents exist, who built them, which systems they can access, whether two agents are doing the same job, whether they are pulling from the same source of truth, or which outputs are reliable. This is why AI agent sprawl may become the next shadow IT problem. Shadow IT was about employees using unsanctioned apps. AI agent sprawl is about employees creating unsanctioned workers. That is a much bigger shift because software mostly waits for someone to use it. Agents can act. They can search, summarize, route, decide, escalate, generate, recommend, and in some cases execute. Without clear governance, the organization does not just lose control of the tool stack. It loses visibility into how work is actually getting done. This is where the real leadership challenge begins. The question is no longer, “Are our employees using AI?” The better question is, “Do we understand the new layer of work being created underneath the org chart?” One unmanaged agent may not seem like a problem. But hundreds or thousands of agents, connected to different systems, using different data, and owned by nobody in particular, can quietly reshape the organization. The irony is that companies may create agent sprawl while trying to become more efficient. They may remove friction in one part of the business while creating confusion somewhere else. They may speed up tasks while weakening accountability. This does not mean companies should stop experimenting with AI agents. That would be the wrong lesson. The lesson is that experimentation needs architecture. Leaders need to know what is being built, who owns it, what it touches, what it is allowed to do, and when a human needs to step in. AI adoption was the opening chapter. AI governance is where the real leadership test begins.

The biggest AI risk right now is not that employees will refuse to use it. It is that leaders will mistake usage for progress. That is already starting to happen. Companies are tracking logins, tokens, prompts, adoption rates, and internal leaderboards because those things are easy to measure. But easy to measure does not mean meaningful. If you tell people the target is AI usage, people will use AI. If you tell them the target is token consumption, they will burn tokens. The dashboard improves while the actual work may stay the same or even get worse. This is the trap. AI creates the appearance of transformation long before it creates the reality of transformation. That is why the measurement question has to change. The question is not, “Are our people using AI?” The better question is, “Is AI helping us make better decisions, produce better work, serve customers faster, and maintain clearer accountability?” Because AI does not just change how work gets done. It changes how leaders know whether the work is any good. That is the part most organizations are not ready for. When AI enters the workflow, visibility changes. It becomes harder to tell who made the decision, who shaped the output, who checked the work, and where human judgment actually mattered. And when leaders lose visibility, the instinct is usually to add more tracking, more monitoring, and more surveillance. But surveillance is not the same as trust. The real work ahead is not just AI adoption. It is redesigning the trust, measurement, and accountability systems around AI so leaders can separate real progress from adoption theater. Better work is the goal. AI usage is only useful if it gets us there.

The companies most eager to replace managers with AI may be the ones that need better managers the most. That sounds backward, but it is the real tension in this conversation. If a manager's role is mostly chasing updates, routing information, summarizing meetings, creating dashboards, and reminding people what they already agreed to do, then yes, AI is going to make that version of management look expensive very quickly. But that does not prove management is obsolete. It proves we have allowed too many management roles to become buried under administrative work. The real test is what remains after AI removes the coordination layer. Someone still has to translate strategy into the daily decisions people make. Someone still has to coach junior employees before they develop bad judgment at scale. Someone still has to know why a customer exception exists, why one team does not trust another, why a process failed the last time it was attempted, and why the obvious answer in the dashboard might be the wrong answer in the organization. That work is harder to measure, which is exactly why it is easy to undervalue. This is where companies can make a very expensive mistake. Cutting bloated layers is not the same as hollowing out the middle. One removes drag. The other removes context. A lot of organizations probably do need fewer managers doing coordination work. But they may need more leaders who can create clarity, build trust, develop talent, and turn executive priorities into execution without losing the human reality of the business. AI should make management less administrative. It should not make leadership disappear. The smarter question is not how many managers AI can replace. It is what kind of management work should disappear so the people left in those roles can finally do the work that actually creates value. That is the difference between redesigning the organization and simply cutting into the muscle.

Diversity of thought propels us forward because it’s where we get solutions and new ideas. - Debbie Pickle, CHRO of @WilliamsUpdates

AI is not ending work. That headline is lazy. The more accurate headline is that AI is changing the shape of work faster than most companies are prepared for. That is a very different conversation. It is less dramatic, but far more useful for leaders, employees, and anyone trying to understand what the next few years will actually look like. Recent data from @BCG shows that 50% to 55% of U.S. jobs will be reshaped by AI over the next two to three years. Not eliminated. Reshaped. That word matters because it forces us to move beyond the simplistic idea that if AI can do part of a job, the entire job disappears. That is not how work usually changes. A fraud analyst does not disappear because AI can scan transactions. The role shifts from manually reviewing every transaction to overseeing the system, catching exceptions, interpreting patterns, and making judgment calls when something does not look right. A financial advisor does not disappear because AI can generate reports or recommend investment options. The role moves toward trust, relationships, context, storytelling, and understanding the family or business behind the numbers. An engineer does not disappear because AI can write code. The role moves toward architecture, quality standards, review, systems thinking, and deciding whether what was generated actually works in the real world. This is the part of the AI jobs conversation that keeps getting flattened. AI is not replacing "jobs" in one clean motion. It is replacing tasks inside jobs. Some parts of work will become automated. Some will become less valuable. Other parts will become more important than ever. That means the real leadership question is not, "How many people can we cut?" The better question is, "What should this job become now?" Because when AI takes over more of the repetitive execution layer, the human layer does not disappear. It changes. Judgment matters more. Context matters more. Trust matters more. Critical thinking matters more. The ability to ask better questions and make better decisions becomes more valuable, not less. The real danger is not that AI instantly ends work. The real danger is that companies adopt AI faster than they redesign work, reskill people, and help employees move into the parts of the role where humans create the most value. That is a leadership problem. not an AI problem.