Author Archives: Braden Kelley

About Braden Kelley

Braden Kelley is a Human-Centered Experience, Innovation and Transformation consultant at HCL Technologies, a popular innovation speaker, and creator of the FutureHacking™ and Human-Centered Change™ methodologies. He is the author of Stoking Your Innovation Bonfire from John Wiley & Sons and Charting Change (Second Edition) from Palgrave Macmillan. Braden is a US Navy veteran and earned his MBA from top-rated London Business School. Follow him on Linkedin, Twitter, Facebook, or Instagram.

The AI New Deal

Another AI Soft Landing Scenario Exploration — Government as the Employer of First Resort

LAST UPDATED: May 2, 2026 at 5:33 PM

The AI New Deal

by Braden Kelley and Art Inteligencia


The Structural Gap: Why Process Automation Requires a Civic Pivot

As we navigate the accelerating displacement of cognitive and administrative labor, the conversation around the “AI soft landing” has reached a critical juncture. In my previous explorations, I’ve examined how our future might mirror the extreme wealth gaps of Victorian England and how we might witness a Human Premium Renaissance, where uniquely human traits become our most valuable currency.

However, a significant structural link is missing. While AI is exceptionally efficient at automating process, it is incapable of automating presence. This creates a dangerous void: as middle-class administrative roles evaporate, we risk losing the economic liquidity and social cohesion that sustain our communities.

The prevailing solution often discussed is Universal Basic Income (UBI). But as I have argued, UBI is a fiscal mirage — a passive mechanism that fails to account for the human need for agency and the staggering mathematical reality of devalued tax bases. We don’t need a handout; we need a Civic Dividend. We must move from a scarcity mindset focused on protecting obsolete jobs to an abundance mindset that funds the essential work we have historically neglected. This is the foundation of the AI New Deal: positioning the government as the Employer of First Resort.

The Fiscal and Psychological Mirage of UBI

Universal Basic Income (UBI) is often presented as the “silver bullet” for the AI age, but a closer look at the mechanics reveals it to be a flawed tool for a human-centered transition. From a design perspective, UBI solves for survival but fails to solve for contribution.

First, we must confront the Math Problem. Funding a meaningful UBI requires a robust and consistent tax base. However, as AI drives down the cost of labor toward zero, the income tax pool — the traditional engine of government revenue — shrinks alongside it. Relying on passive redistribution in a devalued labor market is a race to the bottom that risks a permanent “subsistence trap” for the majority of the population.

Second, there is the Agency Problem. Innovation thrives on human agency — the ability to act, create, and impact one’s environment. UBI provides a safety net but offers no platform for growth. By decoupling income from contribution, we risk creating a “useless class” not because humans lack value, but because we have failed to design systems that utilize their unique “Human Premium.”

Finally, we must consider the Inflation Trap. Without a mechanism to ensure the circulation of capital through local, human-to-human services, stagnant UBI payments are easily consumed by the rising costs of private-sector essentials. To achieve a soft landing, we need a dynamic model that prioritizes the Velocity of Money over the mere distribution of funds.

The Core Concept: The Civic Dividend

To bridge the gap between AI-driven efficiency and human necessity, we must introduce the Civic Dividend. This is not a social safety net designed for the desperate; it is a strategic economic platform designed for a high-functioning society. At its heart is a fundamental shift in the social contract: the Government as the Employer of First Resort.

In this model, the government doesn’t just step in when the private market fails; it proactively identifies and funds the “work that matters” — the essential maintenance of our physical, social, and cultural existence. These are the roles that require empathy, physical dexterity, and contextual judgment — capabilities that remain firmly in the human domain.

The Civic Dividend operates on the principle that human labor is a public asset. By offering potential employment in public works, care networks, and community resilience projects, the state ensures that most citizens have the opportunity to contribute. This creates a “Social Floor” of activity and income that is immune to algorithmic displacement.

Crucially, this work is not “make-work” intended to keep hands busy. It is the vital labor required to repair our crumbling infrastructure, support our aging population, and revitalize our neighborhoods. Unlike a handout, these wages are earned, providing the dignity of contribution while fueling the Velocity of Money. As these wages are spent at local bakeries, barbershops, and bookstores, they sustain a secondary human-to-human service economy that AI simply cannot replicate.

Three Pillars of AI New Deal

The Three Pillars of the AI New Deal

The success of the AI New Deal rests on a strategic focus on the “Un-automatable.” We must direct our collective energy toward three specific domains where human presence, judgment, and physical interaction are not just preferred, but essential for a thriving society.

Pillar 1: Physical and Digital Infrastructure

We are currently witnessing a “Tragedy of the Commons” in our physical world. Our bridges, transit systems, and power grids require more than just algorithmic optimization; they require physical intervention. The AI New Deal would mobilize a modern workforce to focus on Community Resilience — retrofitting cities for climate adaptation, urban “rewilding” to restore local ecosystems, and maintaining the physical nodes that allow our digital world to function. This work creates a tangible, high-quality public environment that serves as a shared wealth for all citizens.

Pillar 2: The Social and Care Fabric

As we automate cognitive tasks, the “Human Premium” in care becomes our most valuable asset. We are facing a global loneliness epidemic and an aging demographic that requires empathy, companionship, and nuanced psychological support. By professionalizing and scaling roles in elder care, mental health mentorship, and early childhood development, we transform these from marginalized sectors into the prestigious cornerstones of our new economy. These are roles where the goal is not “efficiency” (doing more with less time), but “effectiveness” (the quality of the human connection).

Pillar 3: Community Vitality and Cultural Resilience

In an era of AI-generated noise, local culture and verified information are at risk of erosion. The AI New Deal funds the “Civic Architects” — the local journalists, community theater directors, and public artists who document and celebrate the unique identity of a place. This pillar ensures that while our tools become more global and algorithmic, our lived experiences remain local, vibrant, and distinctly human. We aren’t just building roads; we are building the social connective tissue that prevents the isolation often triggered by rapid technological shifts.

Economic Mechanics: The Velocity of Human Connection

Economic Mechanics: The Velocity of Human Connection

The fiscal engine of the AI New Deal is built on a fundamental economic principle: the Velocity of Money. In a hyper-automated private sector, capital tends to pool at the top, concentrating in the hands of those who own the compute and the algorithms. Without a mechanism to pull that capital back into the hands of the many, the local economy — the shops, services, and neighborhood hubs — withers.

The Civic Dividend solves this by creating a continuous loop of circulation. When the government pays a living wage to a community health worker or a local infrastructure specialist, that income doesn’t sit idle. It is immediately recycled into the Human-to-Human (H2H) service economy. This worker buys bread from a local baker, gets a haircut from a neighborhood barber, and visits a local gym. These secondary businesses thrive precisely because their customers have earned, discretionary income to spend.

To fund this transition, we must look toward Automation Royalties or “Compute Taxes.” Rather than taxing labor — which AI is making artificially cheap — we shift the tax burden to the high-margin output of automated systems. This creates a sustainable cycle: the efficiency of AI funds the resilience of the human community.

Furthermore, the AI New Deal acts as a natural Inflation Buffer. By investing in public housing maintenance, efficient public transit, and community-led food resilience, we lower the “floor” of the cost of living. This ensures that the wages provided by the Civic Dividend maintain high purchasing power, shielding the population from the volatility of a purely algorithmic private market.

Addressing the Critics: Efficiency vs. Resilience

Critics often argue that government-led employment is inherently “inefficient” compared to the lean, optimized nature of the private sector. From the perspective of human-centered innovation, this critique misses the mark because it uses the wrong metric for success. In an AI-dominated age, social resilience is a far more valuable outcome than marginal efficiency.

The private sector’s drive for efficiency is exactly what is displacing workers. If we allow that same logic to dictate our social response, we end up with a society that is “optimized” into instability. The AI New Deal isn’t about competing with AI on speed or cost; it is about providing the stability that the private market, by its very nature, cannot offer. We are designing for systemic health, not just quarterly throughput.

Another common concern is the fear of “make-work” or a lack of individual choice. However, the AI New Deal is designed as a platform, not a cage. By providing a guaranteed social floor of meaningful work, we actually increase career mobility. When a citizen’s basic survival and dignity are secured through the Civic Dividend, they are more — not less — likely to take risks, launch their own H2H small businesses, or pursue creative endeavors in the Human Premium Renaissance.

Finally, we must recognize that this is a choice of design. We can choose to view displaced workers as a “surplus” to be managed, or we can view them as a massive, untapped reserve of human talent ready to be deployed toward the public good. The “inefficiency” of paying a human to do what an algorithm could do is only an inefficiency if you ignore the catastrophic social cost of a disengaged, impoverished populace.

AI New Deal: Designing a New Social Contract

Conclusion: Designing a New Social Contract

We stand at a unique design crossroads in human history. The rapid advancement of artificial intelligence has presented us with a fundamental choice: do we design a future of automated irrelevance, where a vast majority of the population subsists on a dwindling digital handout, or do we design a future of civic abundance?

The AI New Deal is more than an economic policy; it is a reaffirmation of the value of human contribution. It recognizes that while technology can manage our systems, only humans can care for our communities, preserve our culture, and maintain our physical world. By moving toward a model of the Government as the Employer of First Resort, we ensure that the wealth generated by the AI revolution is directly reinvested into the human experience.

This “soft landing” requires us to be bold. We must stop asking how we will survive without the jobs of the past and start asking what kind of world we could build if we finally had the resources and the hands to do it. The Civic Dividend offers a path where technology does the “tasks” so that humans can finally do the “work” of being human—creating a society that is not just more efficient, but more resilient, more connected, and more purposeful.

The tools are in our hands, and the need is all around us. Now, we simply need the courage to sign a new contract with ourselves and build the future we actually want to live in.


Braden Kelley is a leading futurist and trusted voice in human-centered innovation and change. Stay tuned for next week’s next installment in this series on the AI Soft Landing.

Frequently Asked Questions

How is the AI New Deal different from Universal Basic Income (UBI)?

While UBI provides a passive payment regardless of activity, the AI New Deal is a “Civic Dividend” based on active contribution. It positions the government as the Employer of First Resort, paying living wages for essential public work — such as infrastructure maintenance and care services — rather than providing a handout that lacks a connection to social agency or the local service economy.

How can the government afford to become the ‘Employer of First Resort’?

The funding shifts from taxing human labor to taxing the high-margin output of automated systems, often referred to as “Automation Royalties” or “Compute Taxes.” By capturing the wealth generated by AI-driven efficiency, the state can reinvest that capital into the Human-to-Human (H2H) economy, ensuring currency continues to circulate through physical communities.

Does this mean the government is creating ‘make-work’ just to keep people busy?

No. The AI New Deal focuses on the “Un-automatable” — high-value needs that are currently neglected, such as climate resilience, elder care, and mental health support. These are not arbitrary tasks; they are the essential services required for a functional, healthy society that AI cannot perform because they require human empathy, physical presence, and contextual judgment.

EDITOR’S NOTE: This is a visualization of but one possible future. I will be publishing other possible futures as they crystallize in my mind (or as you suggest them for me to explore).

Image credits: Google Gemini

Content Authenticity Statement: The topic area, key elements to focus on, etc. were decisions made by Braden Kelley, with a little help from Google Gemini to clean up the article, add images and create infographics.

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Winning with Artificial Intelligence in 90 Days

Winning with Artificial Intelligence in 90 Days

Exclusive Interview with Charlene Li

The rapid evolution of artificial intelligence (AI) has shifted the technology from a futuristic curiosity to the primary engine of modern organizational growth. In an era defined by data-driven decision-making, the ability to effectively harness machine learning and predictive analytics is no longer just a competitive advantage; it is a fundamental requirement for long-term viability. However, the path to integration is rarely linear. Many organizations find themselves caught between the urgent need for transformation and the daunting reality of legacy infrastructure, talent shortages, and the cultural shifts required to move beyond small-scale pilots toward true enterprise-wide intelligence.

While the potential for increased efficiency and innovation is clear, the execution remains a significant hurdle.

The organizations that thrive in this new landscape are those that treat AI as a core strategic pillar rather than a plug-and-play software update. This requires a rethink of how human talent and machine intelligence coexist, ensuring that the technology enhances human capability rather than simply automating existing inefficiencies. Overcoming these challenges involves not just technical prowess, but a disciplined approach to change management and a clear vision for how intelligence will redefine the value the organization provides to its customers.

Today we will dive deep into what it takes to quickly achieve success with artificial intelligence with our special guest.

Creating a 90-Day Blueprint to Win with Artificial Intelligence

Charlene LiI recently had the opportunity to interview Charlene Li, a New York Times bestselling author, keynote speaker, and AI transformation strategist. Her latest book, Winning with AI: The 90-Day Blueprint for Success, co-authored with Dr. Katia Walsh, gives senior leaders a practical framework for moving from AI experimentation to measurable business value. Her prior books include The Disruption Mindset, Open Leadership, and Groundswell. Fast Company named her one of the most creative people in business, and she has worked with global organizations including 14 of the Dow Jones Industrial 30 companies. She is the founder of Altimeter Group (acquired by Prophet) and currently leads Quantum Networks Group.

Below is the text of my interview with Charlene and a preview of the kinds of insights you’ll find in Winning with AI: The 90-Day Blueprint for Success presented in a Q&A format:

1. What confusion is being created by speaking of “AI” as one thing when there are different kinds of AI, and how does this hold back AI adoption?

When people say “AI,” they’re usually thinking ChatGPT. But ChatGPT is generative AI — and that’s just one of three types of AI showing up in business today. There’s also predictive AI, which has been quietly running in your CRM, your fraud detection, and your streaming recommendations for years. And there’s agentic AI, which takes autonomous action toward a goal rather than waiting for a prompt.

The Oracle (predictive), the Creator (generative), and the Agent (agentic) — that’s how Katia and I describe them in Winning with AI. They do fundamentally different things, and they require fundamentally different things from you.

The conflation matters because it leads to bad decisions. Leaders see a generative AI demo, get excited, and ask their teams to “do something with AI” — when the actual business problem might be better solved with predictive AI (and probably already could’ve been three years ago). Or they hear “agentic AI” and assume their organization is ready to deploy autonomous agents when they haven’t even gotten generative AI into their workforce yet.

The winners aren’t choosing among types — they’re using all three strategically, in combination. A customer care transformation might use predictive AI to route inquiries, generative AI to draft responses, and agentic AI to handle routine cases autonomously. Once you can see the three distinctly, the question stops being “what can I do with AI?” and starts being “what can AI do for me?” That’s the question that actually unlocks value.

2. What are some of the key characteristics of AI inertia and some of the best ways to break free?

We call it pilot purgatory — and almost every organization we work with is stuck there. The signs are easy to spot: dozens of disconnected pilots, lots of conference attendance, lots of slide decks, no measurable financial impact. An MIT study found 95% of AI initiatives fail to scale. That’s not a technology failure. It’s a failure of leadership and culture.

The classic characteristics:

    • Use cases as a strategy. Many use cases equals procrastination. A long list of pilots is how organizations look busy without committing to anything.
    • Diffused accountability. When the CIO, CFO, and CMO all “share” responsibility for AI, no one owns the outcome.
    • Waiting for the foundation to be perfect. Clean data, the right platform, the perfect org structure — these become reasons to delay rather than constraints to solve through.
    • Confusing motion with progress. Running pilots feels like progress. It isn’t, unless those pilots are tied to your most important business problems.

To break free: pick your biggest strategic problems, figure out how AI solves them, invest heavily in those solutions, and move with urgency. Appoint one AI value owner who lives, breathes, and dreams AI outcomes. Kill pilots that aren’t on a path to scale. And replace “fail fast” with “learn fast” — nobody actually rewards failure, and the language of failure lets people walk away from things that should be pushed through.
Speed is the new moat. The companies that win aren’t the ones with the best technology. They’re the ones that adapt faster than their competitors.

3. There are still a lot of people out there not using AI (or not realizing that they are). What are some of the best ways for people to get started with AI?

Most people are already using AI — every spam filter, every Google Maps route, every recommendation on a streaming service is AI. So the real question is: how do you get started with the kind of AI that’s reshaping work right now, which is generative AI?

My advice is genuinely simple. Pick one of the major tools — Claude, ChatGPT, Gemini, Copilot — and start using it for one real task you do every week. Not a toy task. A real one. Drafting an email. Prepping for a meeting. Summarizing a long document. Brainstorming an approach to a problem you’re stuck on.

Two practical tips that make a big difference:

Write better prompts. A good prompt has a role (“Act as a marketing strategist”), instructions (what you want done), context (the background the AI needs), and an output format (memo, table, slide outline). Then refine through dialogue. Most people give AI two sentences and judge it on the result. Give it two paragraphs and you’ll be amazed.

Try the flipped interaction. Instead of asking AI for an answer, ask it to ask you questions until it has enough context to give a good answer. For example, at the end of a prompt, add this sentence: “Ask me any clarifying questions you may have.” It turns your prompt into a conversation.

I think of AI fluency as learning to eat with chopsticks: at first you’re concentrating on every motion, and eventually it’s just how you eat. You won’t get there by reading about it. You get there by using it. Every day. On real work.

4. Does AI safety really matter? It seems like all of the major AI players are just focused on speed and getting to AGI before China, am I wrong?

You’re not wrong about what the AI players are doing. But you’re probably not playing that game – more on that below. First, I’d push back on the framing that safety and speed are opposites.

Think of Formula 1. The drivers who win championships have absolute confidence in their brakes, their crash structures, their fire suppression systems. That’s why they can push so hard on speed. Safety is what makes speed possible. The companies moving fastest on AI adoption aren’t the ones cutting corners on responsibility — they’re the ones with the highest ethical standards, because trust eliminates friction. When your team knows where the guardrails are, when your customers trust your intentions, when your board has confidence in your approach, you can move at the speed AI demands.

The 2024 Edelman Trust Barometer found that 43% of people would reject AI in products and services if they don’t believe the innovation has been thoroughly scrutinized. That’s not a PR problem — it’s a revenue and competitive position problem.

On the AGI race specifically, the geopolitical framing oversimplifies what’s actually a much more textured conversation about how AI is deployed within companies, governments, and communities. Most leaders I work with aren’t worrying about AGI — they’re worrying about whether their AI customer service tool is treating customers fairly, whether their AI-driven hiring screen is introducing bias, and whether their data is being used in ways customers didn’t consent to. Those are the safety questions that matter for the next five years, regardless of what the frontier players are doing.

5. Where is the government being too hands off with AI and its impacts, and what conversations should governments and societies be having about AI and its impacts that they’re not?

I’ll be careful here because I’m not a policy person — I work with the leaders implementing AI inside organizations. But from that vantage point, a few things stand out.

The conversation we aren’t having enough is about workforce transition. Not “will AI take jobs” — we’ve been arguing about that abstractly for three years. The real question is what happens to the millions of people whose roles will substantially change in the next five years, and who’s responsible for helping them adapt. Right now, that’s mostly being left to individual employers, and the gap between what enlightened employers are doing and what the median employer is doing is enormous. That gap will become a societal problem long before regulators catch up.

The second underdiscussed conversation is about education. We’re training a generation of students with curricula designed for a pre-AI world. By the time we figure out what AI fluency looks like in K–12, the kids who needed it most will be in the workforce.

Third — and this is where I’d actually like to see governments lean in more — is data. Most AI regulation focuses on the models. The leverage is in the data: who owns it, how it can be used, what consent looks like in a world where data collected for one purpose can be repurposed for AI training that wasn’t imagined when it was collected.

That said, regulations always lag technology. Anchoring your responsible and ethical AI policy in your organization’s values rather than waiting for rules is the right move, regardless of what governments do.

6. What are the key pillars that form the basis of a strong AI foundation for those who seek to take full advantage of AI in their organization?

In Winning with AI, Katia and I lay out four building blocks. They develop together, not sequentially.

Mindset — the cultural ability to move at AI’s speed. Speed, focus, customer-centricity, experimentation, and learning from setbacks rather than treating them as evidence that the technology doesn’t work. Without the right mindset, you can have the best tools in the world, and they’ll sit unused.

Skillset — AI fluency across the workforce, not just in IT. Everyone needs to understand what AI can and can’t do, how to use it responsibly, and how to apply it to their actual work.

Toolset — the technical foundation. We tell leaders to build with LEGO, not cathedrals. Modular, interchangeable components you can swap as the technology evolves, sitting on top of data that’s good enough to start with.

Decision-set — the governance and decision-making structures that let you move fast without breaking things. Who decides what, how quickly, with what oversight.

The mistake organizations make is treating these as a sequence — first we’ll fix the data, then we’ll train people, then we’ll deploy. That sequence will take you a decade. The right approach is to build the blocks while delivering value, using each AI application to strengthen multiple blocks at once.

And one piece that wraps all four: leadership. Without active, visible commitment from the top, the four building blocks don’t compound. With it, they accelerate.

7. Of all the outcomes that the different types of AI can achieve, which activities create the most value for organizations?

Winning with AIWe frame the value AI creates in three areas: engagement, efficiencies, and reinvention.

Engagement is about deepening relationships with customers and employees through personalization, prediction, and proactive service. Anticipating what someone needs before they articulate it.

Efficiencies are about doing what you already do, faster and cheaper. This is where most organizations start — and where most get stuck. Efficiency gains are real, but they’re easy for competitors to replicate, which means they don’t create lasting advantage.

Reinvention is the most transformational and the most uncomfortable. It’s not asking “how can we do what we do faster?” — it’s asking “what becomes possible now that the old constraints are gone?” New business models. New revenue streams. New markets that were never economical before.

The trap is thinking efficiency is AI’s value. We call it the efficiency trap. Companies that limit themselves to efficiency are using a strategic weapon as a cost-cutting tool. The real competitive advantage comes from engagement and reinvention.

A great example: Coursera. Translation used to cost about $10,000 per course, which made global expansion economically impossible at the scale of their 5,000+ course catalog. Generative AI eliminated that constraint overnight. CEO Jeff Maggioncalda saw it immediately and launched Project Genesis by the end of 2022. That’s reinvention — AI removing a constraint that defined the business model.

If I had to pick one activity that creates the most value, it would be: using AI to remove a constraint that has shaped your industry’s economics for so long that nobody questions it anymore.

8. There was a lot of talk for a while about becoming an AI-first organization. Is this something that companies should be trying to do?

No. Be AI-ready instead.

“AI-first” is a technology company’s framing. It puts the technology in the driver’s seat, which sounds visionary but in practice produces dozens of disconnected pilots with no strategic impact. You end up chasing AI because it’s shiny rather than because it solves a real problem.

“AI-ready” is a business leader’s framing. It puts strategy in the driver’s seat. You’re building the culture, the skills, the decision systems, and the technical foundation that let AI create real value against the strategic priorities you already have.

Said simply: AI-first is a technology mindset. AI-ready is a business mindset.

You don’t actually need an AI strategy. You need a business strategy that uses AI. Anyone selling you on an AI strategy is selling you the wrong thing.

9. What should people be doing as individuals to maintain their value to their organizations and to grow their careers?

Three things, in order.

One: develop genuine AI fluency. Not “I’ve used ChatGPT a few times” fluency. Real fluency — the kind where AI is woven into how you think, prepare, decide, and communicate. The people and organizations who get to AI fluence in 2026 will pull dramatically ahead of those who don’t, and the gap will be very hard to close once it opens.

Two: deepen what’s uniquely human. AI can amplify cognition at speeds and scales no individual can match. What it can’t do is exercise empathy, self-reflection, intuition, judgment, and wisdom. These five traits — the foundation of what Katia and I call “superhumans” in the book — become more valuable, not less, as AI handles more of the cognitive work. The leaders who pair AI’s reach with these distinctly human capacities are the ones creating the most value.

Three: build a lifelong learning practice. The shelf life of any specific skill is shrinking. The skill that doesn’t depreciate is the ability to learn — quickly, repeatedly, with intellectual humility. Normalize not knowing. Embed reflection into how you work. Treat curiosity as a professional asset, not a side hobby.

If you do those three things, you’ll be more valuable in the future than you are today, regardless of what happens to your specific role.

10. What have organizations gotten wrong about rolling out AI and what can the early adopters do to recover from botched initial rollouts?

The biggest things organizations get wrong:

  • Treating AI as a technology project. It’s a business initiative for value creation that happens to use technology. When IT owns it, it stays small.
  • Use cases instead of strategy. A laundry list of pilots is procrastination dressed up as progress.
  • Diffused accountability. Without a single AI value owner, the work fragments.
  • Skipping the people work. Throwing tools at employees without addressing the fear underneath. Until fear is replaced by trust, no amount of training will change behavior.

If you’ve already botched the rollout, here’s the recovery path:

Stop and audit. What’s actually scaling, what’s not, what’s draining resources without producing value? Be honest. Sunset the dead ends.

Appoint one accountable AI leader. If no single person is accountable for AI value creation across the enterprise, fix that this quarter. Not part-time, not committee-led — one person whose performance is measured on the value that AI creates.

Pick one strategically meaningful problem and go after it. Not the easiest problem. The one whose solution would matter most to the business.

Learn from Ally Bank. When generative AI emerged, Ally’s CIO Sathish Muthukrishnan deliberately chose the most resistant audience — customer service agents — and a low-stakes problem: summarizing customer calls. The result was so valuable that the agents who’d been most skeptical became the loudest advocates: “Don’t take this away from me.” Targeting the skeptics with a real win is one of the most powerful change strategies we’ve seen.

A botched rollout isn’t a death sentence. It’s actually a useful clearing of the underbrush — assuming you learn from it.

11. Several studies have come out recently about the negative effects of AI on human cognition. Any tips for how to best use AI without degrading your brain?

This is a real concern and worth taking seriously. The risk isn’t AI itself — it’s lazy AI use. Using AI to skip thinking rather than to enhance it.

A few habits I’ve found useful:

Think first, then prompt. Before going to AI for an answer, write down what you think. Coursera’s Jeff Maggioncalda calls this cognitive bootstrapping — write your perspective on a decision, then ask AI to challenge it: “What are the strengths and weaknesses of this view? What are my blind spots? What would you recommend I improve?” AI sharpens your thinking instead of replacing it.

Treat AI outputs as drafts, not deliverables. Read critically. Push back. Ask why. Verify facts. The moment you stop questioning AI’s outputs is the moment your thinking starts to atrophy.

Protect deep work. Schedule time for thinking that doesn’t involve AI at all. Reading, writing, reflecting, walking — the unstructured time where your brain consolidates what it knows. AI can compress research, but it can’t compress wisdom. That still has to come from lived experience, integrated over time.

Notice the difference between using AI to accelerate something you understand and using AI to substitute for understanding. Acceleration is healthy. Substitution erodes you.

The promise of AI isn’t to do our thinking for us. It’s to help us think better. The discipline is staying on the right side of that line.

12. Any question you wish I had asked but didn’t?

Yes — I’d love a question about the human possibility on the other side of this.

Most AI conversation is about risk, displacement, and disruption. Those are real. But the conversation Katia and I get most excited about is what becomes possible when AI handles the cognitive work that has been depleting people for decades — the synthesis, the routing, the routine analysis — and frees up human capacity for what only humans can do.

We call those people “superhumans” — not because they’re enhanced by technology in some sci-fi sense, but because they finally have the room to be more deeply human. To exercise empathy, self-reflection, intuition, judgment, and wisdom at a level that’s been crowded out by cognitive overload.

The first companies to deliberately develop and organization filled with superhumans won’t just have a competitive advantage. They’ll be creating an entirely new form of value — one we haven’t fully named yet. That’s the future I want leaders thinking about. Not “how do I survive AI?” but “what becomes possible for my people on the other side of this?”

Dream it. Then build it.

Conclusion

Thank you for the great conversation Charlene!

I hope everyone has enjoyed this peek into the mind of one of the women behind the insightful new title Winning with AI: The 90-Day Blueprint for Success!

Image credits: Charlene Li, Pexels

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Designing Work for Humans and AI Agents to Do Together

LAST UPDATED: April 29, 2026 at 6:28 PM

Designing Work for Humans and AI Agents to Do Together

by Braden Kelley and Art Inteligencia


The Work Design Gap

We are not struggling to build artificial intelligence. We are struggling to design work for it.

Across industries, organizations are layering AI onto workflows that were never meant for collaboration. The result is predictable: inefficiency, mistrust, and unrealized value.

The real divide is not human versus AI. It is between work that is intentionally designed for collaboration and work that is not.

Why Traditional Tools Fail Us

Most of our management tools were built for a different era.

  • Process maps assume predictability
  • Org charts assume static roles
  • RACI models assume clear ownership

But human and AI collaboration is dynamic, contextual, and continuously learning. These tools help us optimize yesterday’s work, not design tomorrow’s.

What we need is a new visual language for collaboration.

Introducing the Human–AI Collaboration Canvas

The infographic below is not just a diagram. It is a thinking tool.

Its purpose is to make invisible interactions visible, clarify roles without over-constraining them, and embed judgment, trust, and learning into how work gets done.

This is a shift from process design to system design for collaboration.

Designing Work for Humans and AI Infographic

The Three-Lane Model: A More Honest Representation of Work

The canvas is built around three interconnected lanes:

The Human Lane

Where judgment, empathy, ethics, and accountability live. Humans frame the problem, not just solve it.

The AI Agent Lane

Where scale, speed, pattern recognition, and automation operate. AI expands what is possible.

The “Together” Lane

This is where value is actually created. Co-creation, co-decision, and co-learning happen here.

If you are not explicitly designing the middle lane, you are leaving value on the table.

The Work Journey: Sense → Decide → Act → Learn

Instead of rigid workflows, the canvas maps work as an adaptive cycle:

  • Sense: Understand context and gather signals
  • Decide: Blend human reasoning with AI recommendations
  • Act: Execute with scale and oversight
  • Learn: Reflect, adapt, and improve

Learning is not the end of the process. It feeds everything.

Collaboration Nodes: Where the Magic (or Failure) Happens

At key points in the journey are collaboration nodes—the moments where humans and AI interact.

Each node forces three critical questions:

  • Who leads?
  • What is the role of the other?
  • What is at stake?

Most AI failures are not technical failures. They are interaction design failures.

Making Judgment Visible

One of the biggest risks in AI adoption is invisible decision-making.

The canvas highlights:

  • Where human judgment is required
  • Where AI recommendations are sufficient
  • Where escalation is necessary

Automation without explicit judgment design is just risk at scale.

Designing for Trust, Not Just Performance

Capability alone is not enough. Systems must be trusted to be used effectively.

This requires:

  • Transparency
  • Explainability
  • Auditability

The real question is not “Can the AI do this?” but “Will humans trust and use this appropriately?”

Learning Loops: The System That Gets Smarter

The canvas includes two reinforcing learning loops:

  • AI Learning Loop: Data → Model → Output → Feedback → Improvement
  • Human Learning Loop: Experience → Reflection → Insight → Better decisions

The real competitive advantage is not AI itself. It is how quickly your combined system learns.

Risk, Ethics, and Failure by Design

No system is perfect. The best systems are designed with failure in mind.

The canvas highlights:

  • Bias and fairness
  • Privacy and security
  • Safety and compliance

It also asks essential questions:

  • What happens if the AI is wrong?
  • What happens if the human is wrong?
  • How do we recover?

Resilience comes from designing for breakdowns, not ignoring them.

Human-AI Agent Work Collaboration Canvas

How to Use This Canvas

This is a practical tool, not a theoretical one.

  • Use it in workshops to map collaboration
  • Audit existing workflows
  • Design new human–AI systems from scratch

A simple place to start:

  1. Map one critical workflow
  2. Identify collaboration nodes
  3. Redesign the “together” lane first

Designing for a More Human Future

AI does not reduce the need for humans. It raises the bar for how we design work.

The goal is not efficiency alone. The goal is better decisions, better experiences, and better outcomes.

The organizations that win will not be the ones with the most AI. They will be the ones who best design how humans and AI work together.

EDITOR’S NOTE: You should read this article too to learn more about atomizing work for man and machine to do together.

Content Authenticity Statement: The topic area, key elements to focus on, etc. were decisions made by Braden Kelley, with a little help from ChatGPT and Google Gemini to clean up the article, add images and create infographics.

Image credits: Google Gemini, ChatGPT

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Go Beyond SLAs and Measure Human Success with the New XLM Matrix (free download)

LAST UPDATED: April 29, 2026 at 12:03 PM

Go Beyond SLAs and Measure Human Success with the XLM Matrix

by Braden Kelley


The Crisis of the “Efficient but Empty” Experience

In our current landscape of rapid digital transformation, we have achieved unprecedented levels of speed and automation. Organizations have mastered the “how” of delivery, yet many find themselves facing a growing paradox: processes are becoming more efficient while human satisfaction is simultaneously declining. We are successfully building faster systems that often leave the user feeling more like a cog in a machine than a valued participant.

The root of this issue lies in our reliance on traditional Service Level Agreements (SLAs). For decades, SLAs have served as the gold standard for operational success, measuring technical markers like system uptime, response times, and throughput. While these metrics are essential for maintaining infrastructure, they are fundamentally “cold” metrics. They can tell you that a system is functioning, but they cannot tell you if the person using that system is thriving, frustrated, or merely exhausted by the interaction.

To innovate effectively in a human-centered future, we must look beyond technical availability and begin measuring the actual quality of the human encounter. We need a shift in perspective—moving from monitoring system performance to measuring human success. This evolution requires a new framework: Experience Level Measures (XLMs). By focusing on how an innovation impacts the user’s cognitive load, sense of agency, and emotional resonance, we can move past “efficient but empty” outputs and toward solutions that deliver genuine value.

Introducing the XLM Matrix

To bridge the gap between technical output and human success, we developed the XLM (Experience Level Measure) Matrix. This visual framework is designed to help innovation teams move beyond abstract empathy and toward concrete, measurable experience improvements. By visualizing the relationship between friction, measurement, and action, teams can align their efforts with the outcomes that actually move the needle for their users.

The matrix is structured as a series of concentric rings, requiring teams to work from the “inside out” to ensure every innovation is rooted in a real-world human need:

  • The Inner Circle (The Friction Point): This is the starting line. Here, teams identify the specific “ugh” moment—the point in the journey where the user currently feels confused, slowed down, or disempowered.
  • The Middle Ring (The XLM): This layer transforms qualitative frustration into a quantitative metric. It asks: “How do we measure the absence of that friction?” An XLM isn’t about system uptime; it’s about the user’s success rate in reaching their goal without cognitive fatigue.
  • The Outer Ring (The Innovation Lever): Once the friction is identified and the metric is set, the outer ring focuses on the solution. It identifies the specific change in the product, service, or workflow that will directly influence the XLM and eliminate the friction point.

By using this “Target Logic,” teams ensure that they aren’t just innovating for the sake of novelty, but are strategically pulling levers that have a measurable impact on the human experience.

The XLM (Experience Level Measure) Matrix

The Four Pillars of Human-Centered Innovation

To provide a comprehensive view of the user experience, the XLM Matrix is divided into four critical quadrants. Each quadrant represents a fundamental pillar of how humans interact with technology and services. By examining an innovation through these four lenses, teams can uncover hidden friction points and prioritize improvements that resonate most deeply with their audience.

1. Cognitive Load

“Does this make the user’s life simpler or more complex?”

In an age of information abundance, mental energy is a finite resource. This pillar focuses on the mental effort required to complete a task. Innovation here is about reducing noise, simplifying navigation, and ensuring that the “cost of thinking” is kept to an absolute minimum.

2. Time-to-Value

“How quickly does the user reach their ‘Aha!’ moment?”

Success is often determined by the distance between a user’s first interaction and their first realization of value. This quadrant measures the speed of relevance. Effective innovation in this space removes barriers to entry and streamlines the path to a meaningful outcome.

3. Agency

“Does the user feel in control, or like a cog in the process?”

As systems become more autonomous, maintaining human agency is vital. This pillar explores whether a tool empowers the user or forces them into a rigid, predetermined path. High-agency innovations provide the user with the autonomy to make meaningful choices and direct the outcome.

4. Emotional Resonance

“Does the interaction build trust or cause frustration?”

Every interaction leaves an emotional footprint. This quadrant assesses the “vibe” of the experience. It looks beyond function to ask if the solution feels reliable, empathetic, and aligned with the user’s values, transforming a transactional moment into a relational one.

How to Use the Matrix with Your Team

The XLM Matrix is most effective when used as a collaborative workshop tool. By gathering cross-functional perspectives—from product and design to engineering and customer success—you can ensure a 360-degree view of the human experience. Follow these three steps to run your first experience audit:

Step 1: The Empathy Audit

Focus on the Inner Circle. Select one of the four quadrants and ask the team to identify the most persistent “ugh” moment currently facing the user. Be specific. Instead of saying “the checkout process is slow,” identify the exact friction point, such as “the user feels overwhelmed by the number of form fields.”

Step 2: Defining the Metric

Move to the Middle Ring. Once the friction point is clear, brainstorm how you would measure its absence. This is your Experience Level Measure (XLM). If the friction is cognitive overload from form fields, your XLM might be “reduction in time spent on the checkout page” or “a 20% increase in completion rate without support intervention.”

Step 3: Pulling the Innovation Lever

Reach the Outer Ring. Now, identify the specific technical or design change that will move that metric. This is your “Innovation Lever.” It could be an AI-driven auto-fill feature, a progress bar to improve the sense of agency, or a “save for later” option to reduce immediate emotional pressure.

Repeat this process for each quadrant to build a robust, human-centered innovation roadmap that prioritizes meaningful outcomes over simple feature checklists.

Conclusion: Creating a Human-Centered Future

The transition from measuring system performance to measuring human success is not just a technical shift; it is a cultural one. As we move deeper into an era of agentic AI and rapid digital acceleration, the organizations that thrive will be those that prioritize the human experience as their primary north star. Innovation is no longer defined solely by what we can build, but by how effectively we enable people to feel, act, and succeed.

The XLM Matrix provides a structured, repeatable path to this future. By moving from the friction of the “ugh” moment to the strategic clarity of the innovation lever, your team can ensure that every project delivers meaningful, human-centered value. It is time to stop guessing how our users feel and start building for their success.

Start Your Experience Transformation Today

Ready to move beyond SLAs? Download the high-resolution, 11″x17″ (works as A3 too) printable version of The XLM Matrix and begin identifying the measures that truly matter for your innovation team. You can also use it virtually by uploading it and locking it down as a background in Miro, Mural, LucidSpark, Figjam or the FREE Microsoft Whiteboard or Google Jamboard.


Download the Free XLM Matrix Canvas

Frequently Asked Questions

What is the difference between an SLA and an XLM?

A Service Level Agreement (SLA) measures technical system performance, such as uptime or response speed. An Experience Level Measure (XLM) focuses on human outcomes, measuring how effectively an innovation reduces cognitive load, increases user agency, or builds emotional resonance.

How does the XLM Matrix help innovation teams?

The XLM Matrix provides a visual framework to move from identifying user friction (“ugh” moments) to defining specific metrics and identifying the technical or design “levers” required to improve the human experience.

Can the XLM Matrix be used for internal digital transformation?

Yes. The matrix is highly effective for internal projects. By measuring the cognitive load and time-to-value for employees using new internal tools, organizations can ensure their digital transformation efforts actually increase productivity rather than just adding complexity.

Image credits: Braden Kelley, Google Gemini

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Is Your Innovation Fire Fading?

LAST UPDATED: April 28, 2026 at 3:46 PM

Is Your Innovation Fire Fading?

by Braden Kelley

A common misconception in business is that innovation fails simply because of a shortage of good ideas. In reality, the “fire” is more often extinguished by the structural context in which those ideas are born.

Organizations often focus their energy on brainstorming sessions and ideation workshops, assuming that more ideas will lead to more success. However, volume and diversity are merely preconditions; they cannot overcome a rigid organizational environment.

The Reality: Strategic and Cultural Fire Extinguishers

Innovation is frequently hindered by structural barriers, poor information flow, and misaligned psychology. Without the right enabling conditions, even the most brilliant concepts will stall.

Key Themes for Transformation

  • Strategy vs. Experimentation: Innovation without strategy is merely experimentation, while strategy without innovation results in nothing more than incremental improvement.
  • Human-Centered Insight: Sustainable innovations are almost always rooted in deep, human-centered insights regarding customer needs and frustrations.
  • Structural Alignment: True innovation capability requires organizational structures and digital infrastructure that support rapid experimentation and collaboration across teams.

The Ten Dimensions of Innovation Health

To build a sustainable innovation capability, an organization must evaluate its performance across ten core diagnostic areas. These dimensions help identify whether your innovation “fire” has a strong foundation or is being restricted by hidden barriers.

  1. Vision: A compelling, shared starting point that inspires people to challenge the status quo.
  2. Strategy: Integrating innovation efforts into the broader strategic framework to avoid random experimentation.
  3. Goals: Using specific, measurable targets and leading indicators to focus creative energy.
  4. Insights: Generating deep, human-centered data about customer frustrations and unmet desires.
  5. Idea Generation: Creating conditions for a high volume and wide diversity of ideas across the organization.
  6. Idea Evaluation: Ensuring fair, rigorous, and innovation-friendly processes that guard against incremental bias.
  7. Idea Development: Providing dedicated pathways, resources, and rapid prototyping to turn concepts into reality.
  8. Organizational Psychology: Addressing the mindsets, autonomy, and fear of failure that dictate innovation behavior.
  9. Information and Structural: Optimizing organizational structures and information flows to remove “innovation drag.”
  10. Sustainability: Building innovation as a lasting, self-reinforcing capability rather than a one-time initiative.

Download Your FREE Innovation Health Checks

The Innovation Health Checks are designed to move beyond subjective feelings and toward evidence-based diagnostics. To get the most value from these tools, leadership teams should follow a disciplined approach to the audit process.

Evidence Over Aspiration

When rating your organization, it is critical to be honest and specific. You must base your scores on evidence and observable behavior rather than your intentions or what you believe should be happening. Scoring statements honestly ensures that you are diagnosing the actual state of your innovation “fire.”

Continuous Improvement and Maturity

Innovation health is not a one-time measurement. By repeating these health checks every 6–12 months, you can track your progress over time and identify new barriers that may emerge as your organization’s innovation capability matures.

From Diagnosis to Roadmap

While the Innovation Health Checks provide the diagnostic tools to identify where your fire is fading, they are designed to work in tandem with deeper strategic frameworks. These checks reveal the “what” and the “where,” serving as the essential starting point for any leader committed to building a sustainable culture of innovation and purpose.

Take the Next Step

Ready to clear the barriers identified in your scores?

Stoking Your Innovation Bonfire provides the comprehensive roadmap and deep-dive strategies required to transform these insights into a lasting competitive advantage.


Free download

10 Innovation Health Checks

Audit your leadership team’s innovation capacity with the full PDF toolkit drawn from Braden Kelley’s framework.

⇓  Download PDF


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Stoking Your Innovation Bonfire

Move your organization from incremental improvement to transformational growth with Braden Kelley’s complete roadmap.

Read the book →

Image credits: ChatGPT

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The Human-Premium Renaissance

Another AI Soft Landing Scenario Exploration

LAST UPDATED: April 24, 2026 at 6:52 PM

The Human-Premium Renaissance

by Braden Kelley and Art Inteligencia


I. Beyond the “Empty Desk”

The prevailing narrative surrounding embodied AI and robotics is often one of inevitable displacement. As automation reaches a scale where it can replicate human labor at a fraction of the cost, the fear of an “empty desk” economy—one where human participation is optional—has become a central anxiety of the 2020s.

Defining the “Soft Landing”

A soft landing represents a societal transition that sidesteps the extremes of total economic collapse or violent revolution. It is the search for a new equilibrium where human value is not just preserved, but reimagined within a landscape of infinite machine productivity.

The Core Thesis: Value in the Biological

While many forecast a return to a “Victorian” class structure defined by service and servitude, this scenario proposes a more viable, long-term alternative. The Human-Premium Renaissance suggests that:

  • Commoditized Perfection: As AI makes perfect execution free, the market value of “flawless” drops to zero.
  • The Premium of Imperfection: Economic value will migrate to the “biological origin”—the hand-carved, the human-thought, and the uniquely flawed.
  • Narrative over Utility: We are moving toward an era where we no longer pay for what a product does, but for the human story behind its creation.

In this scenario, human labor isn’t a cost to be minimized; it is the unique identifier that prevents a product from becoming a valueless commodity.

II. The Framework: Utility Floor vs. Premium Ceiling

The viability of this soft landing rests on a bifurcation of the economy into two distinct layers. This structure allows for mass survival through automation while preserving a high-value labor market for human endeavor.

The Utility Floor: The World of “Perfect Commodities”

In this layer, AI and embodied robotics handle the fundamental requirements of modern life. Logistics, basic food production, energy management, and routine diagnostics are optimized to a point where the marginal cost of production approaches zero.

  • Standardization: Everything produced at the floor is “perfect” but uniform.
  • Abundance: Scarcity is eliminated for basic needs, preventing the societal collapse often predicted in mass-unemployment scenarios.
  • Devaluation: Because these goods are generated without human effort, they lack the “prestige” required to command a premium price.

The Premium Ceiling: The Human Narrative

Above the utility floor sits the “Premium Ceiling.” This is a market tier where consumers—who now have their basic needs met by the floor—spend their discretionary wealth on items and services that possess a biological provenance.

  • Authenticity as the New Scarcity: In a world of infinite digital and robotic replicas, the one thing that cannot be mass-produced is the unique perspective and history of a specific human being.
  • The Human-Centric Premium: We see the rise of “Slow Innovation,” where the value is found in the time, struggle, and intent behind the creation rather than the speed of its delivery.

The Strategic Shift: From Utility to Origin

This transition represents a fundamental shift in how we define economic value. We move away from asking “What can this do for me?” (Utility) and toward asking “Who made this, and what is their story?” (Origin).

While the Utility Floor keeps society running, the Premium Ceiling gives society a reason to keep trading, creating, and connecting.

III. Economic Viability: Why This Model Works

The skeptic’s immediate response to a “human-premium” model is usually grounded in the cold logic of the bottom line: If a machine can do it cheaper, why would anyone pay for a human? The answer lies in the shifting definition of value in a post-scarcity utility environment.

The Scarcity of Authenticity

In an era of infinite AI-generated content and robotic manufacturing, “perfection” is no longer a differentiator—it is a baseline requirement. When every digital image is flawlessly composed and every physical object is mathematically precise, human attention, history, and original thought become the only truly non-fungible resources.

  • Effort Heuristic: Humans are psychologically predisposed to value objects and services more highly when they perceive a high degree of effort or “struggle” behind them.
  • Biological Connection: We are social animals who seek the “ghost in the machine.” We don’t just want a solution; we want to know another consciousness intended for us to have it.

The Veblen Good Effect

As basic needs are met by the Utility Floor, discretionary spending migrates toward status symbols. In this scenario, human labor becomes a Veblen Good—a luxury item where demand increases as the price (and the perceived exclusivity of the human touch) rises.

“The hand-carved chair with its slight, organic imperfections becomes a status symbol of the elite, while the flawless, 3D-printed alternative becomes the hallmark of the masses.”

Democratization of Expertise and the “Company of One”

Unlike previous industrial shifts that required massive capital for factories, AI is a capital of the mind. This technology allows individual artisans and “augmented experts” to compete with monolithic corporations.

  • Skill Augmentation: AI doesn’t just replace the expert; it allows the “middle-skill” human to perform at an elite level, spreading the ability to generate high-value, personalized work across a much larger population.
  • Niche Viability: Lowering the cost of production allows for the “Long Tail” of human services to thrive. Small-scale, highly specialized human businesses become economically sustainable because their overhead is managed by AI.

By moving the human worker from a “cost to be minimized” to a “feature to be highlighted,” companies can maintain high margins and justify the continued circulation of capital back into human hands.

Preventing the Consolidation - Breaking the Monopoly on Production

IV. Preventing Wealth Consolidation: Breaking the Monopoly on Production

One of the greatest risks of an AI-driven economy is the “Winner-Take-All” effect, where the owners of the most powerful algorithms capture the entirety of global productivity. However, the Human-Premium Renaissance offers structural defenses against this consolidation by shifting the power of production from centralized capital to distributed intelligence.

The “Company of One” Era

In previous industrial revolutions, scale was a prerequisite for success. You needed a factory to compete with a factory. Today, AI acts as a force multiplier for the individual. When the cost of sophisticated research, design, and logistics drops to near zero, the competitive advantage of a massive corporation—its ability to manage complexity—evaporates.

  • Democratized Innovation: Individual creators can now orchestrate global supply chains and reach global audiences with the same efficiency as a Fortune 500 company.
  • Agility over Scale: Smaller, human-led entities can pivot and personalize their offerings faster than a shareholder-beholden giant, allowing wealth to remain with the creator.

The Circular Human Economy

As global logistics become a commodity (the Utility Floor), we anticipate a resurgence in localized, high-trust commerce. AI-assisted cooperatives and local “Experience Stewards” can replace centralized “Gig Economy” platforms.

  • Localism: Trust is a human currency that does not scale well in an algorithm. By focusing on community-specific needs, human workers can create “walled gardens” of value that shareholders cannot easily penetrate.
  • Profit Retention: When the “platform” is a decentralized protocol rather than a Silicon Valley intermediary, more of the transaction value stays in the pockets of the local human service provider.

Narrative Ownership and Provenance

To prevent AI from simply harvesting and replicating human creativity for the benefit of shareholders, this scenario relies on Digital Provenance.

  • Certification of Origin: Using watermarking and blockchain-based verification, human-made products carry a “digital signature.” This allows creators to maintain the equity of their original work.
  • The Authenticity Tax: If a company uses AI to mimic a specific human’s style or narrative, the legal and social frameworks of the Renaissance model demand a “royalty of origin,” ensuring capital flows back to the human inspiration.

Wealth consolidation occurs when production is centralized. The Renaissance scenario is inherently decentralizing, as it prizes the one thing that cannot be mass-produced: the individual human perspective.

V. Comparing the “Soft Landings”: Victorian vs. Renaissance

To understand the trajectory of our economic future, we must distinguish between two types of “soft landings.” While both scenarios avoid immediate catastrophe, they offer fundamentally different versions of human dignity and wealth distribution.

Feature Victorian England Scenario Human-Premium Renaissance
Core Driver Inequality of Wealth and Power. Inequality of Authenticity and Scarcity.
The Human Role Tasks: Performing labor AI won’t do (low-cost servitude). Meaning: Performing labor AI can’t do (high-value narrative).
Economic Logic Humans as “Cheap Alternatives” to expensive robots. Humans as “Luxury Exceptions” to cheap, mass-produced AI.
Social Structure Centralized and Rigidly Hierarchical. Decentralized and Networked Communities.
Primary Value Obedience and Time. Trust and Shared Experience.
Role of AI The “Master’s Tool” for efficiency. The “Artisan’s Apprentice” for augmentation.

The Crucial Distinction

In the Victorian Scenario, the “servant class” is trapped by a lack of access to capital and a surplus of desperate labor. Success is measured by how well one can serve the elite.

In the Renaissance Scenario, the “artisan class” is empowered by AI to bypass traditional gatekeepers. Success is measured by how well one can connect with other humans through unique, un-automatable narratives. One is a world of servitude; the other is a world of stewardship.

While the Victorian model is a race to the bottom in cost, the Renaissance model is a race to the top in meaning.

Innovation Challenge - From Optimization to Orchestration

VI. The Innovation Challenge: From Optimization to Orchestration

For decades, the core driver of innovation has been Efficiency—doing things faster, cheaper, and with less friction. In the Human-Premium Renaissance, this paradigm reaches its logical conclusion: AI handles all optimization. When efficiency is “solved,” the new frontier of innovation becomes the Human Experience.

The Innovation of “Friction”

In a world of instant gratification provided by the Utility Floor, value is created by intentionally “slowing down” the experience. This is the art of Meaningful Friction.

  • Intentionality over Velocity: Future innovation won’t focus on how to get a product to a customer in ten minutes, but on how to make the ten minutes they spend with your brand the most memorable part of their day.
  • Biological Synchronization: Designing systems that align with human circadian rhythms, emotional cycles, and social needs rather than purely digital throughput.

The New Leadership Role: The Narrative Orchestrator

The role of the leader must shift. We are moving away from the “Optimization Officer” model toward the Narrative Orchestrator.

  • Curation as Strategy: Leaders will spend less time managing processes (AI will do this) and more time curating the talent, stories, and human connections that define the brand’s “Premium” status.
  • Stewardship of Trust: Because trust is a non-automatable resource, the primary job of leadership is to protect and grow the “Trust Equity” between the human staff and the customer base.

Redefining Innovation Maturity

In this scenario, a “mature” organization is not one with the most advanced tech stack, but one that has successfully integrated AI to the point of Invisibility.

Innovation maturity will be measured by an organization’s ability to use AI to automate the “Work” so it can empower its people to perform the “Art.”

This shift forces a total rethink of R&D. We are no longer just solving technical problems; we are solving for human belonging, status, and meaning in a post-labor world.

VII. Conclusion: Choosing Our Trajectory

The transition to an economy defined by embodied AI and mass automation does not have a predetermined destination. While the technical capabilities of generative systems and robotics are advancing at an exponential rate, the social and economic architecture we build around them remains a matter of human agency.

A Choice of Valuations

The “Victorian” and “Renaissance” scenarios represent two distinct paths for the future of work. One path values human time as a commodity—a low-cost alternative to a machine. The other values human time as a canvas—the unique source of narrative and meaning that an algorithm cannot replicate.

The Final Frontier of Competitive Advantage

As we move deeper into the 2030s, the most successful organizations will not be those that achieved the highest level of automation, but those that used that automation to solve the “Utility Floor” problem so they could focus entirely on the “Premium Ceiling.”

The ultimate goal of AI should not be to replace the worker, but to replace the “work”—the repetitive, the mundane, and the soul-crushing—thereby freeing the human to perform the “art” that only they can provide.

The soft landing is within reach, but it requires us to stop asking how we can compete with machines and start asking how we can better complement each other. The future isn’t defined by the artificial; it is defined by what becomes possible when the artificial is so ubiquitous that the human finally becomes the premium.

Frequently Asked Questions: The Human-Premium Renaissance

1. What is the difference between the “Utility Floor” and the “Premium Ceiling”?

The Utility Floor refers to the baseline economy where AI and robotics produce essential goods (food, logistics, basic software) at near-zero marginal cost, making them affordable commodities. The Premium Ceiling is the high-value market tier where consumers pay a significant markup for products and services with a “biological provenance”—meaning they are created, curated, or delivered by humans.

2. How does this scenario prevent massive wealth consolidation?

Unlike previous industrial shifts that required massive capital, AI acts as a “capital of the mind.” This allows for the rise of the Company of One, where individuals use AI to handle complex operations, allowing them to compete with large corporations. Furthermore, because “authenticity” cannot be mass-produced by a central algorithm, the value remains distributed among individual human creators and local communities.

3. Why is “human imperfection” considered an economic asset?

In a world where AI can generate “perfect” results instantly, perfection becomes a devalued commodity. Human “errors” or “uniqueness” serve as proof of biological origin—a signal of authenticity that AI cannot authentically replicate. This creates an Effort Heuristic, where consumers psychologically value the struggle and intent of a human creator over the sterile precision of a machine.

EDITOR’S NOTE: This is a visualization of but one possible future. I will be publishing other possible futures as they crystallize in my mind (or as you suggest them for me to explore).

Image credits: Google Gemini

Content Authenticity Statement: The topic area, key elements to focus on, etc. were decisions made by Braden Kelley, with a little help from Google Gemini to clean up the article, add images and create infographics.

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AI State of the Union

Image Generation Edition

LAST UPDATED: April 26, 2026 at 11:39 AM

AI State of the Union - Image Generation Edition

by Braden Kelley


Watching the evolution of AI over the past eighty years (83 actually) has been fascinating to watch (admittedly, I haven’t been alive long enough to watch all of it), but the evolution over the past 3 1/2 years following an extended AI winter has been nothing short of amazing. To anchor us and set context for what’s next, here is ChatGPT’s evolution over the current AI spring:

The Evolution of GPT Models

A quick reference for the major milestones in generative AI development:

Version Release Date Key Achievement
GPT-3 June 2020 The first massive 175-billion parameter model.
ChatGPT Nov 2022 Brought generative AI to the general public via a chat interface.
GPT-4 March 2023 Introduced advanced reasoning and multimodal (image) support.
GPT-5 August 2025 A “network of models” approach for complex problem-solving.
GPT-5.5 April 2026 Current state-of-the-art model for nuanced reasoning.

Earlier this week OpenAI released a new image model and people were wondering why, after killing of their video model Sora to focus their limited resources, would they introduce a new, potentially resource hungry image model that will burn more of their compute?

My uninformed user perspective is that perhaps OpenAI’s leaders saw what it could do and they just couldn’t justify depriving the public of it given their stated mission to “ensure artificial general intelligence (AGI) benefits all of humanity.”

Creativity and Innovation and Change Quote

I’ve created more than 1,200 quote posters over the past few years for people to use in their meetings, presentations, keynotes and workshops (download them for FREE at http://misterinnovation.com) using freely available images initially from sites like Pixabay, Unsplash, Pexels and Wikimedia Commons like the one above because the image generation capabilities of the AI models were so bad.

Anticipatory Leader Quote

Then about eight months ago when Google launched Nano Banana the AI image generation started to be good enough at capturing the essence of a quote to use an AI generated image instead of a photo (see the example above), before layering the quote in a translucent layer on top of it.

Cognitive Resilience Quote

But then in March 2026 I started using Gemini’s Nano Banana 2 to start creating hand drawn style images for the quote posters (like the one above) because of it’s ability to MUCH BETTER handle the inclusion of text into an image. You can see in this image, not only was it able to include the quote in the image, but it was able to add some other supplementary text (on its own) into the image AND an image of me, without me asking it to!

I started using this hand drawn style for many of the quote posters I’ve created over the past couple of months, doing a daily bake-off between Gemini, ChatGPT and Grok (which loses 99% of the time) and in March 2026 Gemini was winning most of the bake-offs until maybe April when it started to be about 50-50 between Gemini and ChatGPT.

BUT, with the release of OpenAI’s new image model earlier this week, ChatGPT has been winning every day and it is because it has been creating images like this one off a single, simple text prompt with the quote, author and requested style provided:

Remote-First Intentional Design Quote

Now remember, all I gave ChatGPT was the quote and the author and asked it to capture the essence of the quote in a hand-drawn style. IT decided to add all of these other informational, education, inspirational elements and my jaw literally dropped.

If I was an OpenAI executive and saw this result to my prompt, I too would have argued for the release of this image model given OpenAI’s mission. This ability is superhuman. I as a human would have stopped at finding an image that reinforces or enhances the meaning of the quote.

This image model turned the quote into a multi-dimensional learning tool that transmits far more insight and information in a single document than the already powerful single sentence did.

The quote is still an important distillation that is far easier to remember and thus to drive behavior change from, but the rest of the content that the OpenAI image model created of its own volition adds value for those who want to quickly double-click on the essence and learn more.

So, this is where we are with AI image generation now, this is the kind of power these tools now have. The only question is:

What are you going to do with them next?

Image credits: Google Gemini and http://misterinnovation.com (download all 1,200+ FREE)

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Which of the Nine Innovation Roles do you play? (A Quiz)

Which of the Nine Innovation Roles do you play? (A Quiz)

by Braden Kelley

Too often we treat people as commodities that are interchangeable and maintain the same characteristics and aptitudes. Of course, we know that people are not interchangeable, yet we continually pretend that they are anyway — to make life simpler for our reptile brain to comprehend.

I’m of the opinion that all people are creative, in their own way. That is not to say that all people are creative in the sense that every single person is good at creating lots of really great ideas, nor do they have to be. I believe instead that everyone has a dominant innovation role at which they excel, and that when properly identified and channeled, the organization stands to maximize its innovation capacity. I believe that all people excel at one of Nine Innovation Roles, and that when organizations put the right people in the right innovation roles, that your innovation speed and capacity will increase.

The Nine Innovation Roles as a concept were introduced in my bestselling book Stoking Your Innovation Bonfire and people have always asked me if I had a quiz people could take to see what their primary and secondary roles are and my answer has always been NO, until now, when thanks to Claude I’ve been able to create one for the world to enjoy. I think it turned out pretty well and I’ve embedded it here in this article and also create a Nine Innovation Roles Quiz sub-page for it live on in perpetuity.

I hope you enjoy it!

Discover Your
Innovation Role

Answer 20 questions to uncover where you add the most value in any innovation effort. Based on Braden Kelley’s Nine Innovation Roles framework.

Question 1 of 20 0%
Question 1

The Nine Innovation Roles

If you’re not familiar with the Nine Innovation Roles, they are:

Nine Innovation Roles Revolutionary

1. Revolutionary

The Revolutionary is the person who is always eager to change things, to shake them up, and to share his or her opinion. These people tend to have a lot of great ideas and are not shy about sharing them. They are likely to contribute 80 to 90 percent of your ideas in open scenarios.

Nine Innovation Roles Conscript

2. Conscript

The Conscript has a lot of great ideas but doesn’t willingly share them, either because such people don’t know anyone is looking for ideas, don’t know how to express their ideas, prefer to keep their head down and execute, or all three.

Nine Innovation Roles Connector

3. Connector

The Connector does just that. These people hear a Conscript say something interesting and put him together with a Revolutionary; The Connector listens to the Artist and knows exactly where to find the Troubleshooter that his idea needs.

Nine Innovation Roles Artist

4. Artist

The Artist doesn’t always come up with great ideas, but artists are really good at making them better.

.

Nine Innovation Roles Customer Champion

5. Customer Champion

The Customer Champion may live on the edge of the organization. Not only does he have constant contact with the customer, but he also understands their needs, is familiar with their actions and behaviors, and is as close as you can get to interviewing a real customer about a nascent idea.

Nine Innovation Roles Troubleshooter

6. Troubleshooter

Every great idea has at least one or two major roadblocks to overcome before the idea is ready to be judged or before its magic can be made. This is where the Troubleshooter comes in. Troubleshooters love tough problems and often have the deep knowledge or expertise to help solve them.

Nine Innovation Roles Judge

7. Judge

The Judge is really good at determining what can be made profitably and what will be successful in the marketplace.

.

Nine Innovation Roles Magic Maker

8. Magic Maker

The Magic Makers take an idea and make it real. These are the people who can picture how something is going to be made and line up the right resources to make it happen.

.

Nine Innovation Roles Evangelist

9. Evangelist

The Evangelists know how to educate people on what the idea is and help them understand it. Evangelists are great people to help build support for an idea internally, and also to help educate customers on its value.

So, which one(s) resonate most with you? Want to find out if you’re right? Take the quiz!

Free Stuff

If you go to the main Nine Innovation Roles page you’ll find all kinds of free downloads and sub pages, including:

  1. Nine Innovation Roles pages in Spanish, Portuguese, French and Swedish (and I’m always happy to give credit and link to anyone willing to translate them into other languages)
  2. Nine Innovation Roles card design to download for printing with adMagic (or your vendor)
  3. Nine Innovation Roles downloadable presentation
  4. Nine Innovation Roles team worksheet
  5. Nine Innovation Roles introductory video to use in workshops
  6. This Nine Innovation Roles Quiz!

Keep innovating!

Click here to access your Nine Innovation Roles freebies

Image Credits: Braden Kelley, Google Gemini

Why an AI Soft Landing Might Look Like Victorian England

LAST UPDATED: April 18, 2026 at 3:29 PM

Why an AI Soft Landing Might Look Like Victorian England

by Braden Kelley and Art Inteligencia


The Mirage of the Post-Scarcity Utopia

For decades, the prevailing narrative surrounding artificial intelligence has been one of a post-scarcity “Star Trek” future. The logic was simple: as machines took over the labor, the dividends of automation would be harvested by the state and redistributed via Universal Basic Income (UBI), freeing humanity to pursue art, philosophy, and leisure.

The AI Promise vs. The Fiscal Reality

However, this utopian vision ignores the gravity of The Great American Contraction. As we approach 2026 and beyond, the friction between exponential technological growth and a $37 trillion+ national debt (with a $2 trillion annual budget deficit) creates a structural barrier to redistribution. When the tax base of human labor erodes, the math for a livable UBI simply fails to compute.

The Victorian Hypothesis

If UBI is a mathematical and political impossibility fueled by corporate and human greed, we must look toward an alternative “soft landing.” This hypothesis suggests a vertical restructuring of society. As AI drives the cost of production and the demand for goods into a deflationary spiral, the purchasing power of the remaining “employed elite” will skyrocket.

The result isn’t a horizontal distribution of wealth, but a return to a Neo-Victorian social hierarchy. In this reality, the new digital gentry will use their outsized wealth to employ a massive “servant class” to maintain stately homes and personal lives, creating a world where status is defined by the human labor one can afford to command.

Neo-Victorian Hypothesis Infographic

The Great American Contraction: Why UBI is a Non-Starter

The conversation around the transition to an AI-driven economy often treats Universal Basic Income as an inevitability — a safety net that will naturally catch those displaced by the silicon wave. However, this assumes a level of fiscal elasticity that no longer exists. We are entering The Great American Contraction, a period where the traditional levers of government spending are restricted by the sheer weight of historical obligation and systemic greed.

The Debt Ceiling of Compassion

With a national debt exceeding $37 trillion, a $2 trillion budget deficit and rising interest rates, the federal government’s “room to maneuver” has effectively vanished. A livable UBI requires a massive, consistent tax base. As AI begins to hollow out the middle class, the very tax revenue needed to fund such a program disappears. To fund UBI under these conditions would require a level of sovereign borrowing that the global markets simply will not support, leading to a reality where the government cannot afford to be the savior of the displaced.

The Greed Variable

Even if the math were more favorable, the human element remains a constant. Corporate interests, focused on margin preservation and shareholder value, are unlikely to support the aggressive taxation required to fund a social floor. In the race to the bottom of production costs, the primary goal of the “winners” in the AI revolution will be wealth concentration, not social equity. The political willpower to force a massive transfer of wealth from AI-profiting corporations to the idle masses is a historical outlier that we should not count on repeating.

The Velocity of Displacement

Finally, the speed of the AI transition is its most disruptive feature. Legislative bodies move in years, while AI cycles move in weeks. By the time a political consensus for UBI could be formed, the economic floor will have already fallen out. This lag time creates a vacuum that will be filled not by government checks, but by a desperate search for subsistence, setting the stage for the return of the domestic labor economy.

The Deflationary Paradox: Collapse of Demand and Cost

In a traditional economy, unemployment leads to recession, which usually leads to stagflation or managed recovery. However, the AI-driven “soft landing” introduces a unique mechanical failure: the Deflationary Paradox. As AI and advanced robotics permeate every sector, the labor cost of producing goods and services begins to approach zero, but the pool of consumers capable of buying those goods simultaneously evaporates.

The Production Floor Drops

We are witnessing the end of the labor theory of value. When an AI can design, a robot can manufacture, and an automated fleet can deliver a product without a single human touchpoint, the marginal cost of production hits the floor. In a desperate bid to capture the dwindling “active” capital in the market, companies will engage in a race to the bottom, causing the prices of physical and digital goods to deflate at a rate unseen in modern history.

The Demand Vacuum

While cheap goods sound like a boon, they are a symptom of a deeper rot: the Demand Vacuum. As the middle class is hollowed out, the velocity of money slows to a crawl. The economy shifts from a mass-consumption model to a precision-consumption model. Most businesses will fail not because they can’t produce, but because there are no longer enough customers with a paycheck to buy, even at rock-bottom prices.

The Purchasing Power of the “Remaining”

This is where the Victorian shift begins. For the small percentage of Americans who retain their income — the innovators, the orchestrators, and the entrepreneurs — this deflationary environment is a golden age. Their dollars, fixed in value while the cost of everything else drops, suddenly possess exponential purchasing power. When a gallon of milk or a digital service costs mere pennies in relative terms, the “wealthy” find themselves with a massive surplus of capital that cannot be spent on “things” alone. This surplus will naturally be redirected toward the one thing that remains scarce and high-status: the dedicated service of another human being.

The New “Stately Home” Economy

As the Deflationary Paradox takes hold, we will see a fundamental shift in the definition of luxury. In the pre-AI era, luxury was defined by the acquisition of high-tech gadgets or rare goods. In the Neo-Victorian era, where machines produce goods for nearly nothing, “luxury” will pivot back toward the human-centered experience. Status will no longer be measured by what you own, but by whose time you command.

From Software to Service

For the “In-Group” — those entrepreneurs and specialized leaders still generating significant revenue — capital will lose its utility in the digital marketplace. When software is free and manufactured goods are commoditized, wealth seeks the only remaining friction: human presence. We will see a massive migration of capital away from Silicon Valley “platforms” and toward the local domestic economy. The wealthy will stop buying more “things” and start buying “lives” — the total dedicated attention of house managers, chefs, valets, and tutors.

The Modern Manor

This economic shift will be physically manifested in the return of the Stately Home. These won’t just be houses; they will be complex ecosystems of employment. Large estates will once again become the primary employer for local communities. As traditional corporate offices vanish, the residence becomes the center of both social and economic power. These modern manors will require extensive human staffs to cook, clean, maintain grounds, and provide security — services that, while technically possible via robotics, will be performed by humans as a deliberate signal of the owner’s immense “effectively wealthy” status.

The Return of the Domestic Professional

Perhaps the most jarring aspect of this transition will be the class of worker entering domestic service. We are not talking about a traditional blue-collar service shift, but the “Victorianization” of the former middle class. Displaced white-collar professionals — accountants, teachers, and middle managers — will find that their highest-paying opportunity is no longer in a cubicle, but in managing the complex domestic affairs, private education, and logistics of the new digital aristocracy. It is a “soft landing” in name only; while they may live in proximity to grandeur, their survival is entirely tethered to the whims of their employer.

Socio-Economic Stratification: The Two-Tiered Reality

The inevitable result of the “Victorian Soft Landing” is the formalization of a rigid, two-tiered social structure. Unlike the 20th century, which was defined by a fluid and expanding middle class, the post-contraction era will be characterized by extreme polarization. The economic “missing middle” creates a vacuum that forces every citizen into one of two distinct realities: the Digital Gentry or the Dependent Class.

The Corporate and Government Gentry

A small percentage of Americans — likely less than 10% — will remain tethered to the engines of primary wealth creation. This “In-Group” consists of high-level AI orchestrators, strategic entrepreneurs, and essential government officials who maintain the infrastructure of the state. Because their income is derived from high-margin automated systems while their cost of living has plummeted due to deflation, they possess a level of functional wealth that rivals the landed gentry of the 19th century. To this group, the “Great Contraction” is not a crisis, but a refinement of their dominance.

The Dependent Class

For those outside the digital fortress, the reality is stark. Without a national UBI to provide a floor, the majority of the population becomes the “Dependent Class.” Their economic utility is no longer found in the marketplace of ideas or manufacturing, but in the marketplace of personal service. In this neo-Victorian landscape, you either work for the companies that own the AI, work for the government that protects it, or you work directly for the individuals who do.

The Choice: Service or Scarcity

This stratification reintroduces a primal power dynamic into the American workforce. When the cost of basic survival (food and shelter) is low due to deflation, but the opportunity for independent income is zero, the wealthy gain total leverage. The “soft landing” is, in truth, a forced labor transition. Those who are not “useful” to the gentry — either as specialized labor or domestic support — face the grim reality of the Victorian workhouse era: they must find a patron to serve, or they will starve in a world of plenty.

Experience Design in the Neo-Victorian Era

Experience Design in the Neo-Victorian Era

From the perspective of experience design and futurology, the shift toward a Victorian-style social structure will fundamentally alter the aesthetic of status. In a world where AI can generate perfect, flawless goods and digital experiences at zero marginal cost, “perfection” becomes a commodity. Status, therefore, will be redesigned around human friction and intentional inefficiency.

The Aesthetic of Inequality

We will see a move away from the sleek, minimalist “Apple-esque” design of the early 21st century toward a more ornate, human-heavy luxury. Experience design for the elite will emphasize things that AI cannot authentically replicate: the slight imperfection of a hand-cooked meal, the presence of a uniformed gatekeeper, and the physical maintenance of vast, non-automated gardens. Architecture will pivot back to “human-centric” layouts—designing spaces not for efficiency, but to accommodate the movement and housing of a live-in staff.

Designing for Disconnect

The most challenging aspect of this new era will be the Experience of the Invisible. Designers will be tasked with creating systems that allow the Digital Gentry to interact with their environment without acknowledging the vast economic disparity surrounding them. This involves “Social UX” — designing layers of intermediation where the “Dependent Class” provides the comfort, but the “Gentry” only interacts with the result. It is a return to the “back-stairs” architecture of the 19th century, modernized for a digital age.

The UX of Survival

For the majority, the “User Experience” of daily life will be one of Hyper-Personal Patronage. Navigation of the economy will no longer be about interfaces or platforms, but about the “UX of Relationships.” Survival will depend on the ability to design one’s persona to be indispensable to a wealthy patron. In this reality, human-centered design takes on a darker, more literal meaning: the human becomes the product, the service, and the infrastructure all at once.

Conclusion: Preparing for the Retro-Future

The “Soft Landing” we are currently engineering is not the one we were promised. As the Great American Contraction forces a collision between astronomical debt and the deflationary power of AI, the middle-class dream of a subsidized leisure class is evaporating. In its place, we are seeing the blueprints of a Retro-Future — a world that looks forward technologically but moves backward socially.

A Call for Human-Centered Transition

If we continue to view innovation solely through the lens of efficiency and margin preservation, the Victorian outcome is not just possible — it is inevitable. We must realize that without a radical redesign of how we value human contribution beyond mere “market productivity,” we are simply building a more efficient feudalism. True Experience Design must now focus on the social fabric, or we risk creating a world where the only “innovation” left is finding new ways for the many to serve the few.

Final Thought: The Soft Landing Paradox

We must be careful what we wish for when we ask for a “seamless” transition. A landing that is “soft” for the Digital Gentry is one where the friction of poverty and the noise of the displaced have been successfully silenced by the return of the servant class. History doesn’t repeat, but it does rhyme — and right now, the future sounds remarkably like 1837. The question is no longer if AI will change our world, but whether we have the courage to design a future that doesn’t require us to retreat into our past.

Frequently Asked Questions

Why would prices deflate if the economy is struggling?

In this scenario, AI and robotics drive the marginal cost of production toward zero. Simultaneously, massive job displacement creates a “demand vacuum.” To capture what little liquid currency remains, companies must drop prices drastically, leading to a reality where goods are incredibly cheap but income is even scarcer.

How does this differ from the 20th-century middle class?

The 20th century was defined by a “horizontal” distribution where many people owned moderate assets. The Neo-Victorian model is “vertical.” The middle class disappears, replaced by a tiny, hyper-wealthy elite (Digital Gentry) and a large class of people who provide them with personalized human services (the Servant Class).

Isn’t UBI a more logical solution to AI displacement?

While logical in theory, the “Great American Contraction” hypothesis suggests that high national debt and corporate prioritisation of margins make a livable UBI politically and fiscally impossible. Without a state-funded floor, the market defaults to the oldest form of social safety: personal patronage and domestic service.

EDITOR’S NOTE: This is a visualization of but one possible future. I will be publishing other possible futures as they crystallize in my mind (or as you suggest them for me to explore).

Image credits: Google Gemini

Content Authenticity Statement: The topic area, key elements to focus on, etc. were decisions made by Braden Kelley, with a little help from Google Gemini to clean up the article, add images and create infographics.

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The Agentic Paradox

Why Giving AI More Autonomy Requires Us to Give Humans More Agency

LAST UPDATED: April 10, 2026 at 7:11 PM

The Agentic Paradox

by Braden Kelley and Art Inteligencia


The Rise of the Machine “Doer”

For the past few years, we have lived in the era of Generative AI — a world of sophisticated chatbots and creative assistants that respond to our prompts. But as we move deeper into 2026, the landscape has shifted. We are now entering the age of Agentic AI. These are not just tools that talk; they are autonomous systems capable of executing complex workflows, making real-time decisions, and acting on our behalf across digital ecosystems.

On the surface, this promises the ultimate efficiency. We imagine a future where the “busy work” vanishes, leaving us free to innovate. However, a troubling Agentic Paradox has emerged: as we grant machines more autonomy to act, many humans are finding themselves with less agency. Instead of feeling liberated, workers often feel like they are merely “babysitting” algorithms or reacting to a relentless stream of machine-generated outputs.

This disconnect creates a high-stakes leadership challenge. If we focus solely on the autonomy of the machine, we risk creating an “algorithmic anxiety” that stifles the very human creativity we need to thrive. To succeed in this new era, leaders must realize that the more powerful our AI agents become, the more we must intentionally “upgrade” the agency, authority, and strategic focus of our people.

The Thesis: The goal of innovation in 2026 is not to build the most autonomous machine, but to build a human-centered ecosystem where AI agents manage the tasks and empowered humans manage the intent.

The Hidden Cost: The Cognitive Load Crisis

The promise of Agentic AI was a reduction in workload, but for many organizations, the reality has been a shift in the type of work rather than a reduction of it. This has birthed the Cognitive Load Crisis. While an autonomous agent can process data and execute tasks 24/7, it lacks the contextual wisdom to understand the nuances of organizational culture or ethical gray areas. This leaves the human “orchestrator” in a state of perpetual high-alert.

Instead of performing deep, meaningful work, leaders and employees are becoming trapped in the Supervision Trap. They are forced to manage a relentless firehose of machine-generated notifications, approvals, and “check-ins.” This creates a fragmented mental state where the human mind is constantly context-switching between different agent streams, leading to a unique form of 2026 burnout — digital exhaustion without the satisfaction of tactile achievement.

Furthermore, as AI agents take over more of the “doing,” we see an erosion of Deep Work. When every minute is spent verifying the output of an algorithm, the quiet space required for radical innovation and strategic foresight vanishes. We are effectively trading our long-term creative capacity for short-term operational speed.

  • Notification Fatigue: The mental tax of being the constant “emergency brake” for autonomous systems.
  • Loss of Intuition: The danger of becoming so reliant on agentic data that we lose our “gut feel” for the market.
  • The Feedback Loop: A system where humans spend more time managing machines than mentoring people.

To break this cycle, we must stop treating AI agents as simple productivity tools and start treating them as entities that require a new architecture of human attention. If we don’t manage the cognitive load, our most talented people will eventually shut down, leaving the “Magic Makers” of our organization feeling like mere cogs in a machine-led wheel.

Agentic Paradox Spectrum Infographic

Redefining Roles: From “The Conscript” to “The Architect”

As the landscape of work shifts, so too must our understanding of how individuals contribute to the innovation ecosystem. In my work on the Nine Innovation Roles, I’ve often highlighted how different archetypes fuel organizational growth. In this agentic age, we are seeing a dramatic migration of these roles. If we are not intentional, our best people will default into the role of The Conscript — those who are merely drafted into service to support the AI’s agenda, performing the monotonous tasks of verification and data cleanup.

The goal of a human-centered transformation is to automate the role of the “Conscript” and elevate the human into the role of The Architect or The Magic Maker. When the AI handles the heavy lifting of execution, the human is finally free to focus on Intent. This is where true agency resides. Agency is not the ability to do more; it is the power to decide what is worth doing and why it matters to the human beings we serve.

However, there is a dangerous “Agency Gap” emerging. If an organization implements AI agents without redefining human job descriptions, employees lose their sense of ownership. When the machine becomes the primary creator, the human “spark” is extinguished. We must ensure that AI serves as the support staff for human intuition, not the other way around.

The Migration of Value

The AI Agent Role The Human Agency Role
The Conscript: Handling repetitive execution and data synthesis. The Architect: Designing the systems and ethical frameworks for the AI.
The Facilitator: Coordinating schedules and managing basic workflows. The Revolutionary: Identifying the “radical” shifts the AI isn’t programmed to see.
The Specialist: Performing deep-dive technical analysis at scale. The Magic Maker: Applying empathy and storytelling to turn data into a movement.

By clearly delineating these roles, leaders can close the Agency Gap. We must empower our teams to move away from “monitoring” and toward “orchestrating.” This transition is the difference between a workforce that feels obsolete and one that feels essential.

Agentic Workforce Migration Infographic

FutureHacking™ the Cognitive Workflow

To navigate the complexities of 2026, organizations cannot rely on reactive strategies. We must use FutureHacking™ — a collective foresight methodology — to map out how the relationship between human intelligence and agentic automation will evolve. This isn’t just about predicting technology; it’s about engineering the “Human-Agent Interface” so that it scales without crushing the human spirit.

The core of this approach involves identifying the Innovation Bonfire within your team. In this metaphor, the AI agents are the fuel — abundant, powerful, and capable of sustaining a massive output. However, the humans must remain the spark. Without the human spark of intent and empathy, the fuel is just a cold pile of logs. FutureHacking™ allows teams to visualize where the “fuel” might be smothering the “spark” and adjust the workflow before burnout sets in.

By engaging in collective foresight, teams can proactively decide which cognitive territories are “Human-Core.” These are the areas where we intentionally limit AI autonomy to preserve our creative agency and cultural identity. It’s about choosing where we want the machine to lead and where we require a human to hold the compass.

  • Mapping the Friction: Identifying which agent-led tasks are creating the most mental “drag” for the team.
  • Defining Non-Negotiables: Establishing which parts of the customer and employee experience must remain 100% human-centric.
  • Intent Modeling: Shifting the focus from “What can the agent do?” to “What outcome are we trying to hack for the future?”

When we FutureHack our workflows, we move from being passive recipients of technological change to being the active architects of our organizational destiny. We ensure that as the machine gets smarter, our collective human intelligence becomes more focused, not more fragmented.

Framework: The “Agency First” Operating Model

Building a resilient organization in the age of Agentic AI requires more than just new software; it requires a new operating philosophy. We must move away from a model of Machine Management and toward a model of Intent Orchestration. This framework provides three critical steps to ensure that human agency remains the primary driver of your business value.

1. Cognitive Offloading, Not Task Dumping

The goal of automation should be to reduce the mental noise for the employee, not just to move a task from a human to a machine. If a human still has to track, verify, and worry about every step the agent takes, the cognitive load hasn’t decreased — it has merely changed shape.
The Strategy: Design “set and forget” guardrails that allow agents to operate within a defined ethical and operational “sandbox,” only alerting the human when a decision falls outside of those parameters.

2. The “Human-in-the-Loop” Upgrade

We must shift the role of the worker from Monitor to Mentor. In the old model, the human checks the machine’s homework for errors. In the “Agency First” model, the human coaches the agent on why certain decisions are better than others, treating the AI as an apprentice. This reinforces the human’s position as the source of wisdom and authority, preventing the “Conscript” mentality.

3. Intent-Based Leadership

Management must evolve to focus on the Intent rather than the Activity. In a world where agents can generate infinite activity, “busyness” is no longer a proxy for value. Leaders must empower their teams to spend their time defining the “Commander’s Intent” — the high-level objectives and human-centered outcomes that the AI agents must then figure out how to achieve.

Intent Based Leadership Blueprint Infographic

The Agency Audit: Ask your team this week: “Does this new AI agent give you more time to think strategically, or does it just give you more machine-generated work to manage?” The answer will tell you if you are facing an Agentic Paradox.

Conclusion: Leading the Human-Centered Revolution

The true test of leadership in 2026 is not how quickly you can deploy autonomous agents, but how effectively you can protect and amplify the human spirit within your organization. As we navigate the Agentic Paradox, we must remember that technology is a force multiplier, but it requires a human “integer” to multiply. Without a clear sense of agency, even the most advanced AI becomes a source of friction rather than a source of freedom.

By addressing the Cognitive Load Crisis and intentionally moving our teams out of “Conscript” roles and into “Architectural” ones, we do more than just improve efficiency — we future-proof our culture. We ensure that our organizations remain places of meaning, creativity, and purpose.

The “Year of Truth” demands that we be honest about the mental tax of automation. It calls on us to use FutureHacking™ not just to map out our tech stacks, but to map out our human potential. The companies that win the next decade won’t be those with the smartest agents; they will be the ones that used those agents to give their people the time and agency to be truly, radically human.

“Innovation is a team sport where the machines play the support roles so the humans can score the points.”

Are you ready to hack your agentic future?

Frequently Asked Questions

What is the primary difference between Generative AI and Agentic AI?

Generative AI focuses on creating content (text, images, code) based on human prompts. Agentic AI goes a step further by having the autonomy to execute multi-step workflows, make decisions, and interact with other systems to complete a goal without constant human intervention.

How can leaders identify if their team is suffering from the Agentic Paradox?

Look for signs of the “Supervision Trap,” where employees spend more time managing and verifying machine outputs than performing strategic work. If your team feels busier but reports a decline in creative output or “Deep Work,” they are likely experiencing the paradox.

What role does FutureHacking™ play in managing AI integration?

FutureHacking™ is a collective foresight methodology used to visualize the long-term impact of AI on organizational roles. It helps teams proactively define “Human-Core” territories, ensuring that as AI scales, it supports rather than smothers human agency and innovation.

Image credits: Google Gemini

Content Authenticity Statement: The topic area, key elements to focus on, etc. were decisions made by Braden Kelley, with a little help from Google Gemini to clean up the article, add images and create infographics.

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