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 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.
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
Sign up here to get Human-Centered Change & Innovation Weekly delivered to your inbox every week.
I want to forewarn you that this article is quite long. For those of you who prefer delving into it at your leisure, I’ve arranged for a downloadable version. Happy reading, and I look forward to your insights and discussions in the comments section.
The Concept
Most ROI comes from productivity improvements, and most productivity improvements come from releasing trapped value. The reason is simple. All systems trap value all the time, the only question is, where is it getting trapped today? That is, systems are implemented to help make people more productive than they were, and they do so with varying degrees of success. But to whatever degree that success has been achieved, that simply resets the bar. The old bottlenecks have been addressed, but that just surfaces the new bottlenecks. There is no such thing as a system with no bottlenecks (see Second Law of Thermodynamics 😉), so there is always the opportunity to release trapped value.
Let me give some examples:
On a macro scale, much of the trapped value that IT released in the 1980s and 1990s was in the supply chain. The technology that broke through the bottlenecks of communication and coordination included ERP systems for global commerce, the internet for global communications, and client-server infrastructure for standardized universal enablement.
In the 2000s attention shifted from the supply chain to the delivery chain with a focus on consumer markets, and especially those that dealt in services and digital goods. Here traditional media, broadcast advertising, and retail distribution, as powerful as they all were, represented massive waste as well as lost opportunity because they could not close the loop with the prospect nor serve them in the moment they were ready to transact. Smart mobile devices, cloud computing, machine learning, predictive analytics, real-time transaction processing, and home delivery were able to close this loop and thereby transform whole swaths of the consumer economy.
In the current era, at the macro level, the trapped value of highest priority has shifted back to enterprise markets, in particular those that require professional engagement to deliver products, sales, services, and customer success. Here generative AI and data amalgamation look to be game-changing resources, the former enabling untrained users to interact directly with the most sophisticated IT systems available, the latter feeding those systems with an ever-broadening stream of real-time data and transaction history. The trapped value to be released is tied to the current lack of user empowerment in the moment of engagement. That is, while predictive AI has for some time been able to come up with the right answers, most professionals are unable to access that help in real-time; and while ML and AI could be fed some of the data it craves, much more was trapped in data silos and thus not available in any timely manner. As a consequence, although we have had business intelligence for some time, we have largely been unable to translate it into operational intelligence in a scaled way.
There is one final point to make at the macro level before we transition to major account selling. How does releasing trapped value translate into customer return on investment, and how does that in turn help vendors set a good price? Here’s the deal. If you help your customer release a dollar of trapped value, they are happy to give you a dime. If you ask for fifteen cents, they hesitate, if you ask for twenty cents, they begin to think you’re gouging. So, let’s use ten percent to set our sights if for no other reason than it makes the math easier. The equation is simple. You want a million-dollar deal? Find a way to release ten million dollars of trapped value. You want a ten-million-dollar deal? Find a way to release a hundred million dollars worth. You want a hundred-million-dollar deal? Find a way to release one billion dollars in trapped value. Yes, these are very large numbers, but the larger the target enterprise, the more plausible they become, so this playbook is directed toward the Global 2000 and the public sector, two places where billions of dollars of trapped value are commonplace.
Creating the multi-million-dollar deal
So much for the macro level. Multi-million-dollar don’t happen there. They happen at the level of specific accounts, in specific industries, in specific geographies, at specific points in time. The question we need to answer is, how does trapped value show up locally?
It turns out this is a tough question to answer. After all, it is not as if your prospects haven’t been trying to improve their productivity already. Nonetheless, simply by asking the question from an outsider’s perspective, and by being intellectually curious as to where the real answers might lie, account teams can bring unique value-add to their target customers. Specifically, they can help construct a trapped value map.
A trapped value map is analogous to what oil companies create when their exploration & production divisions are prospecting for petroleum reservoirs. It’s very expensive to come up empty in that business, and so they invest considerably in seismic studies before they commit. By contrast, how many sales interactions have you witnessed where the team, to stick with the oil industry analogy, begins by presenting their drilling history, then demos their oil rigs, and then, because they always want to be closing, asks the prospect when they can get started drilling? They call it “solution selling,” but they don’t even know what the problem is.
Co-creating a trapped-value map
The goal is to co-create this map with your target customer. They are stuck, so they need you to help them get unstuck. But you need them too, not only because they have the domain knowledge as to where the bodies are buried, but also because it is their buy-in that will drive the deal. Both of you need to bring imagination, intellectual curiosity, and attention to detail to this effort because it won’t be easy. Wherever the trapped value is, it is not obvious, or it would have already been detected and dealt with.
One way to start the journey is to begin by just asking people. You want to engage with a cross-section of managers, work teams, and executives. In each case, the dialog is informal, the questions you pose are open-ended. Start with “What is working well?” Be sure to capture their answers because this is the stuff you will likely want to protect. Then move on to, “What is holding you back?” Sometimes they know and can tell you, sometimes they know but are reluctant to tell you, and sometimes you just have to hold up a mirror so they can see it for themselves. Regardless, you need to spend time walking in their shoes, observing what they do, inspecting the way they are using their systems, and just as importantly, how their systems are using them. You need to bring a beginner’s mind and design thinking to develop a fresh perspective that could support taking novel actions. Specifically, you are looking for the intersection of their trapped value with your disruptive innovation, the one that will release the trapped value, the place where you will drill for oil.
To give you a closer look at the work involved, here is an outline for a typical trapped value discovery workshop:
Kickoff
Explain the concept of releasing trapped value as the foundation for ROI.
Use the example of Amazon Prime as compared with brick-and-mortar retail, or the example of Amazon Web Services as compared with enterprise data centers.
Share personal experiences of trapped value—e.g. stuff that gets in the way of you doing your best work or getting things done expeditiously.
Brainstorm trapped-value bottlenecks in your enterprise’s operating model from multiple points of view, including those of:
A customer
A customer-facing employee
An internal-facing employee
A partner
An investor
Identify bottlenecks in your overall industry’s operating model, examining things like:
Resource-consuming regulatory regimes
Fragmented installed bases
Locked-in customers
Process steps that add more cost than value
Dropped connections due to latency delay
“Brittle” communication mechanisms that cause outages
Absence of telemetry and lack of available data
Prioritization disconnects leading to poor implementations
Prioritize bottlenecks in terms of potential ROI from removing them:
Target the “big rocks”
Don’t “major in minors”
Don’t try to solve these problems yet
Do try to quantify them and put them in rank order
Double-click on the top priority items:
Employ a “Five Whys?” approach to begin to get at root causes.
Identify “interventions” that could materially improve things.
Discuss past attempts that may not have succeeded.
Discuss the potential impact a disruptive technology could have
Discuss customer examples or war stories that reflect successes.
Summarize and outline next steps.
Sometimes you may find that the trapped value is glaringly obvious, but that might just mean you don’t really understand the trap. In other words, if the right answer is staring everyone in the face, but no one is doing anything about it, then it is likely for some reason there is no permission to pursue it. It may be political, it may be cultural, but intransigent resistance to change is at least part of the problem. Now, do you still want your multi-million-dollar deal? Well then, you not only will have to break the bottleneck at the operational level, you’ll have to solve for the change management problem as well.
That said, keep in mind that your goal at this point is not to solve the problem. Rather, it is to understand it deeply. You are doing diagnosis, not prescription. Eventually, you will convert to prescription, but know that when you do, you will also be capping the size of the deal. That is, one of the barriers to closing a multi-million-dollar deal is to close a million dollar deal instead. Everything has to close eventually, and sometimes the right thing to do is to take the million dollar deal (or the one hundred thousand dollar deal, or even the ten thousand dollar deal) today, and kick the multi-million can down the road. But don’t kid yourself. You don’t get a lot of bites at the apple, and the probability is, once you have set your price envelope, it will not get expanded any time soon.
The trapped value map, by contrast, represents an open-ended narrative, one that can be taken on in chapters, with more to come. At present, we don’t know what the answers will be. Nobody does. We are just assessing whether the problem is material enough to spend the time, talent, and management attention necessary to come up with a feasible solution. Facilitating this assessment is a gift that the account team can bring to the prospect. When conducted with integrity and skill, it positions your company as a trusted advisor, regardless of whether this particular effort bears fruit or not. That’s because you and the customer have been sitting on the same side of the table, working together to co-create something that uniquely describes their challenges in a way that makes them more actionable to address.
Transitioning to the Proposal: Co-creating a V2MOM
A great way to transition from the trapped value map to a full-on proposal is to use the V2MOM framework as a template for getting everyone on the same page. Working one-on-one with your customer sponsor, or in an ideation workshop with a small customer team, address the following:
Vision. What is the outcome we are seeking to bring about? Where is the trapped value today? What will things look like once the trapped value has been released? Why is this a big deal?
Values. What values get realized if we accomplish our vision? One of these should highlight the financial ROI, but the others can be more qualitative. Will this effort improve our ability to deliver on our mission? Will it help us fulfill one of our brand promises? Will it free our workforce to be more effective? Will it help us recruit and retain the talent we need?
Methods. What are all the things we have to get done in order to secure the outcome promised by our vision? The goal here is to describe the whole product, which includes not only whatever products and services are funded by the proposal but also any other deliverables from partners or from the customer team itself that will be required to achieve the desired outcome.
Obstacles. For each method in the whole product, what are the challenges we anticipate having to overcome? What is our current thinking about how we will do so?
Measures. What are the measures that will confirm we are realizing the outcome promised in our vision? What are the intermediate milestones that will ensure we are progressing toward that goal in a timely fashion?
It is hard to overestimate the positive impact of doing this work with the customer prior to developing a proposal. Not only does it get everyone on the same side of the table, all pulling together, but the level of confidence that the vision can be achieved goes way up, as does the sense of inclusion resulting from simply being heard.
Converting the V2MOM into a formal proposal
Creating major proposals is something account teams do for a living, so we don’t need to address all that here. What is needed, however, is a playbook that constructs that proposal from the outside in rather than from the inside out.
Bad proposals are all about you. They are inside-out presentations and documents that explain what a great company you are, how wonderful your products are, how many references and endorsements you have, why you are so superior to the competition, and why all those bad things they say about you aren’t true. Just remember one thing — nobody cares!
Great proposals, on the other hand, are all about the customer:
They start with grounding everyone in the problem to be solved or the opportunity to be captured. They do so in an authentic way that is neither slanted nor self-serving but genuinely positions the customer to make good, if challenging, choices.
They “size the prize.” The co-creation team gives its best assessment of the trapped-value costs it seeks to eliminate as well as the unrealized gains it seeks to achieve. Taken together these constitute the targeted ROI and set the 10X mark for positioning a fair price for the solution.
They map the solution to the problem, not the other way around. Each plank in the proposal has a clear reason to be, all based on releasing trapped value.
They address the whole product, focusing on the sold products and services, but also including both the roles of partners and allies and their responsibilities to the customers themselves, thereby giving the customer a complete picture of what it will take to succeed.
They position the proposed solution relative to reference competitors who represent the best alternatives to what is being proposed. These alternatives are honored for what they are. At the same time, the proposal makes clear why they fall short and why what is being proposed is preferable instead.
Building a Stairway to Heaven
Multi-million-dollar deals have grandiose objectives that capture the minds and hearts of visionaries, raise skeptical hackles with pragmatists, and scare the pants off of conservatives. Getting them funded normally requires building a coalition of the willing across all three constituencies. The framework for so doing is called a stairway to heaven.
Here’s the framework:
The point of the framework is that all four steps will play a part in capturing the total ROI from the proposal. Conservative personas will be most interested in the bottom stair, pragmatists under duress, the second one up, pragmatists with options, the third, and visionaries, the topmost. To build the kind of coalition of the willingness necessary to fund a multi-million dollar deal, you meet with as many key stakeholders one-on-one as you can, directing their attention to the stair that is of most interest to them, and showing how the plan will meet their needs, when and where that stair is expected to be addressed, and what measures will verify and validate that this has been achieved.
Conclusion
Freud is famous for saying, “Sometimes a cigar is just a cigar.” The same is true of frameworks. By themselves they achieve nothing. People do all the work. But people can often work at cross purposes not only for each other but for their intended objectives as well. Good frameworks can help them align to be more effective, and with that thought in mind, let me wish you and your team great success.
That’s what I think. What do you think?
Image Credit: Pexels, Geoffrey Moore
Sign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.
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.
Vision: A compelling, shared starting point that inspires people to challenge the status quo.
Strategy: Integrating innovation efforts into the broader strategic framework to avoid random experimentation.
Goals: Using specific, measurable targets and leading indicators to focus creative energy.
Insights: Generating deep, human-centered data about customer frustrations and unmet desires.
Idea Generation: Creating conditions for a high volume and wide diversity of ideas across the organization.
Idea Evaluation: Ensuring fair, rigorous, and innovation-friendly processes that guard against incremental bias.
Idea Development: Providing dedicated pathways, resources, and rapid prototyping to turn concepts into reality.
Organizational Psychology: Addressing the mindsets, autonomy, and fear of failure that dictate innovation behavior.
Information and Structural: Optimizing organizational structures and information flows to remove “innovation drag.”
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.
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.
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.
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.
Sign up here to get Human-Centered Change & Innovation Weekly delivered to your inbox every week.
Why Your Best Ideas May Already Be on Your Payroll
GUEST POST from John Bessant
‘With every pair of hands you get a free brain!’
That’s the promise of high involvement innovation (HII) – engaging everyone in the organization in the innovation mission. And it’s got a lot to offer.
Take the case of Denny’s shipyard in Dumbarton, Scotland. They introduced a simple HII scheme to encourage anyone in their 350-strong workforce to make suggestions on how they could improve the company’s performance. Within their first year they’d managed to cut the time to build a warship from six months to four while also improving quality, adding new features and reducing waste.
Impressive stuff – but also a reminder that HII isn’t new. That story comes from 1871! Nor is theirs an isolated case; organized HII was happening at least a hundred years before that. The 8th Shogun of Japan, Yoshimuni Tokugawa, tried it out in 1721 with his “Meyasubako”, a box placed at the entrance of the Edo Castle for written suggestions from his subjects.
And the British navy pioneered a similar scheme in 1770, asking its sailors and marines for their ideas — significantly reassuring them that such suggestions would not carry the risk of punishment!
From pioneering efforts like John Patterson’s attempt to harness what he called ‘the hundred headed brain’ in the National Cash Register company in 1892 (eagerly imitated by the Eastman Kodak company in 1896) through to Toyota’s famous Kaizen commitment in the 1970s which mobilized over 50 million suggestions and helped put them at the forefront of productivity performance in the global car industry.
The evidence is clear – HII works. Building on ideas from across the organization can contribute significant competitive advantage and deliver multi-million dollar savings. As companies as diverse as Haier, Conoco-Philips, Liberty Global, Fujitsu or Nokia continue to attest.
Right now there’s great emphasis on looking outside – the world of open innovation in which ‘not all the smart guys work for us’ is recognized and driving a search to find those smart guys out there with whom we could connect. Whilst this is undoubtedly a rich source of inspiration we shouldn’t forget the internal world of employees and their ideas.
It’s one of the paradoxes of modern management that we have the key resource of creativity fitted as standard equipment in every person we employ – yet many organizations fail to recognize or manage to tap into this. In fact, according to the Gallup State of the Global Workplace 2026 report, global employee engagement has plummeted to just 20%—its lowest level since 2020. This is not merely a human resources issue, it’s a massive innovation drain.
One of the fathers of modern quality management, Joseph Juran, famously called this internal potential “the gold in the mine”. He argued that every pair of hands comes with a “free brain”—a reality we often ignore in our search for the next external breakthrough. Our challenge today is not just about finding more talent; it is about finding up-to-date and effective ways to extract the mineral of creativity already sitting in our offices, factories, and remote hubs.
But it’s not a magic trick. These results only emerge from an organizational culture which makes contributing to innovation a key part of ‘the way we do things around here’.
It’s not a one-off initiative; it’s a pattern of behavior which has become reinforced to the point that it’s a routine. Like professional dancers who have learned and rehearsed their intricate steps to the point where they don’t think about it; they just dance.
And it’s worth doing. Organizations which invest in creating a HII culture can reap impressive rewards. For example:
ConocoPhillips: Their “Doing things better” program saved over $100 million in a single year. By focusing on “winterization” in their Canadian operations, just three implemented ideas provided exactly the process optimization they needed.
Liberty Global: Their “Spark” program generated a €25 million return on investment over ten years, largely through “KISS” (Keep It Smart and Simple) campaigns.
BAE Systems: Their “Empower” program has been so consistently valuable in terms of generating savings and improvements that the innovation team now has its own $1 million budget to fund employee ideas, expecting a return of five to ten times that investment.
It’s all about finding ways to bring the ‘hundred-headed brain’ to bear on the challenges facing the organization. Trouble is we sometimes forget this potential. In one financial services organization a single idea from a long-serving (17 years) employee helped save £250,000in its first year alone. When he was asked why he waited 17 years to share his thoughts, he simply replied: “Nobody’s ever asked me before!”.
So if we want the benefits that HII clearly has to offer we need to understand just what behaviors we are talking about and how they might move from being unfamiliar faltering new steps to become embedded routines.
Back in the mists of time (the late 1990s) we began a research program trying to understand this question, working with a wide range of organizations, large and small, in manufacturing, services and not-for-profit. And a pattern gradually began to emerge; although what they all shared was a desire to embed HII in their organizations the real challenge was in changing the culture, introducing and then reinforcing new ways of behaving. It involved a journey where progress was measured not in terms of time or money invested but in how well the organization learned and mastered key behaviors.
It’s worth looking a little more closely at what each of these stages in evolving maturity looks like – and the challenges posed in moving to the next level of capability.
Level 1 might be described as ‘getting the innovation habit’. Organizations at this level are often newcomers to the idea, playing around with it and exploring before fully committing themselves. Their activities involve small-scale pilots and their impact is limited, picking up some low-hanging fruit but not really engaging with big challenges. Support and sponsorship for the approach is often limited and of a temporary nature – there’s little or no long-term commitment from the top.
The big risk in this is that early users will be turned off because nothing seems to happen with their ideas – it’s just been another of those ‘interesting initiatives’ which go nowhere. There’s little or no training provided so most learning comes about by doing; at best facilitation and support is provided by someone inside the organization doing it on a part-time basis or else from external consultants doing it on a temporary basis.
The focus is on local-level issues with little cross-functional or interdisciplinary activity. Knowledge management is rudimentary – perhaps a simple spread sheet on which to record ideas coming into the system. And there’s little in the way of a reward/recognition scheme, not much in the way of motivation to keep going.
By contrast an organization at level 2 would be much more systematic in its approach. It takes HII seriously and has made the decision to invest – not just in an enabling platform but in providing facilitation and encouraging people to participate. This is not just an initial wave of enthusiasm; people join in with their ideas but also with comments, refinements, improvements – a collaborative innovation activity. There’s an idea management system in place to enable ideas to move from initial suggestion, through refinement and improvement to downstream implementation and different pathways for implementation have been identified.
And there is more evidence of support from senior leadership, in terms of both commitment of resources and active sponsorship for the program. But this still takes the form of overall umbrella support rather than directly linked to the line or operating structure of the organization. And the targets for ideas are still mostly bottom-up suggestions; there is little in the way of linkage to the strategic goals of the organization.
Some consideration has gone into the motivation question – there is some form of reward and recognition coming back to people in return for their engagement. Training is provided to help people learn to use the platform and develop their skills and understanding around innovation.
Knowledge management is on a more organized basis now but is still mostly around capturing and storing information – for example recording suggested ideas.
Level 3 brings in the strategic dimension, hooking up the innovation engine which has been built in level 2 and driving it in a particular direction. Campaigns are clearly identified and explained, they are sponsored from a high enough level to communicate that this is an important direction for the organization to move in. And there is a clear owner, interested in the innovations which emerge because they’ll help move the organization forwards. With clear targets comes the possibility of measuring progress against those strategic objectives – something which helps justify the costs (in terms of time and other resources) invested in HII by the organization.
By their nature many of the campaigns cut across organizational boundaries and so the platform increasingly engages people from different parts – there may even be scope for working with external players like suppliers or customers in key campaigns.
At this level the underlying structure for HII is in place and working well. There is extensive facilitation, perhaps involving more than one person working full-time to review and improve the system and help develop it further. Participation rates are higher, appropriate to the nature of the challenge, and spreading out across the organization and people are regularly engaged in the full spectrum of activity on the platform, from ideation through comment and refinement, judging and helping focus and supporting implementation of the strongest ideas. In particular the selection/judgment phase now has clear criteria against which to assess ideas, and many people can help bring ‘the wisdom of crowds’ to this process.
People are experienced in using the platform and continue to be trained in innovation-related skills. In particular the organization has a growing library of tools and techniques available to support the innovation process and the role of facilitators has moved to include a core training, coaching and development one.
Knowledge is now not only being created and stored in the form of ideas – it is being recombined and deployed, key lessons from one area being available to others to use. As a result there is less re-invention of the wheel, more sharing of good ideas and practices.
Level 4 builds on this but also starts to provide an environment in which bigger ideas can be explored alongside the steady stream of campaign-focused innovations. Participation is now at a high level, broadly spread across the organization and engaged in ideation, judgment and implementation. In addition there is now encouragement of highly committed internal entrepreneurs – ‘intrapreneurs’. Teams of people form around these major projects and work off-line to develop them further to create detailed business cases and models. To support this there is extensive training and skills development in key areas such as business planning, project management and financing plus the allowance of time and other resources to the team to support their efforts. People by this time are learning to use the innovation process autonomously – enacting entrepreneurship.
The nature of both campaigns and team-driven entrepreneurial ideas increasingly moves the organization towards cross-functional engagement, linking up across various boundaries and even to outside organizations such as suppliers.
When the ideas have matured they are presented in a ‘pitching’ session to senior management for possible further development and adoption within the organization’s major innovation portfolio. This places a challenge on senior management, not only now to provide support and encouragement but also to commit to seeing the ideas that fit their need through. Just like the role of sponsors as ‘owners’ in the campaign-led route this stage requires active leadership.
Knowledge management at this level operates in sophisticated fashion, not only capturing and storing ideas in a ‘knowledge warehouse’ but also actively searching and using the knowledge to support a wide range of projects. In particular it allows for recombination and redeployment across different areas; the role of supporting and enabling this becomes one of significance. Organizations begin to think about ‘knowledge curation’ as a key activity.
Level 5 involves the strategic use of HII capability, spreading it widely. It is about building and growing innovation communities – with clients, with the external crowd, with suppliers. In a sense the organization becomes increasingly ‘borderless’, operating several parallel innovation activities with these communities but ensuring they remain aligned and focused. There is extensive use of the online functionality in the platform but a growing parallel offline organization of active entrepreneurial groups.
Knowledge management becomes central to the organization, harvesting, processing and redeploying a wide range of knowledge assets and engaging increasingly in open innovation fashion with a wide range of players and stakeholders. The platform becomes the intelligent infrastructure on which a community of sharing co-creators operate.
So how do we climb the staircase – how to build a high involvement culture?
Most organizations start at level 1 – getting the innovation habit —where the biggest risk is turning people off by doing nothing with their ideas. The turning point comes at level 3 – strategic innovation – where the innovation engine finally connects to the organizations actual goals. And the vision is level 5 , a connected but borderless organization where innovation is a way of life. It’s a journey – but at its heart its about changing the culture – ‘the way we do things around here…’
Cultures don’t just happen – they’re built up in a hierarchical way. At the base we have individual values and beliefs – the things which matter to us and which shape the way we think about the world. We share these with others and arrive at some common views – norms – which shape how we behave alongside each other in our organizations.
Over time these patterns of behavior are rehearsed and repeated to the point where we no longer think consciously about them. Eventually they become ‘hard-wired’ into our organization’s processes and procedures, its rules and structures.
Building a high involvement innovation culture
What are the underlying values and beliefs we need to build? Our research identified ten key building blocks; in a high involvement culture we’d expect to find evidence that reflects the belief that:
1. ideas from everyone matter – everyone is capable of contributing to innovation
2. HII needs a core enabling process – it’s not about sudden flashes of inspiration but a systematic process for listening to, sharing and taking good ideas forward. And allowing time and space for it to operate
3. Ideas are not the problem – enabling them to create value is the key. We need an idea management system which gives recognition, feedback and ways to take them forward
4. People can learn how to innovate – innovators are made, not born. But they need support in the form of training and development, tools and techniques to help them become more effective innovators
5. Leadership matters – people who believe the HII story and enable the narrative, providing guidance, direction and support
6. Ideas have real impact when they are strategically directed, HII works when bottom up capability meets top down clear direction about where and why improvements matter
7. HII needs a supporting structure – facilitation, coaching, training, etc. And this structure needs continuous review and development, updating it to provide the scaffolding for the future
8. Knowledge lies at the heart of innovation and people are key carriers of it
9. Knowledge is distributed across the organization so HII needs to enable inclusiveness, openness and free flow of knowledge across boundaries
10. Motivation matters – people need an incentive to share their ideas. This is less about money than about recognition, feeling listened to, empowered, enabled to contribute
Where do we start?
The good news is that we now have some powerful new enabling technologies and a wealth of shared experience to draw upon to help us build such a culture. We’ve come a long way from the simple days of the suggestion box – but HII won’t happen by waving a magic wand and pronouncing the high involvement spell. The conclusions from our research are simple; organizations need to work on four things:
· Articulate what we want to see people doing, and hear them saying as they go about their work? What stories do they tell about success – and failure – in innovation, and what behaviours underpin that?
· Enable those behaviors. Put in place mechanisms to help people learn and practice these behaviors. This might involve training them in specific skills, such as problem finding and solving or using design thinking. It might include providing structures to support and guide the behaviors – the policies and procedures to follow. It may be creating an enabling platform – for example, using a collaboration platform to provide a way to share and build on ideas, collecting and deploying them.
· Reinforce them – If these behaviors are to become ‘the way we do things around here’ then we need to reinforce them through feedback, rewards, and incentives. For instance, celebrate innovation achievements, recognize teams and individuals who make a contribution, and above all make sure that people who take risks or move outside the expected don’t get punished or blamed if they fail!
· Review, reflect and pivot. For a resilient HII culture, we also need the capacity to review and adapt. It’s a learning journey, a continuous process of adapting, adjusting and occasional major resetting.
In today’s turbulent world the need to extract the “gold in the mine” has never been greater. The good news – which we’ve known about for hundreds of years – is that engaging the ‘hundred-headed brain’ can and does work. Today’s resilient organizations are those which have moved past the “faltering new steps” of a pilot program to reach the higher levels of maturity where innovation is a strategic, autonomous engine.
This transformation is not a “magic trick” or a one-off initiative; it is a dedicated learning journey.
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.
Hover over a card to preview each role · Cards courtesy of Braden Kelley / 9roles.com
✦
Question 1 of 200%
Question 1
The Nine Innovation Roles
If you’re not familiar with the Nine Innovation Roles, they are:
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.
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.
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.
4. Artist
The Artist doesn’t always come up with great ideas, but artists are really good at making them better.
.
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.
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.
7. Judge
The Judge is really good at determining what can be made profitably and what will be successful in the marketplace.
.
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.
.
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:
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)
Nine Innovation Roles card design to download for printing with adMagic (or your vendor)
Nine Innovation Roles downloadable presentation
Nine Innovation Roles team worksheet
Nine Innovation Roles introductory video to use in workshops
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.
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
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.
Sign up here to get Human-Centered Change & Innovation Weekly delivered to your inbox every week.
In 2007, our media company in Ukraine completed its IPO and would soon be valued at $100 million. For a rough and tumble organization that, just a few years before, was a relatively small business, it was exhilarating. We had big plans and were eager to execute them. It was a “damn the torpedoes, full speed ahead” moment.
We also had an innovative strategy that we thought was a clear winner — a bet on Ukrainian language media. Although the Russian language was dominant at the time, we thoroughly researched the idea and found that a large part of the market said they preferred Ukrainian. To grab the opportunity, we launched three major brands in a year.
It was a disaster. Although the first launch was cause for concern, we were moving so fast the other two were too far along to stop. Then the 2008 global financial crisis hit and we were soon struggling to stave off bankruptcy. It was a brutal lesson. You can research an idea, but you never really know what you have until you’ve actually tested it in the marketplace.
The Rule Following Paradox
The Philosopher Ludwig Wittgenstein famously wrote, “no course of action could be determined by a rule, because every course of action can be made out to accord with the rule.” He meant that every rule is subject to some interpretation and, given varying contexts, interpretations are bound to vary.
That’s essentially what happened to us. We did our research and combed through all the evidence. Television and advertising was, by law, in Ukrainian and not Russian. Consumer surveys consistently showed that a significant portion of the Ukrainian public preferred Ukrainian language media. There were plenty of signs that we were on to something.
Given that analysis, our course seemed clear. We should not only launch Ukrainian language products, we should proceed at a rapid pace so that we could move out ahead of the pack. Surely, once competitors saw how big the opportunity was, they would pounce and our opportunity would be squandered.
Except that there was no opportunity. We weren’t acting on facts, but our interpretation of them and that interpretation was horribly, drastically wrong. To make matters worse, all this was happening as the Ukrainian media market was hitting its peak and the world was about to head off a cliff into the worst financial crisis since the Great Depression.
Survivorship Bias
Business school professors and consultants gain fame—not to mention large fees—when they are able to define a novel concept or success factor. If you are able to isolate one thing that organizations should do differently, you have a powerful product to sell. A single powerful insight can make an entire career, which is probably why so many cut corners.
For example, in their study of 108 companies, distinguished INSEAD professors W. Chan Kim and Renée Mauborgne found that “blue ocean” products, those in new categories without competition, far outperform those in the more competitive “red ocean” markets. Their book, Blue Ocean Strategy, was an immediate hit, selling over 3.5 million copies.
Bain consultants Chris Zook and James Allen’ book, Profit from the Core, boasted even more extensive research encompassing 200 case studies, a database of 1,854 companies, 100 interviews of senior executives and an “extensive review” of existing literature. They found that firms that focused on their ”core” far outperformed those who strayed.
It doesn’t take too much thinking to start seeing problems. How can you both “focus on your core” and seek out “blue oceans”? It betrays logic that both strategies could outperform one another. Also, how do you define “core?” Core markets? Core capabilities? Core customers? While it’s true that “blue ocean” markets lack competitors, they don’t have any customers either. Who do you sell to?
Yet there is an even bigger, more insidious problem called survivorship bias. Notice how “research” doesn’t include firms that went out of business because there were no customers in those “blue oceans” or because they failed to diversify outside of their “core.” The data only pertains to those that survived.
The Problem With Case Studies
The gold standard for research is randomized, double blind trials in which some of the subjects receive some sort of intervention, a control group gets a placebo and no one, not even those conducting the study, know which subjects are in which group. This design minimizes the chance of bias affecting results.
Yet this type of design is impractical for studying real businesses that are competing in the marketplace. So researchers largely depend on case studies in which participants are interviewed after the fact. These can be helpful in that they offer first-person perspectives of events and their context, but have obvious problems.
First, much like in Wittgenstein’s rule-following paradox, a lot is left up to interpretation. There are rarely more than a half-dozen people interviewed and they tend to be insiders. We almost never hear from competitors, customers or lower level employees. Then the researchers themselves bring their own biases to what they see and hear.
There are also issues with survivorship bias. Clearly, key players will be much more forthcoming about successes than failures. So we tend to hear about strategies that worked when, for all we know, those same strategies may have failed in other organizations and other contexts. There’s simply no real way for us to know.
Incidentally, researchers did a series of case studies on our company and I was struck by how much they depended on who was interviewed. While there wasn’t anything factually wrong, a different sample of perspectives would have led to very different interpretations.
Adopting A Bayesian Strategy
Traditionally, strategy has been seen as a game of chess. Wise leaders survey the board of play, plan their moves carefully and execute flawlessly. That’s always been a fantasy, but it was close enough to reality to be helpful. Organizations could build up sustainable competitive advantage by painstakingly building up bargaining power within the value chain.
Yet as Mike Tyson pointed out, “everybody has a plan until they get hit.” We can research and plan all we want, but the real world is a messy place. The facts, as we see them, are really just interpretations of the data we have available to us. Invariably, there are other data we’re not seeing and, even that which we have in front of us, can be interpreted in multiple ways.
That’s why we need to take a more Bayesian approach to strategy, in which we don’t pretend that we have the “right strategy,” but endeavor to make it less wrong over time. As Rita Gunther McGrath has put it, it’s no longer as important to “learn to plan” as it is to “plan to learn.” We need to be more iterative, see what works and change course as needed.
Today, instead of thinking about strategy as a game of chess, we’d do better to envision an online role-playing game, in which you bring certain capabilities and assets and connect with others to go on quests and discover new things along the way. Unlike chess, where everyone knows that their objective is to capture the opponent’s king, we need to expect the rules to change over time and adapt accordingly.
— Article courtesy of the Digital Tonto blog
— Image credit: Unsplash
Sign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.
Heapy has long been a quiet force in the evolution of healthcare design – not with grand pronouncements, but with deep, thoughtful work that reshapes how we experience care. For decades, they have approached hospitals and clinics not as static buildings, but as living ecosystems – places where healing does not happen despite the surroundings, but because the space was designed to make it possible.
Their work goes beyond sustainability in the traditional sense – energy efficiency, material choices, LEED certifications – though they lead there, too. What sets Heapy apart is their commitment to human sustainability: designing spaces that support not just the planet, but the people within them. Clinicians. Patients. Families. The entire care team.
They understand that a healing environment is not just about clean lines and natural light – though those things matter. It is about creating places that reduce stress, prevent burnout, and foster connection. Spaces that are flexible enough to adapt to a pandemic, yet intimate enough to embrace the ailing or comfort a grieving family.
And they do this not in isolation, but in partnership – with providers, communities, vendors, and innovators who recognize that the future of healthcare is not only about smart technologies, but about deep human intention. It is not just what we build, but why – and for whom.
It was in that spirit last week, I had the honor of serving as the keynote speaker at Heapy’s Symposium on Sustainability in Healthcare, hosted in the beautiful “Queen City” of Cincinnati, Ohio – a gathering of dreamers and designers from across industries, all united by a shared belief: that the future of care must be human-centered.
It was in that room, surrounded by industry pioneers, who see beyond efficiency and into empathy, that the vision for a different kind of healthcare took shape – not as a distant ideal, but as a gentle uprising already underway.
We have spent decades optimizing a system that was not built to heal. It was not built for people at all. It is a machine – and both patients and caregivers are just trying to survive it.
We have chased speed, throughput, and cost-cutting – as if care were an assembly line. But in the rush to do more, faster, we have lost something irreplaceable: the human connection that lies at the heart of healing.
Clinicians drown in documentation; their eyes fixed on screens instead of faces. Patients feel like data points, shuffled through impersonal workflows. And hospital administrators, well-meaning as they are, focus on numbers that measure activity, not meaning.
But what if we stopped trying to make the machine run faster – and started asking: How might we build something entirely different? Not a smarter system, but a human one?
Not a system that grinds, but one that breathes. Not one that manages, but cares.
That is the future we are stepping into – not as a distant dream, but as a calm, determined shift, unfolding from the electricians who wire our buildings to the executives who shape our boardrooms. Not a future where technology replaces humanity, but one where it finally sees us – amplifies us – and reminds us why we are here.
And this future – the heart of healing — rests on four pillars, championed by forward-thinking organizations like The American College of Healthcare Executives (ACHE): liberating clinicians, designing for resilience, committing to learning, and personalizing care.
Liberating Clinicians: Letting Humans Be Humans
Imagine a clinic where the doctor looks at you – not at a screen. Where nurses spend their shifts at the bedside, not buried in charts. Where the administrative load does not fall on the shoulders of those already stretched thin – like patients juggling multiple portals, passwords, and fragmented records.
That is not fantasy. It is the promise of AI as an ally, not an agitator.
We are already seeing systems where AI stealthily handles prior authorizations, drafts clinical notes, and surfaces critical data – not to replace clinicians, but to free them. Early adopters report not just time savings, but better patient outcomes. But the real win? Time. Time to listen. Time to notice. Time to care.
Because healing is not transactional. It is relational. It lives in the pause, the eye contact, the hand on the shoulder. And when we automate the mechanical, we make space for the meaningful. The metric should not be how many patients we see – but how deeply we see them.
Designing for Resilience: Spaces that Adapt, Not Just Endure
Now picture the places where care happens.
Too often, they feel like relics – rigid, impersonal, built for a world that no longer exists. The next generation of healing environments must be different. They must be resilient, not just in structure, but in spirit.
We need hospitals that can withstand storms – literal and metaphorical. That can scale during surges, pivot during pandemics, and adapt to the rapid pace of change. Modular walls. Flexible rooms. Infrastructure that evolves.
But resilience is not just about durability – it is about humanity.
It is peaceful zones for staff to decompress. Natural light in every patient room. Wayfinding that feels intuitive, not clinical. It is designing for emotional endurance as much as physical strength.
Because burnout is not just caused by workload – it is shaped by environment. A space that feels cold, chaotic, or dehumanizing wears people down. One that feels calm, connected, and cared for – even in a crisis – helps them endure.
So let us stop building facilities and start creating healing ecosystems. Places that support not just survival, but the fullness of life – where healing and wholeness go hand-and-hand.
Committing to Lifelong Learning: Growing…Together
Even the smartest tools and strongest walls will not matter if we do not equip people with the knowledge, skills, and supportive environment they need to grow.
That is why ongoing education is not just a nice-to-have – it is non-negotiable. But not the kind of training that feels like a box to check. We need learning that is alive, adaptive, and human-centered.
Leaders, clinicians, and designers need to understand not just how to work with AI – but why it matters to their work. It is not about compliance – it is about curiosity. Not just in operating it but partnering with it. We need safe spaces to experiment, explore, grow – and yes, even fail. No innovation happens without change – and no meaningful change happens without real learning.
Micro-learning modules. Peer mentorship. Protected time for reflection. These are not luxuries – they are lifelines of learning and innovation.
And when leaders model learning – when they say, “I don’t know, let’s figure it out together” – they signal that growth matters more than perfection.
Because the future of care is not about mastering technology – it is about forming partnerships. With each other. With patients. With tools that extend our capacity, not replace our judgment.
Personalizing Care: Seeing the Person, Not the Problem
Finally, imagine care knows you.
Not in a surveillance way – not data hoarded, but wisdom shared. AI that can tailor treatments plans, adjust room settings, and anticipate needs – always with consent, transparency, and control.
This is not about efficiency. It is about dignity.
It is remembering the patient’s name. Honoring their preferences. Adapting to their story. Adjusting to their situation. The most powerful curative is still human attention – and AI can help us focus it.
We are already seeing systems where AI personalizes everything from medication timing to discharge planning – not to automate empathy, but to boost it.
Because when care feels seen and heard, the healing penetrates deeper.
Five Actions for Leaders: From Vision to Practice
So, what can leaders do – right now – to turn this vision into reality?
Redesign Workflows Around Human Dignity: Stop measuring success by speed. Reengineer processes to reduce burnout and restore time for true connection. Use AI to handle the mechanical – documentation, scheduling, billing – and let it also surface critical insights, flag at-risk patients, and streamline workflows so clinicians can focus on what they do best: medicine. Measure moments of care, not mouse clicks – and allow AI to illuminate what truly matters: patient healing and well-being.
Co-Create with Frontline Teams: No more top-down rollouts. Invite nurses, doctors, and support staff into the design of every new tool, space, workflow, and policy. – and use AI to elevate their voices, not override them. Imagine AI that analyzes frontline feedback in real-time, surfaces hidden pain points, and co-generates solutions alongside those who know the work best. Ask: Does this help you provide better care? Their lived experience, supported by intelligent insight, guide what gets built – because the best solutions do not emerge from closed boardroom doors, but from the open collaborative hands and hearts within the community of care.
Build Spaces that Breathe: Invest in modular, adaptable infrastructure – but go further. Design for emotional resilience: tranquil zones, natural light, intuitive layouts, and AI-enhanced environments that respond to human needs in real-time. Imagine rooms that adjust lighting and temperature based on patient stress levels, or corridors that guide staff to moments of respite between high-pressure tasks. A healing space is not just durable – it is humane, alive with invisible intelligence that supports the whole-person: mind, body, heart, and spirit.
Champion Learning as an Act of Care: Make continuous education protected time, not an afterthought. Offer micro-learning, peer mentorship, and collaborative spaces – and harness AI as a dynamic learning partner. Imagine intelligent systems that surface personalized insights, adapt to individualized learning styles, and guide clinicians through real-time decision support that doubles as on-the-job training. When leaders model curiosity and embrace AI not just as a tool, but as a catalyst for growth and innovation, they create cultures where learning is ongoing and invigorating.
Personalize Without Surveillance: Use data to deepen trust, not erode it. Implement AI that personalizes care – predicting needs, tailoring environments, and adapting support – but always with consent, transparency, and patient control. Let personalization mean dignity: remembering a name, honoring a preference, adapting to a story, adjusting to a changing situation, and above all, putting people, not patterns, at the center.
A Future That Feels Human, Beautifully Imperfect
This is not about replacing the system. It is about reimagining it.
From one that manages people to one that sees them.
From one that measures output to one that values presence.
From one that optimizes speed to one that honors slowness – personal focus, deep listening, and the easy moments of connection that no algorithm can replicate.
The tools are here. The insights are clear. The question is no longer can we – but will we?
Will we choose efficiency – or humanity?
Will we build systems that merely function – or ones that truly heal?
The answer lies not in technology, but in where we choose to place our attention – and our intention.
As a Triple Negative Breast Cancer survivor, I have felt firsthand how cold and mechanical care can be – and how profoundly a space can either deepen that pain or help heal it. I have also seen how systems can exhaust the very people meant to deliver care. But I hold onto a belief: healing begins when we return to our humanity. From designers and clinicians to administrators and patients, each of us plays a vital role in co-creating a whole-health environment where care is not just delivered, but genuinely experienced.
And perhaps the most revolutionary act in healthcare today might just be this: to care, deeply, as beautifully imperfect humans – and to let everything else serve a universal truth – one rooted in compassion, true connection, and shared humanity.
Image credits: Kellee M. Franklin
Sign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.
Why the Great American Contraction is leading to a crisis of demand and a re-imagining of the American Social Contract.
LAST UPDATED: April 17, 2026 at 3:58 PM
GUEST POST from Art Inteligencia
The Ghost in the Shopping Mall
In our previous exploration, “The Great American Contraction,” we identified a fundamental shift in the American story. For the first time in our history, the foundational assumption of “more” — more people, more labor, and more expansion — has been inverted. We discussed how the exponential rise of AI and robotics is dismantling the traditional value chain of human labor, moving us from a nation of “doers” to a necessary, albeit smaller, elite class of “architects.”
However, as we move closer to the two-year horizon of the next United States Presidential election, a more insidious shadow is beginning to fall across the landscape. It is no longer just a crisis of employment; it has evolved into a crisis of consumption. This is the “Feedback Loop of Irrelevance.”
The logic is as cold as the algorithms driving it: As increasing numbers of knowledge workers and service providers are displaced by autonomous agents, their disposable income evaporates. When people lose their financial footing, they spend less. When they spend less, the revenue of the very companies that automated them begins to shrink. To protect their margins in a declining market, these companies are forced to cut back even further — often doubling down on automation to reduce costs — which in turn removes more consumers from the marketplace.
We are witnessing the birth of a deflationary death spiral where corporate efficiency threatens to cannibalize the very markets it was designed to serve. Over the next 24 months, this cycle will redefine the American psyche and set the stage for an election year unlike any we have ever seen.
It is time to look beyond the immediate shock of job loss and examine the structural integrity of our economic operating system. If the “Old Equation” of labor-for-income is a sinking ship, we must decide what happens to the passengers before we reach the horizon of 2028.
The Vicious Cycle of Automated Austerity
The transition from a growth-based economy to a Great Contraction is not a linear event; it is a recursive loop. As AI adoption accelerates, we are witnessing a phenomenon I call “Automated Austerity.” This is the process where short-term corporate gains from labor reduction lead directly to long-term market erosion. The cycle progresses through four distinct, overlapping phases:
Phase 1: The First Wave Displacement
We are currently seeing the replacement of both low-skilled physical labor and high-skilled knowledge work by autonomous systems. This isn’t just about factory floors; it’s about the “Architect” roles we once thought were safe. As companies replace $150k-a-year analysts with $15-a-month compute tokens, the immediate impact is a massive surge in corporate profit margins.
Phase 2: The Wallet Effect
The friction begins here. Displaced workers initially rely on savings or severance, but as those dry up, the “gig economy” safety net is nowhere to be found — because AI is already performing the freelance writing, coding, and administrative tasks that used to provide a bridge. Disposable income doesn’t just dip; for a significant percentage of the population, it vanishes. This causes a sharp contraction in discretionary spending.
Phase 3: The Revenue Mirage
This is the trap. Companies that automated to save money suddenly find their top-line revenue shrinking because their customers (the former workers) can no longer afford their products. The efficiency gains are real, but the market size is artificial. We are entering a period where companies may be 100% efficient at producing goods that 0% of the displaced population can buy.
Phase 4: The Secondary Contraction
Faced with shrinking revenues, boards of directors demand even deeper cost-cutting to protect investor dividends. This leads to a second, more desperate wave of layoffs, further reducing the tax base and consumer spending power. This feedback loop creates a Deflationary Death Spiral that traditional monetary policy is ill-equipped to handle.
“When you automate the consumer out of a job, you eventually automate the business out of a customer.” — Braden Kelley
Over the next two years, this cycle will move from the periphery of Silicon Valley to the heart of every American household, forcing a radical re-evaluation of how we distribute the abundance that AI creates.
The Two-Year Horizon: 2026–2028
As we navigate the next twenty-four months, the gap between traditional economic indicators and the lived reality of American citizens will become a canyon. We are entering a period of Economic Bifurcation, where the distance between those who own the “compute” and those who formerly provided the “labor” creates a new social stratification.
The Rise of the ‘Hollow’ Recovery
Expect to hear the term “efficiency-led growth” frequently in the coming months. Wall Street may remain buoyant as AI-integrated corporations report record-breaking margins per employee. However, this is a hollow success. While the stock market reflects corporate optimization, our Alternative Economic Health Measures—like the Genuine Progress Indicator (GPI) — will likely show a steep decline. We are becoming a nation that is technically “wealthier” while the average citizen’s ability to participate in that wealth is structurally dismantled.
The Shift from ‘Doer’ to ‘Architect’ Burnout
The “Great American Contraction” is not just about those losing roles; it is about the immense pressure on those who remain. The survivors — the Architect Class — are tasked with managing sprawling AI ecosystems. This creates a new kind of cognitive load. By 2027, I predict we will see a peak in “Technological Burnout,” where the speed of AI-driven change outpaces the human capacity to design for it. This is where Human-Centered Innovation becomes a survival skill rather than a corporate luxury.
The Mindset of Survivalist Innovation
As the feedback loop of shrinking revenue intensifies, we will see American citizens taking radical actions to decouple from a failing labor market. This includes:
Hyper-Localization: A resurgence in local bartering and community-based resource sharing as a hedge against the volatility of the automated economy.
The ‘Off-Grid’ Digital Economy: Individuals utilizing open-source AI models to create value outside of the traditional corporate gatekeepers, leading to a “shadow economy” of peer-to-peer services.
Consumption Sabotage: A psychological shift where citizens, feeling irrelevant to the economy, consciously reduce their consumption to the bare essentials, further accelerating the contraction.
This period will be defined by a search for meaning in a post-labor world. The American citizen of 2027 is no longer asking “How do I get ahead?” but rather “How do I remain relevant in a world that no longer requires my effort to function?”
Beyond GDP: New Vitals for a Contracting Economy
As the “Old Equation” fails, the metrics we use to measure national success are becoming dangerously obsolete. In a world where AI can drive productivity while simultaneously hollowing out the consumer class, GDP is no longer a compass; it is a rearview mirror. To navigate the next two years, we must shift our focus to alternative economic health measures that prioritize human vitality over transactional velocity.
1. The Genuine Progress Indicator (GPI)
Unlike GDP, which counts the “cost of cleaning up a disaster” as a positive, the GPI factors in income inequality and the social costs of underemployment. As we move toward 2028, we must demand a GPI-centered view of the economy. If AI-driven efficiency creates wealth but destroys the social capital of our communities, the GPI will show we are regressing, providing a much-needed reality check to “hollow” stock market gains.
2. The U-7 ‘Utility’ Rate
Standard unemployment figures (U-3) are increasingly irrelevant. We need a U-7 ‘Utility’ Rate to track those who are “technologically displaced”—individuals whose roles have been absorbed by algorithms or whose wages have been suppressed to the point of working poverty. This metric will highlight the Architect Gap: the growing number of people who have the capacity for high-value human contribution but lack access to the compute resources required to compete.
3. The Social Progress Index (SPI)
The goal of an automated economy should be to improve the human condition. The SPI measures outcomes that actually matter: Access to advanced education, personal freedom, and environmental quality. By 2027, the SPI will be the most honest indicator of whether the Great Contraction is a managed transition to a better life or a chaotic collapse of the middle class.
4. Value of Organizational Learning Technologies (VOLT)
We must begin measuring the “Agility Score” of our nation. VOLT measures how effectively we are using AI to solve complex problems rather than just replacing workers. A high VOLT score paired with a low SPI suggests we are building a “learning machine” that has forgotten its purpose: to serve the humans who created it.
“A high-GDP nation with a crashing Social Progress Index(SPI) is merely a failed state in a gold tuxedo.”
The political battleground of the next two years will be defined by a new set of metrics similar to these (but likely different). The 2028 election will not just be a choice between candidates, but a choice between maintaining the illusion of growth or designing a system of sovereignty for the American citizen.
The Sovereign Tech-Stack & The Localized Pivot
As the “Feedback Loop of Irrelevance” continues to shrink traditional income, we are witnessing a radical grassroots response: The Localized Pivot. When the macro-economy fails to provide value to the individual, the individual stops providing value to the macro-economy and turns inward to their community.
The Rise of the ‘Personal AI’ Infrastructure
By 2027, the barrier to entry for sophisticated production will vanish. We will see a surge in “Sovereign Tech-Stacks” — individuals and small collectives using localized, open-source AI models to run micro-manufactories, automated vertical farms, and peer-to-peer service networks. This is Innovation as a Survival Tactic. These citizens are essentially “unplugging” from the hollowed-out corporate ecosystem and creating a shadow economy that traditional GDP cannot track.
From Global Chains to Hyper-Local Resilience
The contraction of consumer spending will lead to the death of the “long supply chain” for many goods. In its place, we will see the rise of Regional Circular Economies. AI will be used not to maximize global profit, but to optimize local resource sharing. Imagine community AI agents that manage local energy grids or coordinate the bartering of skills — human-centered design at its most fundamental level.
The ‘Architect’ of the Commons
In this phase, the “Architect” role I’ve discussed previously becomes a civic one. These are the individuals who design the systems that keep their communities thriving while the national revenue shrinks. They are the ones building the Human-Centered Guardrails that ensure technology serves the neighborhood, not the shareholder. This shift represents a move from Global Consumerism to Local Sovereignty.
“When the national economic engine stops fueling the household, the household must build its own engine, or it dies.” — Braden Kelley
This localized movement will be the wild card of 2028. It creates a class of “Un-Architected” citizens who are no longer dependent on the federal government or major corporations, creating a profound tension for any political candidate trying to promise a return to the ‘Old Equation’.
The Road to 2028: The Politics of Human Relevance
As we approach the next Presidential election, the political discourse will undergo a seismic shift. The traditional “Left vs. Right” battle lines over tax rates and social issues will be superseded by a more existential debate: The Individual vs. The Algorithm. The 2028 election will likely be the first in history centered entirely on the consequences of a post-labor economy.
The ‘Humanity First’ Tax and Sovereign Solvency
The most contentious issue will be how to fund a shrinking state as the labor-based tax system collapses. We will see the rise of the “Compute Tax” — a proposal to tax AI tokens and robotic output rather than human hours. This isn’t just about revenue; it’s about sovereign solvency. When companies reinvest profits into compute rather than wages, the “Economic OS” crashes. Expect candidates to run on a platform of Universal Basic Everything (UBE) — providing the results of automation (healthcare, housing, and energy) directly to the people as the tax base from labor vanishes.
The Death of Traditional Immigration Debates
As I noted in our initial look at the Contraction, the old argument about immigrants “taking jobs” or “filling gaps” is dead. In 2028, the focus will shift to “Strategic Talent Acquisition.” The debate will center on how to attract the world’s few remaining irreplaceable “Architect” minds while managing a domestic population that is increasingly surplus to the needs of capital. This will create a strange political alliance between protectionists and humanists, both seeking to shield human value from digital devaluation.
Mindset and Likely Actions of the Citizenry
By the time voters head to the polls, the American mindset will have shifted from aspiration to preservation. We are likely to see:
The Rise of ‘Neo-Luddite’ Activism: Not a rejection of technology, but a demand for “Human-Centered Guardrails” that prevent AI from cannibalizing the last remaining sectors of human connection.
The Search for Non-Monetary Meaning: A surge in candidates who focus on “Quality of Life” metrics rather than fiscal growth, appealing to a class of people who no longer derive their identity from their “job.”
Algorithmic Populism: Politicians using AI to personalize fear and hope at scale, creating a feedback loop where the technology used to displace the worker is also used to win their vote.
The central question of the 2028 election will be simple but devastating: “What is a country for, if not to support the thriving of its people — even when those people are no longer ‘productive’ in a traditional sense?” The winner will be the one who can design a new social contract for a smaller, more resilient, and truly innovative nation.
Conclusion: Designing a Thrivable Contraction
The Great American Contraction is no longer a theoretical “what-if” for futurists to debate; it is an active restructuring of our reality. As the feedback loop of automated austerity begins to bite, we are discovering that a country built on the relentless pursuit of “more” is fundamentally ill-equipped to handle the arrival of “enough.”
The next two years will be a period of intense friction as our legacy systems — our tax codes, our education models, and our social safety nets — grind against the frictionless efficiency of the AI era. We will see traditional economic metrics fail to capture the quiet struggle of the consumer, and we will watch as the 2028 election turns into a referendum on the value of a human being in a post-labor world.
But contraction does not have to mean collapse. If we shift our focus from transactional velocity to human vitality, we have the opportunity to design a new version of the American Dream. This new dream isn’t about the quantity of jobs we can protect from the machines, but the quality of the lives we can build with the abundance those machines create. It is about moving from a nation of “doers” who are exhausted by the grind to a nation of “architects” who are inspired by the possible.
“The goal of innovation was never to replace the human; it was to release the human. We are finally being forced to decide what we want to be released to do.” — Braden Kelley
The road to 2028 will be defined by whether we choose to cling to the wreckage of the growth-based model or whether we have the courage to embrace a smaller, smarter, and more human-centered future. The contraction is inevitable, but the outcome is ours to design.
STAY TUNED: On Tuesday my friend Braden Kelley (with a little help from me) is publishing an article featuring one hypothesis for what an AI SOFT LANDING might look like.
Image credits: Google Gemini
Sign up here to get Human-Centered Change & Innovation Weekly delivered to your inbox every week.
Sign up here to get Human-Centered Change & Innovation Weekly delivered to your inbox every week.
