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.
One of the best ways to learn is to make a prototype. Prototypes come in many shapes and sizes, but their defining element is the learning objective behind them. When you start with what you want to learn, the prototype is sure to satisfy the learning objective. But start with the prototype, and no one is quite sure what you’ll learn. When prototypes come before the learning objective, prototypes are inefficient and ineffective.
Before staffing a big project, prototypes can be used to determine viability of the project. And done right, viability prototypes can make for fast and effective learning. Usually, the team wants to build a functional prototype of the product or service, but that’s money poorly spent until the business model is validated. There’s nothing worse than building expensive prototypes and staffing a project, only to find the business model doesn’t hold water and no one buys the new thing you’re selling.
There’s no reason a business model can’t be validated with a simple prototype. (Think one-page sales tool.) And there’s no reason it can’t be done at the earliest stages. More strongly, the detailed work should be held hostage until the business model is validated. And when it’s validated, you can feel good about the pot of gold at the end of the rainbow. And if it’s invalidated, you saved a lot of time, money and embarrassment.
The best way to validate the business model is with a set of one-page documents that define for the customer what you will sell them, how you’ll sell it, how you’ll service it, how you’ll train them and how you’ll support them over the life of your offering. And, don’t forget to tell them how much it will cost.
The worst way to validate the business model is buy building it. All the learning happens after all the money has been spent.
For the business model prototypes there’s only one learning objective: We want to learn if the customer will buy what we’re selling. For the business model to be viable, the offering has to hang together within the context of installation, service, support, training and price. And the one-page prototype must call out specifics of each element. If you use generalities like “we provide good service” or “our training plans are the best”, you’re faking it.
Don’t let yourself off the hook. Use prototypes to determine the viability of the business model before spending the money to build it.
Image credit: Google Gemini
Sign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.
I. Introduction: The Tension Between Renewal and Waste
In the world of innovation, we often talk about the “fire” of creativity — the energy that drives us to build the next great breakthrough. But in the current industrial landscape, we must ask ourselves: are we stoking a sustainable Innovation Bonfire, or are we simply burning the furniture to keep the room warm for a single night?
Planned obsolescence has long been the silent engine of the consumer economy, a strategy designed to ensure that the products of today become the landfill of tomorrow. It creates a fundamental tension between the mechanical need for economic growth and the human-centered need for enduring value.
“To truly innovate for humanity, we must pivot from a strategy of deliberate failure to one of intentional resilience.”
As change leaders, we must recognize that planned obsolescence is an industrial-age relic masquerading as a modern innovation strategy. This article explores whether this cycle of constant replacement truly fuels progress or if it acts as a “wet blanket” that dampens our ability to solve the world’s most pressing, wicked problems.
II. The Case for the “Pro”: Obsolescence as a Catalyst for Speed
While it is easy to dismiss planned obsolescence as purely cynical, from a strategic standpoint, it has functioned as a powerful — if aggressive — accelerant for the adoption curve. By shortening the lifecycle of a product, organizations force a faster cadence of iteration. This “forced evolution” ensures that new technologies, safety standards, and efficiencies are pushed into the hands of users at a rate that a “buy-it-for-life” model simply couldn’t sustain.
Consider the following drivers that proponents argue fuel the innovation engine:
R&D Capitalization: The consistent revenue generated by replacement cycles provides the massive capital reserves required for “Big Bang” breakthroughs. Without the “Small Bangs” of incremental sales, the long-term, high-risk research into materials science or AI might never be funded.
The Velocity of “Innovation”: When a product is designed to be replaced, designers are freed from the “legacy trap.” They can experiment with radical new interfaces or hardware configurations, knowing that the next cycle provides an immediate opportunity to course-correct based on real-world human feedback.
The Psychology of the “New”: In our work on Stoking Your Innovation Bonfire, we recognize that emotion is a primary driver of change. The “Fashion of Tech” creates a sense of momentum. This psychological pull toward the “New” keeps markets liquid and encourages a culture of constant curiosity and upgrade.
In this light, obsolescence isn’t just about things breaking; it’s about keeping the market in motion. It prevents stagnation by ensuring that the “Stable Spine” of our infrastructure is constantly being tested and refreshed by the latest “Modular Wings” of technological advancement.
III. The Case for the “Con”: The “Wet Blankets” of Planned Obsolescence
If innovation is a fire, planned obsolescence often acts as a massive “wet blanket” — smothering the very progress it claims to ignite. When we design for failure, we aren’t just creating a product; we are creating environmental friction. The “Invisible Drain” of e-waste and resource depletion represents a systemic failure that our current economic operating system is struggling to process.
From a human-centered design perspective, the downsides extend far beyond the landfill:
The Erosion of Trust: A core pillar of Experience Design is the relationship between the brand and the human. When a user realizes a device was intentionally throttled or made unrepairable, it creates a “Customer Experience (CX) Betrayal.” This loss of trust is a psychological friction that makes future change adoption much harder.
Innovation Fatigue: There is a limit to how much “New” a human can process. When consumers feel they are on a hamster wheel of meaningless upgrades, they develop an apathy toward genuine breakthroughs. We risk a future where the “latest” no longer feels like the “greatest” — it just feels like a chore.
The Circular vs. Linear Conflict: Planned obsolescence is the hallmark of a linear economy (Take-Make-Waste). To move toward a sustainable future, innovation must embrace circularity, where products are designed as “Stable Spines” that can be updated, repaired, and kept in the ecosystem indefinitely.
By focusing our creative energy on how to make things break, we divert talent away from solving “wicked problems” — like true energy efficiency or radical durability. We are effectively choosing Quantity of Sales over Quality of Impact, a trade-off that rarely benefits humanity in the long run.
IV. The Impact on Innovation: Quality vs. Quantity
One of the most dangerous side effects of planned obsolescence is how it reshapes the innovation mindset. When a company’s primary metric for success is a yearly replacement cycle, the engineering focus shifts from transformational leaps to incremental tweaks. We find ourselves trapped in a cycle of “Innovation Theater” — releasing shiny new features that mask the lack of fundamental progress.
The shift in focus creates several systemic challenges:
The Maintenance Trap: In a human-centered world, we should be designing for longevity. However, planned obsolescence forces our best creative minds to spend their energy designing “points of failure” rather than points of resilience. This is a massive diversion of intellectual capital away from the wicked problems that actually matter to humanity.
Incrementalism vs. Transformation: If you know your product only needs to last 24 months, why solve the difficult problems of battery degradation or heat management for the long term? The “yearly release” schedule creates a treadmill effect where we are running faster but not necessarily moving further.
Systems Thinking Failure: We often view a product as a standalone unit, but in a connected world, every device is a node in a larger infrastructure. When we design for a short lifecycle, we create fragility in the entire system. True innovation requires a Stable Spine Audit — evaluating whether the core of our solution is robust enough to support years of evolving “Modular Wings.”
To move the needle, we must stop measuring innovation by the volume of patents or the frequency of launches. Instead, we should measure the durability of the value created. If an innovation cannot stand the test of time, is it truly an innovation, or is it just a temporary distraction?
V. Is it Good for Humanity? (The Human-Centered Audit)
When we apply a Human-Centered Audit to planned obsolescence, the results are deeply conflicted. Innovation should serve as a tool for human empowerment, yet the cycle of forced replacement often creates new forms of dependency and inequality. We must ask: are we designing for the flourishing of the person, or simply for the health of the balance sheet?
To understand the true impact on humanity, we must look at three critical dimensions:
The Ethics of Accessibility: Planned obsolescence often creates a “digital divide.” When software updates outpace hardware capabilities, we effectively lock out those who cannot afford to stay on the upgrade treadmill. If the tools for modern life — education, banking, and communication — require the latest hardware, then deliberate obsolescence becomes a barrier to global equity.
Autonomy vs. Dependency: There is a subtle shift occurring from ownership to renting. Through un-repairable hardware and “software locks,” users lose the autonomy to maintain their own tools. This creates a fragile relationship where the human is entirely dependent on the manufacturer, eroding the sense of agency that good design should foster.
The Prosperity Balance: Proponents point to the short-term job creation in manufacturing and the “Great American Contraction” as reasons to keep the wheels turning. However, we must weigh these temporary economic gains against the long-term cost of environmental degradation and the loss of organizational agility. A society that spends its energy replacing what it already had is a society that isn’t moving forward.
Ultimately, an innovation strategy that relies on things breaking is fundamentally at odds with a Human-Centered philosophy. If our “Innovation Bonfire” requires us to constantly toss our previous achievements into the flames just to keep the fire going, we haven’t built a fire — we’ve built an incinerator.
VI. The Path Forward: From Obsolescence to Innovation
The shift from a Linear Economy to a Circular Economy requires more than just better recycling; it requires a fundamental redesign of our innovation frameworks. We must move toward Innovation — where the value of a product remains constant or even improves over time, rather than degrading by design.
To transition from a strategy of failure to a strategy of resilience, organizations should embrace three core principles:
Designing for Durability: The next truly “disruptive” move in many industries isn’t adding a new sensor; it’s creating a product that lasts a decade. Durability is becoming a premium feature in a world of disposable goods. By focusing on high-quality materials and Human-Centered engineering, brands can build a legacy rather than just a quarterly report.
The Modular Revolution: We must apply the “Stable Spine” and “Modular Wings” philosophy to hardware. Imagine a device where the core processor (the spine) is built to last, while the specific sensors or interface components (the wings) can be swapped out as technology advances. This allows for evolution without the need for total replacement.
New KPIs for a New Era: We need to stop measuring success solely by unit sales. Forward-thinking companies are moving toward “Value-in-Use” and Experience Level Measures (XLMs). When a company is incentivized by how well a product performs over its entire lifecycle, the motivation to build in failure points disappears.
This isn’t just about “being green”; it’s about Organizational Agility. A company that doesn’t have to reinvent its basic hardware every twelve months can redirect its R&D energy toward solving the deep, systemic challenges that humanity actually faces. It’s time to stop stoking the bonfire with our own waste and start building a fire that truly illuminates the future.
VII. Conclusion: Stoking a Sustainable Flame
As we look toward the future of human-centered change, we must decide what kind of “Innovation Bonfire” we want to build. Is it a flash in the pan that requires the constant sacrifice of resources and consumer trust, or is it a steady, illuminating heat that powers real progress?
Planned obsolescence was a 20th-century solution to a 20th-century problem — the need for rapid industrial scale. But in an era defined by digital transformation and the “Great American Contraction,” the old rules no longer apply. To continue designing for failure is to ignore the wicked problems of our time: climate change, resource scarcity, and the erosion of human agency.
“The true measure of an innovation isn’t how many units we sold this year, but how much better the world is because that product exists ten years from now.”
My challenge to you — the executives, the designers, and the change agents — is this: Stop designing for the landfill. Start designing for the legacy. When we shift our focus from Obsolescence to Resilience, we don’t just save the planet; we save the very soul of innovation.
Let’s stop stoking the fire with our own waste and start building a future that is truly made to last.
Frequently Asked Questions
How does planned obsolescence impact human-centered innovation?
Planned obsolescence often acts as a “wet blanket” on true innovation by forcing creators to focus on incremental tweaks and deliberate failure points rather than solving “wicked problems.” From a human-centered design perspective, it erodes consumer trust and prioritizes short-term sales over long-term value and sustainability.
Can planned obsolescence ever be good for humanity?
Proponents argue it accelerates the adoption curve and provides the R&D capital necessary for major breakthroughs. However, a human-centered audit suggests these economic gains are often offset by environmental degradation, increased e-waste, and the creation of a “digital divide” where only the wealthy can afford to stay on the upgrade treadmill.
What is the alternative to planned obsolescence in design?
The primary alternative is moving toward a “Circular Economy” using a “Stable Spine” and “Modular Wings” philosophy. This involves designing products for durability and repairability, where core components last for years while specific features can be upgraded or replaced, shifting the focus from “quantity of sales” to “value-in-use.”
Image credits: Gemini
Content Authenticity Statement: The topic area, key elements to focus on, etc. were decisions made by Braden Kelley, with a little help from Gemini to clean up the article and add citations.
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.
