Part Four: What Your AI Job Titles Are Hiding
The roles are new. The logic is old. Here’s how to fix the metabolism of your company’s innovation strategy.
Author Note: This is Part Three of a five-part series on Rewilding America’s Creative Economy to Meet the Demands of a Cognitive Industrial Revolution. Each part builds on the last, so I recommend Part One, Part Two , and Part Three for additional context.
And You May Ask Yourself…
I love living in Colorado’s Front Range. There’s a lot that makes it special. In Denver, our immersive theater scene is world-class. A couple of years ago, I had one of those full-circle moments when I met David Byrne during his time here, working with the Denver Center for the Performing Arts on a world premiere theater project grounded in neuroscience.
We talked some. I kept my cool on the outside. But inside, it was still the early 1980s, and I was still the neurodiverse kid in southeastern Kentucky, hard-tuned to MTV as a lifeline to the world beyond the mountains and finding myself deep in the Talking Heads Once in a Lifetime video. The lyrics spinning out of an old TV the size of a washing machine.
The beat was hypnotic. The voice was jittery. The lyrics seem disjointed. But to me, they felt like the most logical thing I had ever heard.
You may ask yourself, "Where does that highway go to?"
You may ask yourself, "Am I right, am I wrong?"
And you may ask yourself… how did I get here?
These were flares fired straight from the heart of American culture. I’ve been seeing them again these days. Not just in my work, but in how companies are responding to organizational creativity in the age of AI.
Signals Beneath the New AI Job Roles
You can spot a system’s capability gaps by looking at the roles it invents to cover them.
In a recent New York Times Magazine piece, Robert Capps outlines 22 jobs likely to emerge in the AI era. He sorts them into three categories: trust, integration, and taste. The roles are plausible. But the deeper signal isn’t in the job titles. It’s in what they’re compensating for.
Hiring is massively important and the old playbook has shifted. That’s why leaders must be sure they’re hiring in ways that build organizational creativity across their entire innovation ecosystem as opposed to just one siloed department.
If your soil is depleted, nothing takes root no matter where you plant it. The roles Capps describes are reactive. They offer a patch for fear but may unintentionally reinforce fragmentation between the parts of a company that need to work more closely. In a cognitive industrial revolution, what you call a job role matters less than whether a company’s culture can absorb complexity and generate resonance.
The roles Capps describes are symptoms of creative intelligence deficits within organizations. They signal a deeper tension that AI alone can’t resolve: meaning that doesn’t scale.
To thrive now, trust, integration, and taste are best cultivated through Agile Imagination: a system-level capacity for teams to turn uncertainty into decision-grade insight and translate it into creativity that drives business outcomes.
Without it, organizations spin their wheels, launch redundant pilots, and misread market shifts.
Trust Is More Than Compliance. It’s the Coherence of Insight.
Capps references roles such as AI ethicists and auditors, which are useful but still grounded in procedural logic. Compliance is necessary. The coherence of your intelligence is strategic. If trust in your data and insights lives only in the risk function, you’re downstream from the real work. In a complex system, trust operates as a regenerative capability that ensures the intelligence you act on is sound, even when it's incomplete. It is how meaning coheres in the face of uncertainty.
Integration Is More Than Implementation. It’s Intelligence Flow.
The rise of “AI integrators” and “systems plumbers” shows that technical bottlenecks are real. But implementation alone won’t fix brittle structures. Quality integration demands intelligence flow, how insights move across disciplines, roles, and time horizons to inform a coherent strategic choice. Without this, your organization’s ability to act on the intelligence it gathers stalls.
Taste Is More Than Subjectivity. It’s Signal Intelligence.
Capps references Rick Rubin as a symbol of taste. That’s a start. But in a volatile environment, taste is a disciplined practice of signal intelligence. It’s the capacity to detect what matters before the data confirms it. It’s your early-warning radar for relevance.
The Frame Is Still Role-Based. The Shift Is Systemic.
The article maps job titles. It doesn’t map relationships. That’s the flaw. These aren’t discrete roles to fill. They’re signals that your systems lack connective tissue. Skill gaps matter. But focusing on job titles is the feel-good play. It gives the illusion of control without questioning the conditions that necessitated the role. Don’t staff symptoms. Build systems that metabolize them.
When you hire to solve for coherence, but your culture can’t metabolize tension, you’re not building strategy. You’re staging triage.
Here’s how to spot a system that’s triage staffing:
Job titles proliferating without role clarity
Redundant pilots solving the same innovation problem
Internal tool adoption outpacing actual decision alignment
Risk functions setting culture instead of enabling it.
The Work of Creativity
What if artificial intelligence is our big chance to make the work of creativity and innovation more human than ever?
In previous articles, I’ve shared that the length of tasks AI can complete with 50% reliability has doubled every seven months for six consecutive years. At that pace, by 2030, machines could independently design creative campaigns, build apps, or run multi-day workflows that previously required expert teams to manage.
This may be our Moore’s Law of Organizational Creativity. As AI, networks, and cognitive science converge, the capacity of human-AI teams to generate and execute novel ideas may be accelerating, too. But are organizations ready to co-create with AI at scale?
The new competitive divide isn’t technical. It’s cognitive, emotional, and social.
Agile Imagination is essential in the cognitive industrial revolution. It brings a DevOps-like mindset (Development and Operations) into creative work, aligning teams around collaboration, automation, and continuous improvement. When applied with intention, it becomes a way to move through complexity. Teams can turn uncertainty into insight, friction into momentum, and ambiguity into shared direction.
This capacity doesn’t emerge on its own. Wondervation is the system that builds it over time. It’s a method for generating intelligence under pressure, and it gives teams a rhythm for making creativity sustainable. The performance signal is Creative Brain Capital. The outcome is Agile Imagination, not as a one-time spark, but as a durable operating capacity for innovation.
The reflex to respond with new job titles is understandable. It feels concrete. It gives us something to hire for. However, it also reveals something deeper: we’ve trained ourselves to treat emerging tension as a staffing issue, rather than a shared team challenge in creative cognition.
The noise around AI and the future of work often misses the point. We aren’t lacking ideas. We’re recycling anxiety and mapping old corporate assumptions onto new technological shocks without changing the system underneath.
The Gray-Out
Some days it feels like we are creeping toward a sterile future of hyper-efficient, AI-generated sameness. A world rich in answers but barren of soul, where every product is optimized and every experience is frictionless, but nothing lands and sticks. Optimization becomes its logic, tightening the system until the friction that once signaled meaning begins to disappear.
Fifteen years ago, after experiencing corporate burnout, I decided to get an MFA (Master of Fine Arts). During that time, I taught the foundation class for undergraduates. One of the first lessons was color theory. I taught my students that colors possess a secret logic, a kind of relational physics. Pairing complementary hues, such as blue and orange, creates vibrancy. Each sharpens the other. That tension carries the charge that makes color sing.
But mix all the pigments together without clarity or care, and the result is always the same: a noncommittal, neutral gray. Not because the inputs are wrong, but because the process erases their distinctions.
It is the inevitable result of wielding a powerful tool without the wisdom to guide it.
Large AI models function like a wheel of endless pigments. Trained on vast cultural inputs, they are designed to remix, smooth, and synthesize. What they generate reflects what’s most statistically coherent. Not because they are trying to suppress variation, but because that is the math behind their predictions.
As this logic gets applied across culture into our stories, products, experiences, and decisions. A believable pattern seems to emerge. Tension softens. Surprise becomes rare. Emotional depth thins out. The work still looks polished. But it no longer moves.
That creeping neutral is what I’ve named the Gray-Out.
The Gray-Out did not appear from nowhere. Its logic traces back through the cultural scaffolding we’ve been building for decades.
In Lois Lowry’s The Giver, sameness becomes stability. Emotion is leveled. Color disappears. That future doesn’t collapse. It flattens.
Aldous Huxley gave us something similar in Brave New World. Pleasure softens every edge until even discontent feels excessive. In that world, the tax on comfort is the loss of clarity.
Herbert Marcuse saw it coming, too. In One-Dimensional Man, he warned of systems so efficient at satisfying manufactured needs that real alternatives get absorbed before they have a chance to live.
All three offer a different lens. Taken together, they frame the deeper pattern: systems that over-optimize eventually erase the conditions for meaning.
Cultural metabolism depends on friction, contrast, and variation. These are not accidents. They are the raw material for emotional and cognitive movement.
Economic systems know this. Joseph Schumpeter coined the term "creative destruction," referring to the idea that innovation emerges through disruption. Chicago Booth School Economist Ufuk Akcigi defines it as the cycle that clears out incumbents, not just to make room, but to keep growth adaptive and alive.
When that cycle slows down, consequences build. Outdated systems persist. New ideas lose their grip. The signal fades. Sometimes, it stops moving altogether.
That kind of stall is what the Gray-Out reveals. The aesthetic goes flat. The emotional system dulls. And the cultural memory begins to blur.
The Data Reveals the Drift
The Gray-Out is already embedded in how we use artificial intelligence across creative systems.
A recent Gallup study shows that most employees are utilizing AI for idea generation and consolidation. These are surface-level tasks. The data tells a story of tools being used to produce faster, not to think differently. AI has become a personal assistant for productivity, not a shared system for cognitive stretch.
Team creativity suffers in this model. Collaboration stalls because the technology remains individualized. What could be a platform for new collective insight is being shaped into another tool for isolated output.
The drift shows up in leadership. Senior executives are among the least likely to use AI for exploration. They tend to trust it for confirming decisions they have already made, not for challenging those decisions. What appears as caution may be a deeper reflex: staying grounded in systems that reward certainty and penalize ambiguity.
These usage patterns reveal a structural mismatch. We are integrating fast tools into slow cultures. We are building capacity for speed without adjusting the systems that metabolize new signals into shared understanding.
This drift is rarely accidental. It is often reinforced by leaders who choose familiarity over friction. Strategic discomfort gets deferred, and outdated models stay in place because changing them would mean challenging the roles and rhythms that protect executive certainty.
Some may argue that AI, in itself, is not the cause of cultural stagnation. Fair enough. The Gray-Out doesn’t emerge from the tools, but rather grows from how we deploy them, how we shape their training data, and the choices we make (or fail to make) in their design. Generative systems are probabilistic engines. They reflect what we ask of them.
The Gray-Out is the cultural result of deploying this technology widely without any balancing force. With AI, unique anomalies, radical ideas, and new aesthetics are seen more as statistical mistakes to be corrected rather than as sources of future innovation. Progress stalls into endless, subtle variations of the past. Culture stops being a lively, evolving force and instead becomes a hall of mirrors, constantly remixing what has already been done.
Moving Beyond the Gray-Out
The Gray-Out spreads quietly. It moves through choices that seem efficient, but gradually wear down the system’s ability to metabolize creative tension. Over time, the conditions for meaning begin to break apart.
Its spread follows three key vectors:
1. Prioritizing metrics over meaning
When success is defined by what’s easiest to measure, harder questions recede. Engagement becomes the outcome. Efficiency becomes the goal. What cannot be captured in a traditional dashboard begins to feel optional. The result is drift. We lose contact with why the work mattered in the first place.
2. De-risking creativity into predictability
In systems shaped by pressure and fear, safety becomes strategy. Ideas that don’t offer early proof are set aside. Surprise is treated like threat. Projects are forced to resolve before they get a chance to reveal. What gets funded is what fits, not what shifts. Innovation shrinks to what feels familiar enough to survive.
3. Automating taste through design logic
When algorithms curate what we see, small acts of discernment fade. Discovery becomes passive. Judgment recedes behind personalization. Every time we unthinkingly accept an algorithm's recommendation for a book, a film, or a piece of music, we outsource a small act of human judgment. This is a subtle but profound transaction. Over time, it atrophies our capacity for aesthetic discernment. We forget how to choose and, eventually, we forget what we even like.
These are signals of a crisis in the human spirit. The system is still running, but its metabolism for difference has started to slow. Meaning struggles to land. The signal doesn’t break through. It diffuses.
The Gray-Out creates a world where our choices are made for us, our tastes are suggested to us, and our innate capacity for wonder is engineered out of our lives. It is a comfortable cage that slowly but surely erodes our ability to be the authors of our own experience. For businesses, this leads to a brutal race to the bottom where the only differentiator is price. For individuals, it leads to the atrophy of agency.
Wondervation is the process that prevents these from becoming our new status quo. It is a structured method to train teams to work alongside AI as a co-creative partner. It helps them to learn to interrogate AI outputs, rather than outsource their own. Wondervation builds team capacity through disciplined creative rhythm.
Agile Imagination is the capability that grows from that rhythm. It is a mindset for using creativity as change management. It is the shared creative performance of an innovation system that knows how to navigate uncertainty and still achieve traction.
Wondervation is implemented as a regular practice across the three vectors of a team’s Creative Brain Capital:
Cognitive fights the generic by giving chaos a novel and resonant form.
Emotional fights automated taste with the perspicacity of human experience.
Social fights for uniformity by building communities around unique, challenging truths.
The Gray-Out is the world we get by default. The world of wonder, color, and meaning is the one we must choose to build. It requires active, courageous, and sustained creative work. It requires us to embrace a repeatable system of practice-based creativity.
The Olympian & The Streaming Service
To see what metabolized creativity looks like as a series of deliberate choices, consider the case of Allyson Felix. Her story is a playbook for how to translate systemic friction into a new operational model.
In 2019, Felix, already the most decorated U.S. track and field athlete in history, was offered a contract renewal from Nike. The offer included a reported 70 percent pay cut during her pregnancy. Speaking publicly about it carried risk. Remaining silent would have reinforced the problem.
Instead of trying to reform the system from within, she created a new one.
Felix co-founded Saysh, a lifestyle and footwear brand designed by and for women. The company didn’t just respond to a market gap. It challenged the logic that created the gap in the first place. Saysh designed shoes for women’s feet and introduced a ground-breaking Maternity Returns Policy. If a customer’s foot size changed during pregnancy, she could exchange her shoes at no additional cost.
Her story is a live case study in the full realization of these skills working together to metabolize constraints and transform them into possibilities. This same process is available to systems analysts who see a new way to connect data, logistics managers who reframe a bottleneck as an opportunity, and any arts and culture leader willing to question the 'why' behind their 'what'.
I’m not promoting an anti-incumbent narrative here. The capacity to metabolize creative tension into value is a discipline, not an inherent trait of size or agility. I’m making a pro-metabolism argument. Any organization, from a startup to a global giant, can suffer from creative entropy or build systems to reverse it. The challenge for incumbents is often the weight of their own success, which can calcify systems.
For comparison with Felix, consider Netflix’s content strategy from 2020 to 2022. In that period, the company dramatically increased its pace of original production without a matching investment in narrative coherence. Audiences began to disengage. Nearly one in five viewers abandoned their session before selecting a title, citing choice overload and a lack of emotional clarity. (It's an issue Netflix still grapples with today.) Series like Jupiter’s Legacy, built with data-backed expectations and high production budgets, were canceled after one season due to low engagement and weak resonance.
By 2023, Netflix began to pull back. U.S. production volume dropped significantly, and the company shifted back toward curated storytelling, showrunner autonomy, and fewer, deeper projects. The shift reflected a deeper realization: systems that optimize for precision without metabolizing meaning often find themselves on shaky ground.
Neither Felix and her startup team nor the corporate team at Netflix built their creative strategy around inspiration. Both moved through the discomfort of creative disruption, reframed value, and translated systemic creative tension into different kinds of cultural foresight and response. One generative. One corrective.
This is what Wondervation prepares a team to do. It’s the repeatable method that builds the shared capacity Felix demonstrated, and that Netflix had to rediscover through constraint.
That capacity is Agile Imagination. It allows teams to hold what feels unresolved until it forms into something that others can follow, use, and grow.
A 5-Minute Diagnostic for Agile Imagination
The Gray-Out is a systemic drift that degrades your organization's ability to see clearly. To counter it, you must learn to detect it. This is fundamentally an intelligence-gathering operation. Use these prompts, with your team or for your own analysis, to pinpoint where your organization's intelligence-gathering metabolism is weak.
The Metric Test:
Name one project or idea that was sidelined not because it was bad, but because a pre-existing KPI couldn't capture its value. What unmeasured potential was lost when you prioritized what was easy to measure over what was meaningful?The Friction Test:
In your last major creative review, identify the moment of greatest tension or ambiguity. Was that friction explored as a source of energy and potential, or was it immediately "solved" and smoothed over to achieve a false consensus? What did that "solution" cost the final product?The Agency Test:
Where in your workflow are you outsourcing a critical decision to data or an algorithm that would be better served by human judgment, taste, or intuition? Are you using technology as a partner for judgment or as a machine for justification?
Recognizing these patterns is the first, non-negotiable step toward building Agile Imagination and creating the conditions for creativity to survive.
UP NEXT: PART FIVE
We’ve named what ails us: The Gray-Out. And we’ve identified the capacity required to counter it: Agile Imagination. It’s a team-based capability built through deliberate, repeatable practice. In the final part of this series, I’ll walk you through the Wondervation process: a six-phase method for growing Agile Imagination at the team level and embedding creative metabolism into your operating system. This is the work.
©2013-2025 Theo Edmonds | All Rights Reserved.
This article contains original intellectual property. No part of it may be reproduced, distributed, or adapted without attribution. Quotation or reference is permitted for non-commercial use with proper credit. The views expressed here are mine alone and do not necessarily represent those of any affiliated organization.
Co-Edit Statement
As a neurodiverse writer, I use AI to support editorial clarity and structure. The conceptual frameworks, metaphors, and systems logic reflect my thinking, grounded in lived experience and interdisciplinary research. AI contributed to refining language, not generating ideas. Original analysis, arguments, and insights are my own.
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