Author Archives: Chateau G Pato

About Chateau G Pato

Chateau G Pato is a senior futurist at Inteligencia Ltd. She is passionate about content creation and thinks about it as more science than art. Chateau travels the world at the speed of light, over mountains and under oceans. Her favorite numbers are one and zero. Content Authenticity Statement: If it wasn't clear, any articles under Chateau's byline have been written by OpenAI Playground or Gemini using Braden Kelley and public content as inspiration.

Measuring the Unmeasurable – Metrics for Curiosity, Trust, and Openness

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LAST UPDATED: December 8, 2025 at 2:34PM

Measuring the Unmeasurable - Metrics for Curiosity, Trust, and Openness

GUEST POST from Chateau G Pato

In the relentless pursuit of Human-Centered Change and innovation, we face a critical paradox: the most impactful drivers of breakthrough ideas—things like curiosity, trust, and openness—are often dismissed as “soft” or “unmeasurable.” We diligently track KPIs related to output, revenue, and efficiency, yet overlook the very inputs that foster an environment where these outputs can thrive. This is a profound mistake. What gets measured gets managed. What isn’t measured often languishes.

To truly build resilient, innovative organizations, we must unlearn the rigid assumption that only direct, quantitative metrics hold value. Instead, we must embrace the art and science of inferential measurement, building a mosaic of data points that, together, illuminate the state of these crucial, yet intangible, human qualities. These are not about vanity metrics; they are about understanding the health of your innovation ecosystem.

Visual representation: An infographic illustrating how indirect metrics (e.g., questions asked, cross-functional collaboration, idea submissions) can be proxies for Curiosity, Trust, and Openness.

The Triangulation Approach: Unlocking Hidden Insights

Measuring the unmeasurable is not about finding a single, perfect number. It’s about triangulation: combining multiple, often indirect, indicators to create a robust picture. Here’s how we can approach curiosity, trust, and openness:

1. Measuring Curiosity: The Fuel for Exploration

Curiosity is the impulse to explore, learn, and question. It drives individuals to seek new solutions and challenge assumptions. To measure it, look for behavioral proxies:

  • “Why?” Question Frequency: In meetings, workshops, and project discussions, track the number of times individuals or teams ask fundamental “why” questions rather than just “how” or “what.” A higher frequency suggests deeper inquiry.
  • Cross-Departmental Inquiry: Track the number of informal (coffee chats) and formal (shadowing, interviews) information-seeking interactions employees initiate outside their immediate team or department. Tools like communication platforms or internal social networks can help monitor this.
  • Learning Resource Engagement: Monitor engagement with internal learning platforms, external courses, industry reports, and innovation labs. How many unique topics are explored? How many non-mandatory courses are completed?
  • Idea Submission Diversity: Beyond just the number of ideas, analyze the breadth of domains or problem spaces addressed in idea submissions. Are people exploring completely new territories, or just iterating on existing ones?

By combining these, you can gauge whether your culture is merely allowing curiosity or actively fostering it.

2. Measuring Trust: The Foundation of Collaboration

Trust is the belief that others will act in good faith and that one’s vulnerabilities will not be exploited. It is essential for sharing nascent ideas and taking risks. Proxies for trust include:

  • Psychological Safety Index: Utilize anonymous surveys (e.g., Google’s Project Aristotle model) to gauge employees’ comfort level with speaking up, admitting mistakes, and sharing unconventional ideas without fear of negative repercussions. Focus on statements like, “If I make a mistake on this team, it is not held against me.”
  • Feedback Loop Activity: Track the volume and bidirectional nature of constructive feedback. Are people giving and receiving candid feedback freely, or is it primarily top-down and formal? High-trust environments foster frequent, informal feedback exchanges.
  • Cross-Functional Resource Sharing: Beyond simple collaboration, look at the willingness to share sensitive information, critical resources, or even temporary team members between departments. This indicates a deeper level of inter-team trust.
  • Conflict Resolution Patterns: Observe how conflicts are resolved. Is it through formal escalation (low trust) or direct, informal discussion and negotiation (high trust)?

A thriving innovation culture cannot exist without strong inter-personal and inter-team trust. Building this foundation is not soft; it is strategic.

3. Measuring Openness: The Gateway to New Possibilities

Openness is the willingness to consider new ideas, approaches, and perspectives, even if they challenge existing paradigms. It’s about shedding cognitive biases and embracing ambiguity. Metrics for openness include:

  • Experimentation Rate: Track the number of small-scale experiments, MVPs, and pilots initiated monthly. More importantly, measure the learning cycle time—how quickly experiments are run, results analyzed, and decisions made.
  • Diversity of Input Sources: Where do new ideas originate? Are they solely internal, or is there a strong influx from external sources (customer co-creation, academic partnerships, competitor analysis, diverse new hires)?
  • Resistance-to-Change Index: Use pulse surveys or qualitative interviews to identify explicit and implicit resistance to new processes, technologies, or strategies. Look for patterns in objections—are they evidence-based, or fear-based?
  • Leadership Receptiveness: Assess how often leaders genuinely seek out dissenting opinions, actively listen to junior staff ideas, and publicly acknowledge when their own assumptions were challenged and proven incorrect. This sets the tone for the entire organization.

Ultimately, openness determines an organization’s capacity for true transformation, not just incremental improvement.

Case Study 1: Reinvigorating a Stagnant R&D Lab

Challenge: Declining Innovation Output in a Legacy R&D Division

A global pharmaceutical company (“PharmaCo”) noticed its once-pioneering R&D lab was becoming risk-averse, producing fewer novel compounds. Direct output metrics remained stable due to incremental improvements, but true breakthrough innovation had stalled.

Measurement Intervention: Curiosity & Openness Proxies

PharmaCo introduced new “soft” metrics alongside traditional KPIs:

  • Curiosity: Tracked participation in cross-disciplinary “Lunch & Learn” sessions (informal scientific sharing), internal publication of research outside one’s core specialty, and spontaneous “deep dive” requests to the central knowledge repository.
  • Openness: Monitored the number of “negative result” reports (failures leading to new insights), external collaboration proposals, and employee-initiated “exploratory project” pitches outside core mandates.

The Innovation Impact:

By explicitly measuring and rewarding these proxies, PharmaCo shifted its culture. Within two years, cross-disciplinary “Lunch & Learns” increased by 300%, and “negative result” reports (previously buried) became celebrated learning documents. This led to a 15% increase in novel drug candidate proposals from unexpected combinations of research, demonstrating that measuring inputs can drive groundbreaking outputs.

Case Study 2: Building Inter-Departmental Trust in a Tech Giant

Challenge: Siloed Teams and Blame Culture Post-Acquisition

A rapidly growing tech company (“MegaTech”) experienced significant friction and blame-shifting between its engineering and product teams following a major acquisition. This eroded trust, slowed development cycles, and increased employee turnover in critical roles.

Measurement Intervention: Trust & Openness Proxies

MegaTech launched a Human-Centered Change initiative focusing on trust. Metrics included:

  • Trust: Anonymous pulse surveys on psychological safety (e.g., “I feel safe disagreeing with my manager”), and “shadowing days” where engineers spent a day with product teams, and vice versa.
  • Openness: Tracked the number of “feedback sessions” where teams collectively reviewed each other’s work (not just managers), and the explicit mention of “lessons learned” in post-mortems, rather than just “root causes.”

The Innovation Impact:

Over 18 months, the psychological safety score increased by 25%. More importantly, the quality and speed of conflict resolution improved dramatically, and employee retention in critical engineering roles stabilized. By making trust and openness measurable, MegaTech systematically dismantled silos, fostering a culture where inter-team learning and mutual respect became the norm.

Conclusion: The Strategic Imperative of Intangible Metrics

Ignoring curiosity, trust, and openness as “unmeasurable” is a strategic blunder. These are not optional nice-to-haves; they are the fundamental operating system of an innovative enterprise. By adopting a triangulation approach—combining observable behaviors, qualitative insights, and intelligent proxies—leaders can gain unprecedented visibility into the health of their innovation culture. This shift from purely output-driven metrics to input-driven insights is the next frontier of Human-Centered Change. Start measuring these “unmeasurables” today, and watch your innovation capacity soar.

“If you only measure the easy things, you’ll miss the most important things.” — Braden Kelley

Frequently Asked Questions About Measuring Intangible Metrics

1. Why are curiosity, trust, and openness considered “unmeasurable”?

They are often considered unmeasurable because they are subjective human qualities that cannot be directly counted or quantified in a simple numerical way. Traditional metrics focus on outputs (e.g., sales, production), whereas these are inputs that describe psychological states and behaviors, requiring more nuanced, indirect measurement approaches.

2. What is the “triangulation approach” to measurement?

The triangulation approach involves using multiple, different data sources and types (e.g., surveys, behavioral observations, usage logs) to gain a comprehensive understanding of an intangible quality. Instead of relying on a single “perfect” metric, it combines several indirect indicators to form a more robust and reliable picture.

3. How can I start measuring these in my own team?

Start small with a single proxy. For curiosity, try tracking “why” questions in team meetings. For trust, implement a quick, anonymous psychological safety pulse survey. For openness, monitor the diversity of idea sources. The key is to pick observable behaviors or simple self-reports and consistently track changes over time, then discuss the insights with your team.

Your first step toward measuring the unmeasurable: Convene your innovation leadership team. Instead of asking, “What new products did we launch?” ask, “What new questions did our team ask last month that challenged our core assumptions?” Document these, and you’ve begun to measure curiosity.

Extra Extra: Because innovation is all about change, Braden Kelley’s human-centered change methodology and tools are the best way to plan and execute the changes necessary to support your innovation and transformation efforts — all while literally getting everyone all on the same page for change. Find out more about the methodology and tools, including the book Charting Change by following the link. Be sure and download the TEN FREE TOOLS while you’re here.

Image credit: Pexels

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The Secret Skill of the Modern Innovator

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Psychological Flexibility

LAST UPDATED: December 7, 2025 at 12:43AM

The Secret Skill of the Modern Innovator - Psychological Flexibility

GUEST POST from Chateau G Pato

We celebrate the external skills of the innovator: the design thinking workshops, the pitch decks, the engineering prowess. But the greatest innovation hurdle is not external; it is internal. It is the human brain’s innate desire for certainty and comfort. Innovation, by definition, requires uncertainty, risk, and repeated failure. The skill that allows an individual and an organization to navigate this emotional terrain is Psychological Flexibility.

Psychological Flexibility is the ability to fully contact the present moment—including undesirable thoughts, feelings, and sensations—and, depending on what the situation affords, persist or change behavior in the service of chosen values. It’s the opposite of rigidity. Rigidity manifests in the innovation space as Idea Attachment (holding onto a failed concept too long) or Emotional Avoidance (shying away from projects that induce fear of failure). True Human-Centered Change demands that we unlearn avoidance and embrace the discomfort as a necessary input for growth.

Visual representation: A diagram illustrating the key components of Psychological Flexibility: Acceptance, Cognitive Defusion, Contact with the Present Moment, Self-as-Context, Values, and Committed Action.

The Four Practices of the Flexible Innovator

To cultivate this internal agility, innovators must master four practices adapted from Acceptance and Commitment Therapy (ACT):

1. Cognitive Defusion (Disentangling from Thoughts)

Innovation is besieged by self-doubt: “This idea is stupid,” “The market will never accept this,” or “I’m going to lose my job.” Cognitive Defusion is the practice of seeing thoughts not as literal truths, but as mere words or mental events. The flexible innovator does not try to fight or suppress the negative thought; they simply observe it and continue acting in alignment with their goal. The key phrase is, “I am having the thought that I will fail,” instead of “I will fail.” This distance creates mental space for bold action.

2. Values Clarity (Knowing the North Star)

Change often feels chaotic. Psychological Flexibility requires a clear, defined sense of Values Clarity. Why are we innovating? Is it to enhance customer dignity, improve planetary health, or simplify an essential process? When the inevitable setback occurs (a failed MVP, a budget cut), the innovator relies on their North Star values to guide the next move. They don’t pivot arbitrarily; they pivot toward the value, not away from the pain. This turns a moment of crisis into a Commitment Test.

3. Acceptance (Embracing the Error)

Innovation failure is data, but emotionally, it feels like rejection. Acceptance is not resignation; it is the active, non-judgmental embrace of uncomfortable emotions (frustration, anger, sadness) and market realities (the product is flawed). The rigid innovator wastes energy trying to rationalize or ignore the failure. The flexible innovator accepts the emotional hit, processes the data, and redirects that saved energy into Course Correction. This radically accelerates the Build-Measure-Learn loop.

4. Self-as-Context (Fluid Identity)

Many innovators tie their personal worth to the success of their project. When the project fails, their self-esteem is crushed. Self-as-Context means recognizing that one’s identity is the container holding all experiences, not the experiences themselves. The failure of Project X does not mean “I am a failure.” It means “The container is holding the experience of a failed project.” This internal decoupling protects the innovator’s psychological resources, allowing them to remain resilient and return to the challenge without the debilitating fear of identity loss.

Case Study 1: The Media Company’s Content Pivot

Challenge: Market Collapse of Traditional Revenue Stream

A mid-sized media conglomerate (“GlobalNews”) saw its core print advertising revenue rapidly evaporate. The leadership team had spent years successfully managing a highly stable business, and the sudden shift induced profound anxiety and Cognitive Fusion with limiting beliefs (“We are print people,” “Digital is too chaotic”).

Flexibility Intervention: Values-Driven Defusion

The CEO mandated a Human-Centered Change program focusing on psychological skills. The team practiced Cognitive Defusion to observe their limiting thoughts without acting on them. The core value was redefined from Delivering Print to Delivering Trusted Information. They accepted the pain of losing their old model (Acceptance) and used the value of Trusted Information to pivot.

  • The pivot was towards building a paywalled, high-fidelity data analytics service for businesses, not just a news website.
  • The value (Trust) defined the new product’s identity and its business model, moving them out of the volatile ad market.

The Innovation Impact:

By using their core value as the flexible guide and practicing defusion, the team avoided the rigid response of simply cutting costs or doubling down on failed strategies. They achieved a strategic pivot within 18 months, leveraging their expertise in a new, high-growth format, driven entirely by their newfound psychological tolerance for market upheaval.

Case Study 2: The Software Team’s Feature Kill

Challenge: Attachment to a High-Cost, Low-Value Feature

A software development team (“HelixTech”) spent six months and significant budget on a highly complex, technically impressive new feature. Upon launch, Big Data revealed near-zero user adoption. The product manager, having personally championed the feature, experienced intense Idea Attachment and resisted the recommendation to “kill” the feature.

Flexibility Intervention: Acceptance and Self-as-Context

The leadership team intervened by applying the Acceptance and Self-as-Context practices. They explicitly coached the manager: “The failure of Feature Z is not a failure of your competence. It is data showing an unmet customer need.” They asked the manager to practice accepting the data and the resulting negative emotion (frustration/embarrassment) as temporary states, not definitions of self.

  • The manager was then empowered to lead the decommissioning project, re-framing the effort as cleaning up the roadmap (a new valued action).
  • The time saved was immediately reinvested in a small, customer-validated MVP, allowing the manager to instantly re-engage in creative work.

The Innovation Impact:

By separating the innovator’s identity from the idea’s outcome, HelixTech avoided the common inertia where teams waste months supporting defunct features. The quick Acceptance and re-framed Committed Action allowed the team to recover the initial investment of time and maintain high morale, reinforcing the organizational value that failure is simply a learning input.

Conclusion: Building Resilient Organizations

Psychological Flexibility is not a soft skill; it is the hardest skill in innovation. It is the prerequisite for speed, resilience, and true market responsiveness. Organizations focused on Human-Centered Change must recognize that the biggest brake on progress is the collective rigidity of their people, fueled by fear and the desire for emotional comfort. By embedding the practices of Cognitive Defusion, Values Clarity, Acceptance, and Self-as-Context, leaders don’t just build resilient innovators; they build resilient organizations capable of navigating any disruptive shift.

“Innovation is a contact sport. You must be willing to feel the pain of rejection and keep moving towards successful value creation that can overcome market inertia.” — Braden Kelley

Frequently Asked Questions About Psychological Flexibility

1. How does Psychological Flexibility differ from simple Resilience?

Resilience is typically defined as the ability to bounce back from adversity. Psychological Flexibility is a broader, active skill set: the ability to engage fully with painful or undesirable thoughts and feelings (Acceptance) while simultaneously taking effective action aligned with one’s values. It’s about adapting behavior in the face of internal discomfort, not just enduring it.

2. What is “Cognitive Fusion” and why does it stop innovation?

Cognitive Fusion is when a person believes their thoughts are literal truths that must be acted upon or obeyed (e.g., “I am stupid” means I cannot try the hard project). This stops innovation because it prevents the individual from taking valued risks when the inevitable, self-critical thoughts arise. Cognitive Defusion is the opposite skill, allowing the innovator to observe the thought without obeying it.

3. How can a team encourage the practice of “Acceptance”?

Teams encourage Acceptance by making failure an explicit, non-punitive data event. This involves celebrating the learning derived from a failed experiment, publicly discussing the difficult emotions that arose, and immediately reassigning resources to the next valued action. It shifts the culture from failure avoidance to learning acceleration.

Your first step toward cultivating Psychological Flexibility: The next time a new project feels overwhelming or terrifying, pause. Don’t fight the fear. Simply acknowledge it by saying internally, “I am having the feeling of fear, and I choose to start the first task anyway.” Use your values, not your feelings, to guide your immediate action.

Extra Extra: Because innovation is all about change, Braden Kelley’s human-centered change methodology and tools are the best way to plan and execute the changes necessary to support your innovation and transformation efforts — all while literally getting everyone all on the same page for change. Find out more about the methodology and tools, including the book Charting Change by following the link. Be sure and download the TEN FREE TOOLS while you’re here.

Image credit: Pexels

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How to Make Virtual Experiences Feel Real

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Designing for Presence

LAST UPDATED: December 6, 2025 at 11:05AM

How to Make Virtual Experiences Feel Real

GUEST POST from Chateau G Pato

In the world of Human-Centered Innovation, the most powerful tool is often one that can induce a profound psychological shift. Virtual Reality (VR) promises this, but only if it can successfully convince the brain that the experience is real. This is the concept of Presence, and it is defined by the degree to which a user’s consciousness ignores the physical world and accepts the virtual world as the immediate, sensory reality.

Why does this matter for business strategy? When presence is achieved, training is dramatically more effective, collaboration fosters stronger empathy, and therapeutic interventions yield lasting results. When the brain is truly present, the resulting learning and behavioral changes are transferred more reliably back into the real world. We must unlearn the focus on simple immersion and embrace the deep, psychological design principles that create Authentic Presence.

Visual representation: A diagram illustrating the key factors contributing to Virtual Presence: Fidelity, Consistency, and Interactivity.

The Three Pillars of Authentic Presence

Designing for presence requires mastering three non-negotiable psychological and technical pillars. A failure in any one can shatter the illusion of reality, breaking the user’s immersion and effectiveness.

1. Sensorimotor Consistency (No Sickness, No Lag)

The brain’s biggest alarm system is vestibular mismatch (the feeling of motion sickness). If the visual input (seeing motion) does not perfectly match the inner ear’s input (feeling motion), the sense of presence collapses. Therefore, the absolute priority is low-latency tracking (minimal lag) and a high, stable frame rate. When designing a physical training environment, any lag in hand tracking or head movement instantly reminds the user they are wearing a headset. Consistency is not a feature; it is the foundation of reality.

2. Interpersonal Fidelity (The Uncanny Valley of Avatars)

Presence is intensely social. In collaborative VR environments, your avatar and the avatars of your colleagues must move beyond cartoony representations toward Interpersonal Fidelity. This means realistic eye contact, micro-expressions, and hand gestures. The moment you look at a colleague’s avatar and their eyes don’t track your movement correctly — the Uncanny Valley — the emotional connection and, thus, the sense of co-presence are lost. True innovation in virtual meetings must prioritize realistic social cues to enable Authentic Collaboration.

3. Real-Time Physical Agency (The Power to Affect the World)

Presence is cemented when the user can act on the virtual world and receive an immediate, consistent, and logical response. This is Physical Agency. If you reach out to grab a virtual pen and your hand passes straight through it, the brain registers the environment as fake. Every object the user is expected to interact with must have realistic physics, weight, and haptics (via controllers). The ability to truly manipulate the environment is what transforms passive viewing into active engagement and learning.

Case Study 1: High-Stakes Crisis Training

Challenge: Ineffective Role-Playing for Emergency Responders

A national fire and rescue service (“FirstResponse”) found traditional simulation and role-playing exercises to be costly, logistically complex, and emotionally insufficient. Trainees knew they were “faking it,” leading to limited transfer of knowledge when faced with a real-world crisis.

Presence Intervention: Emotional Immersion

FirstResponse implemented VR training for high-stakes emergencies (e.g., collapsed buildings, active hazards). The design team focused heavily on Sensorimotor Consistency (perfect tracking and low lag to prevent sickness during fast movement) and, critically, added immersive audio cues (the sound of debris falling, realistic panic, and muffled radio communications).

  • Trainees reported experiencing the fight-or-flight response identical to real-world scenarios, a direct result of strong presence.
  • The virtual environment allowed for failure consequence (e.g., virtual casualty count), which built muscle memory for managing extreme emotional stress — a key learning outcome impossible to simulate safely otherwise.

The Innovation Impact:

Because the brain experienced the virtual environment as real (Presence), the cognitive and emotional stress responses were authentic. This led to a measured 40% reduction in response time errors during subsequent real-world exercises. The innovation successfully focused on emotional fidelity to drive lasting behavioral change.

Case Study 2: Architectural Co-Design and Empathy

Challenge: Misalignment and Lack of Empathy Between Architects and Clients

A global architectural firm (“FutureBuild”) struggled with design reviews, often finding that clients couldn’t visualize blueprints, leading to late-stage, costly change orders. Furthermore, architects lacked empathy for how a space would truly feel to a non-expert.

Presence Intervention: Shared Physical Agency

FutureBuild adopted shared, mixed-reality co-design sessions. Both the architect and the client (as realistic avatars) could walk through a holographic projection of the building on the physical table.

  • The system prioritized Interpersonal Fidelity by accurately tracking head gaze and pointing gestures between the two people.
  • They emphasized Real-Time Physical Agency: the architect could virtually grab a wall and move it, and the client could “paint” a surface with a different texture, instantly seeing the change.

The Innovation Impact:

By giving the client physical agency within the design, the sense of co-presence allowed for a level of communication and feedback impossible on a flat screen. Clients identified problems (e.g., “The ceiling feels too low when I stand here”) that were based on true spatial feeling, not just interpretation of lines on a page. The firm saw a 60% reduction in late-stage design modifications because they successfully utilized shared reality to accelerate mutual understanding and Human-Centered Decision Making.

Conclusion: Presence as the ROI of Spatial Computing

The return on investment (ROI) for spatial computing is not measured in hardware units sold, but in the intensity of Presence achieved. When you design a virtual experience, you are not building a game; you are constructing a temporary, alternate reality. To be effective, this reality must adhere to the neurological laws of the human mind.

Leaders must mandate that their innovation teams unlearn the focus on simple graphical output and prioritize the three pillars: Sensorimotor Consistency, Interpersonal Fidelity, and Real-Time Physical Agency. When the technology fades into the background, and the reality of the environment takes over, Authentic Presence is achieved—and that is where true, lasting change begins.

“The goal of VR is not to simulate reality; it is to create a reality that is perceived as authentic.”

Frequently Asked Questions About Designing for Presence

1. What is “Presence” in the context of virtual experiences?

Presence is the subjective, psychological phenomenon where a user’s consciousness fully accepts the virtual environment as their immediate, sensory reality, causing them to temporarily forget their actual physical surroundings. It is the key factor enabling effective learning and behavioral transfer from the virtual world to the real world.

2. Why is Sensorimotor Consistency the most critical pillar for Presence?

Sensorimotor Consistency (low lag, high frame rate) is critical because vestibular mismatch — when visual movement doesn’t match inner ear motion — immediately triggers the brain’s alarm systems, causing motion sickness and shattering the illusion of presence. If the brain detects inconsistency, it cannot accept the virtual environment as real.

3. What is the “Uncanny Valley” effect in VR design?

The Uncanny Valley refers to the unsettling feeling that occurs when avatars or synthetic human representations are *almost* perfectly realistic but have small, subtle flaws (like poor eye tracking or delayed micro-expressions). These flaws break Interpersonal Fidelity and cause emotional discomfort, instantly destroying the sense of “co-presence” in a shared virtual space.

Your first step toward designing for Presence: Hold a review session for your existing VR/MR training program. Instead of asking, “Did the user complete the task?” ask, “Did the user physically flinch, hesitate, or exhibit any signs of motion or social discomfort?” Use these physical cues to identify and eliminate the moment where Presence was broken.

Extra Extra: Because innovation is all about change, Braden Kelley’s human-centered change methodology and tools are the best way to plan and execute the changes necessary to support your innovation and transformation efforts — all while literally getting everyone all on the same page for change. Find out more about the methodology and tools, including the book Charting Change by following the link. Be sure and download the TEN FREE TOOLS while you’re here.

Image credit: Unsplash

Subscribe to Human-Centered Change & Innovation WeeklySign up here to get Human-Centered Change & Innovation Weekly delivered to your inbox every week.

How to Use Human-Scale Insights to Pivot Strategy

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Small Data, Big Impact

LAST UPDATED: December 5, 2025 at 3:32PM

How to Use Human-Scale Insights to Pivot Strategy

GUEST POST from Chateau G Pato

Your analytics dashboard can tell you what happened: 70% of users abandoned the checkout process at Step 3. Big Data is superb at identifying this pattern. But it is fundamentally incapable of telling you why that abandonment occurred. Was the font confusing? Was the payment system counter-intuitive? Did the user get distracted by a child? The answer to the why requires Small Data.

Small Data refers to the qualitative, non-numerical, contextual information collected through human observation, deep empathy, and ethnographic research. It is the core of Human-Centered Innovation. Strategy that pivots based solely on aggregated trends risks being perpetually incremental. True, disruptive pivots are always rooted in a single, profound Human-Scale Insight — the realization of an unmet need that Big Data cannot quantify because the need is emotional, procedural, or cultural.

The Three-Step Small Data Strategy Pivot

To effectively leverage Small Data, organizations must embed a simple, three-step human-centered process:

1. Embrace Ethnographic Immersion (Discovery)

Strategy cannot be designed purely from behind a desk. Leaders must mandate and participate in ethnographic immersion. This involves frontline engagement: watching how a customer actually uses a product in their home, observing the communication patterns of a surgical team, or shadowing a field technician. The goal is to collect thick description — detailed, contextual field notes that capture the environment, mood, and exact procedural friction points. This practice requires organizational humility and a commitment to unlearn existing assumptions about the customer.

2. Synthesize for “Job-to-be-Done” (Analysis)

Once Small Data is collected, the analysis must focus on the Job-to-be-Done (JTBD) framework. JTBD moves analysis away from product features toward human motivation. Instead of asking, “Why did they buy our software?” ask, “What progress was the customer trying to make in their life when they hired our software?” The qualitative data often reveals that customers hire your product for a completely different job than you think. This Human-Scale Insight is the most common driver of strategic pivots because it exposes an entirely new market definition.

3. Operationalize the Anecdote (Action)

The single greatest challenge for Small Data is scaling it up against the perceived weight of Big Data. To pivot strategy, the Human-Scale Insight must be translated into a compelling narrative and immediately tested as a Minimum Viable Product (MVP). The anecdote must be operationalized. Instead of saying, “We should change the user interface,” say, “During the home visit, Jane mentioned she feels anxious when the software asks for her social security number three times. We need to test an MVP that reduces that anxiety by asking once and explaining the ‘why’ with clear, non-legalistic language.” This grounds the change in empathy and provides clear, immediate action.

Case Study 1: The Insurance Company’s Claims Process Pivot

Challenge: Low Digital Adoption Despite App Redesign

A major insurance provider (“SecureCo”) launched a highly publicized, expensive app redesign to modernize its claims process. Big Data analytics confirmed the app was technically sound, yet 80% of major claims were still submitted via phone call or physical mail. The Big Data showed what was happening, but offered no useful path for a strategic pivot.

Small Data Intervention: Ethnographic Claims Shadowing

A human-centered innovation team decided to shadow a handful of claimants. They observed one customer, an elderly woman named Helen, trying to submit a complex claim. The Small Data revealed the following Human-Scale Insight: Helen wasn’t confused by the interface; she was terrified of making a single, irrecoverable mistake that would void her payment.

  • The app’s clean, modern interface, which minimized text to look “sleek,” made her feel unsupported.
  • The phone call, despite the wait time, provided the emotional reassurance that a human was accountable for her process.

The Strategic Pivot: Designing for Emotional Safety

The strategic pivot was not a technical fix, but an emotional one. SecureCo unlearned the assumption that speed was the top priority. They redesigned the app to include a permanent, dedicated “Help Desk Chat” button staffed by a specific, named agent for complex claims. They introduced a feature that explicitly allowed the user to undo any step, assuring them that the process was safe. By focusing on the human fear of permanent error (Small Data), the company achieved a 75% digital adoption rate for complex claims within nine months, proving that emotion drives adoption.

Case Study 2: The SaaS Firm’s Enterprise Feature Failure

Challenge: Zero Adoption of a Flagship Enterprise Feature

A B2B SaaS company (“DataStream”) developed a powerful, highly complex “Advanced Analytics Module” for its largest enterprise clients. Despite being a required feature in high-cost contracts, Big Data showed near-zero usage. Usage logs confirmed that every user who clicked the module abandoned it within 30 seconds.

Small Data Intervention: “Desk-Side” Observation

The innovation team conducted in-person, desk-side observation with five key users at a major client. The Small Data analysis showed that the official reason for the product’s existence — “complex data correlation” — was not the user’s Job-to-be-Done. The users were highly stressed analysts who needed a quick snapshot to answer a simple, recurring question from their executive team: “Is this number trending up or down today?”

  • The Advanced Analytics Module required 15 clicks and 5 minutes to generate this answer (procedural friction).
  • The analysts were actually hiring a spreadsheet hack, a complicated but reliable 30-second shortcut they had built themselves.

The Strategic Pivot: The “Executive Answer”

DataStream performed a major strategic pivot, unlearning the notion that “more complex is more valuable.” They immediately launched an MVP dashboard called the “Executive Answer” (Stage 3). This dashboard, which used the same backend data, generated the required snapshot in a single click. The pivot was based entirely on observing five users and understanding their actual Job-to-be-Done. Usage of the original, complex module remained low, but usage of the new, Small-Data-driven dashboard became mandatory within all top-tier accounts, significantly improving client retention.

Small Data as the Change Fuel

Big Data provides the destination (e.g., “Grow revenue 15%”). Small Data provides the ignition — the human-scale insight needed to change course dramatically. Strategic change is often blocked by inertia and a fear of the unknown. By grounding a strategic pivot in a specific, observable human anecdote, leaders can create a compelling narrative that overcomes organizational resistance. The clarity and empathy derived from Small Data is the most potent fuel for Human-Centered Innovation.

“If Big Data is the map, Small Data is the compass that tells you the correct direction of travel.”

Frequently Asked Questions About Small Data

1. What is Small Data and how is it different from Big Data?

Big Data is aggregated, quantitative, and large-scale (the what and how many). Small Data is qualitative, contextual, and human-scale (the why and how). Small Data is collected through deep observation, ethnographic research, and in-depth interviews, focusing on a small number of users to gain deep, empathetic insights into their emotional and procedural friction points.

2. What is a “Human-Scale Insight”?

A Human-Scale Insight is a profound realization about user behavior, often revealed by Small Data, that exposes a latent or unmet need, emotional driver, or procedural friction point. This insight often reframes the “Job-to-be-Done” and is potent enough to drive a strategic pivot—changing not just how a product works, but why the company offers it.

3. Why is organizational “Humility” required to use Small Data effectively?

Humility is required because effective Small Data collection, like ethnographic immersion, demands that leaders and designers unlearn their existing assumptions about the customer and admit that the company may not understand the user’s true needs. It requires leaving the boardroom and observing the customer in their own environment, often revealing uncomfortable truths about product failure.

Your first step toward leveraging Small Data: Choose a product feature with low adoption, but high perceived value. Find three customers who stopped using it. Send a designer or product manager to spend 90 minutes observing them use a competitor’s product. Document the friction points, and use that Small Data to define a simple, empathetic MVP.

Extra Extra: Because innovation is all about change, Braden Kelley’s human-centered change methodology and tools are the best way to plan and execute the changes necessary to support your innovation and transformation efforts — all while literally getting everyone all on the same page for change. Find out more about the methodology and tools, including the book Charting Change by following the link. Be sure and download the TEN FREE TOOLS while you’re here.

Image credit: Pixabay

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Why Authentic Leadership is a Change Accelerator

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The Vulnerable Visionary

LAST UPDATED: December 4, 2025 at 4:01PM

Why Authentic Leadership is a Change Accelerator

GUEST POST from Chateau G Pato

In a predictable world, certainty from the top was a virtue. In today’s hyper-disruptive environment, certainty is a liability. Leaders who pretend to have a flawless roadmap for the next three years — when no one can predict the next three quarters — are seen by their teams as disingenuous or dangerously naive. This destroys the fundamental ingredient for successful change: Trust.

Authentic Leadership, defined by the courage to be vulnerable, transforms this dynamic. Vulnerability in leadership is not passive; it is an active, human-centered strategy that accelerates change by normalizing risk and failure throughout the organization. When the leader admits, “I don’t know the answer, and this path is ambiguous,” they grant every employee the permission to also be uncertain, to experiment, and to speak up when something is wrong. This creates a feedback loop essential for fast, continuous innovation.

The Three Catalysts of Vulnerable Leadership

The Vulnerable Visionary utilizes three specific, repeatable behaviors to drive organizational change:

1. Modeling the Acceptance of Failure

A change initiative often requires teams to unlearn old, successful behaviors and embrace new, untested ones. This transition inevitably involves mistakes. If the leader’s default reaction is to punish mistakes or demand a flawless execution, teams retreat to safe, incremental work. The Vulnerable Visionary, by contrast, must deliberately and publicly recount a recent, significant failure — and explain what they learned from it. This behavioral modeling provides Psychological Safety and shifts the organizational reward structure from minimizing mistakes to maximizing learning velocity.

2. Actively Asking for Help (The Anti-Expert Stance)

The myth of the heroic leader is that they must be the ultimate expert in all domains. The Vulnerable Visionary understands that the complexity of modern challenges exceeds any single person’s capacity. They actively ask their subordinates and cross-functional partners, “What critical blind spot am I missing here?” and “I need your domain expertise to solve this.” This simple act of seeking input not only gathers crucial data but also creates an environment of Collective Confidence, empowering employees who feel their specialized knowledge is genuinely valued at the highest level.

3. Communicating Strategic Uncertainty

In times of massive disruption (e.g., a major technology shift or market collapse), employees crave honesty more than false confidence. The Vulnerable Visionary communicates the high-level vision (the North Star) but admits that the specific path (the GPS route) is still being discovered. Phrases like “We are entering an ambiguous zone” or “We are committed to the customer, but we need to test three different business models to get there” are powerful. This honesty replaces anxiety with reciprocal accountability, turning passive observers into active co-creators of the change journey.

Case Study 1: The Healthcare System’s Digital Transformation

Challenge: Doctor Resistance to a New Patient Portal

A large, national healthcare system (“HealthNow”) launched a costly digital patient portal intended to improve care coordination. The rollout failed because senior doctors, citing the project’s complexity and poor interface, actively resisted inputting data. The CEO, who had championed the project, initially pushed back, fearing it would signal weakness to admit the failure.

The Vulnerable Intervention: Public Ownership of Failure

The CEO decided to shift strategy. In a major staff meeting, she opened with, “My vision for the digital portal was right, but my execution plan failed. That is entirely on me.” She specifically pointed out that she had relied too much on her external tech team and failed to seek adequate, early input from the doctors themselves (admitting a strategic blind spot).

  • She immediately killed the existing portal (modeling the acceptance of costly failure).
  • She announced a new project structure, stating, “We will rebuild this, and the medical staff, not the IT team, will be the ultimate owner and decision-maker.” (Actively asking for help and sharing power).

The Change Accelerator Lesson:

By taking ownership of the failure, the CEO instantly dissolved the defensive posture of the medical staff. The subsequent co-creation process was rapid and effective. The Vulnerable Visionary secured buy-in not through coercion, but through humility and strategic honesty, accelerating a crucial digital transformation that was previously blocked by the leader’s initial need to appear infallible.

Case Study 2: The Energy Company’s Sustainability Pivot

Challenge: Cultural Inertia Against Radical Change

A long-established energy company (“PowerGrid”) needed to pivot from fossil fuels to renewables. The majority of the senior engineering staff, who had built successful careers in the legacy sector, felt threatened and resisted the aggressive timeline set by the new CEO.

The Vulnerable Intervention: Shared Uncertainty and Purpose

The CEO gathered the senior leadership and, instead of presenting a polished, detailed financial plan for the pivot, he presented a clear, values-driven vision: “This is our ethical North Star. We must survive for the next 50 years.” He then stated, “I am the leader, but I cannot tell you exactly how to rebuild our infrastructure. My expertise is in strategy; yours is in physics. I need you to tell me where the technology is currently falling short and how we structure R&D funding for the next decade.”

  • He publicly acknowledged that transitioning the workforce would involve personal and professional uncertainty for everyone, including himself.
  • He framed the change as a moral and engineering challenge that he could not solve alone (Communicating strategic uncertainty and creating reciprocal accountability).

The Change Accelerator Lesson:

By admitting the magnitude of the engineering challenge and acknowledging the personal risks involved, the CEO shifted the focus from compliance to purpose. The engineers, respected as domain experts, embraced the new mandate with ownership. Vulnerability unlocked the necessary technical expertise, accelerating the company’s R&D efforts because the people closest to the legacy systems felt safe enough to dismantle them.

Leading with Your Human-Centered Self

The Vulnerable Visionary is the ultimate expression of Human-Centered Change. Leaders who believe their team is capable of handling the truth about risk and uncertainty are repaid with maximum commitment and creativity. Stop seeking to be the flawless hero. Start modeling the behavior you need to see in your teams: the courage to try, the permission to fail, and the conviction to speak truth to power.

This is the new definition of leadership strength: the ability to embrace your own humanity and use it to activate the potential of those around you.

“Authentic leadership doesn’t mean sharing everything; it means sharing what is necessary to give your people permission to operate without fear.”

Frequently Asked Questions About the Vulnerable Visionary

1. What is the fundamental difference between Vulnerability and Weakness in leadership?

Weakness is passive and involves a lack of capability or resolve. Vulnerability is an active, strategic choice to share uncertainty, admit a mistake, or ask for help, specifically to build psychological safety and trust in the team. It is a strength because it maximizes collective performance by lowering the fear of interpersonal risk-taking among employees.

2. How does a leader communicate “Strategic Uncertainty”?

Strategic Uncertainty means communicating the clear, values-driven ultimate goal (the North Star) while openly admitting that the specific path, technology, or business model to reach it is still ambiguous and requires collective discovery. This prevents anxiety by replacing false confidence with transparent, reciprocal accountability.

3. Why is Vulnerability considered a “Change Accelerator”?

It accelerates change because it is the fastest way to build Psychological Safety. When a leader models failure and admits a need for help, they signal that it is safe for the rest of the organization to experiment, push boundaries, and report errors quickly. This speed in learning and course correction is essential for driving successful, continuous innovation.

Your first step toward becoming a Vulnerable Visionary: In your next project review, publicly identify and admit one assumption you personally held that turned out to be wrong, and explain how the team’s data corrected your course. Do not assign blame — just model the learning.

Extra Extra: Because innovation is all about change, Braden Kelley’s human-centered change methodology and tools are the best way to plan and execute the changes necessary to support your innovation and transformation efforts — all while literally getting everyone all on the same page for change. Find out more about the methodology and tools, including the book Charting Change by following the link. Be sure and download the TEN FREE TOOLS while you’re here.

Image credit: Unsplash

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Mapping the Journey of an Internal Innovation

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Idea to P&L

LAST UPDATED: December 3, 2025 at 4:09PM

Mapping the Journey of an Internal Innovation

GUEST POST from Chateau G Pato

The biggest enemy of internal innovation is not a lack of funding or creativity; it is organizational friction. We’re excellent at the initial spark — the hackathon, the idea challenge — but we fail consistently in the messy middle: the transition of an idea from a protected Innovation Lab to an accountable Business Unit. This journey requires a structured approach that explicitly manages the shift from high-tolerance learning metrics to high-pressure revenue metrics.

This challenge demands a Human-Centered approach. The people and teams must change their behaviors, their metrics, and their risk profile at specific points. We must stop thinking of the innovation pipeline as a single, fluid pathway and recognize it as three distinct environments, each with its own governance and culture. The map from Idea to P&L is defined by three critical Change Management Gates.

The Three-Stage Innovation Transit System

An internal innovation must successfully transit three stages, each marked by a change in focus, metrics, and most importantly, ownership.

Stage 1: The Innovation Lab (Discovery & Validation)

This is the protected environment where the idea is born and the Problem/Solution Fit is established. The culture is one of Psychological Safety and rapid, low-cost experimentation. The goal here is purely learning velocity.

  • Owner: Innovation Team / Dedicated Idea Owner.
  • Funding: Grant or Seed Budget (focused on burn rate).
  • Metrics: User desirability scores, successful MVP pivots, cost of learning, and completion of Risk Reduction Milestones (e.g., proving the technology works, proving the customer needs it).

The first Change Management Gate — The Validation Gate — is passed when the team can prove, with early user data, that the innovation solves a genuine, urgent customer problem and has a clear, if small, path to monetization.

Stage 2: The Accelerator (De-risking & Scaling)

Once validated, the innovation needs protection from the rigid P&L structures of the core business while being forced to adopt Business Rigor. This stage is about establishing Product/Market Fit and building the operational infrastructure required for scale. The team must unlearn the scrappy habits of the lab and learn the discipline of business planning.

  • Owner: The new Venture or dedicated Accelerator Leadership, often with joint sponsorship from the Innovation Team and a potential Business Unit leader.
  • Funding: Transitional Budget (focused on operational scaling).
  • Metrics: Customer acquisition cost (CAC), lifetime value (LTV), early revenue figures, and stability of the Minimum Viable Operation (MVO). The focus shifts from “Can we build it?” to “Can we sell it sustainably?”

The second Change Management Gate — The Commercialization Gate — is passed when the innovation achieves established, repeatable commercial traction, a positive unit economic model, and the core business unit agrees to assume financial responsibility.

Stage 3: The Business Unit (Optimization & P&L)

The innovation now transitions to the operational world. It receives full funding and full P&L Accountability. The culture shifts from experimentation to optimization and continuous improvement. The innovation leader must now unlearn the constant need for radical change and learn to operate within established corporate constraints (e.g., compliance, annual budgeting, HR structure).

  • Owner: Core Business Unit (BU) Leadership, often with the original innovation champion integrating into the BU team.
  • Funding: Operational Budget (focused on margin).
  • Metrics: Gross Margin, market share, YoY growth, and integration with existing corporate systems.

Case Study 1: The Manufacturing Giant’s Digital Service Offering

Challenge: New Business Model Cannibalization Fear

A global industrial manufacturer (“MegaCorp”) developed an internal IoT-enabled maintenance service (a subscription model) in its Lab. The innovation team proved Problem/Solution Fit, showing customers wanted to buy “uptime” rather than “parts.” However, the core Parts Sales BU feared the new service would cannibalize their highly profitable parts revenue, leading them to resist taking ownership at the Commercialization Gate.

The Human-Centered Intervention: The Transitional BU

MegaCorp created a separate, temporary Transitional Business Unit (TBU) reporting directly to the CEO for 18 months. This TBU served as the Accelerator Stage (Stage 2) and was given a specific mandate: Generate $5M in revenue using the service model, and its success metrics would explicitly ignore any perceived impact on the Parts Sales BU. This TBU was shielded from the core P&L fears.

The P&L Lesson:

The TBU successfully proved the revenue model was additive, not just cannibalistic, targeting a new segment of customers. Critically, it allowed the Parts Sales BU leadership to unlearn their fear of service revenue through observed data rather than abstract analysis. After 18 months, the successful TBU was folded into a newly-formed Digital Services BU with clear P&L accountability, proving that organizational structures must be designed to manage fear and resistance during the crucial transition phase.

Case Study 2: The Retail Bank’s Automated Lending Tool

Challenge: Regulatory Friction Stalling Scale

A regional bank (“SafeBank”) developed an AI-driven lending tool in its Lab, proving it could process small business loan applications in minutes instead of weeks — a clear Product/Market Fit. However, the Legal and Compliance BU halted the innovation at the Commercialization Gate (Stage 2 to 3 transition), citing regulatory risk associated with AI model transparency and auditability.

The Human-Centered Intervention: Embedded Compliance Design

Instead of battling the Legal team, the Accelerator team embedded a compliance officer into their engineering team (Co-Creation). The compliance officer’s success metric was changed from preventing deployment to designing a compliant path to deployment. The innovation team had to unlearn their engineering-first mindset and learn to design compliance rules directly into the code as a core feature.

The P&L Lesson:

By treating compliance as a design constraint rather than a roadblock, the team created a tool that automatically generated the required audit reports. This collaborative approach built reciprocal trust. The Legal BU confidently signed off on the innovation, and it was integrated directly into the Retail Lending P&L with immediate positive impact on customer experience and loan volume. The innovation successfully reached the P&L stage because its owners proactively managed systemic friction.

Conclusion: It’s All About Governance

The journey from Idea to P&L is fundamentally a governance journey. Innovation leaders must create clear, documented rules for each of the three stages, defining metrics that prioritize learning in the Lab, rigor in the Accelerator, and return in the Business Unit. When ownership is ambiguous, the innovation stalls; when metrics are misaligned, the innovation dies. Your internal innovation pipeline must be a deliberate, human-centered change management mechanism.

“An innovation without a path to P&L is a hobby. An innovation with a mapped journey is a growth engine.”

Frequently Asked Questions About Idea to P&L Mapping

1. What is the “Commercialization Gate”?

The Commercialization Gate is the critical transition point between the Accelerator Stage (Stage 2) and the final Business Unit Stage (Stage 3). It is passed when the internal innovation achieves repeatable commercial traction, proves a positive unit economic model, and a Core Business Unit agrees to assume full financial (P&L) accountability for its scaling and operations.

2. How should metrics change between the Lab and the Accelerator?

Metrics must shift from focusing on learning to focusing on business rigor. In the Lab (Stage 1), metrics are qualitative (e.g., user desirability, risk reduction milestones). In the Accelerator (Stage 2), metrics become quantitative business indicators (e.g., Customer Acquisition Cost, Lifetime Value, early revenue figures, and operational stability).

3. What is the biggest organizational friction point in the Idea to P&L journey?

The biggest friction point is often the fear of cannibalization and the resistance to unlearning. Core Business Units resist adopting innovations that might disrupt their existing profitable model (even if necessary for future growth). Effective change management must be employed to provide transitional governance (like a TBU) to de-risk the new idea until it proves its value.

Your first step toward mapping your Idea to P&L journey: Create a simple one-page checklist for your Commercialization Gate. Include five non-negotiable proof points that must be achieved before any Core Business Unit is asked to assume P&L responsibility (e.g., $1M in revenue, 80% customer retention, zero critical security flaws).

Extra Extra: Because innovation is all about change, Braden Kelley’s human-centered change methodology and tools are the best way to plan and execute the changes necessary to support your innovation and transformation efforts — all while literally getting everyone all on the same page for change. Find out more about the methodology and tools, including the book Charting Change by following the link. Be sure and download the TEN FREE TOOLS while you’re here.

Image credit: Pixabay

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A New Blueprint for Government Innovation

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Human-Centered Public Policy

LAST UPDATED: December 2, 2025 at 12:21PM

A New Blueprint for Government Innovation

GUEST POST from Chateau G Pato

The core dysfunction in government — whether it’s a baffling tax form, a convoluted permit process, or an inaccessible public service — stems from a design failure. Policy is often designed for the convenience of the bureaucracy, not the dignity of the citizen. This bureaucratic friction, which I call the Public Policy Drag, erodes participation, trust, and the very effectiveness of governance.

Human-Centered Public Policy (HCPP) rejects the notion that complex problems require complex solutions for the user. Instead, it applies the deep empathy and iterative testing principles of Human-Centered Design (HCD) to law and administration. HCPP demands that we understand the citizen’s true needs and pain points before drafting the first line of legislation or code. The result is policy that achieves its stated goal with maximum efficiency and fairness.

The Three Pillars of Human-Centered Policy Design

HCPP transforms the traditional policy lifecycle by emphasizing three key shifts:

1. From Political Intent to Ethnographic Empathy (Discovery)

Traditional policy starts with a high-level goal (e.g., “reduce homelessness”). HCPP starts with deep ethnographic research. Policy designers must immerse themselves in the lived experience of the target population. This means sitting in the queue at the DMV, observing a family applying for social aid, or walking the streets with those without shelter. The goal is to move beyond statistical averages and understand the emotional and procedural friction that causes programs to fail. The policy becomes a solution to a documented human problem, not an abstract legislative goal.

2. From Top-Down Rulemaking to Citizen Co-Creation (Design)

Too often, policy is written in isolation. HCPP mandates co-creation. This involves working directly with citizens, frontline workers (the ultimate policy implementers), and even advocacy groups to design the actual service delivery flow and forms. This ensures that the policy isn’t just feasible; it’s desirable and usable. When citizens have a stake in the design, they gain a sense of ownership and dignity, leading to higher compliance and engagement.

3. From Compliance Audit to Iterative Learning (Delivery)

Government traditionally measures success via budgetary compliance or output (e.g., “We processed 10,000 forms”). HCPP measures success via citizen outcomes and iterative learning. Policies must be launched as Minimum Viable Products (MVPs), with clear metrics tied to human behavior (e.g., “What percentage of eligible citizens successfully accessed the benefit?”). This rapid feedback loop allows policymakers to unlearn what doesn’t work and pivot quickly, institutionalizing Learning Velocity as a governmental core competency.

Case Study 1: Redesigning the Job Seekers’ Benefit Application

Challenge: High Drop-off Rates and Fraud Risk

A national labor department struggled with its unemployment benefit application. The form was over 50 pages long, required numerous attachments, and resulted in a 60% drop-off rate for eligible, low-literacy applicants. Simultaneously, the complexity created loopholes exploited by fraudsters. The policy was designed to prevent fraud, but in doing so, it became anti-citizen.

HCPP Intervention: Focus on the “Moment of Need”

A human-centered team began by observing applicants. They quickly realized the high cognitive load of the form, which compounded the emotional stress of unemployment. The redesign focused on the citizen journey:

  • The 50-page document was unlearned and replaced with a phased, conversational digital interface that dynamically asked questions based on previous answers, reducing cognitive load.
  • Document submission was replaced by back-end automated verification using existing government databases, eliminating the burden of citizens having to retrieve obsolete paperwork.

The Outcome:

The redesign led to a 90% completion rate among eligible users and an 80% reduction in processing time. By applying deep empathy to simplify the process, the new system paradoxically reduced fraud risk (as data capture was cleaner) while dramatically improving citizen access and restoring faith in the department’s mission.

Case Study 2: Municipal Permit Reform for Small Businesses

Challenge: Stifled Entrepreneurship via Bureaucratic Maze

A large metropolitan city noticed a sharp decline in new small business openings, particularly in marginalized neighborhoods. The key roadblock was the municipal permit process, which required navigating ten different departments, each with its own paper forms and siloed timelines. The policy was designed for departmental control, not economic growth.

HCPP Intervention: A Unified Permit Journey

The innovation team mapped the small business owner’s journey — the end-user. They discovered the owner didn’t care about the internal departmental structure; they cared about a single, predictable timeline. The intervention:

  • Created a single digital Small Business Portal (the MVP) that served as the sole entry point for all permits and licenses, acting as an internal orchestrator.
  • Mandated an internal unlearning of departmental silos, replacing the individual forms with a single, integrated data submission that populated all necessary back-end systems simultaneously.
  • Implemented a public-facing service standard: a maximum 30-day decision deadline, creating reciprocal accountability between the city and its citizens.

The Outcome:

The permit reform dramatically increased the number of new small business registrations, particularly in the target neighborhoods. The city unlearned its rigid, siloed structure, proving that policy simplicity for the citizen drives the desired economic outcome more effectively than bureaucratic control.

A New Covenant of Trust

Human-Centered Public Policy is not just a framework for better government services; it is a blueprint for rebuilding the covenant of trust between the governed and the governing. When citizens encounter policy that is intuitive, respectful, and effective, they regain faith in the system’s competence and intent.

Leaders in government must stop writing policies that sound good in a committee room and start designing services that work brilliantly in a citizen’s life. Embrace the empathy required to understand the friction, the courage to challenge obsolete procedures, and the humility to launch policies as MVPs ready for iteration.

“The measure of good government is not the wisdom of its laws, but the effectiveness with which those laws translate to human dignity and ease of use.”

Frequently Asked Questions About Human-Centered Public Policy

1. How does HCPP differ from standard policy development?

Standard policy development is often top-down, driven by legislative goals and budget. HCPP is bottom-up, starting with deep ethnographic research to understand the lived experience and pain points of the citizen (the end-user) before any rules or regulations are drafted. It prioritizes maximizing citizen outcomes and minimizing systemic friction.

2. What is “Public Policy Drag”?

Public Policy Drag is the term for the systemic friction and complexity created by policy and procedure that makes government services difficult, confusing, or inaccessible for the citizen. This drag erodes trust, lowers compliance, and reduces the overall effectiveness of a government program.

3. What role does “Co-Creation” play in HCPP?

Co-creation involves actively designing services and policy mechanisms with citizens and frontline workers, not just for them. By including the end-users in the design process, HCPP ensures the policy is not only feasible but also usable and desirable, leading to higher levels of citizen engagement and dignity.

Your first step toward Human-Centered Public Policy: Gather a cross-functional team of policymakers, IT experts, and front-line staff. Choose one high-friction citizen process (e.g., getting a required license). Ask the team to go through the process as if they were the citizen — no using internal shortcuts. Document the emotional and procedural friction points, then use these insights to co-create a single-page improvement plan.

Extra Extra: Because innovation is all about change, Braden Kelley’s human-centered change methodology and tools are the best way to plan and execute the changes necessary to support your innovation and transformation efforts — all while literally getting everyone all on the same page for change. Find out more about the methodology and tools, including the book Charting Change by following the link. Be sure and download the TEN FREE TOOLS while you’re here.

Image credit: Pixabay

Subscribe to Human-Centered Change & Innovation WeeklySign up here to get Human-Centered Change & Innovation Weekly delivered to your inbox every week.

The End of the Lone Genius Myth

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Building Collective Confidence

The End of the Lone Genius Myth

LAST UPDATED: December 1, 2025 at 5:55PM

GUEST POST from Chateau G Pato

Innovation is rarely a bolt of lightning striking a solitary mind. Look closer at history’s greatest inventions, and you’ll find a network of collaborators, financiers, critics, and technical experts. Yet, in business, we persistently mythologize the Lone Genius — the charismatic individual who will single-handedly disrupt the market. This myth is more than just bad history; it’s bad strategy, creating fragile, single-point-of-failure dependencies and actively diminishing the potential of entire workforces.

The human-centered change leader knows that sustainable, continuous innovation flows from Collective Confidence — the shared belief among team members that their group is competent, capable of generating novel solutions, and resilient enough to overcome inevitable failures. It is the core mechanism that allows an organization to embrace ambiguity and initiate radical change without succumbing to fear or internal politics.

Individual confidence is essential, but it is Collective Confidence that translates great ideas into scaled reality. This is the difference between a brilliant patent and a world-changing product pipeline.

The Three Pillars of Collective Confidence

Collective confidence is not built through motivational posters or annual retreats; it is engineered through three core, systemic pillars:

1. Engineered Psychological Safety

The single most powerful predictor of team effectiveness is Psychological Safety — the shared belief that the team is safe for interpersonal risk-taking. If an engineer fears ridicule for suggesting a crazy idea, or if a junior employee risks reprisal for questioning a flawed decision, innovation dies. Collective Confidence requires institutionalizing safety through: a) Leadership modeling vulnerability and failure; and b) Process design that frames experiments as learning opportunities, not judgment opportunities. When teams know they can fail safely, they will dare to succeed boldly.

2. Shared Context and Innovation Language

Confidence is impossible without clarity. Teams cannot feel confident about solving a complex problem if they don’t share a common language for defining the problem, measuring progress, and articulating risk. This means moving beyond departmental jargon to create a common, human-centered language (e.g., using “minimum viable product” and “discovery phase” consistently across engineering, marketing, and finance). This shared context minimizes miscommunication, builds trust, and ensures everyone is confidently pulling in the same direction — even if the path forward is ambiguous.

3. Reciprocal Accountability, Not Individual Blame

In the Lone Genius model, if a project fails, a single person is blamed and removed. In the Collective Confidence model, accountability is reciprocal. It is a shared responsibility to ensure success and to support the process of learning from failure. When a project hits a roadblock, the question shifts from “Who made the mistake?” to “What did our process allow to happen, and how can we, as a team, strengthen our controls for the next iteration?” This shared burden of responsibility reinforces trust and encourages team members to speak up early when risks are identified.

Case Study 1: The Manufacturing Firm’s Quality Transformation

Challenge: Inconsistent Quality Control and Blame Culture

A precision parts manufacturing firm (“PrecisionCo”) struggled with unacceptable error rates. Their existing culture relied on a “Quality Genius” — a single, highly experienced supervisor responsible for final sign-off. When failures occurred, the supervisor was blamed, which led the rest of the team to practice defensive mediocrity — avoiding responsibility and relying solely on the genius’s final check.

The Collective Confidence Intervention: Decentralizing Quality Ownership

PrecisionCo decided to deliberately dismantle the Quality Genius role. Instead, they:

  • Implemented mandatory cross-functional training, teaching every line worker not just their task, but the context of the next person’s task.
  • Introduced a rule: Errors were investigated, not to find the individual responsible, but to identify the process step that failed.
  • Empowered every team member with the stop work authority, encouraging them to halt production if a process felt wrong, making their voice a valued tool, not a potential liability.

The Human-Centered Lesson:

The shift was profound. By building Collective Confidence in the process and in each other, error rates dropped dramatically. The value was not in creating 100 individual geniuses, but in creating a system where the collective capability of the team — backed by psychological safety — eliminated the need for a single hero.

Case Study 2: The Design Agency’s Client Pitch Success

Challenge: Dependence on the Principal Designer for High-Stakes Pitches

A renowned digital design agency (“DesignPro”) often won major contracts, but only when the celebrated Principal Designer (the “Genius”) led the pitch. Junior designers, fearing the Principal’s perfectionism, rarely contributed their best work until it was safe, resulting in bottlenecks and burnout for the lone star.

The Collective Confidence Intervention: Structured Co-Creation and ‘Shitty First Drafts’

DesignPro focused on teaching the team to confidently engage with ambiguity early. They instituted:

  • The “Shitty First Draft” (SFD) ritual: Every project phase required a deliberately low-fidelity, unfinished draft from every team member to normalize imperfection and minimize the fear of judgment.
  • Reciprocal Feedback Systems: Instead of the Principal critiquing down, junior members were mandated to lead critique sessions for the Principal’s work, encouraging Psychological Safety and flattening the expertise hierarchy.

The Human-Centered Lesson:

The result was a dramatic decrease in the Principal Designer’s pitch lead rate, and a massive increase in overall client pitch wins led by various team members. The Collective Confidence enabled the entire firm to innovate consistently. The Principal Designer, freed from the necessity of being the lone hero, shifted into a strategic coaching role, enhancing the firm’s overall innovation capacity.

The Call to Action for Human-Centered Leaders

Stop searching for the next Lone Genius. The genius is already within your walls, diffused across your teams, waiting for the right conditions to emerge. Your role as a leader is to stop rewarding individual heroism and start engineering the systems that build Collective Confidence.

This means moving from a culture of individual brilliance to a culture of systemic capability. The breakthroughs you need won’t come from a single hero’s desk, but from the collaborative, confidently ambiguous work of teams that trust their process and, most importantly, trust each other.

“Individual brilliance gets you a great idea. Collective Confidence gets you a sustainable organization capable of perpetual innovation.”

Frequently Asked Questions About Collective Confidence

1. What is the difference between Individual and Collective Confidence?

Individual confidence is a person’s belief in their own capability to perform a task. Collective Confidence (or team efficacy) is the shared, reciprocal belief among team members that their group, as a whole, can successfully organize and execute the courses of action required to produce given levels of attainment, especially when facing ambiguous or complex problems.

2. How does the “Lone Genius” myth harm organizational innovation?

The myth harms innovation by creating single points of failure, suppressing employee voice (lowering psychological safety), and encouraging “defensive mediocrity” where employees avoid responsibility and rely on the identified “genius” instead of contributing their own creative input. This slows down learning and reduces organizational resilience.

3. How do you “engineer” Psychological Safety?

Psychological Safety is engineered, not wished into existence, through formal processes: 1) Leadership explicitly modeling vulnerability and admitting mistakes; 2) Implementing formal feedback loops that focus on process learning instead of individual blame; and 3) Decentralizing decision-making (like the “stop work” authority) to empower all voices equally.

Your first step toward building Collective Confidence: Next time an experiment fails, deliberately avoid asking, “Who was responsible?” Instead, ask the entire team, “What weakness in our shared process or communication enabled this outcome? And what will we change next time?”

Extra Extra: Because innovation is all about change, Braden Kelley’s human-centered change methodology and tools are the best way to plan and execute the changes necessary to support your innovation and transformation efforts — all while literally getting everyone all on the same page for change. Find out more about the methodology and tools, including the book Charting Change by following the link. Be sure and download the TEN FREE TOOLS while you’re here.

Image credit: Pixabay

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Why Unlearning is More Critical Than Continuous Learning

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LAST UPDATED: November 30, 2025 at 7:32PM

Why Unlearning is More Critical Than Continuous Learning

GUEST POST from Chateau G Pato

In the age of exponential change, organizations are suffocated not by a lack of information, but by an abundance of obsolete information. Every firm champions Continuous Learning — the idea that employees must constantly acquire new skills. Yet, few acknowledge that the space in our cognitive and organizational structures is finite. New knowledge cannot take root if the old, comfortable certainties are still occupying the ground.

This is where the Skill of Unlearning becomes paramount. Unlearning is not about forgetting; it’s about choosing to discard the relevance of previously successful mental models and organizational processes that are no longer fit for the current context. It is the conscious, human-centered decision to create cognitive capacity for the new, disruptive ideas necessary for survival.

Unlearning is a strategic necessity. If you cannot unlearn the operating principles of the last decade, you will apply last decade’s solutions to this decade’s problems, and you will fail not from a lack of effort, but from a failure of release.

The Unlearning Imperative: Removing the Ruts of Success

The biggest blocker to unlearning is often past success. When a strategy or process works brilliantly for ten years, it hardens into dogma. This dogma creates three primary barriers that must be addressed through human-centered change:

1. The Organizational Identity Barrier

Many firms derive their identity from their history (“We are the best analog camera manufacturer,” or “We are the best provider of physical media”). When the market shifts, employees struggle to let go of the core competency that defined their professional value. Unlearning requires redefining the organizational mission from what we produce to what problem we solve for the customer, regardless of the technology.

2. The Procedural Rigidity Barrier

The “way we’ve always done things” acts as concrete, resisting new methodologies (e.g., trying to implement Agile development using a rigid waterfall budgeting process). Unlearning requires disrupting the processes that reward the old behavior. You can’t learn radical new product development if the budget cycle punishes every failed experiment. The process itself must be unlearned.

3. The Cognitive Comfort Barrier

For individuals, unlearning is emotionally taxing. It means admitting that a skill they spent decades mastering is now worth less than a skill they don’t yet possess. Leaders must create Psychological Safety where employees are allowed to be temporarily incompetent as they transition to the new model. The fear of looking foolish is the number one killer of unlearning.

Case Study 1: The Insurance Giant and the Digital Channel Shift

Challenge: Dominance of an Obsolete Sales Channel

A large, established insurance company (“LegacyInsure”) dominated its market through a massive, highly successful network of local, commissioned agents. When digital-native competitors offered instantaneous online quotes and sign-ups, LegacyInsure lagged. The problem wasn’t a lack of digital investment; it was the cultural inability of its regional managers to unlearn the value structure of the agent-led model.

The Unlearning Intervention: Mandatory Dual Operating Models

The leadership knew forcing a sudden shift would alienate key personnel. Instead, they mandated a Dual Operating Model for three years, creating a separate, digitally-focused division with zero dependence on the agent network. Critically, regional managers were assigned metrics that rewarded both the old and the new model, forcing them to:

  • Unlearn the assumption that high-touch contact was required for every sale.
  • Learn to value data from self-service customers (Learning).

The Human-Centered Lesson:

By separating the models, the company created a safe space for the new to grow without being suffocated by the old, profitable dogma. The regional managers who embraced the unlearning process transitioned into roles overseeing both digital and agent channels, becoming change champions. Those who couldn’t unlearn their previous success were gently transitioned out over time. The company unlearned its channel dependency and survived the digital wave.

Case Study 2: The Software Company and the Product Pivot

Challenge: Sticking to a Feature Set That No Longer Solved the Core Problem

A B2B software firm (“FeatureSoft”) built its reputation on a product with deep, complex, and highly customizable features. However, the market had shifted to favoring simple, intuitive, cloud-based solutions (the SaaS Revolution). FeatureSoft’s engineers were resistant to the pivot; their professional identity was tied to building complexity.

The Unlearning Intervention: The ‘Kill Your Darling’ Mandate

The CEO issued a direct mandate to unlearn complexity. They created an internal innovation challenge: “Build the simplest possible version of our product that delivers 80% of the customer’s value in a pure SaaS model, using only 20% of the original codebase.” The prize was funding for the team to become the new core product unit.

  • Engineers were forced to unlearn the value of complexity and mastery of the legacy code.
  • They had to learn the value of abstraction and minimal viable product (MVP).

The Human-Centered Lesson:

The challenge transformed the culture. By making the act of simplifying the primary goal, the company inverted the value hierarchy. The engineers, highly intelligent and competitive, embraced the new challenge. They successfully unlearned the need for feature depth and focused on solving the core user problem elegantly, facilitating a market-saving pivot that would have been blocked by the cognitive inertia of its legacy code experts.

Mastering the Unlearning Skill

Unlearning is an active, not passive, process. It requires leadership to institutionalize rituals that challenge the status status quo:

  1. The Pre-Mortem: Before launching any major project, assume the project failed spectacularly two years in. Discuss what old assumption was responsible for that failure.
  2. The Stop Budget: Allocate a percentage of budget not to R&D, but to identifying and stopping obsolete projects, processes, and products. Reward the teams that successfully kill their own initiatives and free up resources.
  3. Reverse Mentoring: Mandate senior leaders be mentored by new, entry-level employees specifically on their lack of knowledge — their fresh, unburdened perspective on customer friction.

Focusing on continuous learning fills the organizational tank; mastering unlearning ensures the tank can be properly emptied and refilled. This is the ultimate skill of human-centered change.

“The measure of intelligence is the ability to change. The measure of organizational intelligence is the ability to unlearn.”

Frequently Asked Questions About the Skill of Unlearning

1. What is the fundamental difference between Unlearning and Forgetting?

Unlearning is not about forgetting information; it is the conscious, deliberate act of discarding the relevance or applicability of a previously successful mental model, process, or assumption. It’s creating space for new knowledge, while forgetting is a passive failure of memory.

2. Why is past success the biggest barrier to Unlearning?

Past success creates dogma. When a process works for a long time, it becomes an entrenched part of the organizational identity and reward system, leading to procedural and cognitive rigidity. This comfort and certainty actively resist any new information that contradicts the profitable “way we’ve always done things.”

3. What is the “Stop Budget” and why is it important for Unlearning?

A Stop Budget allocates funds specifically to identifying and terminating obsolete projects, processes, or products. It’s important because it institutionalizes the reward structure for unlearning, shifting the focus from simply starting new things to actively clearing the internal roadblocks created by the old, allowing resources and attention to be intentionally freed up.

Your first step toward mastering Unlearning: Hold an “Assumption Audit” meeting for your next major project. Before discussing the solution, have everyone write down three ‘truths’ they hold about the market or the customer based on the last five years of success. Then, for 15 minutes, debate why each of those ‘truths’ might be completely false today.

Extra Extra: Because innovation is all about change, Braden Kelley’s human-centered change methodology and tools are the best way to plan and execute the changes necessary to support your innovation and transformation efforts — all while literally getting everyone all on the same page for change. Find out more about the methodology and tools, including the book Charting Change by following the link. Be sure and download the TEN FREE TOOLS while you’re here.

Image credit: Pixabay

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Integrating AI into the Innovation Pipeline

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From Ideation to Execution

LAST UPDATED: November 30, 2025 at 8:21AM

Integrating AI into the Innovation Pipeline

GUEST POST from Chateau G Pato

The quest for innovation has always been constrained by human bandwidth: the time it takes to conduct research, synthesize data, and test concepts. Artificial Intelligence shatters these constraints. However, simply using AI to generate more ideas faster leads to digital noise. True competitive advantage comes from using AI to enhance the quality of human judgment and focus our finite human empathy where it matters most: the Moments of Insight.

We must move beyond the narrow view of AI as just a tool for cost reduction and embrace it as a partner that dramatically accelerates our Learning Velocity. The innovation pipeline is no longer a linear process of discovery, design, and delivery; it is a Synergistic Loop where AI handles the heavy lift of data synthesis, freeing human teams to focus on unstructured problem-solving and radical concept generation.

The AI Augmentation Framework: Three Critical Integration Points

To integrate AI mindfully, we must define clear roles for the human and the machine at three stages of the pipeline:

1. Deepening Empathy through AI Synthesis (Discovery Phase)

The Discovery Phase is traditionally dominated by ethnographic research. While human observation remains irreplaceable for capturing nuance and emotion, AI excels at processing vast, disparate datasets that inform that empathy. An AI system can ingest millions of customer service transcripts, social media sentiment, competitor product reviews, and historical sales figures to immediately generate a prioritized list of friction points and unmet needs. This doesn’t replace the human ethnographer; it provides the ethnographer with a laser-focused map, allowing them to spend their time understanding the why behind the patterns AI found, rather than manually searching for the patterns themselves.

2. Augmenting Ideation through AI Diversification (Design Phase)

Human teams tend to cluster around familiar solutions (Affinity Bias). AI breaks this pattern. In the Design Phase, after the human team defines the core problem, AI can be tasked with generating radical concept diversification. By training an AI on solutions from entirely different industries (e.g., applying aerospace logistics solutions to retail inventory management), it can suggest analogous concepts that humans would never naturally connect. The human team’s role shifts from generating 100 average ideas to selecting the best 5 from 1,000 machine-generated, diverse, and well-researched concepts — a massive multiplier on human creativity.

3. Accelerating Validation through AI Simulation (Delivery Phase)

The most time-consuming step is validation (prototyping, testing, and iterating). AI, specifically in the form of digital twins and sophisticated simulation models, can dramatically accelerate this. For complex physical products (like self-driving cars or new materials), AI can run thousands of scenario tests in a virtual environment before a single physical prototype is built. This shifts the human team’s focus from slow, expensive physical validation to data interpretation and hypothesis refinement. The human only builds the prototype when the AI simulation suggests a 95% likelihood of success, maximizing the efficiency of capital and time.

Case Study 1: The Financial Institution and Regulatory Forecasting

Challenge: Slow Time-to-Market Due to Regulatory Risk

A global financial institution (FinCorp) found its innovation pipeline paralyzed by regulatory uncertainty. Every new product launch required months of legal review and risked fines if the regulatory landscape shifted mid-deployment. The fear of compliance risk stifled breakthrough innovation.

AI Integration: Predictive Compliance Synthesis

FinCorp deployed an AI system trained on global regulatory history, legal documents, and legislative debate transcripts. This AI was integrated into the Discovery Phase:

  • The AI scanned new product proposals and immediately generated a “Compliance Risk Score” based on predicted future regulatory shifts.
  • It identified regulatory white space — areas where new products could be safely launched with minimal legal friction.
  • Human compliance officers shifted their role from reactive policing to proactive strategic guidance, advising innovation teams on how to shape products to be future-compliant.

The Human-Centered Lesson:

The AI removed the fear of the unknown, boosting the team’s psychological safety. Time-to-market for new financial products was reduced by 40% because the human teams were empowered to innovate within a clear, AI-forewarned boundary. The risk management was automated, freeing the humans to focus on value creation.

Case Study 2: The Consumer Goods Company and Material Innovation

Challenge: Years-Long Material R&D Cycle

A major consumer goods company (ConsumerCo) required years to develop new sustainable packaging materials, involving countless failed lab experiments due to the sheer volume of possible chemical combinations.

AI Integration: Generative Material Design

ConsumerCo integrated a generative AI model into the Ideation and Delivery Phase. This model was given constraints (e.g., “must be compostable in 90 days, withstand $180^\circ$C, and cost less than $0.05 per unit”).

  • The AI generated millions of hypothetical chemical formulas and simulated their real-world properties instantly (Accelerated Validation).
  • The human material scientists reviewed the top 0.1% of AI-generated formulas, using their expertise to filter for manufacturing feasibility and supply chain reality.

The Human-Centered Lesson:

The AI transformed the material scientists’ job from performing repetitive, blind experiments to becoming expert filters and hypothesis builders. This augmentation reduced the R&D cycle from four years to 18 months, leading to a massive increase in the Learning Velocity of the entire organization. The result was a successful launch of a proprietary, highly sustainable packaging line, directly attributing its success to the speed of AI-driven simulation.

The Future: Human-AI Co-Creation

The integration of AI into the innovation pipeline must be governed by a single rule: AI handles the volume, humans retain the veto and the empathy. Leaders must focus on training their teams not in how to use the AI, but how to ask it the right, human-centered questions.

Embrace the Synergistic Loop. Use AI to synthesize complexity, diversify ideas, and accelerate validation. Use your people for vision, ethics, and the profound, qualitative understanding of the human condition. That is how you drive sustainable, breakthrough innovation.

“AI does not make humans less creative; it removes the repetitive labor that prevented them from being creative in the first place.”

Frequently Asked Questions About AI in the Innovation Pipeline

1. What is the biggest risk of integrating AI into the innovation pipeline?

The biggest risk is relying on AI to generate ideas without human oversight, which leads to “algorithmic echo chambers” — innovations that are merely optimizations of past successes, not true breakthroughs. Humans must retain the veto and inject radical new, empathetic concepts that defy historical data.

2. How does AI enhance “Discovery” without replacing human ethnographers?

AI enhances discovery by acting as a powerful data synthesizer. It processes massive, unstructured datasets (like customer reviews and call transcripts) to identify patterns, friction points, and statistically significant unmet needs. This information guides the human ethnographer to focus their high-touch observation time on the most critical and complex qualitative problems.

3. What is “Learning Velocity” and how does AI affect it?

Learning Velocity is the speed at which an organization can generate, test, and codify actionable insight from experiments. AI dramatically increases Learning Velocity by accelerating the “Test & Refine” stage through simulation and digital twins, minimizing the time and cost required for physical prototyping and validation.

Your first step toward AI integration: Identify your most time-consuming, data-intensive manual synthesis task in your current Discovery phase (e.g., manually summarizing customer feedback). Prototype an AI solution to automate only that synthesis, then measure how much more time your human ethnographers spend on direct customer interaction rather than data processing.

Extra Extra: Because innovation is all about change, Braden Kelley’s human-centered change methodology and tools are the best way to plan and execute the changes necessary to support your innovation and transformation efforts — all while literally getting everyone all on the same page for change. Find out more about the methodology and tools, including the book Charting Change by following the link. Be sure and download the TEN FREE TOOLS while you’re here.

Image credit: Dall-E

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