Category Archives: Innovation

Unleashing Your Innovation Potential

Leave a reply

Unleashing Your Innovation Potential

GUEST POST from Janet Sernack

It is not unusual, especially in the corporate world, to label someone who disrupts and challenges the status quo, or what others say, as argumentative and “oppositional”. According to Human Synergistics, the Oppositional Style, in their circumplex, when measured as a dominant leadership style, is summarized in their 360-degree Leadership Styles Inventory (LSI) by two items: “usually against things” and “opposes new ideas.” It labels the person, or leader as being aggressively defensive and causes them to disengage by avoiding engaging in any kind of conflict, dissent and disagreement. This inhibits and prevents them from unleashing their creative thinking and their innovation potential.

In my many years as a corporate trainer presenting high-performance cultures and transformational leadership programs, I learned to apply my imagination, curiosity and inquiry skills to be attentively present, listen deeply and ask generative questions. This helped me develop strategies to unleash a person’s innovation potential by encouraging and enabling thoughtful dissent and disagreement.  I learned to uncover and recognize a leader’s energy and positive intent behind many of the scary, argumentative, hostile, contrary, critical, and aggressive behaviors directed at me, especially in my years upfront as a female corporate trainer in many toxic organizational cultures.

I often found that they were compromised in some way by their personal and family needs and values, their assumptions and beliefs about themselves and their performance at work, poor physical and mental health, and/or a lack of emotional well-being. Any one of these factors made them self-protective, leading to either overt aggression toward others, passive avoidance of conflict, or an unconscious refusal to engage in thoughtful dissent and disagreement because they did not want to be perceived as hostile and argumentative.

All of these unconscious reactive responses inhibited them from unleashing their creative thinking and innovative potential. 

This blog explores the power of unleashing creative thinking and innovation potential through supporting people to thoughtfully dissent and disagree – by using conflict as a catalyst for better decision-making, problem-solving, and strengthened collaboration and innovation – not as a barrier.

Unconscious reactive responses

This manifests as a neurologically challenging reactive response, as people’s brains are wired to defend and protect them to ensure their survival. These defensive responses are composed of fight/flight/freeze/fawn reactions and arise when people face what their neurology (the limbic system) perceives as a threat to their security, safety, or survival.

These reactions stem from past experiences and traumas that are often stored in people’s somatic memories. They become neurologically wired and embedded in people’s habitual ways of being unless they are noticed, labelled, accepted, acknowledged and owned. Until there is a safe holding space for this to happen, it will be impossible to unleash their creative thinking and innovation potential.

Safe holding spaces

Once a safe holding space is established, an oppositional person can be carefully and empathetically challenged, disputed, deviated from, and redirected towards more self-regulated, resourceful, and emotionally healthy states and new approaches. This involves bravely engaging them in thoughtful dissent and disagreement, despite being encased in a toxic organizational culture. Its about building permission, safety and trust to encourage creative thinking that unleashes a persons innovation potential.

Fight-flight-freeze and fawn responses

The amygdala is designed to detect threats and trigger the fight-flight-freeze response to help people survive

  •  A fight response involves standing our ground when we feel threatened, driven by anger, fear, and anxiety.
  •  A flight response entails moving away from or avoiding a situation to reduce painful feelings.
  •  A freeze response involves shutting down, feeling numb, or experiencing paralysis to reduce painful feelings, resulting in immobility.
  • A fawn response involves appeasing and placating others or seeking approval while neglecting the need to feel safe.  

Role of smart conflict, thoughtful dissent and disagreement

This is particularly evident in political and organizational contexts, where people are suffering from change fatigue and overwork. As a result, they unconsciously disengage from their work, families, and daily lives through a flight-or-freeze response, driven by burnout, the pursuit of external validation (a fawn response), or frustration and anger (usually a fight response). A combination of any or all of these largely unconscious but fully present reactive responses creates toxic organizational cultures, in which leaders’ behaviors are either passively or aggressively defensive in their attempts to meet people’s core security, safety, and survival needs. Leaders avoid encouraging or engaging in thoughtful dissent and disagreement that mobilizes people’s creative and critical thinking and unleashes their innovation potential.

The constraints and challenges of the pandemic, coupled with threats to people’s stability and the desire for certainty, also led many people in organizations to fear that their needs for security, safety, and survival would not, and could not, be met. These fears unconsciously intensified people’s defensiveness and reactivity, leading to immobilization through passivity, helplessness, hopelessness, powerlessness, disengagement, and detachment. This indicates a lack of emotional intelligence, largely due to the absence of initial self-regulation and self-leadership strategies.  It is easier and sometimes safer to be a victim and blame others for any negative, pessimistic, or painful feelings of shame, guilt, and embarrassment, resulting from competing commitments, value violations, thought distortions, and emotional dissociation.

These emotionally overwhelming and cognitively overloading factors are also accepted inhibitors to unleashing a person’s innovative potential, especially when they are unable to engage in thoughtful dissent and disagreement under the influence of a toxic organizational culture.

Making the shift

To support people to effectively shift this way of being and safely unleash their creative thinking and innovation potential:

It is crucial to develop and apply the generative discovery skillset to welcome dissent and thoughtful disagreement:

  • Notice: by being aware and attentively present to what is happening to, and within the person, neurologically and physiologically, emotionally, cognitively and physically.
  • Disrupt: by being curious and safely asking open, explorative discovery questions, and by listening carefully to hear and notice what is really going on for them.
  • Dispute: by safely summarizing and challenging their assumptions, perceptions and perspectives, asking evocative and provocative questions that encourage contrary thinking, diversity of thought, differences and constructive disagreement.
  • Deviate: by safely summarizing and eliciting a mindset flip or an agility shift in the oppositional person, directing their creative energy towards constructive behaviors that deliver a positive outcome.

This enables people to redirect their emotional energy and move away from unresourceful internal thought patterns and distortions. It also encourages them to open their minds to thoughtful dissent and disagreement, allowing them to think differently and enter creative and innovative realms. 

This is impossible when toxic organizational cultures force people to hide behind conventional, rigid, and closed minds that won’t allow them to rock the boat and be, think and act differently by intentionally using conflict constructively by embracing dissent and thoughtful disagreement 

At the same time, it’s crucial to safely, empathically and compassionately evoke and provoke an opening of people’s hearts by purposefully and meaningfully aligning their needs and values, so that the change motivates them to change meaningfully and purposefully. This creates the safe holding space that allows them to let go of the need to be in control and be open-willed, unleashing their creative thinking and innovation potential to make the shift to the creative and innovative realms.

Empowering people and teams to redirect their emotional energy towards opening their minds to thoughtful dissent and disagreement allows them to handle hard conversations with constructive and creative intent.

Welcoming dissent and thoughtful disagreement involves daring yourself and others to be and think differently. Being able to think creatively maximize a person or a teams innovation potential, unleashes their human ingenuity in the age of AI, and mobilizes their collective intelligence to lead, manage, or implement constructive and sustainable change.

Leveraging constructive conflict, dissent and thoughtful disagreement unleashes people’s creative thinking and innovation potential and is a necessary part of enabling AI, digital transformation and innovation initiatives to succeed in uncertain and disruptive times.

Find out more about our work at ImagineNation™ and The Start-Up Game.™ Discover our collective learning products and tools that can be customized as playful bespoke corporate learning programs. Our blended and transformational change and learning programs provide a deep understanding of the language, principles, and applications of an ecosystem-focused, human-centric approach and emergent structure (Theory U) to innovation and entrepreneurship. It will also upskill people and teams, developing their future fitness within your unique digital transformation and innovation initiatives. Please find out more about our products and tools.

Image Credit: Gemini

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.

Top 10 Human-Centered Change & Innovation Articles of July 2026

Leave a reply

Top 10 Human-Centered Change & Innovation Articles of July 2026Drum roll please…

At the beginning of each month, we will profile the ten articles from the previous month that generated the most traffic to Human-Centered Change & Innovation. Did your favorite make the cut?

But enough delay, here are July’s ten most popular innovation posts:

  1. What Happens When AI Becomes Your Customer? — by Shep Hyken
  2. The Experience Economy 2.0 — by Braden Kelley
  3. How to Calculate the ROI of Customer Experience — by Braden Kelley
  4. Strategic Foresight: A Practitioner’s Guide to Thinking About the Future — by Braden Kelley
  5. Innovation or Not — InTruth — by Braden Kelley
  6. Innovation Framework Examples: 7 Real-World Cases That Show How They Work — by Braden Kelley
  7. The Personal AI Renaissance — by Braden Kelley
  8. Your 3 Phase AI Journey — by Geoffrey Moore
  9. Why So Much Bullshit? — by Greg Satell
  10. Creating an Innovation Edge — by John Bessant

BONUS – Here are five more strong articles published in June that continue to resonate with people:

If you’re not familiar with Human-Centered Change & Innovation, we publish 4-7 new articles every week built around innovation and transformation insights from our roster of contributing authors and ad hoc submissions from community members. Get the articles right in your Facebook, Twitter or Linkedin feeds too!

Build a Common Language of Innovation on your team

Have something to contribute?

Human-Centered Change & Innovation is open to contributions from any and all innovation and transformation professionals out there (practitioners, professors, researchers, consultants, authors, etc.) who have valuable human-centered change and innovation insights to share with everyone for the greater good. If you’d like to contribute, please contact me.

P.S. Here are our Top 40 Innovation Bloggers lists from the last five years:

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

Case Study – Innovating Around a Disruption

Leave a reply

Škoda built a bike bell that beats noise-cancelling headphones

Case Study - Innovating Around a Disruption

GUEST POST from Jason Hauer

A near-miss on a London street became Škoda’s smartest marketing spend in years. The bigger idea: someone else’s AI has broken something in your category too, and fixing it might be your next growth play.

Ben Fraser was walking to work near Borough Market, headphones on, when a cyclist nearly hit him. The cyclist had been ringing his bell the whole way down the street. Fraser never heard a thing.

Fraser works at PHD Media, and the near-miss became a brief. Transport for London data showed bike-pedestrian collisions rose 24% in 2024, with cyclists set to outnumber car drivers in London for the first time this year. One quiet contributor: active noise cancellation. The bike bell had worked for a century. On a growing share of pedestrians, it simply stopped working, because an algorithm in their headphones was scrubbing it out of the air.

Škoda, the Czech carmaker owned by Volkswagen Group and one of Europe’s biggest auto brands, took the problem to acoustics researchers at the University of Salford. Testing turned up something useful: noise-cancelling algorithms struggle with a narrow band between 750 and 780 hertz. So Škoda built the DuoBell, a second resonator tuned to that gap, plus a hammer that strikes in an irregular rhythm the algorithms can’t predict. Fully mechanical. A piece of metal designed to beat software.

They tested it with a fleet of Deliveroo riders in London, people whose income depends on being heard in traffic. One rider said that with the bell, he finally had a voice in the streets. The riders wanted to keep them.

Pedestrians in noise-cancelling headphones gained up to five extra seconds of reaction time and up to 22 metres of additional distance. Roughly four times the cut-through of a standard bell. Škoda published the underlying research as an open-source white paper, and the work took Silver in Creative Data at Cannes in June. One case-study breakdown projects €79 million in vehicle sales connected to the project, which fits how the company sees it. Škoda started life as a bicycle maker in 1895 and still sponsors the Tour de France. The fix sits inside a hundred-year-old brand truth.

Nobody at the headphone companies set out to make cyclists invisible. Noise cancellation was a good product decision that quietly broke a safety system in a completely different category, and the breakage sat unowned for years while collisions climbed. That pattern is everywhere now. Every AI deployment of the last three years created some downstream effect in somebody else’s business.

Somebody is absorbing that cost right now, and no line item owns it.

Look at your own category through that lens. A century-old bell stopped working because ears started running software. Every signal your company sends was built the same way, for a human on the receiving end, and a machine now sits between you and that person. Your buyer’s first impression of you is written by a model you’ve never briefed. Everything you send after that runs through software tuned by companies with no stake in whether you’re heard. Somewhere in that chain, something that worked in your category for decades is being scrubbed out of the air, and the loss surfaces in a metric you’ve been explaining some other way. The collision numbers sat in Transport for London’s data before anyone thought to blame headphones. Your version of that number already exists. It’s in a dashboard right now, filed under something else.

Škoda’s move was to treat the breakage as a market. Find the gap, spend the budget on fixing it, and let the fix carry the brand. The bell exists because someone measured a frequency nobody had bothered to look for.

Skoda DuoBell statistics

WHAT TO DO THIS WEEK

The first conversation moved. Your customer now asks a model before they ask you, whether that’s a buyer building a shortlist or a client second-guessing your advice. This week, put your customers’ most common questions to the major models, worded the way they’d word them. What comes back is your new first impression.

The unfiltered channels are still open. Every digital signal you send now clears someone else’s software before a human sees it. Nothing stands between a room and the people in it. Škoda answered an algorithm with a piece of metal; your equivalent might be a dinner, or a printed report that lives on a desk for a month. Pick the one message this quarter that can’t survive being flattened and spend real money delivering it by hand.

The next breakage is already in somebody’s data. The collision numbers sat in Transport for London’s files before anyone blamed headphones. Spend an hour with your team on two questions. What have our own AI deployments broken for the people downstream, the customer who needed a person, the partner whose emails our triage now buries? And what’s degrading in our category that nobody has claimed yet? One of those answers is a liability you can still price before a competitor does. The other is a bell nobody has built.

Whatever you find, take it all the way to metal. Škoda could have stopped at the white paper. The bell is the reason you’re reading about them.

A century-old bell needed one new frequency to work again. Your category has a gap like that somewhere.

What’s quietly stopped working in your category?

Image credit: Jason Hauer

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.

Category Creation

Leave a reply

Category Creation

GUEST POST from Geoffrey Moore

Category creation is a critical success factor for start-ups bringing to market a disruptive innovation that calls for a new ecosystem, to support a new class of use cases, funded by a new budget line item. If the category does not form, the start-ups have no place to hang their hat. They can acquire early adopter customers via a bespoke project approach, but they cannot scale any further without help from the rest of the marketplace.

Similarly, established enterprises in mature categories also need to find new venues for growth if they are to break free from their value-investor-set market caps and create net new shareholder value. Whether through acquisition or in-house innovation, they, too, can have the challenge of category creation. So, in both cases, companies need to reengineer the marketplace in order to realize their ambitions. The question is, what would make the marketplace want to lean in?

Marketplaces are made up of ecosystem players, be they partners, competitors, or an installed base of customers. All these constituencies keep their eyes out for disruptive developments that could either benefit or jeopardize their future performance. The early adopters are typically motivated by the benefits, seeking a first mover advantage, while the early majority normally takes a wait-and-see approach, thereby creating a chasm, which in turn can be crossed wherever there is an urgent customer problem that is resisting standard solutions and thus warrants taking a novel approach to solve it. If the category does show it is getting traction, then all those wait-and-see pragmatists will begin to feel threatened by FOMO (Fear of Missing Out), and that is what creates a tornado of demand that puts the category permanently on the map.

Okay, so there is reason to believe that under the right circumstances, markets will support the creation of a new category. That said, we should not underestimate the power of inertia. Markets do not welcome transformational changes with open arms. Indeed, their default move is to deflect most attempts. What we need is a proven playbook. Fortunately, there is one, written some forty years ago, written by an old boss of mine, Regis McKenna.

The Regis Touch

It is hard to overstate the impact that Regis had on high-tech marketing, especially in its early days when it was trying to break free from advertising as its primary medium. At the time, the tech sector was just emerging, the bulk of spending was in B2B markets, the focus was on automating core business processes, and every buying decision entailed considerable risk, not only in terms of the product and vendor’s staying power but also in terms of the impact of the business processes themselves. As a result, advertising per se was not sufficiently informative or credible to drive purchasing. Regis saw this and, with the help of a talented set of consultants and communications professionals, developed a public-relations-led approach that successfully launched hundreds of new products and created dozens of new categories.

The key to his approach was a framework called the infrastructure model that organized the various audiences and constituencies that make up a marketplace in what one might call a ladder of communications:

Relationship Marketing Infrastructure Model Geoffrey Moore

Here’s how it works. The goal is to convert prospects into customers. In B2B markets, those prospects organize around three centers of interest—the technology itself, the impact on productivity, and the financial returns. These prospects get their information most directly from the media, the technologists from the technical press, the end users these days from social media (no such thing, of course, back in the day), and the executives from the business press. The agents of the press, in turn, get a lot of their information from opinion leaders, be they the industry analysts for the technical press, the influencers for social media, or the financial investment analysts for the business press. Those opinion leaders, in turn, get their information from their engagement with the marketplace itself, be they customers, partners, or competitors already involved with the disruptive innovation.

The point is, any claims about the disruptive innovation are verified and validated by working down this model, which means any communications program should organize around working up the same model. Skipping over any one of these audiences and going straight to the prospects directly—the way advertising does—is bound to fail because you have not got your references lined up and sufficiently informed to support and endorse a high-risk buying decision. Product launches and category creation initiatives, therefore, work up this ladder of communications, rung by rung, starting in the executive suite, moving from there to the product organization, and from there to the go-to-market team. That team, in turn, needs to start with educating the ecosystem players, typically with talks and panel sessions at industry conferences, then connecting with the opinion leaders, typically via one-on-one briefings that end up being two-way dialogs, and only then out to the media that will engage with the target prospects.

Category Creation Playbook

A lot of what would go into a complete playbook is product and market-specific, but there are audience-centric principles that remain relatively constant. The key question in each case is, what is it about the emerging category that would be of interest to this particular constituency? With that in mind, here is a brief take:

  1. Executive team. This team will value growth to boost market cap, something that participation in an emerging category can be expected to deliver, but it may well be reluctant to take transformational risk to achieve it. If this team is not 100% behind the effort, don’t start, as every other rung on the latter ultimately calls for investments that this team must endorse.
  2. Product team. This team has to be all in for a wild ride—and usually is. You have to pressure test their claims nonetheless, as they can often get over their skis, promising more than they can deliver within the window that matters.
  3. Go-to-market team. This team requires maturity and patience. The big sales commissions won’t come until the category enters the tornado, so for now, the focus is on creating a market, not harvesting it. That means paying deep attention to developing the ecosystem, including bringing along the installed base, helping to engage and enlist partners, and (oddly enough) encouraging competitors. The last one is important because, ultimately, a category is defined by a set of competitors, not just one company, so for a healthy growing category you need to have peers that are winning too—hopefully in target market segments that are distinct from yours.
  4. Ecosystem. These are the people your go-to-market team is engaging with. The sales team has the installed base, the business development team, the partners, and the marketing team, the competitors. The goal is to get everyone speaking from their own perspective to reinforce your story that something big is underway. One item of note: With respect to competitors, marketing needs to develop a narrative that has room for more than one winner while at the same time staking out turf where your own differentiation makes you the obvious choice. What you do not want to do is bad-mouth the other team’s products—that will create anxiety that will cause everyone to wait and see some more. So, save your sharp tongue for when you get inside the tornado—that’s the no-holds-barred battleground where a well-placed elbow can make a real difference.
  5. Opinion leaders. The goal here is to get conceptual endorsement for the claims you will be making via the media. Opinion leaders need to maintain their independence and do not want to shill for you or anyone else. What they do want to do is look intelligent and have something differentiated to say. What they want from you is enough context to do their job and no interference thereafter. In addition, opinion leaders want to share their opinions with you, in part to influence your future investments, and so it is just as important to listen and ask them questions as it is to present your own story. With respect to your presentation, demos can be useful, but repurposing a customer sales pitch is not, as this audience is not going to buy your product but rather is going to opine on the reasons why other people might.
  6. Media. This is the means by which you will communicate with the three prospect audience types—the technical team, the end users, and the executive sponsors. Each has a preferred media type—industry press, social media, and business press—and each of these types wants to be treated in its own special way. The technical press wants to talk about the product itself. They want facts, love demos, and like to talk to specialists more than generalists. They also are often happy to beta test products or get any other kind of advanced notice as to what’s coming next. Social media wants to talk about the applications of the product, and the ways in which it will impact end users’ lives. So demos can work here only if they are in service to an end-user story as opposed to a run-through of all the features and functions. The business press wants to talk about the “size of the prize,” the impact of the new technology on productivity, how it will reengineer bottlenecking processes, and thus how much trapped value it will be able to release. Demos are wasted here, but PowerPoint can help a lot.
  7. Prospects. When category creation is the focus, it is important to engage the three types of prospects in the right order. If the technology is outrageous, you need to start with the technical audience first just to earn the right to talk to anyone else. If it is not outrageous, then the executive sponsor needs to be your first port of call. The reason is that the other two audiences will actually be willing to meet with you to learn about the latest and greatest thing, but they will have budget, not permission to get new budget, if the executive sponsor is not on board. So a typical path through a major account would start with an executive from your company having a conversation with the prospective executive sponsor at your target customer, which would lead to a referral to the technical team to test your bona fides, and then on to the end-user team, to validate your productivity claims. Proof-of-concept projects are necessary at the very beginning, but one of the major milestones in category creation per se is to generate enough marketplace acceptance that future prospects will forgo these tests.

To sum up, category creation is an outbound communications effort to orchestrate a coalition of the willing across a laddered set of constituencies, each with its own set of interests. The goal is to build an inbound path of verification that reinforces the new category’s right to existence. Trying to shortcut the outbound process by skipping over one or more audiences will defeat the purpose, as any doubts raised this early in the game result in lost momentum that can never be recovered. There is no magic here, but patience and discipline are required.

That’s what I think. What do you think?

— Image credit: Pexels

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.

What is Your Problem?

Leave a reply

What is Your Problem?

GUEST POST from Mike Shipulski

If you don’t have a problem, you’ve got a big problem.

It’s important to know where a problem happens, but also when it happens.

Solutions are 90% defining and the other half is solving.

To solve a problem, you’ve got to understand things as they are.

Before you start solving a new problem, solve the one you have now.

It’s good to solve your problems, but it’s better to solve you customers’ problems.

Opportunities are problems in sheep’s clothing.

There’s nothing worse than solving the wrong problem – all the cost with none of the solution.

When you’re stumped by a problem, make it worse then do the opposite.

With problem definition, error on the side of clarity.

All problems are business problems, unless you care about society’s problems.

Odds are, your problem has been solved by someone else. Your real problem is to find them.

Define your problem as narrowly as possible, but no narrower.

Problems are not a sign of weakness.

Before adding something to solve the problem, try removing something.

If your problem involves more than two things, you have more than one problem.

The problem you think you have is never the problem you actually have.

Problems can be solved before, during or after they happen and the solutions are different.

Start with the biggest problem, otherwise you’re only getting ready to solve the biggest problem.

If you can’t draw a closeup sketch of the problem, you don’t understand it well enough.

If you have an itchy backside and you scratch you head, you still have an itch. And it’s the same with problems.

If innovation is all about problem solving and problem solving is all about problem definition, well, there you have it.

Image credits: Pixabay

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.

Innovation or Not — InTruth

Leave a reply

Innovation or Not — InTruth

by Braden Kelley and Art Inteligencia

Section I: The Context — The Friction of Unfiltered Information

We live in an age of hyper-abundant, instantaneous media. Live-streamed political debates, rapid-fire press conferences, corporate town halls, and unscripted interviews broadcast continuously across our screens. Yet, alongside this unprecedented access lies a compounding crisis: information pollution. Misinformation, selective statistics, and outright falsehoods move at the speed of light — frequently outpacing traditional journalism, post-event debriefs, and manual fact-checking by hours or even days.

By the time a correction is published, the narrative has already taken root in the public consciousness. The damage is done, and the systemic cost to societal trust is immense.

The Human Dilemma: Cognitive Load in the Attention Economy

For the average media consumer, keeping up with live information requires an exhausting mental calculus. Viewers are caught between two undesirable options:

  • Passive Acceptance: Consuming content at face value to save energy, thereby absorbing unverified claims and out-of-context assertions.
  • Active Skepticism: Continuously pausing, opening secondary tabs, and manually searching primary sources to verify claims — a process that destroys flow and creates severe cognitive friction.

In an attention economy built around low-effort, lean-back viewing, asking users to perform manual research while watching live video is a fundamental experience design failure. The friction is simply too high.

Enter InTruth: Shrinking the “Truth Latency”

This is where InTruth enters the frame. Operating as a Chrome extension, InTruth aims to bridge the gap between real-time consumption and rigorous verification. By pairing live automated voice-to-text transcription with continuous, real-time database lookups against verified primary sources, InTruth introduces an automated contextual layer directly over any live video stream.

It seeks to collapse the “truth latency” — closing the critical gap between the moment a claim is uttered and the moment a viewer receives verified context. But does having the capability to cross-reference audio in real time make InTruth a true innovation, or merely a powerful technical demo?

Section II: The Innovation Test — Applying the Value Equation

To evaluate whether InTruth is a genuine breakthrough or merely an intriguing technical novelty, we must look beyond raw technology capabilities and apply a core principle of human-centered change:

Innovation = Value Creation × Value Access × Value Translation

Innovation is never just the underlying technology — it is the holistic system that turns potential value into realized human impact. Because this relationship is multiplicative, if any single variable in the equation drops to zero, the total innovation value collapses to zero. Let’s stress-test InTruth across all three dimensions.

1. Value Creation: Closing the “Truth Latency”

Value Creation represents the raw potential utility of a solution. In the case of InTruth, the potential value is immense. By compressing hours of investigative research into split-second automated lookups, it transforms complex, multi-step investigative work into live, contextual intelligence.

Traditional fact-checking suffers from high “truth latency” — the delay between when a misleading claim is broadcast and when verified facts reach the public. InTruth attacks this latency directly, creating value by:

  • Automating the Research Loop: Instantly translating spoken dialogue into structured search queries against primary databases, public records, and vetted archives.
  • Democratizing Verification: Giving everyday viewers access to research capabilities previously restricted to newsrooms and investigative analysts.
  • Neutralizing Misinformation Velocity: intercepting falsehoods at the exact moment of delivery before they can anchor in the viewer’s memory.

2. Value Access: Reducing Cognitive and Experience Friction

Capability without access is useless. Value Access measures how easily a user can tap into the created value within their existing habits and workflows. This is where experience design becomes the ultimate differentiator.

If InTruth forces users to switch tabs, click through complex menus, or read dense paragraphs of text while trying to watch a fast-paced debate, it introduces severe cognitive friction. To succeed on Value Access, the tool must observe key human-centered principles:

  • In-Flow Delivery: Surfacing unobtrusive, glanceable notifications directly inside the existing video player (whether on YouTube, X, or news networks) without forcing context switching.
  • Cognitive Ergonomics: Presenting live receipts using visual micro-cues (such as color-coded trust vectors or short source snippets) that can be processed at a glance without disrupting the viewing experience.
  • Zero Setup Effort: Functioning ambiently in the background so the user gains immediate insight without configuring complex rules or query filters.

3. Value Translation: Overcoming the Trust Paradox

Great capability and seamless access mean nothing if the user doesn’t believe or understand the output. Value Translation is the art of framing information so that it builds trust and inspires meaningful action.

In fact-checking, Value Translation faces a massive hurdle: human confirmation bias and institutional skepticism. Simply labeling a speaker’s statement as “False” often triggers a defensive psychological reaction, causing viewers to reject the tool rather than reconsider the claim. InTruth must solve this trust paradox by:

  • Sourcing Over Assertion: Shifting away from judgmental labels (e.g., “Pants on Fire”) toward objective, primary-source evidence (e.g., “Congressional Record, Vol. 168: Voted YES on Bill X”).
  • Radical Transparency: Making the verification logic clear, showing exact source documents so users feel empowered to make their own judgment rather than being dictated to by an algorithm.
  • Contextual Nuance: Distinguishing between outright fabrication, missing context, or minor statistical rounding, ensuring the audience trusts the nuance and neutrality of the tool.

Section III: Human-Centered Change & Experience Design Lens

Building a powerful real-time fact-checking engine is fundamentally a human experience challenge, not just an algorithmic one. Technology enables capabilities, but human-centered design enables adoption. To transform InTruth from a novel browser extension into an indispensable daily tool, we must examine the behavioral realities of how people consume media, process conflicting information, and react to real-time feedback.

Designing for Cognitive Load: Preserving the Viewing Flow

Watching live video — whether a town hall, press conference, or political debate — is inherently a passive, low-effort experience. Viewers lean back, absorb narratives, and follow emotional cues. By introducing a real-time verification overlay, InTruth asks the brain to switch into an active, analytical processing mode.

If designed poorly, this extra layer leads to immediate cognitive overload and outrage fatigue. Human-centered experience design must protect the user’s focus through careful interface discipline:

  • Glanceable Micro-Interactions: Information must be visual and bite-sized. Complex policy documents should be distilled into clear, single-line contextual highlights with expandable detail for those who want to dig deeper.
  • Temporal Breathing Room: Notifications must not flash continuously across the screen. Applying intelligent thresholding ensures the overlay only triggers when high-confidence discrepancies or critical missing contexts arise.
  • Visual Hierarchy & Ambient Cues: Utilizing peripheral, non-intrusive status indicators allows viewers to remain immersed in the video stream while maintaining awareness of real-time source verification.

The Source Transparency Model: Spectrum vs. Binary Truth

Traditional fact-checking often relies on reductive binary tags: True or False. In human discourse, however, statements rarely fall into neat black-and-white categories. Rhetoric is built on selective framing, outdated statistics, exaggerated figures, and omitted context.

A rigid binary approach degrades user trust and creates friction. InTruth must adopt a Source Transparency Model that reflects nuance across a spectrum of context:

Verified Primary Record  •  Missing Context  •  Outdated Data  •  Unsubstantiated Assertion

By presenting a spectrum rather than a verdict, InTruth respects the user’s intelligence. It transforms the experience from an automated referee telling the audience what to think into a personal research assistant providing the receipts needed to draw independent conclusions.

Overcoming Psychological Defense Mechanisms

When people are presented with facts that contradict their deeply held beliefs, their immediate instinct is rarely acceptance — it is defensiveness. Cognitive dissonance kicks in, and the brain’s immune system seeks reasons to discredit the source, the algorithm, or the platform delivering the correction.

To navigate this psychological hurdle, InTruth must incorporate key behavioral design tactics:

  • Neutral, Non-Judgmental Language: Avoid emotionally charged terms like “Debunked” or “Lies.” Instead, use objective phrasing such as “Official Treasury data shows…” or “Voting records reflect…”
  • Direct Primary Links: Build instant credibility by linking directly to raw, unedited source files — such as legislative bills, economic reports, or court transcripts — rather than third-party commentary.
  • Empowering User Autonomy: Frame corrections as optional context layers that enrich understanding, ensuring users feel empowered rather than challenged.

Section IV: The Futurology Perspective — FutureHacking™ Live Truth

To understand the long-term strategic trajectory of InTruth, we must look beyond its current implementation as a desktop browser extension. By applying a FutureHacking™ framework — scanning weak signals in technological adoption and anticipating systemic shifts — we can map how real-time truth engines will re-architect the broader media landscape over the next 5 to 10 years.

From Weak Signals to Mainstream Realities

Today, real-time fact-checking overlayed on web video is a weak signal — a niche capability used primarily by journalists, policy analysts, and tech-savvy early adopters. However, several converging trends will accelerate this capability into a baseline expectation for ambient intelligence:

  • The Explosion of Synthetic and AI-Generated Media: As deepfakes, automated audio cloning, and AI-driven propaganda proliferate, unassisted human perception will no longer be sufficient to determine authenticity. Real-time verification will evolve from an optional feature into an essential cognitive defense layer.
  • Ultra-Low-Latency Edge AI: On-device AI models and localized vector databases will allow voice transcription, semantic analysis, and cross-referencing to occur locally with zero network latency, making verification instantaneous and completely private.
  • The Shift to Conversational and Live Streams: As traditional written journalism continues to give way to long-form podcasts, live video streams, and interactive town halls, context tools must operate natively in live audio visual environments.

From Browser Extensions to Native Ambient Interfaces

The Chrome extension is merely a stepping stone. As computing paradigms shift away from traditional screens, tools like InTruth will migrate directly into native hardware and ubiquitous spatial interfaces:

  • Spatial Computing & AR Glasses: In augmented reality environments, real-time verification layers will move from screen overlays to ambient heads-up displays during live, in-person events, political rallies, or public lectures.
  • Smart TV and Broadcast Integration: Streaming platforms and television hardware manufacturers will embed native “truth engines” directly into set-top boxes and smart OS environments, allowing viewers to toggle contextual overlays with a button on their remote.
  • Enterprise Video Conferencing: In business settings, real-time verification tools will be integrated into platforms like Zoom and Teams, serving as automated compliance and fact-verification assistants during corporate board meetings, earnings calls, and sales presentations.

The Strategic Counter-Maneuver: Adaptations in Public Rhetoric

Whenever a transformative technology alters the dynamic between speaker and audience, the speaker’s behavior adapts. The widespread adoption of live, automated truth engines will trigger a fundamental evolution in public rhetoric and media strategy:

  • Rhetorical Obfuscation vs. Precision: Bad actors will adapt by shifting from falsifiable statements to hyper-vague, highly emotive language designed to bypass database queries entirely. Conversely, transparent communicators will structure their speeches to explicitly cite primary sources in real time, encouraging instant automated validation.
  • Source Poisoning and Database Wars: The strategic battleground will shift from the speaker’s podium to the underlying reference databases. Public figures and interest groups will actively attempt to flood primary source repositories or manipulate public records to influence real-time algorithmic outputs.

Ultimately, InTruth is not just a tool for today’s web browsing — it is a prototype for the ambient, real-time context infrastructure that will define the future of human communication and public accountability.

Section V: The Verdict — Innovation or Novelty?

When we weigh InTruth against our core criteria for human-centered change, a clear distinction emerges: technological capability alone does not equal innovation. A product can feature cutting-edge machine learning and instantaneous audio transcription, yet remain an intrusive novelty if it fails to solve the broader human experience challenge.

To determine whether InTruth represents a category-defining breakthrough or just another temporary browser plugin, we must compare the characteristics of an incremental feature against those of a true, human-centered innovation:

Dimension Incremental Feature (Novelty) True Human-Centered Innovation
Core Focus Speed, raw transcription accuracy, and automated database lookups. Trust, context retention, and enhancing human comprehension without fatigue.
User Experience Intrusive pop-up overlays that interrupt viewing flow and increase cognitive friction. Non-intrusive micro-insights that empower individual judgment smoothly and ambiently.
Perceived Value An authoritative “referee” dictating rigid binary judgments (True/False). A transparent research assistant providing accessible primary-source receipts.
Systemic Impact A niche fact-checking widget for political hobbyists and journalists. A foundational layer for public accountability in the attention economy.

The Final Verdict

InTruth possesses all the technical ingredients necessary to become a transformative tool. However, its ultimate success will not be decided in the code base — it will be decided in the user experience layer.

If InTruth focuses merely on real-time execution speed and automated scorekeeping, it risks becoming a distraction that users disable after the initial novelty wears off. But if the team designs for low cognitive load, prioritizes source transparency over judgmental scoring, and seamlessly embeds context into the user’s natural viewing flow, InTruth will move from a clever browser extension to a vital engine of trust in the modern digital ecosystem.

Section VI: Strategic Recommendations for the InTruth Team

To successfully cross the chasm from a promising technical prototype to a category-defining human-centered innovation, the InTruth team must execute on four strategic imperatives. These recommendations focus on maximizing value translation, optimizing experience design, and building systemic trust across the entire ecosystem.

1. Prioritize Sourcing Over Scoring

Avoid the trap of playing “automated referee.” Assigning rigid, top-down truth scores or binary labels creates immediate psychological friction and invites accusations of algorithmic bias. Instead, shift the design emphasis entirely toward rapid, neutral receipt delivery:

  • Provide Direct Evidence: Present exact quotes from official public records, legislative documents, or peer-reviewed data alongside the spoken transcript.
  • Highlight Context Gaps: Explicitly show what information was omitted (e.g., “Stat represents 2021 data, excluding 2024 updates”) rather than declaring a statement flatly false.
  • Empower Human Judgment: Frame every overlay as an objective research receipt that respects the viewer’s intelligence to draw their own conclusion.

2. Empower the Nine Innovation Roles

Systemic adoption requires engaging users across distinct behavioral profiles. Product design should specifically accommodate the full spectrum of user roles from the Nine Innovation Roles, particularly:

  • The Troubleshooter: Provide deep-dive analytical modes allowing researchers, journalists, and policy analysts to inspect raw source metadata, API logs, and verification pipelines in real time.
  • The Customer Champion: Ensure the experience is continuously tuned to human needs, minimizing cognitive friction and preventing outrage fatigue so the tool feels like a trusted personal assistant.
  • The Evangelist: Build friction-free “shareable receipt” mechanics, enabling everyday users to export verified video clips and primary-source overlays directly to social networks with a single click.

3. Design for Zero-Friction Cognitive Ergonomics

The ultimate goal of experience design is making powerful capabilities feel effortless. If using InTruth requires conscious effort, adoption will stall among mainstream audiences:

  • Ambient Micro-Interactions: Keep overlays compact, glanceable, and peripheral to the primary video frame. Use non-intrusive status indicators that expand only upon intent.
  • Smart Thresholding: Implement confidence scoring models behind the scenes so the UI only interrupts the viewer when high-value, high-certainty discrepancies occur.
  • Zero-Setup Onboarding: Ensure the extension works immediately out of the box on major video platforms without requiring complex filter configurations or custom API setups.

4. Institutionalize Radical Transparency and Decentralized Trust

In an environment marked by deep institutional skepticism, the platform itself must be beyond reproach. Trust cannot be requested; it must be structurally demonstrated:

  • Open-Source Verification Logic: Make the underlying matching algorithms, prompt architectures, and scoring heuristics publicly auditable on GitHub.
  • Pluralistic Primary Databases: Draw from a diverse, transparently published set of non-partisan archives, government databases, and academic repositories rather than proprietary black-box datasets.
  • Community Audit Mechanisms: Allow trusted independent researchers and public ombudsmen to review, flag, and continuously refine source mappings.

Frequently Asked Questions

Is InTruth a fact-checker or an automated referee?

InTruth functions as a real-time research assistant rather than a judgmental referee. Instead of assigning arbitrary “True” or “False” ratings, it provides instant primary-source receipts — such as official government records, voting histories, and economic datasets — so users can verify live statements and draw their own conclusions without leaving their video stream.

How does InTruth reduce cognitive overload during live videos?

By shifting from active searching to ambient discovery, InTruth eliminates the friction of pausing videos or opening secondary browser tabs. Its glanceable, non-intrusive micro-overlays deliver contextual highlights in real time, preserving the viewer’s natural flow while ensuring critical facts are instantly accessible.

Why is experience design critical for automated fact-checking tools?

Technology enables capability, but experience design enables adoption. If a fact-checking tool uses intrusive pop-ups or accusatory language, it triggers user fatigue or cognitive defensiveness. Human-centered design ensures the tool delivers high value with zero setup effort, low cognitive friction, and maximum source transparency.


Image credits: Gemini

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

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

Constrained Innovation is Beating Unconstrained Innovation – Again

Leave a reply

Constrained Innovation is Beating Unconstrained Innovation - Again

by Braden Kelley and Art Inteligencia

Every few years, Silicon Valley rediscovers a lesson the rest of the innovation world already knows: constraints don’t kill breakthroughs — they focus them.

This week’s AI headlines make the point again. Moonshot’s Kimi K3 and Thinking Machines Lab’s Inkling are not “unlimited compute with unlimited budget” stories. They are constrained-innovation stories — open-weight models built to compete with (and sometimes beat) far richer, closed frontier systems from OpenAI and Anthropic on the tasks that matter to builders. At the same time, a quieter race is packing surprising capability into models small enough to live on a smartphone with 6 GB of RAM or less.

If you lead change, product, or experience design, this is not just a model-release week. It is a reminder of how innovation actually works when resources are scarce, goals are clear, and “more” is not allowed to substitute for “better.”

The unconstrained myth

Unconstrained innovation sounds romantic: infinite GPUs, infinite capital, infinite permission to chase every benchmark.

In practice, unconstrained environments often produce:

  • Feature sprawl instead of sharp value
  • Capability inflation instead of usable outcomes
  • Vendor dependence instead of organizational learning
  • Status races (who has the biggest model) instead of customer impact

Constrained innovation does the opposite. It forces tradeoffs. Tradeoffs force clarity. Clarity forces design.

We’ve seen this movie before — in lean startups, in frugal engineering, in design-to-cost product development, in wartime R&D. The pattern is durable:

When you cannot buy your way to “more,” you must invent your way to “enough.”

AI is now teaching that lesson at planetary scale.

Kimi K3: open weight, frontier pressure

China’s Moonshot AI released Kimi K3 in mid-July 2026 as what it calls the world’s first open ~3T-class model — roughly 2.8 trillion parameters, native vision, and a 1-million-token context window, with full weights promised for public release.

Be precise about the scoreboard, because hype helps no one:

  • Moonshot itself says K3’s overall performance still trails Anthropic’s Claude Fable 5 and OpenAI’s GPT-5.6 Sol.
  • On multiple evaluations, though, K3 is competitive with — and on some coding, agent, long-horizon engineering, and frontend-building tasks ahead of — strong closed models sitting just behind the absolute tip of the spear.
  • Independent evaluators have placed it near GPT-5.5 / Claude Opus-class systems on several complex multi-step workloads, while still acknowledging Fable 5 as the tougher overall ceiling.

That combination is the real story: not “open models already own everything,” but “open models are close enough, open enough, and cheap enough to change the game.”

Constraint here is structural. Moonshot is not playing with the same geopolitical, capital, and closed-ecosystem advantages as the largest U.S. labs. So it optimized for:

  • Open weights (download, run, modify)
  • Architecture efficiency (MoE-style sparsity and novel attention choices)
  • Task-relevant dominance where developers actually feel pain (coding agents, long context, UI building)

That is constrained innovation: win where it matters for users, not where the press release wants a clean sweep.

Inkling: constraint as a product philosophy

Days earlier, Thinking Machines Lab — founded by former OpenAI CTO Mira Murati — released Inkling, its first open-weights model.

Inkling is a multimodal Mixture-of-Experts system (~975B total / ~41B active parameters), trained across text, images, audio, and video, with a large context window and Apache 2.0 weights on Hugging Face. Critically, the lab is not claiming Inkling is the strongest model available, open or closed.

Instead, Thinking Machines is making a different bet — one every human-centered innovator should recognize:

The winning model is not always the biggest generalist. It is the one an organization can shape.

Their framing is customization, efficient controllable “thinking effort,” and a base model designed to be adapted. Alongside Inkling they previewed Inkling-Small (lighter active-parameter footprint) for lower cost and latency.

This is constrained innovation as strategy:

  • Don’t outspend OpenAI/Anthropic on every frontier benchmark.
  • Out-enable customers on fit, control, and adaptation.
  • Treat “open weights + fine-tuning path” as the product, not a side quest.

In experience-design terms: they are optimizing for agency, not spectacle.

The pocket frontier: intelligence that fits in 6 GB

While the giants argue about trillion-parameter scoreboards, another constrained race is rewriting daily experience design: on-device AI.

Phones with ~6 GB of RAM are now practical homes for capable small language models — typically 1B–3B class models under aggressive 4-bit quantization, often with NPU acceleration (Apple Neural Engine, Qualcomm Hexagon, and peers). Families like Gemma’s efficient variants, Phi-class minis, Llama 3.2 small models, and Apple’s on-device foundation model path are not “tiny ChatGPT cosplay.” They are differently designed systems: distillation, quantization-aware training, sliding-window/grouped-query attention, and task specialization.

What becomes possible when intelligence must fit in a pocket?

  • Privacy by architecture (data never leaves the device)
  • Latency that feels like UI, not waiting for a cloud round trip
  • Offline resilience
  • Ambient assistance without a permanent surveillance subscription

This is FutureHacking in the literal sense: the future arriving first where constraint is non-negotiable — battery, thermal envelope, memory bandwidth, and user trust.

Unconstrained cloud models will still win the hardest reasoning contests for a while. Constrained on-device models will win moments — the thousands of tiny interactions that shape whether people feel helped or hunted by technology.

A simple framework: Three Arenas of Constrained AI Advantage

Leaders should stop asking only “Who has the best model?” and start asking which arena they are competing in:

  1. Frontier Arena — Absolute peak reasoning. Still often favors well-funded closed labs (Fable 5 / GPT-5.6 Sol class). Use sparingly for the hardest 10–20% of work.
  2. Open Adaptation Arena — Near-frontier capability + weights you can own, route, fine-tune, and host. Kimi K3 and Inkling are attacking this arena hard. Ideal for product teams, agents, and regulated environments.
  3. Edge Experience Arena — Models compressed into phone-scale memory. Wins on privacy, speed, cost-at-scale, and human experience continuity. This is where unconstrained cloud thinking often fails customers.

Constrained innovation beats unconstrained innovation when the arena rewards focus.

Implications for organizations (not just AI labs)

If you are charting change inside a company, the lesson is operational:

  1. Budget is a design tool. Cap tokens, latency, and model size early. Force product clarity.
  2. Route by job-to-be-done. Don’t send every prompt to the most expensive frontier model. Reserve it for true hard cases.
  3. Prefer adaptable over mythical “best.” An open model you can fine-tune to your workflow may outperform a slightly smarter generalist you can’t shape.
  4. Design for the edge. Anything frequent, personal, or privacy-sensitive should be a candidate for on-device or hybrid architectures.
  5. Measure outcomes, not vibes. Benchmarks matter; customer task completion, cost per successful outcome, and trust matter more.

This is human-centered change applied to AI portfolios: start from experience, not ego.

We’ve seen this movie — and the sequel is here

Constrained innovation beat unconstrained innovation in Japanese postwar manufacturing quality, Israeli “startup nation” necessity engineering, mobile-first product design in bandwidth-poor markets, and every great design brief that began with “You only get X.”

Now it is beating — or at least pressuring — unconstrained AI again.

Kimi K3 shows that open, resource-conscious frontier building can meet or beat closed leaders on key developer battlegrounds even while still trailing at the absolute peak. Inkling shows that refusing the one-size-fits-all arms race can itself be a strategy. Phone-scale models show that the most human future may be the one small enough to live beside us without phoning home.

The organizations that win the next decade will not be those with the least constraint.
They will be those who treat constraint as a creative operating system.

Because in innovation, as in life:

Limits don’t stop the future. They decide who gets there first — and who arrives with something people can actually use.

Image credits: Meta.AI

Content Authenticity Statement: The topic area, key elements to focus on, etc. were decisions made by Braden Kelley, with a little help from Cursor to clean up the article.

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

Innovation Framework Examples: 7 Real-World Cases That Show How They Work

Leave a reply

Innovation Framework Examples: 7 Real-World Cases That Show How They Work

by Braden Kelley and Art Inteligencia

The most common question I get after presenting on innovation frameworks is not “which framework is best?” — it’s “can you show me what this actually looks like inside a real organization?” That question is exactly right. Frameworks are only valuable when you can see how they translate from theory to practice, and the translation is rarely as clean or obvious as the textbook version suggests.

What follows are real examples of organizations applying specific innovation frameworks — what the framework gave them, what it required of them, and what the outcomes looked like. For a complete guide to the major frameworks themselves, see our comprehensive innovation frameworks reference guide.

Design Thinking: IDEO and Bank of America’s “Keep the Change”

Bank of America’s “Keep the Change” savings program is one of the most cited design thinking success stories for good reason — it demonstrates what happens when you apply genuine customer empathy rather than product-feature thinking to a business problem.

The challenge: Bank of America wanted to help customers save more money, but conventional savings products were failing to attract adoption among their target segment of working-age adults. IDEO was brought in to apply design thinking to the problem.

The empathy research revealed something that no amount of market data had surfaced: people found saving difficult not because they lacked discipline, but because saving felt like a deliberate sacrifice that required conscious decision-making every time. The insight was behavioral, not financial.

The solution that emerged from this insight was counterintuitive: make saving automatic and invisible. Every time a customer made a debit card purchase, the amount was rounded up to the nearest dollar, and the difference was automatically transferred to savings. No decision required. No sacrifice felt.

The result: 2.5 million new customers enrolled in the first year, and Bank of America customers saved more than $1 billion through the program in its first year of operation. The program succeeded because the design thinking process surfaced a genuine behavioral insight — that the friction to saving was psychological, not financial — that product-focused thinking had systematically missed.

The framework lesson: Design thinking’s empathy stage is not market research. It surfaces the behavioral and emotional dimensions of a problem that quantitative data can’t see. The “Keep the Change” insight — that automatic saving removes the psychological friction that makes conscious saving feel like sacrifice — was only discoverable through direct human observation.

Jobs to Be Done: McDonald’s Milkshake Story

Clayton Christensen’s milkshake story is the most famous example of Jobs to Be Done thinking in practice — and it’s worth revisiting in detail because it illustrates exactly how differently JTBD reframes a business problem.

McDonald’s wanted to increase milkshake sales. Conventional market research asked customers what they wanted in a milkshake — thicker? sweeter? more flavors? The answers were inconclusive and the improvements they prompted didn’t move the sales needle.

A JTBD researcher took a different approach: instead of asking customers what they wanted in the product, he asked what job they were hiring the milkshake to do. The finding was completely unexpected. The majority of morning milkshake purchasers were buying for the commute — they needed something that would keep them full through a long, boring drive, that they could consume one-handed without making a mess, and that would last long enough to feel like an event rather than a transaction. The milkshake — thick, slow to consume, and easy to hold — was uniquely suited for this job. The alternatives (a banana, a bagel, a coffee) all failed on at least one dimension of the commute job.

The implication was immediately actionable: make the morning commute milkshake even better at its actual job — thicker, available faster at the drive-through, with a thinner straw to make it last longer. Don’t change the flavor. The job, not the product attribute, was the unit of analysis.

The framework lesson: JTBD reframes the competitive set entirely. McDonald’s wasn’t competing with Burger King for milkshake customers — it was competing with bananas and bagels for the morning commute job. That reframe opens completely different improvement directions than conventional competitive analysis would ever produce.

Lean Startup: Dropbox’s Minimum Viable Product

Dropbox’s founding story is the canonical example of Lean Startup’s MVP principle applied to its fullest effect — and what makes it particularly instructive is that the MVP wasn’t even a product. It was a video.

In 2007, Drew Houston had built a working prototype of Dropbox but faced a fundamental challenge: file synchronization is a problem that requires a significant user base to be meaningful, and building that base requires persuading investors and early users that the problem is real and the solution works. The conventional path — build, launch, market, iterate — would require substantial capital for a product whose value proposition was genuinely hard to communicate without experiencing it.

The Lean Startup approach: before investing further in the product, validate that people actually wanted it. Houston created a simple three-minute demo video explaining what Dropbox would do. No working product. No technical demonstration. Just a clear explanation of the problem and how Dropbox would solve it. He posted it on Hacker News.

The waitlist went from 5,000 to 75,000 overnight. The demand signal was unambiguous. The MVP — in this case, a video rather than a product — had validated the core assumption (that people wanted effortless file synchronization across devices) at a cost of hours rather than months of development.

The framework lesson: The point of an MVP is to test the most important assumption at the lowest possible cost, not to build the simplest functional version of the product. In Dropbox’s case, the most important assumption was demand, not technical feasibility — so the MVP was a demand test, not a product prototype.

Three Horizons Framework: Amazon Web Services

Amazon’s development of AWS is the most instructive example of McKinsey’s Three Horizons Framework in practice — partly because Amazon’s leaders almost certainly weren’t thinking about Three Horizons when they built it, but the strategic logic maps perfectly onto the framework.

Amazon’s Horizon 1 business in the early 2000s was e-commerce — the core retail operation that was generating revenue and requiring continuous improvement. The challenge every e-commerce business faces is infrastructure: you need enormous computing capacity to handle peak periods (holiday shopping), but that capacity sits idle for most of the year. Amazon had solved this problem for itself through massive internal infrastructure investment.

The Horizon 2 insight — building an adjacent business from existing capabilities — came from recognizing that the infrastructure Amazon had built to run its own e-commerce operation was itself a valuable product that other companies needed. The capability was already built. The extension was to offer it externally.

The Horizon 3 bet was that computing infrastructure as a service would become a foundational utility — that the long-term market was enormous and that Amazon’s early investment would produce compounding advantages as the market developed. In 2024, AWS generated over $100 billion in annual revenue and represented the majority of Amazon’s operating profit.

The framework lesson: The Three Horizons Framework is most valuable not as a planning tool but as a diagnostic: it forces explicit conversations about whether the organization is investing appropriately across all three time horizons, and whether Horizon 1 pressures are crowding out the Horizon 2 and 3 investments that produce long-term competitive advantage. Amazon’s willingness to invest in and protect Horizon 3 bets — including AWS, Prime, and Alexa — while competitors focused primarily on Horizon 1 optimization is a significant part of why it has compounded value so effectively.

Open Innovation: Procter & Gamble’s Connect + Develop

Procter & Gamble’s Connect + Develop program, launched in 2000 under CEO A.G. Lafley, is the most cited example of open innovation at enterprise scale. Lafley set an ambitious and specific goal: source 50% of P&G’s innovations from outside the company. This was not aspirational language — it was a specific, measurable target that required fundamentally restructuring how P&G approached innovation.

The program built explicit infrastructure for external idea sourcing: a dedicated team for identifying and evaluating external innovations, partnerships with universities and research institutions, a public submission portal for independent inventors, and acquisition strategies that brought external technologies inside P&G’s commercialization machinery.

The results were significant. Spin-off toothbrush innovations, the Swiffer product line, and the Pringles printing technology all came through open innovation channels. By 2006, P&G reported that more than 35% of its new products had elements that originated from outside the company, up from about 15% in 2000. Productivity in R&D improved substantially.

What made Connect + Develop work where most open innovation programs fail was the investment in internal absorption capability — the processes, relationships, and organizational structures that allowed P&G to actually use external ideas rather than just collect them. The “not invented here” syndrome that kills most open innovation programs was addressed through deliberate cultural and process design, not just aspiration.

The framework lesson: Open innovation requires two-sided capability development — not just the ability to attract external ideas, but the organizational capacity to evaluate, integrate, and commercialize them. P&G’s investment in internal absorption capability was as important as its investment in external sourcing.

The Value Innovation Framework: Apple iPad Launch

The Apple iPad launch in 2010 illustrates the Value Innovation Framework’s three components — Value Creation, Value Access, and Value Translation — and specifically demonstrates what happens when Value Translation fails even when the other two are strong.

The iPad’s Value Creation was genuinely significant: a device that made web browsing, email, media consumption, and light content creation dramatically more convenient than a laptop for a large set of use cases. Value Access was strong: the price point was lower than expected, distribution through Apple Stores and carriers was immediate, and the device worked out of the box without configuration.

The initial launch, however, struggled with Value Translation — helping people understand what job the device was actually for. The early marketing positioned it as a larger iPhone or a smaller laptop, both framings that made it seem like a compromise rather than a genuine innovation. Reviews were mixed. The initial sales trajectory was uncertain.

The Value Translation breakthrough came not from a product change but from a single advertising image: a person relaxing on a couch with an iPad in their lap. That image communicated in seconds what no amount of specification comparison could: this is the device for the relaxed, casual computing moment — not the desk, not the commute, but the couch. Sales accelerated dramatically after that visual translation clicked.

The framework lesson: Innovation = Value Creation × Value Access × Value Translation is multiplicative, not additive. The iPad had strong Value Creation and Value Access from day one. The Value Translation gap almost cost Apple the launch. Fixing the translation — not the product — unlocked the market.

Disruptive Innovation: Netflix vs Blockbuster

The Netflix/Blockbuster story has become the defining example of disruptive innovation theory in practice — perhaps because it is unusually clean as a case study, with a visible incumbent, a clear disruption pattern, and a decisive outcome.

Netflix’s initial DVD-by-mail service in 1998 entered the video rental market from exactly the position Christensen’s theory predicts: serving an overlooked segment (frequent renters who resented late fees and found the trip to the store inconvenient) with a simpler, different model that the incumbent (Blockbuster) had no interest in responding to. Blockbuster’s most profitable customers were the casual renters who came into stores and paid late fees — the customers Netflix was serving were not Blockbuster’s priority.

As Netflix improved, it moved upmarket — expanding its library, improving delivery speed, and eventually transitioning to streaming. By the time the threat was obvious to Blockbuster, the incumbent’s response was structurally constrained: its entire business model (physical stores, late fees, walk-in customers) was incompatible with the direction the market was moving. Blockbuster filed for bankruptcy in 2010. Netflix is now a global media company with over 300 million subscribers.

The framework lesson: Disruptive innovation theory’s most valuable practical application is identifying threats that conventional competitive analysis will dismiss. Blockbuster’s leadership could see Netflix’s numbers for years and rationally conclude that the threat was manageable. The framework reveals why that rational conclusion was wrong: the disruption was coming from a direction Blockbuster’s financial incentives prevented it from defending.

Frequently Asked Questions

What are some real-world examples of innovation frameworks in action?

Real-world innovation framework examples include: Bank of America’s “Keep the Change” savings program (design thinking applied to behavioral finance); McDonald’s milkshake insight (Jobs to Be Done reframing the competitive set); Dropbox’s video MVP (Lean Startup demand validation before product development); Amazon Web Services (Three Horizons Framework applied to infrastructure-as-a-service); Procter & Gamble’s Connect + Develop (open innovation at enterprise scale); the Apple iPad launch (Value Innovation Framework showing the importance of Value Translation); and Netflix’s disruption of Blockbuster (Disruptive Innovation theory playing out over a decade). Each example illustrates how frameworks translate from theory to specific, actionable decisions in real organizations.

Which innovation framework is most widely used by large companies?

McKinsey’s Three Horizons Framework and Design Thinking are the most widely adopted innovation frameworks among large organizations. Three Horizons is particularly prevalent in corporate strategy and portfolio management contexts because it provides a common language for conversations about innovation investment allocation. Design Thinking has been widely adopted across industries — from product development to healthcare to public policy — because its human-centered, iterative approach applies to virtually any type of complex problem. In practice, most sophisticated innovation programs use multiple frameworks in combination rather than selecting one exclusively.

How do you choose the right innovation framework for your organization?

Choosing the right innovation framework depends on your primary challenge: if you need to allocate innovation investment across time horizons, use Three Horizons; if you need to identify unmet customer needs, use Jobs to Be Done; if you need to validate a new concept quickly, use Lean Startup; if you need to understand competitive disruption threats, use Disruptive Innovation theory; if you need to access external capabilities, use Open Innovation; if you need to solve a complex human-centered problem, use Design Thinking. Most organizations benefit from using multiple frameworks in combination — each addresses a different dimension of the innovation challenge. For a complete framework selection guide, see our comprehensive innovation frameworks guide.

Want to go deeper on any of these frameworks? Our complete guide to innovation frameworks covers each one in detail — what it does well, where it falls short, and how to choose the right approach for your specific situation.




Bring This Thinking to Your Next Event

Braden Kelley is a LinkedIn Top Voice, bestselling author, and innovation keynote speaker who helps organizations get to the future first and build sustainable innovation cultures.

Book Braden as a Keynote Speaker →

Image Credit: Gemini

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

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

Sometimes with Novelty, Less Can Be More

Leave a reply

The Ghost Pepper Rule

GUEST POST from Mike Shipulski

When it’s time to create something new, most people try to imagine the future and then put a plan together to make it happen. There’s lots of talk about the idealize future state, cries for a clean slate design or an edict for a greenfield solution. Truth is, that’s a recipe for disaster. Truth is, there is no such thing as a clean slate or green field. And because there are an infinite number of future states, it’s highly improbable your idealized future state is the one the universe will choose to make real.

To create something new, don’t look to the future. Instead, sit in the present and understand the system as it is. Define the major elements and what they do. Define connections among the elements. Create a functional diagram using blocks for the major elements, using a noun to name each block, and use arrows to define the interactions between the elements, using a verb to label each arrow. This sounds like a complete waste of time because it’s assumed that everyone knows how the current state system behaves. The system has been the backbone of our success, of course everyone knows the inputs, the outputs, who does what and why they do it.

I have created countless functional models of as-is systems and never has everyone agreed on how it works. More strongly, most of the time the group of experts can’t even create a complete model of the as-is system without doing some digging. And even after three iterations of the model, some think it’s complete, some think it’s incomplete and others think it’s wrong. And, sometimes, the team must run experiments to determine how things work. How can you imagine an idealized future state when you don’t understand the system as it is? The short answer – you can’t.

And once there’s a common understanding of the system as it is, if there’s a call for a clean sheet design, run away. A call for a clean sheet design is sure fire sign that company leadership doesn’t know what they’re doing. When creating something new it’s best to inject the minimum level of novelty and reuse the rest (of the system as it is). If you can get away with 1% novelty and 99% reuse, do it. Novelty, by definition, hasn’t been done before. And things that have never been done before don’t happen quickly, if they happen at all. There’s no extra credit for maximizing novelty. Think of novelty like ghost pepper sauce – a little goes a long way. If you want to know how to handle novelty, imagine a clean sheet design and do the opposite.

Greenfield designs should be avoided like the plague. The existing system has coevolved with its end users so that the system satisfies the right needs, the users know how to use the system and they know what to expect from it. In a hand-in-glove way, the as-is system is comfortable for end users because it fits them. And that’s a big deal. Any deviation from baseline design (novelty) will create discomfort and stress for end users, even if that novelty is responsible for the enhancement you’re trying to deliver. Novelty violates customer expectations and violating customer expectations is a dangerous game. Again, when you think novelty, think ghost peppers. If you want to know how to handle novelty, imagine a green field and do the opposite.

This approach is not incrementalism. Where you need novelty, inject it. And where you don’t need it, reuse. Design the system to maximize new value but do it with minimum novelty. Or, better still, offer less with far less. Think 90% of the value with 10% of the cost.

Image credits: Pexels

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.

Creating an Innovation Edge

Leave a reply

Creating an Innovation Edge

GUEST POST from John Bessant

Have you ever wondered why we say ‘two heads are better than one?’

Mainly because in innovation we’ve learned that the lone genius is a pretty rare animal – we’re actually much better at coming up with new stuff if we collaborate. But here’s the interesting thing – it’s not just doubling our brain power when those two heads work together; the real value comes when they are different heads, bringing different stuff to the ideas party.

Which is what today’s post is all about.

(if you’d prefer to watch/listen please click here)

Valyrian steel from Game of Thrones. Andúril from Lord of the Rings. Excalibur rising from the misty lake to find its way into King Arthur’s hand. We love the myth of the lone, magical elven smith hiding in a mountain forge, infusing magic into metal.

But what if I told you the greatest steel in human history—metal that could bend in a semi-circle without breaking—was completely real? And it wasn’t made by elven magic, but by three completely different cultures sharing a city? A place you could call the Silicon Valley of the 16th century. The city of Toledo, in Spain.

I actually met Don Quixote last week.

Or at least, I think it was him.

Mind you, it was a little hard to tell, me being whisked at 200 km/hour across the plains of La Mancha courtesy of the impressive high-speed train from Barcelona. All I really caught out of the window was the endless Spanish countryside, shifting in character like a slow wide shot in a movie. Red earth, olive trees — and a glimpse of a shadow on a horse with someone else by their side. Maybe a windmill — or was that just my imagination?

So it wasn’t hard to conjure another scene from the possible past, this time catching the glint of polished breastplates as the sun caught the progress of a troop of 16th century Spanish cavalry cresting the ridge up ahead. Their weapons, swinging loosely as they trotted purposefully across the rocks, sheathed in ornate leather scabbards, pecked with jewels which sparkled through the dust.

Not just any weapons. These were the swords which built a global empire. Blades so sharp, and so resilient, that they belong alongside their mythical cousins like Excalibur or Anduril.

Legendary blades – but these are not the product of fiction. Instead they were born not far from where my train was scything its way across southern Spain. In the smoky, sun-baked forges of a single city lying by the side of a river. Toledo — the fortress town which gave birth to a steel like no other.

Its reputation spread around the world; Japanese Samurai masters sought after the secret behind its blades, the conquistadores used them to devastating effect throughout Latin America, and the feared tercios of the Spanish infantry fighting their way through Europe had come to depend on it. And, like Swiss watches or luxury cars today, Toledo steel blades were the item no wealthy aristocrat could be seen without at his belt when posing for the official portrait.

Like good businessmen, the smiths of Toledo played up the mythology which had grown up around their workmanship. The magical waters of the river Tagus, somehow bestowing special power, marking out the difference between good blades and the legendary Toledo variety.

The reality was, of course, a little different — and a fascinating story of how innovation happens.

Once upon a time…

How did those Toledo craftsmen help the Spanish Empire rise to be the great Imperial power of its time? By solving a blacksmith’s ultimate dilemma: making a blade hard enough to hold a razor edge, but flexible enough not to snap in battle. To understand that we need to go back a bit…..

In the very earliest days of making weapons, our stone age ancestors would use sharp-edged rocks crudely fashioned into blades. Adequate, but with an annoying habit of breaking if used too enthusiastically, or if they hit a hard object like an opponent’s stronger blade.

The oldest true swords ever discovered by archaeologists date back to roughly 3300 BCE and were found in modern-day Turkey. These marked the first use of metal: a variant of copper. On its own, copper is soft and malleable — not much use for a blade — but ancient metalworkers found that adding arsenic could harden and stiffen it.

(Unfortunately, sword smithing in that region was not a promising profession to enter, on account of its members dying from blood poisoning caused by the arsenic.)

By 1700 BCE things had improved, as we entered the Bronze Age and the Minoans of Crete and the Chinese independently developed the metallurgy which allowed them to shape blades, pouring liquid metal into carefully designed moulds. A significant improvement on their soft copper cousins, but still brittle if subjected to the kind of shock a battle often involved. (Still, you could decorate the blades with wonderfully delicate patterns.)

Things went a bit wrong around 1200 BCE, when — for reasons still not well understood — civilization around the Mediterranean collapsed, and with it the trade networks that delivered a consistent supply of tin, a key ingredient in bronze. Necessity did its usual maternal thing, and a new source of metal began to appear, derived from the plentiful red rocks containing iron ore.

Iron has a number of advantages for blacksmiths working up blades, but a big problem is that it needs high temperatures to melt. Furnaces at the time couldn’t reach the temperatures needed to pour liquid iron into moulds. So instead, smiths mastered the craft of heating it just enough to make it malleable — and then hammering it into submission. They didn’t have electron microscopes to help them, but by trial and error they learned how to compress iron atoms into wrought iron.

Their experiments also involved quenching a red-hot blade in water and then reheating (tempering) it just enough to give the blade some flexibility without losing its strength. Having a plentiful supply of water in the nearby Tagus was a useful local advantage for the Toledo smiths — not least because it helped foster the mythology around their “super blades.”

But the real source of their edge (excuse the pun) lay in the underlying science of metallurgy that their patient craft experiments were gradually uncovering. Early iron blades were still soft and lost their edge faster than their bronze forefathers. But smiths noticed something else: the longer the iron sat in the hot charcoal embers in which it had first been worked, the harder it became. It wasn’t an accident — tiny amounts of carbon were being absorbed and bonding with the iron. They’d discovered the first steel.

The key to converting trial, error, and accidental discovery into a manageable process lies in developing and passing on the craft. Making a sword blade is about trade-offs: a hard steel blade (with a high percentage of carbon) gives you a razor-sharp edge, but the shocks incurred in battle often cause it to snap, because it’s so brittle. You can soften the steel with less carbon, which makes the blade flexible and shock-absorbing, but then it bends and loses its edge.

What the smiths in Toledo managed was to strike a balance between the two, and bring the process under control. A skilled smith would make a flexible core using low-carbon steel, then wrap it in layers of hard, high-carbon steel to give it an edge like no other. It wasn’t easy — the process involved a lot of hammering and furnaces able to heat the metal to white heat. But it worked: Toledo blades could slice through silk, cut through chain mail, and bend in a semi-circle without breaking.

The real secret behind Toledo’s success? Embracing diversity, building on the presence of multiple different heads, each knowing different things. Welding together different knowledge traditions to create something really special.

Welding the science together

The region had originally been settled by the Moors crossing from North Africa in the early 8th century, and they brought with them knowledge of advanced steel-making from Damascus, drawing on ancient Persian and Indian techniques.

But when the Christian forces retook Toledo and the surrounding towns, they didn’t drive out the incumbents and impose their own ideas. Instead — highly unusual for medieval times — they pursued a policy of co-existence. Christian, Muslim, and Jewish craftsmen were encouraged to live and work alongside each other, with the corresponding interplay of three different knowledge strands.

Diversity drives innovation, and it certainly worked to the advantage of Toledo. It wasn’t a simple convergence — it was an intricate interplay, braiding together complementary strands of knowledge. Jewish and Arabic scholars worked to translate key ancient Roman, Greek, and Persian texts on chemistry, alchemy, and metallurgy. Islamic blacksmiths contributed craft knowledge around temperature control, fuel mixes, and different modes of tempering. And Christian armourers brought their knowledge from European battlefields about the design of armour and weaponry. The city became a giant research laboratory for steel-making.

An ecosystem, centuries before Silicon Valley

Innovation has always been a multiplayer game, and even the most dramatic and radical breakthrough comes from a context of networking and connectivity. These days we talk about “ecosystems,” but southern Spain five hundred years ago was an excellent case example.

One key element was a powerful demand pull, articulated by the Spanish military, which required advanced weaponry and could fund its purchase and improvement. At its peak under Kings Charles V and Philip II, the total standing forces of the Spanish Empire ranged between 150,000 and 200,000 professional soldiers, deployed across a wide expanse of the world — including many European theatres and the vast new American continent. Spanish tercios — elite units of around 150 men — dominated European warfare and provided steady demand for continuous rearmament and upgrading of weaponry. Even with their legendary strength and flexibility, swords needed replacing on an industrial scale.

Funding for all of this came directly from the Spanish Crown, acting as a key defence procurement agency, but it was also backed by the Catholic Church, whose global ambitions drove many of the conflicts of the time. Toledo was not simply a huge armaments factory but also a hub of entrepreneurial activity; every blacksmith with an idea for improving product or process technology would be pitching it enthusiastically. It wasn’t just the promise of direct payment for products — successful entrepreneurs could benefit from licences, tax incentives, even the chance of being ennobled for their services to the Crown. It all helped fuel the creative buzz. Knowledge flowed around the city and found its way into new combinations and start-ups with the same excited bustle you’d find in today’s Silicon Valley.

Unlike so much of Europe, with its either/or approach to religion and its “not-invented-here” resistance to outside ideas, Toledo operated a different model. What was called La Convivencia (the co-existence) meant the city became a huge playground for ideas to flow and experimentation to happen. It was a turbocharged version of what we’d call “open innovation” today — and it worked.

One key element of this knowledge economy was the role played by the Toledo School of Translators (Escuela de Traductores de Toledo). This venerable institution traced its origins to the 12th and 13th centuries, when scholars from all over Europe flocked to Toledo to help translate vast libraries of Arabic, Hebrew, and ancient Greek texts. In doing so, they brought to life — and enabled the sharing of — rich veins of knowledge in disciplines as wide-ranging as geometry, chemistry, medicine, and mechanics. Being close to this knowledge base gave the artisans and craftsmen of Toledo an incredibly powerful resource, one that few other European centres could approach.

Not that the knowledge swirling around the city was entirely open-access; just as today’s innovation businesses manage their intellectual property carefully, so the key coordinators in Toledo took care of who got to learn what. The powerful Swordsmiths’ Guild made sure that core knowledge — chemical formulas, folding and hammering techniques, and other craft secrets — was carefully guarded, passed on from master to apprentice by word of mouth alone. They imposed strict quality control, testing every blade rigorously before allowing it to leave the city, and each smith had a unique hallmark stamped into the steel to protect against counterfeit, inferior blades reaching the market.

Their IP regime was further strengthened by the Spanish Crown, which acted both as a key demanding customer and as the gateway through which exports could be controlled. Given that Toledo steel blades were the equivalent of today’s stealth technology, it was important to make sure they didn’t find their way into the wrong hands.

Swords to ploughshares

Toledo steel blades and armour stayed at the height of weapons technology for two hundred years. But, as with any arms race, they were eventually overtaken — in this case not by a single dramatic breakthrough, but by the slower, grinding disruption of gunpowder. By the 18th century, guns rather than swords were the weapons of choice, and the industry lost its grip; King Charles III had to step in with rescue funding from the state. He set up the Real Fábrica de Espadas de Toledo (Royal Sword Factory of Toledo) to bring together what was left of the old guild workshops and keep the city’s ancient technical knowledge from vanishing into history.

It’s a familiar innovation story in its own right: a core market disappears, and the question becomes whether deep, hard-won expertise can find a second life somewhere else entirely. For Toledo, the answer was yes. The smiths’ knowledge — like their sword blades — proved malleable, and they turned their skills to a more peaceable purpose. The ancient art of damasquinado — creating intricate patterns by hammering gold and silver threads as inlay into steel — had arrived with the early Moors. A technique originating in Damascus, as the name suggests, it was always highly prized, and it gave the Toledo craftsmen a valuable new outlet just as their old market was vanishing. The same hands that had spent two centuries perfecting the tension between hardness and flexibility in a blade now turned that same precision to ornament rather than edge.

A tour of the city today brings this history to life. Of course you’ll find countless souvenir shops selling replicas of Toledo blades, but you can also browse other sites selling exquisite damascene artwork. And in the quieter older parts of town, you might still catch a whiff of smoke or hear the tap-tap of a jeweller’s hammer, carefully creating such pieces — a throwback to earlier times, when it would have been a blacksmith’s hammer beating highly crafted steel into its legendary shape.

It’s a powerful metaphor for successful innovation. The swords themselves were forged from a steel that represented a perfect composite of strength, hardness, and flexibility, conferred through deep understanding of many metallurgical traditions. They resolved the blacksmith’s dilemma — trading off strength and sharpness against flexibility — by finding an integrated solution instead of picking a side.

Their unique metalworking skills could only emerge from a similar integration: a bringing together of rich and diverse cultural and technological traditions. The city’s architecture still reminds us what can happen when different cultures converge and interact — the Muslim, Jewish, and Christian worlds colliding not to explode and shatter, but to combine.

Toledo steel is, in essence, an admixture: a coming together of differences to create something that brings out the best of all of them. Real swords, it turns out, didn’t need elven magic—just eight centuries of competing knowledge traditions forced to share a city.

You can find my podcast here and my videos here

And if you’d like to learn with me take a look at my online courses here

And subscribe to my (free) newsletter here

All images generated by Substack AI unless otherwise indicated

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.