Category Archives: Innovation

Three HOW MIGHT WE Alternatives That Actually Spark Creative Ideas

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Three How Might We Alternatives That Actually Spark Creative Ideas

GUEST POST from Robyn Bolton

Q: How might we brainstorm new ideas to serve our customers better?

A: Have a brainstorming session that starts with “How Might We help customers [Job to be Done/problem]?”

If only it were that simple.

How Might We (HMW) is an incredible tool (not BS, as some would assert), but we misuse it. We focus too much on the “we” and not enough on the “might.”

Might > We

HMW was first used to prompt people to be “wildly creative while simultaneously leveraging [company’s] innate strengths.”

IDEO popularized the prompt as a way to solve “wicked problems” – problems so complex that there is no right or wrong answer.

In both of these cases, the assumption was that the word “might” would free people from the shackles of today’s thinking and constraints and give people permission to dream without fear of judgment and reality.

“We” kept ideas tethered to the reality of the company’s “innate strengths,” providing a modicum of comfort to executives worried that the session wouldn’t result in anything useful and would, therefore, be a waste of time.

We > Might

Alas, as time went on and HMW became more popular, we lost sight of its intent (prompt wildly creative thinking about wicked problems) and twisted it to our purposes.

  • We end the HMW sentence with our problems (e.g., HMW cut costs by getting more customers to use self-service tools?).
  • We use it to brainstorm solutions to things that aren’t even problems (e.g., HMW eliminate all customer service options that aren’t self-serve?)
  • We mentally replace “might” with “will” so we can emerge from brainstorming sessions with a tactical implementation plan.

How Might Can YOU Fix HMW?

If you’re not getting creative, radical, or unexpected ideas from your brainstorming sessions, you have an HMW problem.< As a result, continuing to use HMW as a tool to prompt creative, radical, or unexpected ideas is the definition of insanity. And you are not insane. Instead, mix it up. Use different words to articulate the original intent of HMW.

How would we solve this problem if the answer to every request is YES?

Innovation thrives within constraints. Brainstorming doesn’t.

Even when you tell people not to constrain themselves, even implore them to value “quantity over quality,” you still get more “safe” ideas rather than more “crazy” ideas.

Do more than tell. Make a world without constraints real. Explicitly remove all the constraints people throw at ideas by creating a world of infinite money, people, capabilities, willingness, appetite for risk, and executive support. Doing this removes the dreaded “but” because there is no “but we don’t have the money/people/capabilities” or “but management will never go for it” and creates space for “and.”

What would we ask for if we were guaranteed a YES to only ONE request?

This question is often asked at the end of a brainstorm to prioritize ideas. But it’s equally helpful to ask it at the beginning.

This question shifts our mindset from “the bosses will never say yes, so I won’t even mention it” to “the bosses will say yes to only one thing, so it better be great!”  It pulls people off the sidelines and reveals what people believe to be the most critical element of a solution.   It drives passionate engagement amongst the whole team and acts as a springboard to the next brainstorm – How Might We use (what they said yes to) to solve (customers’ Jobs to be Done/problem)?

How would we solve the problem if the answer to every request is NO?

This one is a bit risky.

Some people will throw their hands in the air, declare the exercise a waste of time and effort, and collapse into a demotivated blob of resignation.

Some people will feel free. As Seth Godin wrote about a journal that promises to reject every single person who submits an article, “The absurdity of it is the point. Submitting to them feels effortless and without a lot of drama, because you know you’re going to get rejected. So instead of becoming attached to the outcome, you can simply focus on the work.”

For others, this will summon their inner rebel, the part of themselves that wants to stick it to the man, prove the doubters wrong, and unleash a great “I told you so” upon the world. To them, “No” is the start of the conversation, not the end. It fires them up to do their best work.

Don’t invite the first group of people to the brainstorm.

Definitely invite the other two groups.

How Might Will/Do YOU Fix HMW?

If you want something different, you need to do something different.

Start your next brainstorm with a new variation on the old HMW prompt.

How do people react? Does it lead to more creative or more “safe” ideas?

How might we adjust to do even better next time?

Image credit: Pexels

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How to Measure and Improve Employee-Driven Innovation

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The Value of Engagement

How to Measure and Improve Employee-Driven Innovation

GUEST POST from Art Inteligencia

In the relentless pursuit of competitive advantage, companies often look outward—to new markets, emerging technologies, and disruptive business models. While these are all valid areas for exploration, the single most powerful and often overlooked engine of innovation lies within: your engaged employees. Innovation is not a top-down mandate; it is a grassroots, human-centered activity. When employees are fully engaged—when they feel a sense of ownership, purpose, and psychological safety—they become a perpetual source of new ideas, process improvements, and breakthrough solutions. As a human-centered change and innovation thought leader, I am here to argue that the true measure of a company’s innovative capacity is not its R&D budget, but the level of its employee engagement. Furthermore, we must move beyond simply measuring engagement and learn to measure and nurture the innovation that it produces.

The link between engagement and innovation is not a coincidence; it is a direct causal relationship. Engaged employees are more likely to take risks, share dissenting opinions, and go above and beyond their job descriptions to solve problems. They are the eyes and ears on the ground, a direct conduit to customer frustrations and operational inefficiencies that leadership teams often miss. However, for this energy to be harnessed effectively, we need a new framework. We need to go beyond the traditional engagement survey and create a system that actively encourages, measures, and rewards employee-driven innovation.

Measuring the Innovation That Engagement Fuels

Traditional metrics for innovation—such as patent counts or new product launches—are often lagging indicators and don’t tell the full story. We need leading indicators that show us the health of our employee-driven innovation pipeline. Here are four key areas to measure:

  • Idea Velocity & Quality: Track the number of ideas submitted by employees across different teams or departments. More importantly, measure the quality and diversity of these ideas. Are they addressing key strategic challenges or just incremental fixes?
  • Experimentation Rate: How many employee-led experiments or pilot projects are being initiated? A high experimentation rate signals a culture where it’s safe to try new things and fail fast. This is a powerful proxy for psychological safety.
  • Cross-Functional Collaboration: Use tools and surveys to measure the frequency and quality of collaboration across different teams. Innovation often happens at the intersections of departments, and a lack of collaboration is a clear red flag.
  • Impact & Implementation: Measure the number of employee ideas that are actually implemented and the tangible business impact they have (e.g., cost savings, revenue increase, customer satisfaction scores). This closes the loop and shows employees that their contributions matter.

“An engaged workforce doesn’t just work harder; it thinks smarter. The role of leadership is to create the ecosystem that turns that thinking into tangible value.”

How to Turn Engagement into a Predictable Innovation Engine

Measuring innovation is only the first step. The real work lies in building the systems and culture that consistently generate new ideas. Here’s how to improve employee-driven innovation:

  1. Empower Ideation: Implement a clear, simple system for employees to submit ideas. This could be an internal platform, a regular brainstorm session, or a dedicated “Innovation Sprint” team.
  2. Provide Resources & Autonomy: Give employees the time, budget, and authority to test their ideas. A small “innovation fund” or a policy of allowing employees 10% of their time to work on personal projects can be a game-changer.
  3. Celebrate Learning, Not Just Success: When an employee idea fails, don’t punish them. Celebrate the learning gained from the experiment. This reinforces psychological safety and encourages future risk-taking.
  4. Create a Feedback Loop: Ensure that every idea, whether implemented or not, receives thoughtful feedback. This shows respect for the employee’s contribution and helps them grow as an innovator.

Case Study 1: Google’s “20% Time” and the Birth of Gmail

The Challenge:

In the early 2000s, Google was a rapidly growing search engine company, but it was at risk of becoming a single-product company. To foster a culture of continuous innovation and keep its employees engaged and creative, leaders faced the challenge of how to formalize a process that would encourage risk-taking and intrapreneurship.

The Engagement-Driven Innovation Model:

Google famously implemented the “20% Time” policy, which allowed engineers to spend 20% of their work week on personal projects that they believed would benefit the company. This was a radical act of trust and empowerment that fundamentally linked employee engagement to innovation. The program was designed to:

  • Encourage Autonomy: Engineers had the freedom to work on whatever they were passionate about, without a top-down mandate.
  • Foster Serendipity: It created an environment where unexpected connections and breakthroughs could occur naturally, outside of a rigid project plan.
  • Signal Trust: The policy sent a powerful message that Google trusted its employees to be responsible for their own innovative contributions.

The Result:

The “20% Time” policy became a legendary driver of some of Google’s most successful products. Gmail, for instance, was famously created by engineer Paul Buchheit during his 20% time. Google Maps and AdSense also have roots in this program. While the formal policy has evolved, the mindset of encouraging employee autonomy and internal entrepreneurship remains a core part of Google’s culture. This case study perfectly illustrates that when you empower employees to follow their curiosity, you can turn engagement into a powerful engine for breakthrough innovation and sustained growth.

Case Study 2: Toyota’s Kaizen – Continuous Improvement at the Grassroots

The Challenge:

Toyota’s success has long been tied to its renowned production system. However, the true genius of their system lies not in its technology, but in its human-centric approach. The challenge was to create a system where every employee, from the factory floor to the boardroom, felt responsible for continuous improvement, thereby keeping the company’s operational processes lean and innovative.

The Engagement-Driven Innovation Model:

Toyota’s solution was the Kaizen philosophy, which translates to “change for the better” or “continuous improvement.” This is a perfect example of employee-driven innovation at scale. Unlike a one-off suggestion box, Kaizen is a deeply embedded cultural practice where every employee is encouraged to identify and propose small, incremental improvements to their daily work. This approach is built on trust and a fundamental belief in the intellectual capacity of every team member.

  • Universal Empowerment: Every employee is a designated innovator, with the authority and encouragement to improve their own work processes.
  • Small, Constant Changes: The focus is not on grand, revolutionary ideas, but on a perpetual stream of small improvements that collectively lead to massive gains in efficiency and quality.
  • Respect for People: The foundation of Kaizen is respect for the employee, recognizing that the person doing the work is the one best equipped to find a better way to do it.

The Result:

The Kaizen system has yielded millions of employee-submitted ideas over the years, many of which have been implemented. These small, incremental innovations have led to significant improvements in quality, safety, and productivity, solidifying Toyota’s position as a global leader. This case study proves that when you democratize innovation and give every employee a voice, you create a powerful, self-sustaining engine of continuous improvement that is incredibly difficult for competitors to replicate.

Conclusion: The Strategic Imperative of Engagement

The future of innovation is not a secret blueprint held by a few executives; it is a collaborative effort fueled by the collective intelligence and passion of your entire workforce. Engaged employees are not just more productive; they are the wellspring of your company’s future. By creating a culture that nurtures curiosity, empowers autonomy, and measures the impact of grassroots ideas, you can transform your organization from a passive recipient of change into a powerful creator of it.

As leaders, our most critical role is to stop seeing employee engagement as a mere HR metric and start seeing it for what it truly is: the ultimate strategic imperative for building a resilient, innovative, and future-ready enterprise. Invest in your people’s curiosity, and they will, in turn, innovate your way to a more prosperous and sustainable future.

Extra Extra: Futurology is not fortune telling. Futurists use a scientific approach to create their deliverables, but a methodology and tools like those in FutureHacking™ can empower anyone to engage in futurology themselves.

Image credit: Pixabay

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Detecting the Seeds of Future Innovation

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Weak Signals, Strong Insights

Detecting the Seeds of Future Innovation

GUEST POST from Chateau G Pato

In our hyper-connected world, we are inundated with information. Market data, analyst reports, and competitive intelligence systems all provide a clear picture of the present. But as a human-centered change and innovation thought leader, I argue that the most transformative opportunities don’t emerge from this flood of “strong signals.” They emerge from the subtle, often contradictory, and easily dismissed weak signals on the periphery. These are the whispers of change, the fringe trends, the unarticulated customer frustrations, and the strange technological mashups that hint at a future yet to be built. The ability to detect, interpret, and act on these weak signals is the single most powerful competitive advantage an organization can cultivate. It’s the difference between reacting to disruption and proactively creating it.

Weak signals are, by definition, not obvious. They are often dismissed as anomalies, niche behaviors, or fleeting fads. They can come from anywhere: a casual comment in a user forum, a viral video that defies a category, a surprising scientific breakthrough in an unrelated field, or a quiet startup with a baffling business model. The challenge for leaders is to move beyond the comfort of big data analytics and embrace the messy, qualitative, and deeply human work of foresight. This isn’t about guesswork; it’s about building a systematic, human-centered practice for sensing the future and turning those faint whispers into a clear vision for innovation.

Why Weak Signals are Your Best Innovation GPS

Cultivating a weak-signal detection capability offers profound benefits:

  • Foresight, Not Just Hindsight: While strong signals confirm what has already happened, weak signals provide clues about what is *about to* happen. This gives you a critical head start in preparing for, or even driving, market shifts.
  • The Source of True Disruption: Most truly disruptive innovations—from personal computing to smartphones—began as weak signals on the fringe, often dismissed by established players who were focused on optimizing their core business.
  • Uncovering Unmet Needs: Weak signals are often an early indicator of deep, unarticulated human needs. They are the seeds of a problem that a current market solution isn’t addressing.
  • Building a Culture of Curiosity: Actively looking for weak signals encourages a culture of curiosity, open-mindedness, and a willingness to challenge assumptions—all essential traits for innovation.

“Strong signals confirm your past. Weak signals whisper your future. The most innovative leaders are the best listeners.”

A Human-Centered Approach to Detecting Weak Signals

Detecting weak signals is not an automated process. It is a deeply human activity that requires a specific mindset and intentional practice:

  1. Go to the Edge: Move beyond your core market and familiar customer base. Talk to fringe users, early adopters, and even those who reject your product. Spend time in adjacent industries and with unconventional thinkers.
  2. Embrace a Beginner’s Mindset: Temporarily suspend your expertise. Look at your industry as if you are seeing it for the first time. Why do customers do what they do? What seems strange or inefficient to an outsider?
  3. Connect the Unconnected Dots: A single weak signal means little. The true insight comes from identifying patterns. Is a new technology in one field combining with a new consumer behavior in another? The unexpected combination of two seemingly unrelated signals is often where the magic happens.
  4. Create “Listening Posts”: Form small, cross-functional teams whose sole purpose is to scan the periphery. Empower them to read obscure journals, follow niche social media communities, and report back on anything that feels “off” or interesting.

Case Study 1: The Rise of Social Media – A Weak Signal Ignored by the Giants

The Challenge:

In the early 2000s, the internet was dominated by large, content-heavy portals like Yahoo! and search engines like Google. Communication was primarily through email and instant messaging. The idea of people building public profiles to share personal updates and connect with friends was seen as a niche, even trivial, activity. It was a weak signal, a seemingly minor behavior on college campuses.

The Weak Signal Ignored:

For established tech giants, the signal was too faint. They were focused on the strong signals of search queries and content monetization. Facebook, MySpace, and Friendster were dismissed as “just for kids” or a “niche social trend.” The idea of a public profile as a primary mode of online identity and communication was too far outside their core business model to be taken seriously. They saw a minor curiosity, not the future of human connection.

The Result:

The companies that paid attention to this weak signal—and understood the human-centered need for connection and self-expression—went on to build a multi-trillion-dollar industry. The giants who ignored it were forced to play a decade-long game of catch-up, and many lost their dominant position. The weak signal of a simple public profile evolved into the foundational architecture of the modern internet and the economy built on it. Their failure to see this wasn’t a failure of technology; it was a failure of imagination and human-centered listening.

Case Study 2: Netflix and the Streaming Revolution – From DVDs to a Weak Signal

The Challenge:

In the early 2000s, Blockbuster was the undisputed king of home entertainment. Their business model was robust, profitable, and built on a physical presence of thousands of stores and a lucrative late-fee system. The internet was a nascent and unreliable platform for video, and streaming was a faint, almost invisible signal on the horizon.

The Weak Signal Detected:

While Blockbuster was focused on optimizing its core business (e.g., store layout, inventory management), Netflix, then a DVD-by-mail service, saw a weak signal. The signal wasn’t just about faster internet; it was about the human frustration with late fees and the inconvenience of physical stores. The company’s leaders started to talk about the concept of “on-demand” content, long before the technology was ready. They were paying attention to the unarticulated desire for convenience and unlimited choice, a desire that was a whisper to Blockbuster but a deafening call to Netflix. They began to invest in streaming technology and content licensing years before it was profitable, effectively cannibalizing their own profitable DVD business.

The Result:

Blockbuster famously dismissed Netflix’s weak signal, seeing it as a minor inconvenience to their existing business model. They believed a physical store experience would always win. Netflix, by acting on the weak signal and a deep understanding of human frustration, was able to pivot from being a DVD service to the global streaming behemoth we know today. Their foresight, driven by a human-centered approach to a technological trend, allowed them to disrupt an entire industry and become a dominant force in the future of entertainment. Blockbuster, unable to see beyond the strong signals of its profitable past, is now a cautionary tale.

Conclusion: The Foresight Imperative

The future is not a surprise that happens to you. It is a collection of weak signals that you either choose to see or ignore. In an era of constant disruption, relying on strong signals alone is a recipe for stagnation. The most resilient and innovative organizations are those that have built a human-centered practice for sensing change on the periphery. They have created a culture where curiosity is a core competency and where questioning the status quo is a daily ritual.

As leaders, our most critical role is to shift our focus from optimizing the past to sensing the future. We must empower our teams to go to the edge, listen to the whispers, and connect the dots in new and creative ways. The future of your industry is already being born, not in the center of the market, but on its fringes. The question is, are you listening?

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

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Responsible Innovation

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Building Trust in a Technologically Advanced World

Responsible Innovation

GUEST POST from Art Inteligencia

In our headlong rush toward the future, fueled by the relentless pace of technological advancement, we have a tendency to celebrate innovation for its speed and scale. We champion the next disruptive app, the more powerful AI model, or the seamless new user experience. But as a human-centered change and innovation thought leader, I believe we are at a critical inflection point. The question is no longer just, “Can we innovate?” but rather, “Should we?” and “How can we do so responsibly?” The future belongs not to the fastest innovators, but to the most trusted. Responsible innovation — a discipline that prioritizes ethics, human well-being, and social impact alongside commercial success—is the only sustainable path forward in a world where public trust is both fragile and invaluable.

The history of technology is littered with examples of innovations that, despite their potential, led to unintended and often harmful consequences. From social media algorithms that polarize societies to AI systems that perpetuate bias, the “move fast and break things” mantra has proven to be an unsustainable and, at times, dangerous philosophy. The public is growing weary. A lack of trust can lead to user backlash, regulatory intervention, and a complete rejection of a technology, no matter how clever or efficient it may be. The single greatest barrier to a new technology’s adoption isn’t its complexity, but the public’s perception of its integrity and safety. Therefore, embedding responsibility into the innovation process isn’t just an ethical consideration; it’s a strategic imperative for long-term survival and growth.

The Pillars of Responsible Innovation

Building a culture of responsible innovation requires a proactive and holistic approach, centered on four key pillars:

  • Ethical by Design: Integrate ethical considerations from the very beginning of the innovation process, not as an afterthought. This means asking critical questions about potential biases, unintended consequences, and the ethical implications of a technology before a single line of code is written.
  • Transparent and Accountable: Be clear about how your technology works, what data it uses, and how decisions are made. When things go wrong, take responsibility and be accountable for the outcomes. Transparency builds trust.
  • Human-Centered and Inclusive: Innovation must serve all of humanity, not just a select few. Design processes must include diverse perspectives to ensure solutions are inclusive, accessible, and do not inadvertently harm marginalized communities.
  • Long-Term Thinking: Look beyond short-term profits and quarterly results. Consider the long-term societal, environmental, and human impact of your innovation. This requires foresight and a commitment to creating lasting, positive value.

“Trust is the currency of the digital age. Responsible innovation is how we earn it, one ethical decision at a time.”

Integrating Responsibility into Your Innovation DNA

This is a cultural shift, not a checklist. It demands that leaders and teams ask new questions and embrace new metrics of success:

  1. Establish Ethical AI/Innovation Boards: Create a cross-functional board that includes ethicists, sociologists, and community representatives to review new projects from a non-technical perspective.
  2. Implement an Ethical Innovation Framework: Develop a formal framework that requires teams to assess and document the potential societal impact, privacy risks, and fairness implications of their work.
  3. Reward Responsible Behavior: Adjust performance metrics to include not just commercial success, but also a project’s adherence to ethical principles and positive social impact.
  4. Cultivate a Culture of Candor: Foster a psychologically safe environment where employees feel empowered to raise ethical concerns without fear of retribution.

Case Study 1: The Facial Recognition Debates – Ethical Innovation in Action

The Challenge:

Facial recognition technology is incredibly powerful, with potential applications ranging from unlocking smartphones to enhancing public safety. However, it also presents significant ethical challenges, including the potential for mass surveillance, privacy violations, and algorithmic bias that disproportionately misidentifies people of color and women. Companies were innovating at a rapid pace, but without a clear ethical compass, leading to public outcry and a lack of trust.

The Responsible Innovation Response:

In response to these concerns, some tech companies and cities took a different approach. Instead of a “deploy first, ask questions later” strategy, they implemented moratoriums and initiated a public dialogue. Microsoft, for example, proactively called for federal regulation of the technology and refused to sell its facial recognition software to certain law enforcement agencies, demonstrating a commitment to ethical principles over short-term revenue.

  • Proactive Regulation: They acknowledged the technology was too powerful and risky to be left unregulated, effectively inviting government oversight.
  • Inclusion of Stakeholders: The debate moved beyond tech company boardrooms to include civil rights groups, academics, and the public, ensuring a more holistic and human-centered discussion.
  • A Commitment to Fairness: Researchers at companies like IBM and Microsoft worked to improve the fairness of their algorithms, publicly sharing their findings to contribute to a better, more ethical industry standard.

The Result:

While the debate is ongoing, this shift toward responsible innovation has helped to build trust and has led to a more nuanced public understanding of the technology. By putting ethical guardrails in place and engaging in public discourse, these companies are positioning themselves as trustworthy partners in a developing market. They recognized that sustainable innovation is built on a foundation of trust, not just technological prowess.

Case Study 2: The Evolution of Google’s Self-Driving Cars (Waymo)

The Challenge:

From the outset, self-driving cars presented a complex set of ethical dilemmas. How should the car be programmed to act in a no-win scenario? What if it harms a pedestrian? How can the public trust a technology that is still under development, and how can a company be transparent about its safety metrics without revealing proprietary information?

The Responsible Innovation Response:

Google’s self-driving car project, now Waymo, has been a leading example of responsible innovation. Instead of rushing to market, they prioritized safety, transparency, and a long-term, human-centered approach.

  • Prioritizing Safety over Speed: Waymo’s vehicles have a human driver in the car at all times to take over in case of an emergency. This is a deliberate choice to prioritize safety above a faster, more automated rollout. They are transparently sharing their data on “disengagements” (when the human driver takes over) to show their progress.
  • Community Engagement: Waymo has engaged with local communities, holding workshops and public forums to address concerns about job losses, safety, and the role of autonomous vehicles in public life.
  • Ethical Framework: They have developed a clear ethical framework for their technology, including a commitment to minimizing harm, respecting local traffic laws, and being transparent about their performance.

The Result:

By taking a slow, deliberate, and transparent approach, Waymo has built a high degree of trust with the public and with regulators. They are not the fastest to market, but their approach has positioned them as the most credible and trustworthy player in a high-stakes industry. Their focus on responsible development has not been a barrier to innovation; it has been the very foundation of their long-term viability, proving that trust is the ultimate enabler of groundbreaking technology.

Conclusion: Trust is the Ultimate Innovation Enabler

In a world of breathtaking technological acceleration, our greatest challenge is not in creating the next big thing, but in doing so in a way that builds, rather than erodes, public trust. Responsible innovation is not an optional extra or a marketing ploy; it is a fundamental business strategy for long-term success. It requires a shift from a “move fast and break things” mentality to a “slow down and build trust” philosophy.

Leaders must champion a new way of thinking—one that integrates ethics, inclusivity, and long-term societal impact into the core of every project. By doing so, we will not only build better products and services but also create a more resilient, equitable, and human-centered future. The most powerful innovation is not just what we create, but how we create it. The time to be responsible is now.

Extra Extra: Futurology is not fortune telling. Futurists use a scientific approach to create their deliverables, but a methodology and tools like those in FutureHacking™ can empower anyone to engage in futurology themselves.

Image credit: Pixabay

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Meet me in Manhattan – Innovation and Change Advisory

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Meet me in Manhattan - Innovation and Change Advisory

As the title of the site says, I focus on human-centered change and innovation, bringing in elements of design thinking, customer experience, employee experience and digital transformation as needed.

On November 18, 2022 I will be in New York City (Midtown Manhattan) and available to connect for any of the following purposes:

  • Private keynote or workshop for your organization

  • Certification session on the Change Planning Toolkit™ and/or FutureHacking™ sets of tools for your team

  • Featured keynote speaker or workshop for a sales event or conference

  • Advisory session to provide input on your innovation or transformation program, or a specific innovation project

  • Audio or video podcast appearance

  • Grab a coffee or a meal — to connect or reconnect

If you work in Manhattan or are willing to travel in from elsewhere in the greater New York City metropolitan area (or the world) and are looking to increase the innovation or transformation capabilities of your organization or to de-risk an innovation project by getting an outside perspective, please contact me.

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Innovating with Competitors for Mutual Benefit

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The Art of Co-opetition

Innovating with Competitors for Mutual Benefit

GUEST POST from Art Inteligencia

For centuries, the business world has been largely defined by a zero-sum game mentality: my gain is your loss, and vice versa. Competition, in its purest form, often paints rivals as adversaries to be defeated. However, in an increasingly complex, interconnected, and rapidly evolving global economy, this outdated mindset is not only limiting; it’s detrimental. As a human-centered change and innovation thought leader, I advocate for a more nuanced and powerful strategy: **co-opetition**. This isn’t just a clever portmanteau; it’s a strategic imperative that combines competition and cooperation, enabling organizations to innovate faster, enter new markets, and tackle grand challenges that no single entity could solve alone. It’s about recognizing that sometimes, the fastest way forward is to build bridges, not just walls, with those who might traditionally be seen as your fiercest rivals.

Co-opetition acknowledges that while companies may compete fiercely for market share on one front, they can also collaborate to expand the entire market, establish industry standards, share costly R&D, or even address systemic societal issues. This requires a significant shift in mindset—from purely adversarial to strategically collaborative—and a deep understanding of shared objectives that transcend individual company interests. It’s about finding those unique, human-centered problems or opportunities that are too big for any single player, and then pooling resources and expertise to collectively unlock new value.

Why Co-opetition is the New Innovation Frontier

Embracing co-opetition offers compelling advantages in today’s innovation landscape:

  • Accelerated Innovation: By sharing research, development costs, or technological expertise, companies can bring new products, services, or industry standards to market much faster than they could individually. This is particularly crucial in rapidly evolving tech sectors.
  • Market Expansion & Creation: Collaborating with competitors can help create entirely new markets or significantly expand existing ones by developing universally accepted standards, educating consumers, or pooling resources for infrastructure development.
  • Shared Risk & Cost Reduction: Tackling complex, high-risk innovation projects (e.g., developing sustainable technologies, exploring new scientific frontiers) becomes more feasible when costs and risks are shared across multiple organizations.
  • Access to Complementary Expertise: No single company has all the answers. Co-opetition allows rivals to leverage each other’s unique strengths, technologies, or market access, creating synergistic solutions.
  • Industry-Wide Problem Solving: Many of today’s grand challenges—climate change, global health, digital ethics—require industry-wide solutions. Competitors often have a shared interest in solving these systemic issues that impact their entire ecosystem.

“In the age of exponential change, the enemy isn’t always your competitor. Sometimes, the real adversary is stagnation, and co-opetition is the antidote.”

The Art of Navigating Co-opetitive Relationships

Successfully engaging in co-opetition requires strategic clarity and careful management:

  1. Clearly Define Collaboration Boundaries: Establish strict rules of engagement, clearly delineating what areas are open for cooperation and what remains fiercely competitive. This prevents valuable intellectual property or sensitive strategies from being compromised.
  2. Identify Mutual Benefits: Both parties must clearly see the tangible advantages of collaboration. The “what’s in it for us” must be explicit and balanced.
  3. Build Trust & Transparency (Within Limits): While sharing proprietary secrets is generally off-limits, a foundational level of trust and transparency is essential for effective collaboration. Clear communication channels are vital.
  4. Focus on Expanding the Pie: The goal of co-opetition is often to grow the overall market or solve a common industry challenge, rather than just fighting over existing slices.
  5. Formalize Agreements: Legal frameworks and clear contracts are crucial to define roles, responsibilities, IP ownership, and dispute resolution mechanisms.

Case Study 1: Payment Networks – Visa, Mastercard, and the Expansion of Digital Commerce

The Challenge:

Before the widespread adoption of credit and debit cards, cash and checks dominated transactions. The challenge for individual banks was to create a universally accepted, reliable, and secure electronic payment system that would build consumer trust and enable widespread merchant adoption. No single bank had the reach or resources to do this alone.

Co-opetition in Action:

Visa and Mastercard emerged from groups of competing banks that understood the need for a shared infrastructure. While banks competed fiercely for customers, they collectively owned and operated these payment networks. These networks, in turn, competed fiercely with each other to sign up banks and merchants. This is a classic example of co-opetition:

  • Shared Infrastructure: Competing banks collaborated to create a vast, reliable network for processing transactions, establishing universal standards that benefited all participants.
  • Market Expansion: By providing a secure and convenient alternative to cash, they jointly expanded the entire market for electronic payments, creating billions in new revenue for the entire banking industry.
  • Innovation in Security & Technology: Both Visa and Mastercard continually innovate in areas like fraud prevention, contactless payments, and digital wallets, often setting industry-wide standards that benefit all banks and consumers using their networks, even as they compete for transaction volume.

The Result:

The co-opetitive model of payment networks led to an explosion in digital commerce, fundamentally transforming how people buy and sell. Competing banks leveraged a shared infrastructure to grow a massive new market. Visa and Mastercard continue to be fierce rivals, yet their foundational co-opetition allows them to jointly build and expand the digital economy, proving that collaboration at a foundational level can drive immense, mutual profit.

Case Study 2: Autonomous Driving Development – The Race to a Shared Future

The Challenge:

Developing fully autonomous driving (Level 5) technology is one of the most complex and capital-intensive engineering challenges of our time. It requires trillions of miles of testing, massive R&D investments in AI, sensors, mapping, and regulatory navigation. No single automaker or tech company possesses all the necessary resources, data, or expertise to bring this to fruition independently, safely, and quickly.

Co-opetition in Action:

In response, we’ve seen an unprecedented wave of co-opetition across the automotive and tech industries. Companies that are fierce competitors in vehicle sales or software platforms are collaborating on specific aspects of autonomous driving:

  • Joint Ventures for Tech Platforms: BMW and Mercedes-Benz (Daimler), for example, have collaborated on developing scalable platforms for automated driving, pooling resources for sensor fusion, perception, and decision-making software. They still compete on car design and brand, but share the foundational, high-cost R&D.
  • Data Sharing & Mapping Consortia: Companies are exploring ways to share vast amounts of road data to improve mapping and perception systems, recognizing that a better shared “map” benefits everyone in the industry.
  • Standardization Efforts: Competitors work together on industry standards for safety, testing protocols, and communication between autonomous vehicles, ensuring public trust and regulatory acceptance for the entire sector.

The Result:

This co-opetitive approach is accelerating the development of autonomous driving technology, making it safer and more viable for wider adoption. While each company still aims to differentiate its final product, the shared investment in foundational technology and standards reduces individual risk, speeds up learning, and helps build public confidence in a nascent industry. It’s a pragmatic recognition that some challenges are simply too big to tackle alone, and mutual benefit can be achieved even among the fiercest competitors.

Conclusion: Redefining Competition for a Collaborative Future

The outdated paradigm of pure, unadulterated competition is no longer sufficient for the complexities of the 21st century. The most forward-thinking, human-centered organizations understand that strategic co-opetition—the art of collaborating with rivals for mutual benefit—is a powerful engine for innovation, market expansion, and systemic problem-solving.

As leaders, our challenge is to identify those critical junctures where collaboration with competitors can expand the overall pie, mitigate shared risks, or accelerate progress on grand challenges. It requires courage, a strategic mindset, and a willingness to see beyond immediate rivalries to shared long-term prosperity. Embrace co-opetition, and you will unlock new frontiers of innovation, build more resilient industries, and collectively shape a more prosperous and sustainable future.

Extra Extra: Futurology is not fortune telling. Futurists use a scientific approach to create their deliverables, but a methodology and tools like those in FutureHacking™ can empower anyone to engage in futurology themselves.

Image credit: Unsplash

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The Future of Service

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Innovating for Seamless and Delightful Interactions

The Future of Service

GUEST POST from Chateau G Pato

In a world where products are increasingly commoditized and competition is just a click away, the true and lasting competitive advantage lies in the quality of your service. But the very definition of “service” is undergoing a profound transformation. It’s no longer just about fixing a problem or answering a question; it’s about creating seamless and delightful interactions that anticipate needs, remove friction, and build deep, lasting relationships. As a human-centered change and innovation thought leader, I believe the future of service is not just about being reactive, but about being proactively human-centric, leveraging technology to amplify empathy and deliver truly exceptional experiences.

The traditional service model often operates in silos, with fragmented touchpoints and a rigid, transactional approach. A customer calls one department, is transferred to another, and has to repeat their story multiple times. This isn’t service; it’s a series of frustrations. The future, however, is unified and intelligent. It’s about designing a holistic service journey that anticipates what the customer needs before they even ask, making every interaction feel intuitive and effortless. This shift requires a fundamental change in mindset, moving from a cost-center view of service to a strategic, value-creation engine.

The Four Pillars of Future-Ready Service Innovation

Building a service model for tomorrow requires a focus on four key pillars:

  • Proactive & Predictive: Leveraging data and AI to anticipate customer needs and issues. This means resolving a problem before the customer even knows they have one, such as notifying them of a potential shipping delay and offering a solution preemptively.
  • Seamless & Omni-Channel: Ensuring that the customer journey is fluid and consistent across all channels—from a website chatbot to a phone call to a social media message. The customer should never have to repeat themselves.
  • Personalized & Empathetic: Using data not just for efficiency, but for personalization. This means interactions feel tailored and human, remembering past conversations and preferences to build a genuine rapport.
  • Delightful & Unexpected: Moving beyond just meeting expectations to exceeding them. This involves small, surprising moments of delight that create memorable experiences and foster brand loyalty.

“The best service is so seamless, it’s invisible. The next best service is so delightful, it’s unforgettable.”

Integrating Technology to Amplify the Human Touch

Technology, particularly AI, is not the enemy of human-centered service; it is the ultimate enabler. When used correctly, it frees up human agents from repetitive, mundane tasks, allowing them to focus on complex, empathetic, and relationship-building interactions. It allows us to scale empathy in ways previously unimaginable.

  1. AI for Triage & Efficiency: Use AI-powered chatbots and voice assistants to handle simple, high-volume queries, and to intelligently route complex issues to the right human expert with all the necessary context.
  2. Data Analytics for Foresight: Analyze customer data to predict churn risk, identify opportunities for upselling, and proactively address pain points before they escalate.
  3. Automation for Seamlessness: Automate routine tasks—like order tracking, appointment scheduling, and password resets—to eliminate friction and create an effortless experience.
  4. CRM for Personalization: Equip human agents with a unified view of the customer’s history, preferences, and past interactions across all channels, enabling them to provide highly personalized and empathetic support.

Case Study 1: The Modern Banking Experience – A Shift from Transactional to Relationship-Driven

The Challenge:

For years, banking was a transactional experience. Customers only interacted with their bank when something went wrong, they needed a loan, or they had a question about a fee. This reactive, low-engagement model was ripe for disruption, especially with the rise of FinTech startups offering more user-friendly digital experiences.

Innovating for a Seamless and Proactive Service Journey:

Forward-thinking banks and FinTechs have used technology to fundamentally redefine the customer relationship:

  • Predictive Insights: Instead of just showing a balance, banking apps now use AI to analyze spending habits. They might send a notification that “you’re close to your budget limit on dining out” or “you have a recurring subscription you might have forgotten about.” This is a proactive, helpful service that anticipates a customer’s financial health.
  • Unified Channels: A customer can start a conversation with a chatbot on the app, and if the issue is complex, seamlessly transition to a human agent who has the full chat history and customer context instantly available. There is no need to repeat the problem.
  • Automated Problem Solving: Basic issues like a temporary debit card freeze or a disputed charge can be handled instantly through the app, without ever needing to call a representative, removing a massive point of friction.

The Result:

This shift from a purely transactional model to a seamless, proactive, and relationship-driven service has drastically improved customer satisfaction and loyalty. By using technology to anticipate needs and remove friction, these institutions have transformed banking from a chore into a tool that genuinely helps customers manage their financial lives. The innovation isn’t in a new product, but in a fundamentally better, more human-centric service experience.

Case Study 2: The E-commerce Returns Process – Turning a Pain Point into a Moment of Delight

The Challenge:

The returns process is often the most frustrating part of the e-commerce experience. It’s a key moment of truth that can either cement brand loyalty or destroy it. Traditional returns often involve printing labels, finding boxes, and a lengthy wait for a refund, all of which creates a high-friction, low-delight experience.

Innovating for a Delightful and Effortless Service Experience:

Some innovative retailers have re-engineered the returns process to be a moment of delight, using technology to enable a human-centered design:

  • Frictionless Returns: Companies like Nordstrom and Amazon have partnered with services that allow for no-box, no-label returns at local drop-off points. The customer simply brings the item in a bag, and the service center scans a QR code. This is an innovation that removes multiple points of friction.
  • Proactive Communication: Customers receive automated, real-time updates on their return status, from “item received” to “refund initiated” to “refund processed.” This removes anxiety and the need to call customer service.
  • AI-Powered Recommendations: Some companies use AI to analyze the reason for a return (e.g., “wrong size”) and then proactively suggest a replacement product that is a better fit, turning a potential lost sale into a new one and creating a helpful, personalized service.

The Result:

By transforming the returns process from a source of friction into a seamless and proactive service, these companies have significantly improved customer satisfaction and repurchase rates. They recognized that the moment a customer wants to return an item is not an endpoint but a critical inflection point in the relationship. By innovating around this service journey, they built immense brand trust and loyalty, proving that great service can turn even the most negative interactions into positive brand-building opportunities.

Conclusion: The Human-Centered Imperative

The future of service is not about automation for the sake of efficiency; it’s about using intelligent technology to enable a more deeply human-centered experience. It’s about anticipating needs, removing friction, and empowering employees to focus on the moments that truly matter. The organizations that will win in the long run are those that view service not as a cost to be minimized, but as a strategic asset to be innovated upon.

As leaders, our challenge is to break down old silos, foster a culture of empathy, and design service journeys that are as delightful and intuitive as the products they support. The goal is to move beyond simply satisfying customers to genuinely delighting them, building a future where service is the ultimate driver of loyalty, innovation, and growth. The future of service is here, and it’s beautifully human.

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

Image credit: Pixabay

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Accelerating Innovation Cycles with AI

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From Idea to Impact

Accelerating Innovation Cycles with AI

GUEST POST from Chateau G Pato

The innovation landscape has always been a race against time. Ideas are plentiful, but transforming them into tangible impact—a new product, an optimized process, a groundbreaking service—often involves arduous cycles of research, development, testing, and refinement. In today’s hyper-competitive, human-centered world, this pace is simply no longer sufficient. As a thought leader in change and innovation, I believe the single most powerful accelerator for these cycles is Artificial Intelligence. AI isn’t just a tool; it’s a paradigm shift, enabling us to move from nascent concepts to measurable outcomes with unprecedented speed and precision.

For too long, the innovation journey has been characterized by bottlenecks: manual data analysis, slow prototyping, biased feedback interpretation, and iterative development that could stretch for months or even years. AI offers a compelling antidote to these challenges, supercharging every phase of the innovation process. It’s about augmenting human creativity and insight, not replacing it, allowing our teams to focus on the truly strategic and empathetic aspects of innovation while AI handles the heavy lifting of data crunching, pattern recognition, and rapid iteration.

The AI Accelerator: How AI Transforms Each Stage of Innovation

The true power of AI in innovation lies in its ability to enhance and speed up various stages of the innovation cycle:

  • Discovery & Ideation: AI can rapidly analyze vast datasets—market trends, customer feedback, scientific research, patent databases—to identify emerging white spaces, unmet needs, and potential synergies that human teams might miss. Generative AI can even assist in brainstorming novel concepts, providing diverse starting points for human ingenuity.
  • Concept Development & Prototyping: AI-powered design tools can generate multiple design variations based on specified parameters, simulate performance, and even create virtual prototypes in a fraction of the time it would take human designers. This allows for faster testing of diverse ideas.
  • Validation & Testing: Predictive AI models can forecast market reception for new products or features by analyzing historical data and customer behavior, reducing the need for extensive, costly live testing. AI can also analyze user feedback (sentiment analysis) from early tests to quickly identify areas for improvement.
  • Optimization & Launch: AI can optimize product features, pricing strategies, and marketing campaigns in real-time, learning from live data to maximize impact post-launch. For internal process innovations, AI can identify inefficiencies and suggest optimal workflows.
  • Learning & Iteration: Post-launch, AI continuously monitors performance, identifies emerging patterns in customer usage, and suggests further improvements or next-gen features, effectively creating a perpetual feedback loop for continuous innovation.

“AI doesn’t just speed up innovation; it fundamentally redefines the possible, turning months into days and guesses into data-driven insights.”

Human-Centered AI for Innovation: A Crucial Distinction

It’s vital to emphasize that integrating AI into innovation must remain human-centered. The goal is not to automate innovation away from people, but to empower people to innovate better, faster, and with greater impact. AI should serve as an invaluable co-pilot, handling the computational burden so that human teams can focus on:

  • Empathy and Understanding: Interpreting the emotional nuances of customer needs that AI cannot grasp.
  • Strategic Vision: Setting the direction, defining the ethical guardrails, and making the ultimate strategic decisions.
  • Creative Problem-Solving: Leveraging AI’s insights to spark truly original, human-relevant solutions.

Case Study 1: Pharma Research Acceleration with AI (BenevolentAI)

The Challenge:

Drug discovery is notoriously slow, expensive, and high-risk. Identifying potential drug candidates for specific diseases often takes years of laborious research, involving sifting through vast amounts of scientific literature and conducting countless lab experiments. The human-driven cycle from initial idea to clinical trial could span a decade or more.

AI as an Accelerator:

BenevolentAI, a leading AI drug discovery company, uses its platform to accelerate this process dramatically. Their AI system can:

  • Analyze Scientific Literature: Rapidly process and understand millions of scientific papers, clinical trial results, and proprietary datasets to identify relationships between genes, diseases, and potential drug compounds that human scientists might overlook.
  • Generate Hypotheses: Propose novel hypotheses for drug targets and disease mechanisms, suggesting existing drugs that could be repurposed or identifying entirely new molecular structures for development.
  • Predict Efficacy and Safety: Use predictive modeling to assess the likelihood of success and potential side effects of drug candidates early in the process, reducing wasted effort on less promising avenues.

The Result:

By leveraging AI, BenevolentAI has significantly reduced the time it takes to identify and validate promising drug candidates. For example, they identified a potential treatment for Parkinson’s disease, successfully repurposing an existing drug, and advancing it to clinical trials in a fraction of the traditional timeframe. This acceleration means getting life-saving treatments to patients faster, transforming the innovation cycle from an agonizing crawl to a rapid, data-driven sprint, all while maintaining strict human oversight and ethical considerations.

Case Study 2: Generative AI in Product Design (Nike)

The Challenge:

Designing high-performance athletic footwear involves a complex interplay of biomechanics, material science, aesthetics, and manufacturing constraints. Iterating on designs to optimize for factors like weight, durability, and shock absorption used to be a time-consuming, manual process involving physical prototypes and extensive testing. The innovation cycle for a new shoe model could take 18-24 months.

AI as an Accelerator:

Companies like Nike have begun integrating generative AI into their product design processes. Generative design algorithms can:

  • Explore Design Space: Given a set of design parameters (e.g., desired weight, material properties, aesthetic guidelines), the AI can rapidly generate hundreds or thousands of unique sole structures or upper designs. These designs often push the boundaries of human intuition, creating novel geometries optimized for performance.
  • Simulate Performance: AI-powered simulation tools can instantly analyze the generated designs for factors like stress points, airflow, and energy return, providing immediate feedback on their potential performance without needing to build physical prototypes.
  • Suggest Material Optimization: The AI can also suggest optimal material combinations or placement to achieve desired characteristics, further speeding up the development process.

The Result:

The integration of generative AI allows Nike’s design teams to explore a vastly larger array of design possibilities and to iterate on ideas at an accelerated pace. What once took weeks or months of manual design and physical prototyping can now be achieved in days. This not only shortens the overall innovation cycle for new footwear (reducing time-to-market) but also leads to more innovative, higher-performing products that better meet the specific needs of athletes. The human designer remains at the helm, guiding the AI and making critical creative choices, but their capabilities are amplified exponentially.

Conclusion: The Future of Innovation is Intelligent

The journey from a raw idea to a market-ready innovation has never been faster, nor more critical. Artificial Intelligence is not merely an optional add-on; it is becoming an essential engine for accelerating innovation cycles across every industry. By intelligently augmenting human capabilities, AI allows organizations to move beyond incremental improvements to truly transformative breakthroughs.

As leaders, our role is to embrace this technological evolution with a human-centered approach. We must leverage AI to free our teams from mundane tasks, empower them with deeper insights, and enable them to focus their unique creativity and empathy where it truly matters. The future of innovation is intelligent, collaborative, and, above all, accelerated. It’s time to harness AI to build a future where every great idea has a fast track to impact.

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

Image credit: Microsoft CoPilot

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Shifting Your Innovation Measurement Focus

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From Outputs to Outcomes

Shifting Your Innovation Measurement Focus

GUEST POST from Art Inteligencia

In the world of innovation, we often find ourselves caught in the trap of measuring what’s easy to count: outputs. We tally up new ideas generated, patents filed, prototypes built, or features launched. While these metrics offer a semblance of progress, they often obscure the true impact of our efforts. The real game-changer isn’t how much we produce, but what difference that production makes – the outcomes.

It’s time for a fundamental shift in how we approach innovation measurement. Instead of focusing solely on the tangible outputs of our innovation processes, we must pivot our gaze towards the meaningful outcomes that those outputs are designed to achieve. This isn’t just a semantic distinction; it’s a strategic imperative that can transform how organizations foster, fund, and ultimately succeed with innovation.

Why the Shift Matters: The Limitations of Output-Centric Measurement

Measuring outputs alone can lead to several pitfalls:

  • False Sense of Progress: An abundance of ideas doesn’t necessarily mean valuable ideas. A high number of prototypes might just indicate a lack of clear direction or rigorous testing.
  • Misguided Incentives: When individuals or teams are rewarded for outputs, they naturally prioritize quantity over quality, potentially leading to wasted resources on initiatives that lack true market fit or user value.
  • Lack of Strategic Alignment: Without a clear link to desired outcomes, innovation efforts can become disconnected from broader business objectives, failing to contribute meaningfully to the organization’s strategic goals.
  • Difficulty in Learning: If we don’t measure the impact, how do we learn what truly works? Without understanding outcomes, it’s challenging to refine our innovation processes and improve future endeavors.

The goal of innovation isn’t merely to create something new; it’s to create something valuable. This value is almost always found in the outcomes – whether that’s increased customer satisfaction, improved operational efficiency, new revenue streams, or enhanced brand perception.

“Innovation isn’t about the number of ideas you generate, but the value those ideas create for your customers and your organization.”

Defining Outcomes: What Are We Really Trying to Achieve?

Before you can measure outcomes, you must clearly define them. This requires a deep understanding of your customers, your market, and your strategic objectives. Ask yourselves:

  • What problem are we trying to solve for our customers?
  • How will this innovation improve their lives or work?
  • What business results do we expect to see as a direct consequence of this innovation?
  • How will this innovation impact our competitive position?

Outcomes should be specific, measurable, achievable, relevant, and time-bound (SMART). They should go beyond simple financial metrics and encompass a broader view of value creation, including customer experience, employee engagement, and societal impact where relevant.

Consider the difference: instead of measuring “number of new features released,” measure “increase in user engagement with new features” or “reduction in customer support calls related to previous pain points.” The latter two directly reflect the value delivered to the user and the business.

Case Study 1: Transforming Customer Experience in Banking

The Challenge:

A large retail bank was struggling with declining customer satisfaction and an outdated mobile banking experience. Their innovation team was measured on the number of new app features released quarterly – a pure output metric.

The Old Approach (Output-Centric):

The team consistently delivered a high volume of new features, including minor UI tweaks, new calculator tools, and incremental additions. Despite this, customer satisfaction scores remained stagnant, and app usage, while present, didn’t show significant shifts in how customers managed their finances.

The Shift to Outcomes:

Recognizing the disconnect, the bank redefined its innovation objective for the mobile app. The new outcome goal was to “increase active mobile banking users by 15% within 12 months by enabling frictionless self-service and personalized financial insights, leading to a 10% reduction in branch visits for routine transactions.”

The innovation team began focusing on features directly tied to these outcomes: a simplified bill pay process, AI-driven spending insights, and integrated chat support. They measured:

  • Outcome Metric 1: Percentage increase in active mobile banking users.
  • Outcome Metric 2: Percentage reduction in branch visits for specific routine transactions (e.g., balance inquiries, transfers).
  • Outcome Metric 3: Net Promoter Score (NPS) specific to mobile banking users.

The Result:

Within 10 months, active mobile users increased by 18%, and branch visits for routine tasks decreased by 12%. NPS for mobile banking saw a 20-point jump. This success wasn’t due to more features, but better, more impactful features driven by clearly defined customer and business outcomes. The team learned to prioritize based on potential impact rather than sheer volume.

Implementing the Shift: Practical Steps

Making this transition requires intentional effort and a cultural change:

  1. Start with the “Why”: For every innovation project, clearly articulate the problem it solves and the desired impact. Why does this innovation matter?
  2. Define Key Outcome Indicators (KOIs): Identify the specific metrics that will tell you if you’ve achieved your desired outcome. These are distinct from Key Performance Indicators (KPIs) that track overall business health. KOIs are directly linked to the specific innovation.
  3. Embed Outcomes into the Innovation Process: From ideation to commercialization, constantly ask: “How does this contribute to our desired outcome?” Use outcome-based criteria for project selection and stage-gate reviews.
  4. Embrace Experimentation and Learning: Measuring outcomes requires a willingness to test hypotheses and learn from failures. If an innovation isn’t delivering the desired outcome, pivot or iterate.
  5. Communicate and Celebrate Outcomes: Share stories of how innovations have positively impacted customers and the business. This reinforces the importance of outcomes and motivates teams.

Case Study 2: Developing Sustainable Packaging Solutions

The Challenge:

A global consumer goods company aimed to reduce its environmental footprint by developing more sustainable packaging. The initial innovation mandate was to “develop 5 new sustainable packaging materials by year-end” – another output-focused goal.

The Old Approach (Output-Centric):

The R&D team generated several promising material prototypes, including biodegradable plastics and recycled content designs. They met their target of 5 new materials. However, many were either too expensive for mass production, lacked the required durability, or didn’t significantly reduce overall carbon emissions across the product lifecycle once tested in real-world scenarios.

The Shift to Outcomes:

The company realized that simply developing new materials wasn’t enough; the true goal was measurable environmental impact and economic viability. Their refined outcome goal became: “Reduce the carbon footprint of our top 3 product lines by 25% within two years by adopting commercially viable and scalable sustainable packaging solutions that maintain product integrity and consumer appeal.”

Innovation efforts shifted. Instead of just developing materials, teams focused on:

  • Outcome Metric 1: Life Cycle Assessment (LCA) scores showing percentage reduction in carbon footprint per product unit.
  • Outcome Metric 2: Packaging cost-per-unit impact (ensuring solutions were scalable).
  • Outcome Metric 3: Consumer acceptance testing (maintaining or improving perception of product quality).

The Result:

By focusing on these outcomes, the team prioritized innovations that offered the best balance of environmental benefit, cost-effectiveness, and consumer experience. They adopted a single, highly innovative recycled plastic solution for one product line and completely redesigned the packaging for another to eliminate unnecessary material, exceeding their 25% carbon reduction goal for those lines within 18 months. The shift ensured that sustainability innovations were not just developed, but actually adopted and impactful.

Conclusion: The Future of Innovation Measurement

The journey from output to outcome measurement is a critical evolution for any organization serious about driving meaningful change and innovation. It demands discipline, a deeper understanding of value creation, and a willingness to challenge traditional metrics. By focusing on the true impact of our efforts, we move beyond simply doing things right to doing the right things, ensuring our innovations not only exist but thrive and make a tangible difference in the world.

Embrace this shift, and watch your innovation efforts transform from a series of activities into a powerful engine of sustainable growth and competitive advantage. The future belongs to those who measure what truly matters.

Extra Extra: Futurology is not fortune telling. Futurists use a scientific approach to create their deliverables, but a methodology and tools like those in FutureHacking™ can empower anyone to engage in futurology themselves.

Image credit: Pixabay

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Striking the Right Balance Between Data Privacy and Innovation

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Striking the Right Balance Between Data Privacy and Innovation

GUEST POST from Art Inteligencia

From my vantage point here in the United States, at the crossroads of technological advancement and community values, I often reflect on one of the most pressing challenges of our digital age: how do we foster groundbreaking innovation without compromising fundamental data privacy rights? There’s a pervasive myth that privacy and innovation are inherently at odds – that one must be sacrificed for the other. As a human-centered change leader, I firmly believe this is a false dichotomy. The true frontier of innovation lies in designing solutions where data privacy is not an afterthought or a regulatory burden, but a foundational element that actually enables deeper trust and more meaningful progress.

Data is the fuel of modern innovation. From AI and personalized experiences to healthcare advancements and smart cities, our ability to collect, analyze, and leverage data drives much of the progress we see. However, this power comes with a profound responsibility. The increasing frequency of data breaches, the rise of opaque algorithms, and growing concerns about surveillance have eroded public trust. When users fear their data is being misused, they become reluctant to engage with new technologies, stifling the very innovation we seek to foster. Therefore, balancing the immense potential of data-driven innovation with robust data privacy is not just an ethical imperative; it is a strategic necessity for long-term success and societal acceptance.

Striking this delicate balance requires a human-centered approach to data management – one that prioritizes transparency, control, and respect for individual rights. It’s about moving from a mindset of “collect everything” to “collect what’s necessary, protect it fiercely, and use it wisely.” Key principles for achieving this balance include:

  • Privacy by Design: Integrating privacy protections into the design and architecture of systems from the very beginning, rather than adding them as an afterthought.
  • Transparency and Clear Communication: Being explicit and easy to understand about what data is being collected, why it’s being collected, and how it will be used. Empowering users with accessible information.
  • User Control and Consent: Giving individuals meaningful control over their data, including the ability to grant, revoke, or modify consent for data usage.
  • Data Minimization: Collecting only the data that is absolutely necessary for the intended purpose and retaining it only for as long as required.
  • Security by Default: Implementing robust security measures to protect data from unauthorized access, breaches, and misuse, making security the default, not an option.
  • Ethical Data Use Policies: Developing clear internal policies and training that ensure data is used responsibly, ethically, and in alignment with societal values.

Case Study 1: Apple’s Stance on User Privacy as a Differentiator

The Challenge: Distinguishing in a Data-Hungry Tech Landscape

In an industry where many tech companies rely heavily on collecting and monetizing user data, Apple recognized an opportunity to differentiate itself. As concerns about data privacy grew among consumers, Apple faced the challenge of maintaining its innovative edge while explicitly positioning itself as a champion of user privacy, often in contrast to its competitors.

Privacy as Innovation:

Apple made data privacy a core tenet of its brand and product strategy. They implemented “Privacy by Design” across their ecosystem, with features like on-device processing to minimize data sent to the cloud, App Tracking Transparency (ATT) which requires apps to ask for user permission before tracking them across other apps and websites, and strong encryption by default. Their messaging consistently emphasizes that user data is not their business model. This commitment required significant engineering effort and, at times, led to friction with other companies whose business models relied on extensive data collection. However, Apple framed these privacy features not as limitations, but as innovations that provide users with greater control and peace of mind.

The Impact:

Apple’s strong stance on privacy has resonated deeply with a growing segment of consumers who are increasingly concerned about their digital footprint. This approach has strengthened brand loyalty, contributed to strong sales, and positioned Apple as a trusted leader in a sometimes-skeptical industry. It demonstrates that prioritizing data privacy can be a powerful competitive advantage and a driver of innovation, rather than a hindrance. Apple’s success proves that safeguarding user data can build profound trust, which in turn fuels long-term engagement and business growth.

Key Insight: Embedding data privacy as a core value and design principle can become a powerful brand differentiator, building customer trust and driving sustained innovation in a data-conscious world.

Case Study 2: The EU’s General Data Protection Regulation (GDPR) and Its Global Impact

The Challenge: Harmonizing Data Protection Across Borders and Empowering Citizens

Prior to 2018, data protection laws across Europe were fragmented, creating complexity for businesses and inconsistent protection for citizens. The European Union faced the challenge of creating a unified, comprehensive framework that would empower individuals with greater control over their personal data in an increasingly digital and globalized economy.

Regulation as a Driver for Ethical Innovation:

The GDPR, implemented in May 2018, introduced stringent requirements for data collection, storage, and processing, focusing on principles like consent, transparency, and accountability. It gave individuals rights such as the right to access their data, the right to rectification, and the “right to be forgotten.” While initially perceived by many businesses as a significant compliance burden, GDPR effectively forced organizations to adopt “Privacy by Design” principles and to fundamentally rethink how they handle personal data. It compelled innovators to build privacy into their products and services from the ground up, rather than treating it as a bolt-on. This regulation created a new standard for data privacy, influencing legislation and corporate practices globally.

The Impact:

Beyond compliance, GDPR has spurred a wave of innovation focused on privacy-enhancing technologies (PETs) and privacy-first business models. Companies have developed new ways to process data anonymously, conduct secure multi-party computation, and provide transparent consent mechanisms. While challenges remain, GDPR has arguably fostered a more ethical approach to data-driven innovation, pushing companies to be more thoughtful and respectful of user data. It demonstrates that robust regulation, rather than stifling innovation, can serve as a catalyst for responsible and human-centered technological progress, ultimately rebuilding trust with consumers on a global scale.

Key Insight: Strong data privacy regulations, while initially challenging, can act as a catalyst for ethical innovation, driving the development of privacy-enhancing technologies and fostering greater trust between consumers and businesses globally.

Building a Trustworthy Future through Balanced Innovation

Throughout the world, the conversation around data privacy and innovation is far from over. As we continue to push the boundaries of what technology can achieve, we must remain steadfast in our commitment to human values. By embracing principles like Privacy by Design, championing transparency, and empowering user control, we can create a future where innovation flourishes not at the expense of privacy, but because of it. Striking this balance is not just about avoiding regulatory fines; it’s about building a more ethical, trustworthy, and ultimately more sustainable digital future for all.

Extra Extra: Futurology is not fortune telling. Futurists use a scientific approach to create their deliverables, but a methodology and tools like those in FutureHacking™ can empower anyone to engage in futurology themselves.

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