Category Archives: Digital Transformation

Using Storytelling to Galvanize Action

From Vision to Reality

Using Storytelling to Galvanize Action

GUEST POST from Chateau G Pato

In the complex landscape of modern business, where data often reigns supreme, we sometimes overlook one of humanity’s oldest and most powerful tools for influence: storytelling. As a human-centered change leader, I’ve seen countless brilliant visions and innovative strategies gather dust because they failed to capture the hearts and minds of the people who needed to bring them to life. The gap between a visionary idea and its tangible implementation is often bridged not by spreadsheets and Gantt charts alone, but by compelling narratives. Storytelling is the essential ingredient that translates abstract concepts into emotional resonance, galvanizing individuals and teams to move from passive understanding to inspired action.

Think about it: our brains are hardwired for stories. From ancient myths to modern advertisements, narratives help us make sense of the world, connect with others, and remember information far more effectively than facts and figures alone. In a corporate context, a well-crafted story can do more than just inform; it can align, motivate, and inspire. It allows leaders to articulate a compelling future, explain the ‘why’ behind challenging changes, and create a shared sense of purpose that unites diverse stakeholders. When people see themselves as part of a larger, meaningful narrative, they are far more likely to embrace change, overcome obstacles, and commit their energy to achieving a common goal. This is the essence of human-centered leadership: connecting with people on an emotional level to drive tangible results.

To effectively use storytelling to galvanize action, consider these human-centered principles:

  • Identify Your Core Message: What is the single, most important idea you want to convey? This becomes the central theme of your story.
  • Know Your Audience: Tailor your story to resonate with the specific experiences, values, and concerns of your listeners. What are their challenges? What inspires them?
  • Craft a Compelling Arc: Every good story has a beginning (the current state/challenge), a middle (the journey/change), and an end (the desired future/impact). Build tension, introduce characters, and show transformation.
  • Emphasize Emotion and Empathy: Stories connect emotionally. Use vivid language, relatable characters, and appeals to shared values to evoke empathy and build a deeper connection.
  • Call to Action: Your story should naturally lead to a clear, actionable next step. What do you want people to do after hearing your story? Make it clear how they fit into the narrative.
  • Be Authentic: The most powerful stories come from a place of genuine belief and vulnerability. Share personal experiences or anecdotes that lend credibility and humanize your message.

Case Study 1: Steve Jobs and the “1,000 Songs in Your Pocket” Story

The Challenge: Introducing a Revolutionary Product in a Skeptical Market

When Apple launched the iPod in 2001, portable music players were not new. However, existing devices were often clunky, had limited storage, and were difficult to use. Steve Jobs faced the challenge of not just introducing another gadget, but convincing the world that this new device was fundamentally different and would change their relationship with music.

Storytelling in Action:

Jobs didn’t lead with technical specifications. Instead, he painted a vivid, relatable picture of a future where music was effortlessly accessible. His iconic phrase, “1,000 songs in your pocket,” wasn’t just a feature; it was a powerful narrative that spoke to a common human desire for convenience, abundance, and personal connection to music. He described a frustration (carrying CDs), then offered a magical solution (the iPod), making it easy for people to envision themselves experiencing this future. He framed the iPod as a gateway to personal freedom and enjoyment, not just a piece of hardware. This simple, elegant story immediately resonated, contrasting sharply with the technical jargon typically used in product launches.

The Impact:

The iPod became an instant sensation and revolutionized the music industry. Jobs’s ability to tell a compelling story about what the product meant for users, rather than simply what it did, was crucial to its adoption. It galvanized both consumers and developers, creating a powerful ecosystem around Apple’s vision for digital music. This case highlights how storytelling can transform a product launch into a movement, demonstrating that emotional connection, not just technical specifications, is key to galvanizing action and achieving market dominance.

Key Insight: Framing innovation as a solution to a relatable human problem, told through a simple yet powerful narrative, can create emotional resonance that drives widespread adoption and market transformation.

Case Study 2: Martin Luther King Jr.’s “I Have a Dream” Speech

The Challenge: Uniting a Nation and Inspiring Action Towards Social Justice

In 1963, the Civil Rights Movement faced immense challenges, including systemic discrimination, violence, and deeply entrenched racial inequality. Martin Luther King Jr. needed to articulate a vision for a just future that could unite diverse groups, give hope to the oppressed, and galvanize an entire nation towards moral and political action.

Storytelling in Action:

King’s “I Have a Dream” speech is a masterclass in using narrative to galvanize action. He began by acknowledging the historical context and the “shameful condition” of racial injustice, creating a shared understanding of the problem. He then transitioned into a vivid, aspirational story of a future America where equality and freedom reigned. Using powerful metaphors, biblical allusions, and repetition, he painted a picture that was both deeply personal and universally resonant. His “dream” was not a policy paper; it was an emotionally charged vision of a better world that listeners could see, feel, and believe in. He skillfully evoked both the pain of the present and the promise of the future, compelling his audience to become active participants in fulfilling that dream.

The Impact:

The speech became a defining moment of the Civil Rights Movement, inspiring millions and contributing significantly to the legislative changes that followed, including the Civil Rights Act of 1964 and the Voting Rights Act of 1965. King’s storytelling didn’t just communicate a message; it ignited a shared sense of purpose and urgency. It demonstrated that by articulating a compelling vision through a narrative framework, leaders can inspire collective action on an unprecedented scale, transforming societies and achieving profound human-centered change.

Key Insight: A compelling, emotionally resonant narrative that bridges the gap between present struggles and an aspirational future can unite diverse groups and inspire collective action for profound societal change.

Bringing Your Vision to Life Through Story

Whether you’re launching a new product, leading organizational change, or advocating for a social cause, the ability to tell a compelling story is your most potent tool for human-centered innovation. Data provides the evidence, but stories provide the meaning. They transform abstract visions into vivid realities, allowing people to see themselves within the narrative and understand their role in bringing it to fruition. By mastering the art of storytelling, leaders can move beyond simply informing their audience to truly inspiring them, galvanizing the action necessary to turn even the boldest visions into tangible, impactful realities.

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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Top 10 Human-Centered Change & Innovation Articles of September 2022

Top 10 Human-Centered Change & Innovation Articles of September 2022Drum 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 September’s ten most popular innovation posts:

  1. You Can’t Innovate Without This One Thing — by Robyn Bolton
  2. Importance of Measuring Your Organization’s Innovation Maturity — by Braden Kelley
  3. 3 Ways to Get Customer Insights without Talking to Customers
    — by Robyn Bolton
  4. Four Lessons Learned from the Digital Revolution — by Greg Satell
  5. Are You Hanging Your Chief Innovation Officer Out to Dry? — by Teresa Spangler
  6. Why Good Job Interviews Don’t Lead to Good Job Performance — by Arlen Meyers, M.D.
  7. Six Simple Growth Hacks for Startups — by Soren Kaplan
  8. Why Diversity and Inclusion Are Entrepreneurial Competencies
    — by Arlen Meyers, M.D.
  9. The Seven P’s of Raising Money from Investors — by Arlen Meyers, M.D.
  10. What’s Next – The Only Way Forward is Through — by Braden Kelley

BONUS – Here are five more strong articles published in August 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!

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 two years:

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Building Seamless Human-AI Workflows

Designing for Collaboration

Building Seamless Human-AI Workflows

GUEST POST from Art Inteligencia

The rise of artificial intelligence is no longer a futuristic fantasy; it’s a present-day reality reshaping our workplaces. However, the narrative often focuses on AI replacing human jobs. As a human-centered innovation thought leader, I believe the true power of AI lies not in substitution, but in synergy. The future of work is not human versus AI, but human with AI, collaborating in seamless workflows that leverage the unique strengths of both. Designing for this collaboration is the next great frontier of innovation.

The fear of automation is understandable, but it overlooks a critical point: AI excels at tasks that are often repetitive, data-intensive, and rule-based. Humans, on the other hand, bring creativity, critical thinking, emotional intelligence, and the ability to handle ambiguity and novel situations. The sweet spot lies in designing workflows where AI augments human capabilities, freeing us from mundane tasks and empowering us to focus on higher-level strategic thinking, innovation, and human connection. This requires a fundamental shift in how we design work, moving away from a purely task-oriented approach to one that emphasizes collaboration and shared intelligence.

Building seamless human-AI workflows is a human-centered design challenge. It demands that we deeply understand the needs, skills, and workflows of human workers and then thoughtfully integrate AI tools in a way that enhances their capabilities and improves their experience. This involves:

  • Identifying the Right Problems: Focusing AI on tasks that are truly draining human energy and preventing them from higher-value work. This means conducting thorough journey mapping and observational studies to pinpoint the most repetitive and tedious parts of a person’s workday. The goal is to eliminate friction, not just automate for automation’s sake.
  • Designing Intuitive Interfaces: Ensuring that AI tools are user-friendly and seamlessly integrated into existing workflows, minimizing the learning curve and maximizing adoption. The user should feel like the AI is a helpful partner, not a clunky, foreign piece of technology. The interaction should be conversational and natural.
  • Fostering Trust and Transparency: Making it clear how AI is making decisions and providing explanations when appropriate, building confidence in the technology. We must move away from “black box” algorithms and towards a model where humans understand the reasoning behind an AI’s suggestion, which is crucial for building trust and ensuring the human remains in control.
  • Defining Clear Roles and Responsibilities: Establishing a clear understanding of what tasks are best suited for humans and what tasks AI will handle, creating a harmonious division of labor. This requires ongoing communication and training to help people understand their new roles in a hybrid human-AI team. The human’s role should be elevated, not diminished.
  • Iterative Learning and Adaptation: Continuously monitoring the performance of human-AI workflows and making adjustments based on feedback and evolving needs. A human-AI workflow is not a static solution; it’s a dynamic system that requires continuous optimization based on both quantitative metrics and qualitative feedback from the people using it.

Case Study 1: Augmenting Customer Service with AI

The Challenge: Overwhelmed Human Agents and Long Wait Times

A large e-commerce company was struggling with an overwhelmed customer service department. Human agents were spending a significant amount of time answering repetitive questions and sifting through basic inquiries, leading to long wait times and frustrated customers. This was impacting customer satisfaction and agent morale, creating a vicious cycle of burnout and poor service.

The Human-AI Collaborative Solution:

Instead of simply replacing human agents with chatbots, the company implemented an AI-powered support system designed to augment human capabilities. An AI chatbot was deployed to handle frequently asked questions and provide instant answers to common issues, such as order status updates and password resets. However, when the AI encountered a complex or emotionally charged query, it seamlessly escalated the conversation to a human agent, providing the agent with a complete transcript of the interaction and relevant customer data, like past purchases and support history. The AI also assisted human agents by automatically summarizing past interactions and suggesting relevant knowledge base articles, allowing them to resolve issues more quickly and efficiently. The human agent’s role shifted from being a frontline information desk to a skilled problem-solver and relationship builder.

The Results:

The implementation of this human-AI collaborative workflow led to a significant reduction in average wait times (by over 30%) and a noticeable improvement in customer satisfaction scores. Human agents were freed from the burden of repetitive tasks, allowing them to focus on more complex and nuanced customer issues, leading to higher job satisfaction and lower burnout rates. The AI provided efficiency and speed, while the human agents provided empathy and creative problem-solving skills that the AI couldn’t replicate. The result was a superior customer service experience that leveraged the strengths of both humans and AI, creating a powerful synergy that improved the entire customer journey.

Key Insight: AI can significantly improve customer service by handling routine inquiries, freeing up human agents to focus on complex issues and build stronger customer relationships.

Case Study 2: Empowering Medical Professionals with AI-Driven Diagnostics

The Challenge: Improving Diagnostic Accuracy and Efficiency

Radiologists in a major hospital were facing an increasing workload, struggling to analyze a high volume of medical images (X-rays, MRIs, CT scans) while maintaining accuracy and minimizing diagnostic errors. This was a demanding and pressure-filled environment where human fatigue could lead to oversights with potentially serious consequences for patients. The backlog of images was growing, and the time a radiologist could spend on each case was shrinking.

The Human-AI Collaborative Solution:

The hospital integrated AI-powered diagnostic tools into the radiologists’ workflow. These AI algorithms were trained on vast datasets of medical images to identify subtle anomalies and patterns that might be difficult for the human eye to detect, acting as a highly efficient “second pair of eyes.” For example, the AI would highlight a small nodule on a lung scan, prompting the radiologist to take a closer look. However, the AI did not replace the radiologist’s expertise. The AI provided suggestions and highlighted areas of concern, but the final diagnosis and treatment plan remained firmly in the hands of the human medical professional. The radiologist’s role evolved to one of critical judgment, combining their deep clinical knowledge with the AI’s data-processing power. The AI’s insights were presented in a clear, easy-to-understand interface, ensuring the radiologist could quickly integrate the information into their workflow without feeling overwhelmed.

The Results:

The implementation of AI-driven diagnostics led to a significant improvement in diagnostic accuracy (reducing false negatives by 15%) and a reduction in the time it took to analyze medical images. Radiologists reported feeling more confident in their diagnoses and experienced reduced levels of cognitive fatigue. The AI’s ability to process large amounts of data quickly and identify subtle patterns complemented the human radiologist’s clinical judgment and contextual understanding. This collaborative workflow enhanced the efficiency and accuracy of the diagnostic process, ultimately leading to better patient outcomes and a more sustainable workload for medical professionals. The innovation wasn’t in the AI alone, but in the thoughtful design of the human-AI partnership.

Key Insight: AI can be a powerful tool for augmenting the capabilities of medical professionals, improving diagnostic accuracy and efficiency while preserving the crucial role of human expertise and judgment.

The Human-Centered Future of Work

The examples above highlight the immense potential of designing for seamless human-AI collaboration. The key is to approach AI not as a replacement for human workers, but as a powerful partner that can amplify our abilities and allow us to focus on what truly makes us human: our creativity, our empathy, and our capacity for complex problem-solving. As we continue to integrate AI into our workflows, it is crucial that we maintain a human-centered perspective, ensuring that these technologies are designed to empower and enhance the human experience, leading to more productive, fulfilling, and innovative ways of working. The future of work is collaborative, and it’s up to us to design it thoughtfully and ethically.

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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Cultivating a Growth Mindset

Your Personal Toolkit for Continuous Evolution

Cultivating a Growth Mindset

GUEST POST from Chateau G Pato

In a world defined by constant change, the most valuable skill is not a specific technology or a particular degree—it’s the ability to learn, unlearn, and relearn. This capacity for continuous evolution is the very essence of a growth mindset, and it is the single most powerful tool for navigating an unpredictable future. But a growth mindset is not a personality trait you are born with; it is a muscle you must actively cultivate. This article is your personal toolkit for building that muscle and unlocking your full potential.

The concept, popularized by Stanford psychologist Carol Dweck, contrasts a fixed mindset with a growth mindset. A fixed mindset is the belief that our abilities and intelligence are static and unchangeable. It leads us to avoid challenges, fear failure, and see effort as a sign of weakness. Conversely, a growth mindset is the belief that our abilities can be developed through dedication and hard work. It sees challenges as opportunities, failure as a crucial learning moment, and effort as the path to mastery. In today’s dynamic landscape, a fixed mindset is a liability, while a growth mindset is the ultimate catalyst for personal and professional growth.

Cultivating a growth mindset is a deeply personal, human-centered journey. It requires a fundamental shift in how we talk to ourselves, how we view challenges, and how we interact with the world around us. Here’s a practical framework for building your personal toolkit:

  • Practice Self-Awareness: The first step is to recognize the voice of your fixed mindset. When you face a challenge, do you hear a voice that says, “I can’t do this”? Acknowledge that voice, but don’t let it dictate your actions.
  • Embrace the “Power of Yet”: Instead of saying “I can’t do this,” reframe it to “I can’t do this… yet.” This simple word transforms a statement of finality into a statement of possibility, reframing a weakness as a temporary skill gap.
  • Reframe Failure as a Learning Opportunity: View setbacks not as a reflection of your worth, but as invaluable data. Ask yourself: “What did I learn from this? How can I do it better next time?”
  • Seek Out and Embrace Challenges: Step out of your comfort zone intentionally. Take on a new project, learn a new skill, or tackle a problem that seems beyond your current capabilities. This is where real growth happens.
  • Learn from Others’ Success: Instead of feeling threatened by the success of others, see it as inspiration. Study their journey, understand their process, and learn from their efforts.

Case Study 1: The Reinvention of an IT Professional

The Challenge: Obsolescence in a Rapidly Changing Field

John, a 20-year veteran in the IT department of a large corporation, had built his career on a specific set of legacy technologies. When the company announced a major shift to cloud computing and DevOps, his initial reaction was fear and resistance. He believed that his skills were becoming obsolete and that he was too old to learn a new, complex field. This fixed mindset led him to avoid training sessions and dismiss the new technology as a “fad,” putting his career in jeopardy.

The Growth Mindset Transformation:

After a frank conversation with his manager, John realized he had to change his perspective. He started by reframing his belief. Instead of “I can’t learn this,” he began to say, “I’m going to start learning this today.” He embraced the “power of yet.” He took online courses, sought out a mentor from a younger team, and even volunteered for a small, non-critical cloud project. He viewed every mistake not as a failure, but as a step in his learning journey. His colleagues noticed his renewed enthusiasm and his willingness to ask questions. He transformed his daily mindset from one of survival to one of learning and curiosity.

The Results:

Within two years, John became a certified cloud architect and a respected resource for his team. His willingness to embrace the new technology not only saved his career but also positioned him as a leader in the department’s transformation. His journey became a powerful case study for the entire organization, proving that a growth mindset is more than a buzzword; it’s a practical, actionable strategy for adapting to change and finding new purpose in a career.

Key Insight: A growth mindset is not limited by age or professional history; it is a choice to engage with a new future rather than retreat from it.

Case Study 2: The Startup Founder and The Failure Pivot

The Challenge: The Pain of a Failed Product Launch

Maria, a talented entrepreneur, poured two years of her life into developing a groundbreaking new SaaS product. After a public and highly anticipated launch, the product failed to gain traction. The market feedback was clear: it solved a problem that customers didn’t feel they had. Maria was devastated. Her initial reaction was to take the failure personally, believing it was a reflection of her inability as a founder. This fixed mindset told her that her idea was flawed and her efforts were wasted.

The Growth Mindset Transformation:

After taking time to process the disappointment, Maria shifted her mindset. Instead of viewing the failure as a dead end, she began to see it as a goldmine of data. She reframed the experience as a “failure pivot.” She brought her team together not to mourn the loss, but to conduct an honest, blameless post-mortem. They meticulously analyzed the customer feedback, interviewing users who didn’t adopt the product to understand their true needs and pain points. They discovered a key insight that was hidden in plain sight, which had been overshadowed by their original vision. They learned that the market needed a solution for a different, yet related, problem.

The Results:

Using the data and insights from their “failed” launch, Maria and her team made a strategic pivot. They built a new product that addressed the newly discovered, deeply felt market need. The lessons learned from the first failure allowed them to build a better, more focused product on their second attempt. This time, the product was a resounding success, and the company quickly grew to profitability. Maria’s story became an integral part of her company’s culture, celebrated as a testament to the power of embracing failure as a critical step on the path to success.

Key Insight: The growth mindset turns the emotional pain of failure into the strategic fuel for future innovation and success.

Your Toolkit in Action: The Path to Mastery

A growth mindset is the foundation of human-centered change. It empowers individuals to adapt, to learn, and to thrive in the face of uncertainty. The toolkit I’ve outlined is not a one-time fix but a daily practice. It requires consistent effort and a willingness to be vulnerable. But the rewards are immeasurable. You’ll find yourself approaching challenges with a sense of excitement rather than dread, you’ll see criticism as a gift rather than a judgment, and you’ll find a deep, lasting satisfaction in the process of continuous learning and improvement. The world is changing faster than ever before. Your greatest asset is your ability to change with it. Start building your toolkit today. The journey of continuous evolution is waiting for you.

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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People Drive the World-Technology as a Co-Pilot via Center of Human Compassion

People Drive the World-Technology as a Co-Pilot via Center of Human Compassion

GUEST POST from Teresa Spangler

People at the Center – Technology as a Co-Pilot

Are people at the center of your innovation and new product plans? Have we made people the center of all things digital? Are human’s and our environment the center of the new world entering the 4th Industrial Revolution? When innovation is during groundbreaking disruptive inventions or whether innovation is iterating into new products… what is placed at the center of your strategies? What are the reasons for these new inventions?

So much is at stake, as the world turns to being driven by AI, humanoids, rockets’ red glare searching for new lands to inhabit, games and more games feeding our brains with virtual excitement and stimulation, devices galore on our bodies, in our hands, in our homes helping us navigate our every move and in many ways directing us on how to think. The acceleration of digital permeating our lives is mind boggling. The news we are fed, seemingly unbiased, the product advertisements that sneak into our feeds, the connections via too many social and work-related networks that appear all too promising and friendly too is overwhelming. Technology is encompassing our lives!

The Power of Technology

Don’t get me wrong, I love technology for all the positive it contributes to the world. Technology is allowing individuals to create! To create and earn! To take control of their lives and build meaningful endeavors. The creation of TIME and SPACE to live how we to live has been a major outcome of

1. technology but also 2. the pandemic.

Let’s explore the creator economy which has experienced an explosion of late. As referenced in the Forbes articleThe Biggest Trends For 2022 In Creator Economy And Web3, by Maren Thomas Bannon, Today, the total size of the creator economy is estimated to be over $100 billion and 50 million people worldwide consider themselves creators. Creators will continue to bulge out of the global fabric as individuals seek to augment their incomes or escape the confines or rigged corporate cultures. Technology is enabling creators no doubt!

Technology is also allowing forward acting organizations to scale growth at unprecedented speeds. Let’s look at a recent survey conducted by Accenture

Curious about the effects of the pandemic, we completed a second round of research in early 2021 and discovered the following:

  1. Technology Leaders have moved even further ahead of the pack and have been growing at 5x the rate of Laggards on average in the past three years.
  2. Among the “Others” there is a group of organizations—18% of the entire sample—that has been able to break previous performance barriers—the Leapfroggers.

Let’s look at a recent survey conducted by Accenture

Curious about the effects of the pandemic, we completed a second round of research in early 2021 and discovered the following:

  1. Technology Leaders have moved even further ahead of the pack and have been growing at 5x the rate of Laggards on average in the past three years.
  2. Among the “Others” there is a group of organizations—18% of the entire sample—that has been able to break previous performance barriers—the Leapfroggers.

Of course, so much technology is doing good things for the world. 3-D printing is emerging at the center of homelessness. As reported in the #NYTIMES, this tiny village in Mexico is housing homeless people. The homes were built using an oversized 3-D printer.

Another example positive outcomes of technology is the emergence of over-the-counter hearing devices. Fortune Business Insights estimates the global hearing aids market is projected to grow from $6.67 billion in 2021 to $11.02 billion by 2028 at a CAGR of 7.4% in forecast period, 2021-2028.

These devices, until this year, were regulated to being sold by medical professionals at, for the majority of population in need, very high prices $2000 to $5000+ per hearing aid. Yes typically you need two. But recent innovations in ear buds and bluetooth are allowing other technology companies into the game! Take Bose for example, the FDA recently approved Bose SoundControl Hearing Aids to be purchased on their website for $895/pair. No need for a hearing professional. This significantly changes the playing field and opens the doors for so many that have put off purchases (of these not covered by insurance by the way) devices.

Entertainment & leisure travel is going to a whole new level with the help of technology. It’s wonderful that anyone with connectivity and travel the world and explore via Virtual Reality. Here are 52 places you can explore in the comfort of your home shared by NY Times. Many of us attended conferences and events over the past two years virtually. We’ll see an exponential growth in virtual reality experiences in the coming year.

So why am I talking about creating a Center for Human Compassion if so much good is really coming out of technology? Because many of the outcomes are also unrealized and not anticipated or at least publicized to prepare people. It is essential for companies, technologists, and product teams to consider the consequences of new technologies. Not as an afterthought but at the forethought, from inception of ideas we must ask what are the downsides? How will people be affected? What could happen?

The quote below is taken from the World Economic Forum report, Positive AI Economic Futures

machines will be able to do most tasks better than humans. Given these sorts of predictions, it is important to think about the possible consequences of AI for the future of work and to prepare for different scenarios. Continued progress in these technologies could have disruptive effects: from further exacerbating recent trends in inequality to denying more and more people their sense of purpose and fulfillment in life, given that work is much more than just a source of income.

WeForum brings 150 thought leaders together to share thoughts on how we create an AI world we want. For all of AI’s good, there are potentials for negative outcomes.

Let’s take the military’s fight again hobbyists and drones. In the recent article from WSJ, The Military’s New Challenge: Defeating Cheap Hobbyist Drones, how much energy was placed on Human Compassion if drone technologies, IoT and AI got in the wrong hands?

The U.S. is racing to combat an ostensibly modest foe: hobbyist drones that cost a few hundred dollars and can be rigged with explosives. @WSJ

I feel certain there was some consideration but not enough to draw out possible negative impacts and how to mitigate them before they could even start. Did we really put people at the center of what is possible with drone technologies? What do you think?

This is no easy task. We know what is good for us can turn to bad for us when in the wrong hands, or if it’s not moderated to healthy limits. How do we help facilitate a more compassionate relationship with technology and put people at the center?

Here are four strategies to ensure you are keeping people at the center of your innovation, new products and technology development efforts.

  1. Create a Center of Human Compassion, or People Centered Technology Consortium, or what ever you wish to brand your initiative. Select trusted advisors from external (customers, partners…) and a select group of internal stake holders to join your collaborative to gather input, feedback and push back!
  2. Discuss with your trusted group very early on. Gamify initiatives around gathering what ifs! Anticipating the worst you will plan better for the best! (leaving the hope out)
  3. Build a continuous feedback loop. It is important that insights and scenarios are revisited and rehashed over and over again.
  4. Join other consortiums and get involved with AI and tech for good initiatives. If you can’t find ones you feel are of value to you and your company, start one!

Mantra for the year: #lucky2022 but not without work and placing people front and center of plans will good fortune and luck come for the masses.

As always, reach out if you have ideas you’d like to share or questions you’d like to discuss!

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Future Trends in Innovation Metrics and Analytics

Future Trends in Innovation Metrics and Analytics

GUEST POST from Art Inteligencia

For decades, organizations have grappled with the elusive challenge of measuring innovation. Traditional metrics—R&D spend, patent counts, or revenue from new products—have offered a rearview mirror perspective, telling us what happened, but rarely why, or what to do next. As a human-centered change and innovation thought leader, I’ve seen firsthand that this limited view often stifles true innovation, pushing teams towards incremental improvements rather than bold, transformative leaps. The future of innovation demands a radical shift in how we measure, analyze, and, crucially, understand its underlying human dynamics.

We are moving beyond simple outputs to a more holistic, predictive, and human-centric approach to innovation analytics. This evolution is driven by the increasing complexity of global markets, the imperative for continuous adaptation, and the undeniable recognition that innovation is ultimately a human endeavor, fueled by curiosity, collaboration, and psychological safety.

The Inadequacy of Yesterday’s Metrics

Relying solely on lagging indicators like “percentage of revenue from new products” can be profoundly misleading. It reveals past success but offers scant insight into the health of your current innovation pipeline or the evolving capabilities of your teams. Patent counts, while indicative of intellectual property generation, do not inherently correlate with market impact or customer value. These metrics often inadvertently encourage a focus on quantity over quality, and a siloed view of innovation as a departmental function rather than an overarching organizational capability.

The inherent challenge is that innovation is fundamentally messy, non-linear, and often unpredictable. Attempting to force it into neat, quantitative boxes inevitably overlooks the rich, qualitative data that truly propels breakthrough ideas.


Pivotal Future Trends in Innovation Metrics and Analytics

1. From Lagging to Leading Indicators: Measuring Potential

The future of innovation measurement lies in rigorously assessing the inputs and processes that *predict* future innovation success. This includes vital metrics around:

  • Experimentation Velocity: How swiftly are new ideas being prototyped, tested, and iterated upon? This reflects learning speed.
  • Psychological Safety Scores: Are employees genuinely comfortable taking calculated risks, openly sharing failures, and constructively challenging existing assumptions without fear?
  • Cross-Functional Collaboration Index: How effectively are diverse teams collaborating and exchanging knowledge on new initiatives and challenging problems?
  • Idea Generation & Diversity: The sheer volume and strategic breadth of new ideas being submitted and actively explored across the entire organization.

2. Qualitative & Behavioral Analytics: Unearthing the “Why”

Beyond raw numerical data, organizations will increasingly leverage rich qualitative insights to deeply understand user behavior, emotional responses, and the true problem-solving effectiveness of their innovations. This sophisticated approach involves:

  • User Journey Analytics: Meticulously mapping emotional highs and lows, identifying points of friction, and recognizing moments of profound delight throughout the user experience.
  • Sentiment Analysis: Analyzing unstructured feedback from customer reviews, social media discussions, and internal communications to accurately gauge perception and emotional resonance.
  • Observed Behavior: Direct, empathetic observation of how users naturally interact with prototypes and finished products, leading to the discovery of unspoken needs and intuitive design opportunities.

3. Ecosystem & Network Metrics: Beyond Organizational Walls

True innovation rarely flourishes in isolation. Future metrics will critically assess the health, vibrancy, and effectiveness of external partnerships, open innovation initiatives, and dynamic internal knowledge networks:

  • Partnership Value Index: Quantifying the strategic value, collaborative output, and mutual benefit derived from external alliances and collaborations.
  • Knowledge Sharing Flow: Measuring the velocity, impact, and reach of knowledge transfer both within and outside the traditional boundaries of the organization.
  • Community Engagement: Tracking active participation and meaningful contribution within open innovation platforms or customer co-creation initiatives.

4. Impact-Oriented Metrics: Holistic Value Creation

Innovation is no longer solely about financial returns. Organizations are increasingly accountable for broader societal and environmental impacts. Future metrics will profoundly reflect this critical shift:

  • Social & Environmental Impact Scores: Quantifying tangible contributions to sustainability, community well-being, or ethical practices, beyond mere compliance.
  • Customer Lifetime Value (CLV) from Innovation: Measuring precisely how new offerings enhance long-term customer relationships and loyalty, not just initial sales.
  • Employee Well-being & Engagement from Innovation: Assessing how innovation initiatives contribute to a positive, empowering, and thriving internal culture.

5. AI and Advanced Analytics: The Intelligent Layer

The sheer volume, velocity, and complexity of these new, diverse data points necessitate sophisticated analytical capabilities. AI and machine learning will move far beyond simple dashboards to provide truly predictive and actionable insights:

  • Predictive Modeling: Accurately forecasting the likelihood of innovation success based on the interplay of various leading indicators.
  • Pattern Recognition: Identifying subtle, non-obvious correlations and emerging trends between vast, diverse data sets that human analysts might easily miss.
  • Automated Anomaly Detection: Instantly flagging unexpected drops in collaboration, experimentation, or sentiment, signaling potential issues or emerging opportunities early.

Transformative Case Studies in Advanced Innovation Measurement

Case Study 1: “The Experimentation Engine” at a Global Consumer Goods Company

A large consumer goods company, historically characterized by slow innovation cycles, strategically shifted its primary focus from post-launch product revenue to experimentation velocity and rapid learning cycles. They implemented a sophisticated digital platform to meticulously track every experiment, ranging from minor packaging tweaks to entirely novel product concepts.

Metrics in Action: Instead of relying on traditional annual reviews of product P&Ls, teams were rigorously measured on:

  • The total number of experiments initiated per quarter.
  • The average time to complete an experiment (from initial hypothesis formulation to validated learning).
  • The number of “failed” experiments that, crucially, yielded significant, actionable insights.
  • The degree of cross-functional participation in experiment design and subsequent analysis.

Outcome:

Within a mere 18 months, the company witnessed an astonishing 300% increase in the number of experiments run annually. While many of these experiments “failed” in their initial hypothesis, the speed of learning accelerated dramatically across the organization. This profound shift led to a remarkable 25% reduction in time-to-market for successful new products and a significant increase in the hit rate of subsequent innovations, as insights gleaned from rapid failures directly informed more successful ventures. The organizational culture fundamentally transformed from risk-averse to a dynamic “fail fast, learn faster” ethos.


Case Study 2: “The Collaborative Ecosystem” at a Public Sector Innovation Lab

A pioneering government innovation lab, specifically tasked with solving complex societal challenges, quickly recognized that traditional metrics like “number of programs launched” were woefully insufficient. Their strategic focus pivoted to rigorously measuring the health, vibrancy, and tangible impact of their collaborative ecosystem.

Metrics in Action: They meticulously developed and tracked a suite of metrics centered around:

  • The diversity of stakeholders actively engaged per project (e.g., direct citizen involvement, NGOs, private sector partners, academic institutions).
  • The frequency and depth of impactful knowledge exchange across various network nodes and partner organizations.
  • Quantifiable social impact indicators (e.g., measurable reduction in specific social issues, demonstrable increase in citizen participation) directly attributable to collaborative initiatives.
  • The total number of truly cross-sector solutions co-created and successfully scaled for broader impact.

Outcome:

By proactively prioritizing these comprehensive ecosystem metrics, the lab underwent a profound transformation, evolving from a previously siloed entity into a dynamic central hub for public sector innovation. They experienced an impressive 50% increase in unique cross-sector partnerships within just two years. More significantly, the quality, scalability, and long-term sustainability of the solutions they developed improved dramatically, leading to measurable positive impacts on critical areas like urban planning and public health initiatives. This powerfully demonstrated that fostering a vibrant, interconnected innovation ecosystem is, in itself, a profoundly powerful metric of success.


Embracing the New Innovation Analytics Paradigm

The fundamental shift to these future-forward innovation metrics requires far more than just new dashboards; it demands a profound cultural transformation across the organization. Leaders must champion a mindset that deeply values continuous learning over elusive perfection, fosters open collaboration over internal competition, and prioritizes holistic impact over narrow financial gains. Investing in the right enabling tools—from sophisticated advanced analytics platforms to robust, intuitive collaboration software—is undeniably crucial, but the true, lasting transformation lies in empowering every team and individual to deeply understand and proactively act upon these richer, more insightful data points.

By courageously embracing these emerging trends, organizations can transcend merely tracking past performance to actively and intelligently shaping their innovative future. It’s about meticulously creating a dynamic, responsive system that not only precisely identifies breakthrough opportunities but also cultivates the essential human potential and collaborative spirit necessary to truly realize them. The future of innovation measurement is not just coming—it’s here, and it’s far more intelligent, comprehensive, and profoundly human-centered than anything we’ve ever witnessed before. 📈💡

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: Pexels

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Reaching Beyond the Limits of Innovation and Transformation

Reaching Beyond the Limits of Innovation and TransformationRecently on Episode #873 of the Marketer of the Day podcast, I had the opportunity to sit down with Robert Plank, have a great conversation, and chat about a number of different topics. Here is a quick excerpt:

“When it comes to innovation, timing is a huge factor. Going in too soon or too late can both cost you lots of money. Innovation isn’t all about creativity and value-creation, it is also about the services that you provide around your new idea and helping people understand how your idea can be of value to their lives. But how can we know if our innovative ideas can really affect people’s lives?”


Click the play button to listen to the podcast right here, right now:

Here is Robert Plank in his own words describing what the Marketer of the Day podcast is all about:

The Marketer of the Day Podcast interviews entrepreneurs who have been through “the struggle.”

They’ve experienced the headaches of repeat failure, trial-and-error, scaling, delegating, course-correcting, and getting their online businesses to succeed beyond their wildest dreams… and want to help you get to where you need to go.

Or visit Robert’s site here for additional information and all of the ways to subscribe to his podcast:

https://www.robertplank.com/873-innovation-change-customer-braden-kelley/

Four Lessons Learned from the Digital Revolution

Four Lessons Learned from the Digital Revolution

GUEST POST from Greg Satell

When Steve Jobs was trying to lure John Sculley from Pepsi to Apple in 1982, he asked him, “Do you want to sell sugar water for the rest of your life, or do you want to come with me and change the world?” The ploy worked and Sculley became the first major CEO of a conventional company to join a hot Silicon Valley startup.

It seems so quaint today, in the midst of a global pandemic, that a young entrepreneur selling what was essentially a glorified word processor thought he was changing the world. The truth is that the digital revolution, despite all the hype, has been something of a disappointment. Certainly it failed to usher in the “new economy” that many expected.

Yet what is also becoming clear is that the shortcomings have less to do with the technology itself, in fact the Covid-19 crisis has shown just how amazingly useful digital technology can be, than with ourselves. We expected technology and markets to do all the work for us. Today, as we embark on a new era of innovation, we need to reflect on what we have learned.

1. We Live In a World of Atoms, Not Bits

In 1996, as the dotcom boom was heating up, the economist W. Brian Arthur published an article in Harvard Business Review that signaled a massive shift in how we view the economy. While traditionally markets are made up of firms that faced diminishing returns, Arthur explained that information-based businesses can enjoy increasing returns.

More specifically, Arthur spelled out that if a business had high up-front costs, network effects and the ability to lock in customers it could enjoy increasing returns. That, in turn, would mean that information-based businesses would compete in winner-take-all markets, management would need to become less hierarchical and that investing heavily to win market share early could become a winning strategy.

Arthur’s article was, in many ways, prescient and before long investors were committing enormous amounts of money to companies without real businesses in the hopes that just a few of these bets would hit it big. In 2011, Marc Andreesen predicted that software would eat the world.

He was wrong. As the recent debacle at WeWork, as well as massive devaluations at firms like Uber, Lyft, and Peloton, shows that there is a limit to increasing returns for the simple reason that we live in a world of atoms, not bits. Even today, information and communication technologies make up only 6% of GDP in OECD countries. Obviously, most of our fate rests with the other 94%.

The Covid-19 crisis bears this out. Sure, being able to binge watch on Netflix and attend meetings on Zoom is enormously helpful, but to solve the crisis we need a vaccine. To do that, digital technology isn’t enough. We need to combine it with synthetic biology to make a real world impact.

2. Businesses Do Not Self Regulate

The case Steve Jobs made to John Sculley was predicated on the assumption that digital technology was fundamentally different from the sugar-water sellers of the world. The Silicon Valley ethos (or conceit as the case may be), was that while traditional businesses were motivated purely by greed, technology businesses answered to a higher calling.

This was no accident. As Arthur pointed out in his 1996 article, while atom-based businesses thrived on predictability and control, knowledge-based businesses facing winner-take-all markets are constantly in search of the “next big thing.” So teams that could operate like mission-oriented “commando units” on a holy quest would have a competitive advantage.

Companies like Google who vowed to not “be evil,” could attract exactly the type of technology “commandos” that Arthur described. They would, as Mark Zuckerberg has put it, “move fast and break things,” but would also be more likely to hit on that unpredictable piece of code that would lead to massively increasing returns.

Unfortunately, as we have seen, businesses do not self-regulate. Knowledge-based businesses like Google and Facebook have proven to be every bit as greedy as their atom-based brethren. Privacy legislation, such as GDPR, is a good first step, but we will need far more than that, especially as we move into post-digital technologies that are far more powerful.

Still, we’re not powerless. Consider the work of Stop Hate For Profit, a broad coalition that includes the Anti-Defamation League and the NAACP, which has led to an advertiser boycott of Facebook. We can demand that corporations behave how we want them to, not just what the market will bear.

3. As Our Technology Becomes More Powerful, Ethics Matter More Than Ever

Over the past several years some of the sense of wonder and possibility surrounding digital technology gave way to no small amount of fear and loathing. Scandals like the one involving Facebook and Cambridge Analytica not only alerted us to how our privacy is being violated, but also to how our democracy has been put at risk.

Yet privacy breaches are just the beginning of our problems. Consider artificial intelligence, which exposes us to a number of ethical challenges, ranging from inherent bias to life and death ethical dilemmas such as the trolley problem. It is imperative that we learn to create algorithms that are auditable, explainable and transparent.

Or consider CRISPR, the gene editing technology, available for just a few hundred dollars, that vastly accelerates our ability to alter DNA. It has the potential to cure terrible diseases such as cancer and Multiple Sclerosis, but also raises troubling issues such as biohacking and designer babies. Worried about some hacker cooking up a harmful computer virus, what about a terrorist cooking up a real virus?

That’s just the start. As quantum and neuromorphic computing become commercially available, most likely within a decade or so, our technology will become exponentially more powerful and the risks will increase accordingly. Clearly, we can no longer just “move fast and break things,” or we’re bound to break something important.

4. We Need a New Way to Evaluate Success

By some measures, we’ve been doing fairly well over the past ten years. GDP has hovered around the historical growth rate of 2.3%. Job growth has been consistent and solid. The stock market has been strong, reflecting robust corporate profits. It has, in fact, been the longest US economic expansion on record.

Yet those figures were masking some very troubling signs, even before the pandemic. Life expectancy in the US has been declining, largely due to drug overdoses, alcohol abuse and suicides. Consumer debt hit record highs in 2019 and bankruptcy rates were already rising. Food insecurity has been an epidemic on college campuses for years.

So, while top-line economic figures painted a rosy picture there was rising evidence that something troubling is afoot. The Business Roundtable partly acknowledged this fact with its statement discarding the notion that creating shareholder value is the sole purpose of a business. There are also a number of initiatives designed to replace GDP with broader measures.

The truth is that our well-being can’t be reduced to and reduced to a few tidy metrics and we need more meaning in our lives than more likes on social media. Probably the most important thing that the digital revolution has to teach us is that technology should serve people and not the other way around. If we really want to change the world for the better, that’s what we need to keep in mind.

— Article courtesy of the Digital Tonto blog
— Image credit: Pexels

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Top 10 Human-Centered Change & Innovation Articles of August 2022

Top 10 Human-Centered Change & Innovation Articles of August 2022Drum 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 August’s ten most popular innovation posts:

  1. Why Amazon Wants to Sell You Robots — by Shep Hyken
  2. Now is the Time to Design Cost Out of Our Products — by Mike Shipulski
  3. How Consensus Kills Innovation — by Greg Satell
  4. The Four Secrets of Innovation Implementation — by Shilpi Kumar
  5. Reset and Reconnect in a Chaotic World — by Janet Sernack
  6. This 9-Box Grid Can Help Grow Your Best Future Talent — by Soren Kaplan
  7. ‘Fail Fast’ is BS. Do This Instead — by Robyn Bolton
  8. The Power of Stopping — by Mike Shipulski
  9. The Battle Against the Half-Life of Learning — by Douglas Ferguson
  10. The Phoenix Checklist – Strategies for Innovation and Regeneration — by Teresa Spangler

BONUS – Here are five more strong articles published in July 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!

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 two years:

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Driving the Next Era of Growth: Leveraging Data to Innovate

Driving the Next Era of Growth: Leveraging Data to Innovate

GUEST POST from Teresa Spangler

“50% of US executives and 39% of European executives said budget constraints were the primary hurdle in turning Big Data into a profitable business asset. Rounding out the top 5 challenges were data security concerns, integration challenges, lack of technical expertise, and proliferation of data silos.” (Capgemini)

“The biggest challenges companies face when implementing Big Data are budget constraints” (Capgemini)

Data analytics is continuously evolving as AI and machine learning applications get faster and smarter. The benefits that may be gained by analyzing massive data sets identifying in seconds patterns, signals, and relationships between nonaligned and aligned areas is intoxicating for savvy companies seeking to innovate. We recognize that companies can make faster and better decisions with strong analytic teams interpreting the findings. Look at what information-driven analytics has done already in cool improvements around us. There are so many good examples of this. Take transportation systems, the use of information analytics to course vehicles round congested areas in actual time is one simple example. Another, that literally may have saved the restaurant industry during the pandemic, is meals delivery services which depend on data collected to forecast demand on menu items, key order times, navigation around cities and streets not to mentioned detailed knowledge individual’s meal preferences. Data helped to optimize driving routes for more efficient delivers.

As data analytics becomes more sophisticated, we might anticipate revolutionary disruptions. However, economists report spending greater funds per capita on research, yet there is a significant decline in rate of successful innovation output. One motive for this could be that we are mistakenly focusing an excessive amount of on R&D instead of on innovation output which takes exceptional justification, funding, and resources. What does data analytics have to do with innovation? Everything! Research is crucial but just one part of a puzzle for developing new products and services. Today, innovation requires a sophistication in data analytics interpretation. There’s also a need for the curiosity, for human evaluation and a bit of intuition and intelligence. Companies need an astute cleverness like no other time in history and an ingenious approach to taking research and turning it into something new and worthwhile.  The process must be diligent, but it must also be agile. Too frequently, organizations get bogged down within the details of research and improvement, without truly questioning outside the boundaries of a container process. As a result, we have delays in the process often stalling out for lack of resource allocations. Even worse, companies not focusing on deep understanding of their data may misinterpret the analytics leaving more to chance that to solid pathways.

It’s worth saying, placing a greater emphasis on creativity and innovation is imperative vs. traditional research and improvement methods. As is deeply dissecting the data in your business. Where does all that data live? What are the hidden signals of the data, what types of converging uses (products/solutions) could you turn that data into?

We are in an era of new growth. Poll your customers! They are changing rapidly and challenged with keeping up with the speed of change but know they must. Where are they doubling down their efforts? How well do they understand their own data? What products and services are they developing, who are they collaborating with and a better question, why are you collaborating with them to innovate around their future needs? Are they investing in developing a more tech and analytic savvy organization? Better question, is your company?

As cliché as it is data is the new oil. Data will be producing its own data (it’s happening today) known as synthetic data. According to Gartner, “By 2025, synthetic data will reduce personal customer data collection, avoiding 70% of privacy violation sanctions.” This begs to question the emphasis companies are placing on developing the skills sets of the organization around analytics and data. And simply put, as oil has an expansive array of products and uses, we’re now in an era of inventing new energy sources to reduce even eliminate dependencies on oil. How might data fit into the effort to transform these dependencies? Data is essential for electric and autonomous vehicle development. Innovative companies are undertaking long tail efforts to drive the next generation of IoE (Internet of everything). Data is the fuel. Let’s explore four ways that organizations can use records analytics to power innovation and stay ahead of the competition.

  1. Design new products that think for themselves: understanding data from a variety of sources may trigger new types of needs and possible new products that could be developed. For example: understanding water needs for new smart and innovative cities being designed takes enormous planning. A partner to Plazabridge Group, designs digital twin environments for the water sector. Cites like Singapore, Houston, Dubai, must anticipate the growing needs for water and plan design and building based on anticipated needs but also, they must plan for worst- and best-case scenarios. They must plan for leakage, or contamination or other possible scenarios that may impact water supplies. Digital twinning these environments is the most cost-effective way to simulate new innovative methods. Leveraging as much data as possible as well as generating newly created synthetic data cities can plan more economically, they can execute faster and prepare for events that may occur. Understanding these models around water, suppliers may produce products that help cities build these digital environments. Not just for water systems but for any part of businesses today; manufacturing, facilities management, construction…
  2. Not all innovation has to be moonshot inventions. Simply identify unmet wishes of customers, consumers or the market creating engaging products and services. UBER goes from just carting us around leveraging an incredible inventive back in logistics infrastructure to launch UBER eats! Why not, the drivers are already out and about, the data collected indicates the most popular spots riders go to for coffee, lunch, dinner, drinks… UBER analysts have vast information on customer interests in turn turned from few riders during a pandemic to delivering food as an essential business during the pandemic. A pivot turns into a scalable source of augmented revenue as the shelter lifts and people get back to riding.
  3. So much opportunity exists to improve customer engagement: records analytics can assist businesses to better understand their clients and their wishes. This expertise can then be used to improve customer service and support future-proofing your business.
  4. Extend efficiency: data crunching algorithms, digital twinning, AR/VR simulations and access to remote experts will help corporations to streamline their operations, digitally transforming themselves for greater efficiency. This increased efficiency can lead to price savings, which can be reinvested in innovation.“90% of CEOs believe the digital economy will impact their industry, but less than 15% are executing on a digital strategy.”

— MIT Sloan and Capgemini. Seek out experts and industry mentors to help your organization make these shifts. We often fear what we cannot see, the beautiful thing about the digital world is you can build a virtual environment visualizing the unseen, and plan for all types of scenarios. A model we developed (not dependent on virtual or digital anything in fact) at Plazabridge Group is around the CIA’s The Phoenix Checklist. Strategies for Regenerating is our formula for going deep into understanding problems, future opportunities, needs, anticipating deeply the “What ifs” of every possible scenario.  When done leveraging data and analytics the possibilities become endless.

Original Article

Image credits: Pixabay

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