Category Archives: Innovation

Uber Economy is Killing Innovation, Prosperity and Entrepreneurship

Uber Economy is Killing Innovation, Prosperity and Entrepreneurship

GUEST POST from Greg Satell

Today, it seems that almost everyone wants to be the “Uber” of something, and why not? With very little capital investment, the company has completely disrupted the taxicab industry and attained a market value of over $100 billion. In an earlier era, it would have taken decades to have created that kind of impact on a global scale.

Still, we’re not exactly talking about Henry Ford and his Model T here. Or even the Boeing 707 or the IBM 360. Like Uber, those innovations quickly grew to dominance, but also unleashed incredible productivity. Uber, on the other hand, gushed red ink for more than a decade despite $25 billion invested. In 2021 it lost more than $6 billion, the company made progress in 2022 but still lost money, and it was only in 2023 that they finally made a profit.

The truth is that we have a major problem and, while Uber didn’t cause it, the company is emblematic of it. Put simply, a market economy runs on innovation. It is only through consistent gains in productivity that we can create real prosperity. The data and evidence strongly suggests that we have failed to do that for the past 50 years. We need to do better.

The Productivity Paradox Writ Large

The 20th century was, for the most part, an era of unprecedented prosperity. The emergence of electricity and internal combustion kicked off a 50-year productivity boom between 1920 and 1970. Yet after that, gains in productivity mysteriously disappeared even as business investment in computing technology increased, causing economist Robert Solow to observe that “You can see the computer age everywhere but in the productivity statistics.”

When the internet emerged in the mid-90’s things improved and everybody assumed that the mystery of the productivity paradox had been resolved. However, after 2004 productivity growth disappeared once again. Today, despite the hype surrounding things such as Web 2.0, the mobile Internet and, most recently, artificial intelligence, productivity continues to slump.

Take a closer look at Uber and you can begin to see why. Compare the $25 billion invested in the ride-sharing company with the $5 billion (worth about $45 billion today) IBM invested to build its System 360 in the early 1960s. The System 360 was considered revolutionary, changed computing forever and dominated the industry for decades.

Uber, on the other hand, launched with no hardware or software that was particularly new or revolutionary. In fact, the company used fairly ordinary technology to dis-intermediate relatively low-paid taxi dispatchers. The money invested was largely used to fend off would-be competitors through promoting the service and discounting rides.

Maybe the “productivity paradox” isn’t so mysterious after all.

Two Paths To Profitability

Anybody who’s ever taken an Economics 101 course knows that, under conditions of perfect competition, the forces of supply and demand are supposed to drive markets toward equilibrium. It is at this magical point that prices are high enough to attract supply sufficient to satisfy demand, but not any higher.

Unfortunately for anyone running a business, that equilibrium point is the same point at which economic profit disappears. So to make a profit over the long-term, managers need to alter market dynamics either through limiting competition, often through strategies such as rent seeking and regulatory capture, or by creating new markets through innovation.

As should be clear by now, the digital revolution has been relatively ineffective at creating meaningful innovation. Economists Daron Acemoglu and Pascual Restrepo refer to technologies like Uber, as well as things like automated customer service, as “so-so technologies,” because they displace workers without significantly increasing productivity.

Joseph Schumpeter pointed out long ago, market economies need innovation to fuel prosperity. Without meaningful innovation, managers are left with only strategies that limit innovation, undermine markets and impoverish society, which is what largely seems to have happened over the past few decades.

The Silicon Valley Doomsday Machine

The arrogance of Silicon Valley entrepreneurs seems so outrageous—and so childishly naive— that it is scarcely hard to believe. How could an industry that has produced so little in terms of productivity seem so sure that they’ve been “changing the world” for the better. And how have they made so much money?

The answer lies in something called increasing returns. As it turns out, under certain conditions, namely high up-front investment, negligible marginal costs, network effects and “winner-take-all markets,” the normal laws of economics can be somewhat suspended. In these conditions, it makes sense to pump as much money as possible into an early Amazon, Google or Facebook.

However this seemingly happy story has a few important downsides. First, to a large extent these technologies do not create new markets as much as they disrupt or displace old ones, which is one reason why productivity gains are so meager. Second, the conditions apply to a small set of products, namely software and consumer gadgets, which makes the Silicon Valley model a bad fit for many groundbreaking technologies.

Still, if the perception is that you can make a business viable by pumping a lot of cash into it, you can actually crowd-out a lot of good businesses with bad, albeit well-funded ones. In fact, there is increasing evidence that is exactly what is happening. Rather than an engine of prosperity, Silicon Valley is increasingly looking like a doomsday machine.

Returning To An Innovation Economy

Clearly, we cannot continue “Ubering” ourselves to death. We must return to an economy fueled by innovation, rather than disruption, which produces the kind of prosperity that lifts all boats, rather than outsized profits for a meager few. It is clearly in our power to do that, but we must begin to make better choices.

First, we need to recognize that innovation is something that people do, but instead of investing in human capital, we are actively undermining it. In the US, food insecurity has become an epidemic on college campuses. To make matters worse, the cost of college has created a student debt crisis, essentially condemning our best and brightest to decades of indentured servitude. To add insult to injury, healthcare costs continue to soar. Should we be at all surprised that entrepreneurship is in decline?

Second, we need to rebuild scientific capital. As Vannevar Bush once put it, “There must be a stream of new scientific knowledge to turn the wheels of private and public enterprise.” To take just one example, it is estimated that the $3.8 billion invested in the Human Genome Project generated nearly $800 billion of economic activity as of 2011. Clearly, we need to renew our commitment to basic research.

Finally, we need to rededicate ourselves to free and fair markets. In the United States, by almost every metric imaginable, whether it is industry concentration, occupational licensing, higher prices, lower wages or whatever else you want to look at capitalism has been weakened by poor regulation and oversight. Not surprisingly, innovation has suffered.

Perhaps most importantly, we need to shift our focus from disrupting markets to creating them, from “The Hacker Way”, to tackling grand challenges and from a reductionist approach to an economy based on dignity and well being. Make no mistake: The “Uber Economy” is not the solution, it’s the problem.

— Article courtesy of the Digital Tonto blog
— Image credits: Pixabay

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Nominations Closed – Top 40 Innovation Bloggers of 2024

Nominations Open for the Top 40 Innovation Bloggers of 2024Human-Centered Change and Innovation loves making innovation insights accessible for the greater good, because we truly believe that the better our organizations get at delivering value to their stakeholders the less waste of natural resources and human resources there will be.

As a result, we are eternally grateful to all of you out there who take the time to create and share great innovation articles, presentations, white papers, and videos with Braden Kelley and the Human-Centered Change and Innovation team. As a small thank you to those of you who follow along, we like to make a list of the Top 40 Innovation Bloggers available each year!

Our lists from the ten previous years have been tremendously popular, including:

Top 40 Innovation Bloggers of 2015
Top 40 Innovation Bloggers of 2016
Top 40 Innovation Bloggers of 2017
Top 40 Innovation Bloggers of 2018
Top 40 Innovation Bloggers of 2019
Top 40 Innovation Bloggers of 2020
Top 40 Innovation Bloggers of 2021
Top 40 Innovation Bloggers of 2022
Top 40 Innovation Bloggers of 2023

Do you just have someone that you like to read that writes about innovation, or some of the important adjacencies – trends, consumer psychology, change, leadership, strategy, behavioral economics, collaboration, or design thinking?

Human-Centered Change and Innovation is now looking for the Top 40 Innovation Bloggers of 2023.

The deadline for submitting nominations is December 24, 2024 at midnight GMT.

You can submit a nomination either of these two ways:

  1. Sending us the name of the blogger and the url of their blog by @reply on twitter to @innovate
  2. Sending the name of the blogger and the url of their blog and your e-mail address using our contact form

(Note: HUGE bonus points for being a contributing author)

So, think about who you like to read and let us know by midnight GMT on December 24, 2024.

We will then compile a voting list of all the nominations, and publish it on December 25, 2024.

Voting will then be open from December 25, 2024 – January 1, 2025 via comments and twitter @replies to @innovate.

The ranking will be done by me with influence from votes and nominations. The quality and quantity of contributions by an author to this web site will be a contributing factor.

Contact me with writing samples if you’d like to publish your articles on our platform!

The official Top 40 Innovation Bloggers of 2024 will then be announced on here in early January 2025.

We’re curious to see who you think is worth reading!

SPECIAL BONUS: From now until December 31, 2024 you can save 30% OFF on my latest best-selling book Charting Change on either the eBook (immediate download) or the hardcover (free shipping worldwide) when using code HOL30.

Support this blog by getting your copy of Charting Change

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Innovation or Not – AI Birdwatching

Innovation or Not - AI Birdwatching

GUEST POST from Art Inteligencia

Welcome to another installment of the “Innovation or Not” series, where we dissect intriguing products and services to determine whether they truly represent ingenuity, or if they’re just another notch on the belt of incremental progress. Today, we’re venturing into the realm of birdwatching — a niche hobby that surprisingly intersects with cutting-edge artificial intelligence and automated insights into our avian neighbors.

Introducing FeatherSnap

The product up for review is the FeatherSnap bird feeder camera. At its core, FeatherSnap is a bird feeder equipped with a camera that not only captures images of our feathery friends but also uses AI to identify species and offer insights to the user. The idea itself blends the tranquility of birdwatching with the technological advancements of AI and machine learning. It also has a smart design to integrate the food storage into the structure itself to save space, and has solar panels to power the onboard technology. But the question remains: is this a pleasant convenience or a groundbreaking innovation?

The Tech Behind FeatherSnap

FeatherSnap integrates a high-quality camera with AI capabilities to recognize and catalog bird species visiting your garden or backyard. It allows the user to receive real-time alerts on their smartphone, providing information about the birds that stop by for a snack. This records data such as the species, time of day, and frequency of visits, creating a rich, personalized avian database over time.

“AI birdwatching may be niche, but it bridges a gap between nature enthusiasts and technology, making the act of observation more engaging and informed.”

Innovation Analysis

When assessing FeatherSnap through the lens of innovation, we explore several key criteria:

  • Originality: AI-augmented birdwatching is a fresh take on a traditional hobby, significantly enhancing the user experience.
  • Technology Application: The application of AI in identifying bird species represents an advancement in both hobbyist technology and AI’s practical capabilities.
  • Value Creation: FeatherSnap adds substantial value to the birdwatching experience by providing educational insights and personalized interaction with nature.
  • Market Impact: While its potential market may seem limited to bird enthusiasts, the push towards automated, intelligent environmental engagement could have broader applications.

Final Verdict: Innovation or Not?

So, is FeatherSnap an innovation or not? Taking all factors into consideration, I would argue that FeatherSnap qualifies as an innovation. Despite its niche market, it presents a clever integration of AI with everyday life that could inspire further applications across different domains. The product encourages a deeper interaction with nature and presents a template for utilizing technology to enrich leisure activities.

In the broader context of our tech-driven world, FeatherSnap’s introduction to the market both exemplifies ingenuity in leisure tech and challenges developers to think creatively about AI’s scope and potential in nature-based contexts.

As we reflect on this product, it reminds us that innovation isn’t always about life-changing inventions but also about elevating the simple joys of life with smart adaptations.

I encourage you to share your thoughts and opinions on FeatherSnap and whether you consider it groundbreaking or just another incremental product in the tech landscape. Until next time, keep questioning and exploring the ever-changing facets of innovation around you.

Image credit: FeatherSnap

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Make the Planet and Your Bottom Line Smile

Make the Planet and Your Bottom Line Smile

GUEST POST from Mike Shipulski

What if the most profitable thing you could do was work that reduced the rise in the earth’s temperature? What if it was most profitable to reduce CO2 emissions, improve water quality or generate renewable energy? Or what if it was most profitable to do work that indirectly made the planet smile?

What if while your competitors greenwashed their products and you radically reduced the environmental impacts of yours? And what if the market would pay more for your greener product? And what if your competitors saw this and disregarded the early warning signs of their demise? This is what I call a compete-with-no-one condition. This is where your competitors eat each other’s ankles in a race to the bottom while you raise prices and sell more on a different line of goodness – environmental goodness. This is where you compete against no one because you’re the only one with products that make the planet smile.

The problem with an environmentally-centric, compete-with-no-one approach is you have to put yourself out there and design and commercialize new products based on this “unproven” goodness. In a world of profits through cost, quality and speed, you’ve got to choose profits through reduced CO2, improved water quality and renewable energy. Why would anyone pay more for a more environmentally responsible product when its price is higher than the ones that work well and pollute just as much as they did last year?

When the Toyota Prius hybrid first arrived on the market, it cost more than traditional cars and its performance was nothing special. Yet it sold. Yes, it had radically improved fuel economy, but the fuel savings didn’t justify the higher price, yet it sold. Competitors advertised that the Prius hybrid didn’t make financial sense, yet it sold. With the Prius hybrid, Toyota took an environmentally-centric, compete-with-no-one approach. They made little on each vehicle or even lost money, but they did it anyway. They did the most important thing. They started.

The Toyota Prius hybrid wasn’t a logical purchase, it was an emotional one. People bought them to make a statement about themselves – I drive a funny-shaped car that gets great gas mileage, I’m environmentally responsible, and I want you to know that. And as other companies scoffed, Toyota created a new category and owned the whole thing.

And, slowly, as Toyota improved the technology and reduced their costs, the price of the Prius dropped and they sold more. And then all the other manufacturers jumped into the race and tried to catch up. And while everyone else cut their teeth on high volume manufacturing a hybrid vehicle, Toyota accelerated.

Below is a chart of hybrid electric vehicles (hev) sold in the US from 2000 to 2017. Each color represents a different model and the Toyota Prius hybrid is represented by the tall blue segment of each year’s stacked bar. In 2000, Toyota sold 5,562 Prius hybrids (60% of all hevs). In 2005, they sold 107,897 Prius hybrids, 17,989 Highlander hybrids and 20,674 Lexus hybrids for a total of 209,711 hybrids (69% of all hevs). In 2007, they sold 181,221 Prius and five other hybrid models for a total of 228,593 (65% of all hevs). In 2017, sold 15 hybrid models and the nearest competitor sold four models. The reduction from 2008 to 2011 is due to reduced gas prices. (Here’s a link to the chart.)

United States Hybrid Electric Vehicle Sales

The success of the Prius vehicle set off the battery wars which set the stage for the plug-in hybrids (larger batteries) and all-electric vehicles (still larger batteries). At the start, the Prius didn’t make sense in a race-to-the-bottom way, but it made sense to people that wanted to make the planet smile. It cost more, and it sold. And that was enough for Toyota to make profits with a more environmentally friendly product. No, Prius didn’t save the planet, but it showed companies that it’s possible to make profits while making the planet smile (a bit). And it made it safe for companies to pursue the next generation of environmentally-friendly vehicles.

The only way to guarantee you won’t make more profits with environmentally responsible products is to believe you won’t. And that may be okay unless one of your companies believes it is possible.

Here’s a thought experiment. Put yourself ten years into the future. There is more CO2 in the atmosphere, the earth is warmer, sea levels are higher, water is more polluted and renewable energy is far cheaper. Are your sales higher if your product creates more CO2, or less? Are your sales higher if your product heats the earth, or cools it? Are your sales higher if your product pollutes water, or makes it cleaner? Are your sales higher because you bet against renewable energy, or because you embraced it? Are your sales higher because you made the planet frown, or smile?

Now, with your new perspective, bring yourself back to the present and do what it takes to increase sales ten years from now. Your future self, your children, their children, and the planet will thank you.

Image credits: Google Gemini

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How to Design Offsites That Generate Revenue

How to Design Offsites That Generate Revenue

GUEST POST from Robyn Bolton

Corporate offsites – the phrase conjures images of everything from “mandatory fun” with colleagues to long and exhausting days debating strategy with peers.  Rarely are the images something that entice people to sit up and shout, “YEA!” But what if the reality could be something YEA! worthy?

That’s exactly what the authors of the recent Harvard Business Review (HBR) article, “Why Offsites Work – and How to Get the Most Out of Them,” describe and offer a guide to accomplish.

Offsites May Be the Answer to the WFH vs. RTO Debate

Offsites aren’t new but they’ve taken on a new role and new significance as companies grapple with how to manage Work from Home (WFH) and Return To Office (RTO) policies. 

As with most things in life, the pendulum swings from one extreme to another until eventually, finally, landing in a stable and neutral midpoint.  When the pandemic hit, we swung from every day in the office to every day at home.  Then society opened back up and corporate landlords came calling for rent, whether or not people were in the offices, so we swung back to Return to Office mandates.

Offsites, the authors suggest, may be the happy medium between the two extremes because offsites:

“give people opportunities for interactions that otherwise might not happen. Offsites create unique opportunities for employees to connect in person, forming new relationships and strengthening existing ones. As a result, offsites help people learn about others’ knowledge and build interpersonal trust, which are both critical ingredients for effective collaboration.”

Offsite Connections Lead to Collaborations that Generate ROI

After analyzing eight years of data from a global firm’s offsites and 350,000 “instances of formal working relationships”  for 750 employees, the authors found that intentionally designed offsites (more on that in a moment) yield surprisingly measurable and lasting results:

  • 24% more incoming requests for collaboration amongst attendees post vs. pre-offsite (silos busted!)
  • 17% of new connections were still active two years after the offsite (lasting change!)
  • $180,000 in net new revenue from collaborations within the first two months post offsite (real results!)

The benefits event extended to non-attendees because they “seemed to get the message that collaboration is important and wanted to demonstrate their commitment to being collaborative team players” and “likely identified new collaborators after the offsite through referrals.”

How to Design Offsites That Get Results

Four key strategies emerged from the authors’ research and work with over 100 other organizations:

  1. Design for the people in the audience, not the people on stage.  Poll attendees to understand their specific needs and goals, then design collaborative activities, not management monologues.
  2. Design for the new hires, not the tenured execs.  Create opportunities for new hires to meet, connect with, and work alongside more experienced colleagues.
  3. Set and communicate clear goals and expectations.  Once the offsite is designed and before it happens, tell people what to expect (the agenda) and why to expect it (your design intentions and goals).  Also, tell them how to make the most of the offsite opportunities by thinking about the skill and network gaps they want to fill.
  4. Track activities to measure ROI.  The connections, collaborations, and commitments that start at the offsite need to continue after it in the form of ongoing communication, greater collaboration, and talent engagement.  Yes, conduct a post-event survey immediately after the event but keep measuring every 2-3 months until the next offsite.  The data will reveal how well you performed against your goals and how to do even better the next time.

Offsites can be a powerful tool to build an organization’s culture and revenue, but only if they are thoughtfully designed to go beyond swanky settings, sermons from the stage, and dust-collecting swag and build the connections and collaborations that only start when people are together, in-person, outside of the office.

Image credit: Unsplash

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Artificial Innovation

Artificial Innovation

by Braden Kelley

Recently several people have asked me whether or not artificial intelligence (AI) has a role to play innovation. One of the ways I’ve answered this question is by speaking about how artificial intelligence can be used to help test/disprove assumptions. Innovation always makes assumptions and often the success or failure of any innovation effort is determined by how well the team identifies the critical assumptions to test, those that if incorrectly assumed to be true could later derail the pursuit of innovation or waste limited innovation investment dollars.

But I thought it could be interesting to use AI to answer this question in more detail, leveraging my Eight I’s of Infinite Innovation framework to highlight how artificial intelligence could be used at each step of the continuous innovation journey.

Below you will find a detailed explanation of the Eight I’s of Infinite Innovation framework along with clearly called out contextual responses generated by Microsoft CoPilot detailing how AI could be used productively during that specific phase of the continuous innovation journey from prompts generated by me after uploading a PDF version of the original Eight I’s of Infinite Innovation article (see the link at the bottom).

Eight I's of Infinite Innovation

Creating a Continuous Innovation Capability

To achieve sustainable success at innovation, you must work to embed a repeatable process and way of thinking within your organization, and this is why it is important to have a simple common language and guiding framework of infinite innovation that all employees can easily grasp. If innovation becomes too complex, or seems too difficult then people will stop pursuing it, or supporting it.

Some organizations try to achieve this simplicity, or to make the pursuit of innovation seem more attainable, by viewing innovation as a project-driven activity. But, a project approach to innovation will prevent it from ever becoming a way of life in your organization. Instead you must work to position innovation as something infinite, a pillar of the organization, something with its own quest for excellence – a professional practice to be committed to.

So, if we take a lot of the best practices of innovation excellence and mix them together with a few new ingredients, the result is a simple framework organizations can use to guide their pursuit of continuous innovation – the Eight I’s of Infinite Innovation. This framework anchors what is a very collaborative process. Here is the framework and some of the many points organizations must consider during each stage of the continuous process:

1. Inspiration

  • Employees are constantly navigating an ever changing world both in their home context, and as they travel the world for business or pleasure, or even across various web pages in the browser of their PC, tablet, or smartphone.
  • What do they see as they move through the world that inspires them and possibly the innovation efforts of the company?
  • What do they see technology making possible soon that wasn’t possible before?
  • The first time through we are looking for inspiration around what to do, the second time through we are looking to be inspired around how to do it.
  • What inspiration do we find in the ideas that are selected for their implementation, illumination and/or installation?

How to leverage artificial innovation during the Inspiration phase (according to AI):

  • AI can help employees find inspiration by analyzing vast amounts of data from various sources, such as social media, news articles, and industry reports. By identifying emerging trends and patterns, AI can provide insights into what is possible and inspire new ideas for innovation. Additionally, AI-powered tools can help employees visualize potential solutions and explore creative possibilities.

2. Investigation

  • What can we learn from the various pieces of inspiration that employees come across?
  • How do the isolated elements of inspiration collect and connect? Or do they?
  • What customer insights are hidden in these pieces of inspiration?
  • What jobs-to-be-done are most underserved and are worth digging deeper on?
  • Which unmet customer needs that we see are worth trying to address?
  • Which are the most promising opportunities, and which might be the most profitable?

How to leverage artificial innovation during the Inspiration phase (according to AI):

  • AI can assist in the investigation phase by processing and analyzing large datasets to uncover hidden insights and customer needs. Machine learning algorithms can identify patterns and correlations that may not be immediately apparent to humans, helping organizations understand which opportunities are most promising and worth pursuing. AI can also automate the process of gathering and organizing information, making it easier for employees to focus on deeper analysis.

3. Ideation

  • We don’t want to just get lots of ideas, we want to get lots of good ideas
  • Insights and inspiration from first two stages increase relevance and depth of the ideas
  • We must give people a way of sharing their ideas in a way that feels safe for them
  • How can we best integrate online and offline ideation methods?
  • How well have we communicated the kinds of innovation we seek?
  • Have we trained our employees in a variety of creativity methods?

How to leverage artificial innovation during the Inspiration phase (according to AI):

  • AI can enhance the ideation process by generating a wide range of ideas based on input from employees and external sources. Natural language processing (NLP) algorithms can analyze and categorize ideas, making it easier to identify the most relevant and promising ones. AI-powered collaboration tools can also facilitate brainstorming sessions, allowing employees to share and build on each other’s ideas in real-time, regardless of their physical location.

4. Iteration

  • No idea emerges fully formed, so we must give people a tool that allows them to contribute ideas in a way that others can build on them and help uncover the potential fatal flaws of ideas so that they can be overcome
  • We must prototype ideas and conduct experiments to validate assumptions and test potential stumbling blocks or unknowns to get learnings that we can use to make the idea and its prototype stronger
  • Are we instrumenting for learning as we conduct each experiment?

How to leverage artificial innovation during the Inspiration phase (according to AI):

  • AI can support the iteration phase by providing tools for rapid prototyping and experimentation. Machine learning models can simulate different scenarios and predict potential outcomes, helping teams identify and address potential flaws in their ideas. AI can also automate the process of collecting and analyzing feedback from experiments, enabling continuous improvement and refinement of prototypes.

Eight I's of Infinite Innovation

5. Identification

  • In what ways do we make it difficult for customers to unlock the potential value from this potentially innovative solution?
  • What are the biggest potential barriers to adoption?
  • What changes do we need to make from a financing, marketing, design, or sales perspective to make it easier for customers to access the value of this new solution?
  • Which ideas are we best positioned to develop and bring to market?
  • What resources do we lack to realize the promise of each idea?
  • Based on all of the experiments, data, and markets, which ideas should we select?

How to leverage artificial innovation during the Inspiration phase (according to AI):

  • AI can help organizations identify the most viable ideas by analyzing data from experiments, market research, and customer feedback. Predictive analytics can assess the potential success of different ideas and prioritize those with the highest likelihood of success. AI can also identify potential barriers to adoption and suggest strategies to overcome them, ensuring that innovative solutions are accessible and valuable to customers.

You’ll see in the framework that things loop back through inspiration again before proceeding to implementation. There are two main reasons why. First, if employees aren’t inspired by the ideas that you’ve selected to commercialize and some of the potential implementation issues you’ve identified, then you either have selected the wrong ideas or you’ve got the wrong employees. Second, at this intersection you might want to loop back through the first five stages though an implementation lens before actually starting to implement your ideas OR you may unlock a lot of inspiration and input from a wider internal audience to bring into the implementation stage.

6. Implementation

  • What are the most effective and efficient ways to make, market, and sell this new solution?
  • How long will it take us to develop the solution?
  • Do we have access to the resources we will need to produce the solution?
  • Are we strong in the channels of distribution that are most suitable for delivering this solution?

How to leverage artificial innovation during the Inspiration phase (according to AI):

  • AI can streamline the implementation process by optimizing production, marketing, and sales strategies. AI-powered project management tools can help teams plan and execute tasks more efficiently, while machine learning algorithms can optimize supply chain and distribution processes. AI can also personalize marketing campaigns and sales approaches, ensuring that new solutions reach the right customers at the right time.

7. Illumination

  • Is the need for the solution obvious to potential customers?
  • Are we launching a new solution into an existing product or service category or are we creating a new category?
  • Does this new solution fit under our existing brand umbrella and represent something that potential customers will trust us to sell to them?
  • How much value translation do we need to do for potential customers to help them understand how this new solution fits into their lives and is a must-have?
  • Do we need to merely explain this potential innovation to customers because it anchors to something that they already understand, or do we need to educate them on the value that it will add to their lives?

How to leverage artificial innovation during the Inspiration phase (according to AI):

  • AI can enhance the illumination phase by helping organizations communicate the value of their innovations to potential customers. NLP algorithms can generate compelling marketing content and product descriptions, while sentiment analysis can gauge customer reactions and adjust messaging accordingly. AI can also identify key influencers and target them with personalized messages to amplify the reach of new solutions.

8. Installation

  • How do we best make this new solution an accepted part of everyday life for a large number of people?
  • How do we remove access barriers to make it easy as possible for people to adopt this new solution, and even tell their friends about it?
  • How do we instrument for learning during the installation process to feedback new customer learnings back into the process for potential updates to the solution?

How to leverage artificial innovation during the Inspiration phase (according to AI):

  • AI can facilitate the installation of new solutions by removing barriers to adoption and ensuring a seamless customer experience. AI-powered customer support tools can provide instant assistance and troubleshooting, while machine learning algorithms can personalize onboarding processes to meet individual customer needs. AI can also monitor usage patterns and gather feedback, enabling continuous improvement and updates to the solution.

Conclusion

The Eight I’s of Infinite Innovation framework is designed to be a continuous learning process, one without end as the outputs of one round become inputs for the next round. It’s also a relatively new guiding framework for organizations to use, so if you have thoughts on how to make it even better, please let me know in the comments. The framework is also ideally suited to power a wave of new organizational transformations that are coming as an increasing number of organizations (including Hallmark) begin to move from a product-centered organizational structure to a customer needs-centered organizational structure. The power of this new approach is that it focuses the organization on delivering the solutions that customers need as their needs continue to change, instead of focusing only on how to make a particular product (or set of products) better.

By leveraging AI at each stage of the innovation process, organizations can enhance their ability to generate, develop, and implement successful innovations.

So, as you move from the project approach that is preventing innovation from ever becoming a way of life in your organization, consider using the Eight I’s of Infinite Innovation to influence your organization’s mindset and to anchor your common language of innovation. The framework is great for guiding conversations, making your innovation outputs that much stronger, and will contribute to your quest for innovation excellence – it is even more powerful when you combine it with my Value Innovation Framework (found here). The two are like chocolate and peanut butter. They’re powerful tools when used separately, but even more powerful when used together.

Click to access this framework as a FREE scalable 11″x17″ PDF download

Click to download the PDF version of this article

People who upgrade to the Bronze Version of the Change Planning Toolkit™ will get access to my Innovation Planning Canvas™ which combines the Value Innovation Framework together with the Eight I’s of Infinite Innovation, allowing you to track the progress of each potential innovation on the three value innovation measures as you evolve any individual idea through this eight step process.

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Don’t ‘Follow the Science’, Follow the Scientific Method

Don't 'Follow the Science', Follow the Scientific Method

GUEST POST from Pete Foley

The scientific method is probably the most useful thing I’ve learnt in my life. It is a near universal tool that can be used in so many ways and so many places.  It unleashes a whole world of assisted critical thinking that is invaluable to innovators, but also in our personal lives.  Teaching it to individuals or teams who are not trained as scientists is one of the most powerful and enabling things we can do for them.  And teaching it to kids, as opposed to endless facts and data that they can easily access from the web is something we should do far more of.  

Recruiting Skills not Expertise:  When I was involved in recruiting, I always valued PhD’s and engineers.  Sometimes that was for their unique, specialized knowledge.  But more often than not it was more for the critical thinking skills they had acquired while gaining that specialized knowledge. In today’s rapidly evolving world, specific knowledge typically has a relatively short shelf-life.  But the cognitive framework embodied by the scientific method is a tool for life, and one that can be reapplied in so many ways.  .

Don’t Follow the Science, Follow the Process:  All too often today the scientific method gets confused with ‘following the science’.  The scientific process is almost infinitely useful, but blindly ‘following the science’ is often far less so, and can be counter productive.  The scientific method is a process that helps us to evaluate information, challenge assumptions, and in so doing, get us closer to truth.  Sometimes it confirms our existing ideas, sometimes it improves them, and sometimes it completely replaces them.  But it is grounded in productive and informed skepticism, and this is at the heart of why science is constantly evolving.

‘Follow the Science’ in many ways the opposite.  It assumes someone in a position of power already has the right answer.  At it’s best it means blindly follow the consensus of today’s experts.  All too often it really means ‘do as you are told’.   Frequently the people saying this are not the experts themselves, but are instead evoking third party expertise to support their viewpoint.  That of course is the opposite of science.  It’s often well intended, but not always good advice.

Science is not a Religion:  At the heart of this is a fundamental misunderstanding of science, and scientists. In today’s media and social media, all too often science and scientists are presented with a quasi-religious reverence, and challenging the current view is framed as heretical.  How often do you here the framing ‘scientists tell us… ‘ as a way of validating a position?   

This is understandable.  The sheer quantity and complexity of information we are faced with in our everyday lives is increasingly unmanageable, while big challenges like climate unimaginably complex.  I find it almost impossible to keep up with my own interests, let alone everything that is happening.  And some topics are so technical that they simply require translation by experts.  When someone announces they’ve discovered the Higgs boson particle, it’s not really practical for any of us to pop over to the local particle accelerator and check for ourselves.  So expertise is clearly an important part of any decision chain. But experts come with their own biases. An engineer naturally tends to see problems and through, an engineering lens, a chemist through a chemical one.

Science in Support of an Agenda:  One danger with the ‘follow the science’ mantra is that it is often used to reinforce a belief, opinion, or even agenda.  I’ve seen this all too often in my work life, with the question, ‘can you find me a paper that supports ‘x’.  This is often benign, in that someone passionately believes something, and wants to find evidence to support it.   But this is fundamentally the wrong question, and of course, completely ‘unscientific’.

The scientific literature is filled with competing theories, disproven or outdated ideas, and bad science.   If you look for literature to support an idea you can usually find it, even if it’s wrong.   Scientists are not gods.  They make mistakes, they run poor experiments, and they are subject to confirmation biases, ego, and other human frailties. There is a good reason for the phrase that science evolves one death at a time. Science, like virtually every human organization is hierarchical, and a prestigious scientist can advance a discipline, but can also slow it down by holding onto a deeply held belief. And mea culpa, I know from personal experience that it’s all too easy to fall in love with a theory, and resist evidence to the contrary. 

Of course, some theories are more robust than others.   Both consensus and longevity are therefore important considerations.  Some science is so well established, and supported by so much observation that it’s unlikely that it will fundamentally change.  For example, we may still have a great deal to learn about gravity, but for practical purposes, apples will still drop from trees.    

Peer Review:  Policing the literature is hard.  Consensus is right until its not. Another phrase I often hear is ‘peer reviewed’, in the context that this makes the paper ‘right’.  Of course, peer review is valuable, part of the scientific process, and helps ensure that content has quality, and has been subject to a high level of rigor.   If one person says it, it can be a breakthrough or utter nonsense.  If a lot of smart people agree, it’s more likely to be ‘right’.  But that is far from guaranteed, especially if they share the same ingoing assumptions. Scientific consensus has historically embraced many poor theories; a flat earth, or the sun revolving around the earth are early examples. More tragically, I grew up with the thalidomide generation in Europe.  On an even bigger scale, the industrial revolution gave us so much, but also precipitated climate change.  And on a personal level, I’ve just been told by my physician to take a statin, and I am in the process of fighting my way through rapidly growing and evolving literature in order to decide if that is the right decision.  So next time you see a scientist, or worse, a politician, journalist, or a random poster on Twitter claim they own scientific truth, enjoin you to ‘follow the science’, or accuse someone else of being a science denier, treat it with a grain of sodium chloride.

They may of course be right, but the more strident they are, or the less qualified, the less likely they are to really understand science, and hence what they are asking you to follow.  And the science they pick is quite possibly influenced by their own goals, biases or experience. Of course, practically we cannot challenge everything. We need to be selective, and the amount of personal effort we put into challenging an idea will depend upon how important it is to us as individuals.      

Owning your Health:  Take physicians as an example.  At some time or other, we’ve all looked to a physician for expert advise.  And there is a good reason to do so.  They work very hard to secure deep knowledge of their chosen field, and the daily practice of medicine gives then a wealth of practical as well as well as theoretical knowledge.  But physicians are not gods either.  The human body is a very complex system, physicians have very little time with an individual patient (over the last 10 years, the average time a physician spends with a patient has shrunk to a little over 15 minutes), the field is vast and expanding, and our theories around how to diagnose and treat disease are constantly evolving.  In that way, medicine is a great example of the scientific method in action, but also how transient ‘scientific truths’ can be.  

I already mentioned my current dilemma with statins.   But to give an even more deeply personal example, neither my wife or I would be alive today if we’d blindly followed a physicians diagnosis.

I had two compounding and comparatively rare conditions that combined to appear like a more common one.  The physician went with the high probability answer.  I took time to dig deeper and incorporate more details.  Together we got to the right answer, and I’m still around!

This is a personal and pragmatic example of how valuable the scientific process can be.  My health is important, so I chose to invest considerable time in the diagnosis I was given, and challenge it productively, instead of blindly accepting an expert opinion. My physicians had far more expertise than I did, but I had far more time and motivation.  We ultimately complemented each other by partnering, and using the scientific method both as a process, and as a way to communicate.   

The Challenge of Science Communication:  To be fair, science communication is hard.   It requires communicating an often complex concept with sufficient simplicity for it to be understandable, often requires giving guidance, while also embracing appropriate uncertainty. Nowhere was this more evident than in the case of Covid 19, where a lot of ‘follow the science’, and ‘science denier’ language came from.  At the beginning of the pandemic, the science was understandably poorly developed, but we still had to make important decisions on often limited data.  At first we simply didn’t understand even the basics like the transmission vectors (was it airborne or surface, how long did it survive outside of the body, etc).  I find it almost surreal to think back to those early months, how little we knew, the now bizarre clean room protocols we used on our weekly shopping, and some of the fear that has now faded into the past.  

But because we understood so little, we made a lot of mistakes.  The over enthusiastic use of ventilators may have killed some patients, although that is still a hotly debated topic. Early in the pandemic masks, later to become a controversial and oddly politically charged topic, masks were specifically not recommended by the US government for the general public. Who knows how many people contracted the disease by following this advice?   It was well intentioned, as authorities were trying to prevent a mask shortage for health workers. But it was also mechanistically completely wrong.

At the time I used simple scientific reasoning, and realized this made little sense.  If the virus was transmitted via an airborne vector, a mask would help.  If it wasn’t, it would do no harm, at least as long as I didn’t subtract from someone with greater need. By that time the government had complete control of the mask supply chain anyway, so that was largely a moot point. Instead I dug out a few old N95 masks that had been used for spray painting and DIY, and used them outside of the house (hospitals would not accept donations of used masks). I was lambasted with ‘follow the science’ by at least one friend for doing so, but followed an approach with high potential reward and virtually zero downside. I’ll never know if that specifically worked, but I didn’t get Covid, at least not until much later when it was far less dangerous.

Science doesn’t own truth: Unlike a religion, good science doesn’t pretend to own ultimate truths.  But unfortunately it can get used that way.  Journalists, politicians, technocrats and others sometimes weaponize (selective) science to support an opinion. Even s few scientists who have become frustrated with ‘science deniers’ can slip into this trap.

Science is a Journey: I should clarify that the scientific method is more of a journey, not so much a single process. To suggest is is a single ‘thing’ so is probably an unscientific simplification in its own right. It’s more a way of thinking that embraces empiricism, observation, description, productive skepticism, and the use of experimentation to test and challenge hypothesis. It also helps us to collaborate and communicate with experts in different areas, creating a common framework for collaboration, rather than blindly following directions or other expert opinions.    

It can be taught, and is incredibly useful.  But like any tool, it requires time and effort to become a skilled user.   But if we invest in it, it can be extraordinarily valuable, both in innovation and life. It’s perhaps not for every situation, as that would mire us in unmanageable procrastination.  But if something is important, it’s an invaluable tool. 

Image credits: Pixabay

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Dynamic Thinking

Dynamic Thinking

GUEST POST from Mike Shipulski

If you’re asked to do cost reduction, before doing that work, ask for objective evidence that the work to grow the top line is adequately staffed. You can’t secure your company’s future through cost reduction, so before you spend time and effort to grow the bottom line, make sure the work to grow the top line is more than fully staffed. Without top-line growth, cost reduction is nothing more than a race to the bottom.

If you’re asked to do more of what was done last time, before doing that work, look back and plot how that line of goodness has improved over time. If the goodness over time is flat (it hasn’t increased), the technology is mature, there’s nothing left, and you should improve something else (a new line of goodness). If the goodness continues to increase over time, ask customers if it’s already good enough. Do this by asking if they’d pay more for more goodness. If they won’t pay more, it’s already good enough. Stop work on that tired, old line of goodness and work on a new one. If goodness over time is still increasing and customers will pay more, teach someone else how to improve that line of goodness so you can establish the next line of goodness which will be needed when the old one gets tired.

If you’re asked to make your product do more, before doing that work, figure out if the planet will be better off if your product does more. If the planet will frown if your product does more, make your product do less with far less. In that way, your customers will get a bit less, but they’ll use far fewer resources and the planet will smile. And when the planet smiles, so will the stockholders of the company that provides less with far less.

If you’re asked to improve a specific line of goodness, before doing that work, look to see if competitive technologies are also improving on that same line of goodness. If their improvement slope is steeper than yours, you will be overtaken. Find a new line of goodness to improve, or buy the dominant company in that’s making progress with the competitive technology. Don’t wait, or sooner rather than later, they’ll buy you.

If you’re asked to make your product do more, before doing that work, look at the byproducts that will increase and how that relates to the regulatory standards. If those nasty byproducts are (or will be) regulated, future improvements will be blocked by regulatory limits. You can argue about when those limits will be a problem, but you can’t argue that those regulatory limits will ultimately take you out by the knees. It’s a tough pill to swallow, but it’s time to look to a new technology because your existing one will soon be outlawed.

Everything changes. Nothing is static. Technologies get better, then it’s difficult to make the next improvement. Competitors get better, then it’s difficult to be better than them. Environmental constraints get tighter, then you’re legally blocked from improvements that violate those constraints. Last year’s solutions become obsolete. Last year’s analysis tools become obsolete. Last year’s best materials are no longer best. And last year’s manufacturing best processes are no longer best. That’s just how it works.

Before you allocate precious resources to do what you did last time, spend a little time to analyze the situation in a dynamic sense. What changed since last time? Has the regulatory environment changed? Have competitors made improvements? Have new competitors emerged with new technologies? Has your legacy technology run out of gas or does it still have legs? Have new tools come of age and who is using them?

Everything has a half-life – technologies, products, services, tools, processes, business models, and people. When new things are come to be, the only thing you can guarantee is that time will run out and they will run their course. Even if your business model has been successful, it has a half-life and it will die.

Success causes us to think statically, but the universe behaves dynamically. The trick is to use the resources created by our success to sow the seeds that must grow into the solutions of an uncertain future. The best time to plant a tree was fifteen years ago, and the next best time to plant one is today.

Image credit: Pexels

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Top 10 Human-Centered Change & Innovation Articles of November 2024

Top 10 Human-Centered Change & Innovation Articles of November 2024Drum 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 November’s ten most popular innovation posts:

  1. A Shared Language for Radical Change — by Greg Satell
  2. Leadership Best Quacktices from Oregon’s Dan Lanning — by Braden Kelley
  3. Navigating Uncertainty Requires a Map — by John Bessant
  4. The Most Successful Innovation Approach is … — by Howard Tiersky
  5. Don’t Listen to These Three Change Consultant Recommendations — by Greg Satell
  6. What We Can Learn from MrBeast’s Onboarding — by Robyn Bolton
  7. Does Diversity Increase Team Performance? — by David Burkus
  8. Customer Experience Audit 101 — by Braden Kelley and Art Inteligencia
  9. Daily Practices of Great Managers — by David Burkus
  10. An Innovation Leadership Fable – Wisdom from the Waters — by Robyn Bolton

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

SPECIAL BONUS: While supplies last, you can get the hardcover version of my first bestselling book Stoking Your Innovation Bonfire for 51% OFF until Amazon runs out of stock or changes the price. This deal won’t last long, so grab your copy while it lasts!

Build a Common Language of Innovation on your team

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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 four years:

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You Are Doing Strategic Planning Wrong

(According to Seth Godin)

You Are Doing Strategic Planning Wrong

GUEST POST from Robyn Bolton

It’s that time of year again – the annual ritual of strategic planning. But as Seth Godin points out in “How to Avoid Strategy Myopia,” we often mistake annual budgets and operational efficiency plans for true strategy. Strategies are not plans or guarantees; they’re informed choices to pursue possibilities that may or may not work.

Godin’s insights, while often associated with innovation, are fundamentally about strategy in its purest form. They challenge us to look beyond next quarter’s earnings and focus on transformative potential just beyond our current vision.

The Myth of “Strategic Planning”

Consider for a moment the last strategic planning session you attended. Was it dominated by discussions of cost-cutting measures, market share percentages, and incremental improvements? If so, you’re not alone. Many organizations focus on optimizing their current operations, behavior that is reinforced by the processes, templates, and forms required to secure next year’s funding.

However, as Godin warns, “When the boss demands a strategy that comes with certainty and proof, we’re likely to settle for a collection of chores, tasks, and tactics, which is not the same as an elegant, resilient strategy. To do strategy right, we need to lean into possibility.”

The Realities We Must Confront

Godin challenges us to confront several uncomfortable truths:

Today’s data doesn’t predict tomorrow: Executives rely heavily on easily measurable metrics based on false proxies when they make decisions. While these metrics provide a sense of control and comfort, they close our eyes to emerging opportunities and threats.  When AT&T’s executives considered exiting the cell phone market in the 1980s, they turned to McKinsey to find data to inform their decision.  Estimating that the total worldwide market for cell phones was 900,000, AT&T executives were comfortable exiting.   It’s unknown if that comfort was worth the $11.5 billion AT&T spent to acquire McCaw Cellular in 1995.

Serving everyone serves no one: “Strategy myopia occurs when we fail to identify who we seek to serve and focus on what we seek to produce instead.”  AMEN!  True strategy begins with a deep understanding of our customers’ evolving needs, not just their current preferences. This requires empathy, foresight, and a willingness to challenge our assumptions.  It also requires us to listen and act on what we hear from customers and not just from our bosses.

“All of the Above” is not an option: Strategy requires that we make choices and is as much about what we choose not to do as what we commit to doing. It requires the courage to say no to good opportunities in service of great ones.  It requires facing your FOMO (Fear of Missing Out), loss aversion bias, and finding the courage to keep going.

5 Practical Steps You Can Take

If any of these sound familiar, it’s because they’re also innovation best practices. 

  1. Dedicate One Day per Month for Strategic Thinking: Set aside one full day each month for long-term strategic questions, free from the “Tyranny of Now.”
  2. Cultivate Diverse Perspectives: Invite and listen to voices from different backgrounds, disciplines, and levels within the organization.
  3. Embrace Small-Scale Experimentation: Run a series of small, low-cost, low-profile experiments instead of betting everything on a single initiative.
  4. Redefine Success Metrics: Move beyond traditional financial metrics to include indicators of future potential, such as customer lifetime value and adaptability to change.
  5. Foster a Culture of Questioning: Channel your inner two-year-old and ask “why” with genuine curiosity. Encourage your team to challenge assumptions because the most transformative strategies often emerge from questioning the status quo.

As we continue through this season of strategic planning, let’s challenge ourselves to think beyond the annual budget. Let’s envision the future we want to create and chart a course to get there. After all, in the words of Godin himself, “It doesn’t matter how fast you’re going if you’re headed in the wrong direction.”

Image credit: Pexels

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