Tag Archives: Psychology

Let Yourself Draw Inspiration from Others

Let Yourself Draw Inspiration from Others

GUEST POST from Mike Shipulski

When you try something new, check to see who has done something similar. Decompose their design approach. What were they trying to achieve? What outcome were they looking for? Who were their target customers? Do this for at least three existing designs – three real examples that are for sale today.

Here’s a rule to live by: When trying something new, don’t start from scratch.

What you are trying to achieve is unique, but has some commonality with existing solutions. The outcome you are looking for is unique, but it’s similar to outcomes others have tried to achieve. Your target customers are unique, but some of their characteristics are similar to the customers of the solutions you’ll decompose.

Here’s another rule: There are no “clean sheet” sheet designs, so don’t try to make one.

There was an old game show called Name That Tune, where contestants would try to guess the name of a song by hearing just a few notes. The player wins when they can name the tune with the *fewest* notes. And it’s the same with new designs – you want to provide a novel customer experience using the fewest new notes.

A rule: Reuse what you can, until you can’t.

Because the customer is the one who decides if your new offering offers them new value, the novel elements of your design don’t have to look drastically different in a side-by-side comparison way. But the novel elements of your offering do have to make a significant difference in the customer’s life. With that said, however, it can be helpful if the design element responsible for the novel goodness is visually different from the existing alternatives. But if that’s not the case, you can add a non-functional element to the novelty-generating element to make it visible to the customer. For example, you could add color, or some type of fingerprint, to the novel element of the design so that customers can see what creates the novelty for them. Then, of course, you market the heck out of the new color or fingerprint.

A rule: It’s better to make a difference in a customer’s life than, well, anything else.

Don’t be shy about learning from what other companies have done well. That’s not to say you should violate their patents, but it’s a compliment when you adopt some of their best stuff. Learn from them and twist it. Understand what they did and abstract it. See the best in two designs and combine them. See the goodness in one domain and bring it to another.

Doing something for the first time is difficult, why not get inspiration from others and make it easier?

Image credit: Unsplash

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When Innovation Becomes Magic

When Innovation Becomes Magic

GUEST POST from Pete Foley

Arthur C Clarke’s 3rd Law famously stated:

“Any sufficiently advanced technology is indistinguishable from magic”

In other words, if the technology of an advanced civilization is so far beyond comprehension, it appears magical to a less advanced one. This could take the form of a human encounter with a highly advanced extraterrestrial civilization, how current technology might be viewed by historical figures, or encounters between human cultures with different levels of scientific and technological knowledge.

Clarke’s law implicitly assumed that knowledge within a society is sufficiently democratized that we never view technology within a civilization as ‘magic’.  But a combination of specialization, rapid advancements in technology, and a highly stratified society means this is changing.  Generative AI, Blockchain and various forms of automation are all ‘everyday magic’ that we increasingly use, but mostly with little more than an illusion of understanding around how they work.  More technological leaps are on the horizon, and as innovation accelerates exponentially, we are all going to have to navigate a world that looks and feels increasingly magical.   Knowing how to do this effectively is going to become an increasingly important skill for us all.  

The Magic Behind the Curtain:  So what’s the problem? Why do we need to understand the ‘magic’ behind the curtain, as long as we can operate the interface, and reap the benefits?  After all, most of us use phones, computers, cars, or take medicines without really understanding how they work.  We rely on experts to guide us, and use interfaces that help us navigate complex technology without a need for deep understanding of what goes on behind the curtain.

It’s a nuanced question.  Take a car as an analogy.  We certainly don’t need to know how to build one in order to use one.  But we do need to know how to operate it and understand what it’s performance limitations are.  It also helps to have at least some basic knowledge of how it works; enough to change a tire on a remote road, or to have some concept of basic mechanics to minimize the potential of being ripped off by a rogue mechanic.  In a nutshell, the more we understand it, the more efficiently, safely and economically we leverage it.  It’s a similar situation with medicine.  It is certainly possible to defer all of our healthcare decisions to a physician.  But people who partner with their doctors, and become advocates for their own health generally have superior outcomes, are less likely to die from unintended contraindications, and typically pay less for healthcare.  And this is not trivial.  The third leading cause of death in Europe behind cancer and heart disease are issues associated with prescription medications.  We don’t need to know everything to use a tool, but in most cases, the more we know the better

The Speed/Knowledge Trade-Off:  With new, increasingly complex technologies coming at us in waves, it’s becoming increasing challenging to make sense of what’s ‘behind the curtain’. This has the potential for costly mistakes.  But delaying embracing technology until we fully understand it can come with serious opportunity costs.  Adopt too early, and we risk getting it wrong, too late and we ‘miss the bus’.  How many people who invested in crypto currency or NFT’s really understood what they were doing?  And how many of those have lost on those deals, often to the benefit of those with deeper knowledge?  That isn’t to in anyway suggest that those who are knowledgeable in those fields deliberately exploit those who aren’t, but markets tend to reward those who know, and punish those who don’t.    

The AI Oracle:  The recent rise of Generative AI has many people treating it essentially as an oracle.  We ask it a question, and it ‘magically’ spits out an answer in a very convincing and sharable format.  Few of us understand the basics of how it does this, let alone the details or limitations. We may not call it magic, but we often treat it as such.  We really have little choice; as we lack sufficient understanding to apply quality critical thinking to what we are told, so have to take answers on trust.  That would be brilliant if AI was foolproof.  But while it is certainly right a lot of the time, it does make mistakes, often quite embarrassing ones. . For example, Google’s BARD incorrectly claimed the James Webb Space Telescope had taken the first photo of a planet outside our solar system, which led to panic selling of parent company Alphabet’s stock.  Generative AI is a superb innovation, but its current iterations are far from perfect.  They are limited by the data bases they are fed on, are extremely poor at spotting their own mistakes, can be manipulated by the choice of data sets they are trained on, and they lack the underlying framework of understanding that is essential for critical thinking or for making analogical connections.  I’m sure that we’ll eventually solve these issues, either with iterations of current tech, or via integration of new technology platforms.  But until we do, we have a brilliant, but still flawed tool.  It’s mostly right, is perfect for quickly answering a lot of questions, but its biggest vulnerability is that most users have pretty limited capability to understand when it’s wrong.

Technology Blind Spots: That of course is the Achilles Heel, or blind spot and a dilemma. If an answer is wrong, and we act on it without realizing, it’s potentially trouble. But if we know the answer, we didn’t really need to ask the AI. Of course, it’s more nuanced than that.  Just getting the right answer is not always enough, as the causal understanding that we pick up by solving a problem ourselves can also be important.  It helps us to spot obvious errors, but also helps to generate memory, experience, problem solving skills, buy-in, and belief in an idea.  Procedural and associative memory is encoded differently to answers, and mechanistic understanding helps us to reapply insights and make analogies. 

Need for Causal Understanding.  Belief and buy-in can be particularly important. Different people respond to a lack of ‘internal’ understanding in different ways.  Some shy away from the unknown and avoid or oppose what they don’t understand. Others embrace it, and trust the experts.  There’s really no right or wrong in this.  Science is a mixture of both approaches it stands on the shoulders of giants, but advances based on challenging existing theories.  Good scientists are both data driven and skeptical.  But in some cases skepticism based on lack of causal understanding can be a huge barrier to adoption. It has contributed to many of the debates we see today around technology adoption, including genetically engineered foods, efficacy of certain pharmaceuticals, environmental contaminants, nutrition, vaccinations, and during Covid, RNA vaccines and even masks.  Even extremely smart people can make poor decisions because of a lack of causal understanding.  In 2003, Steve Jobs was advised by his physicians to undergo immediately surgery for a rare form of pancreatic cancer.  Instead he delayed the procedure for nine months and attempted to treat himself with alternative medicine, a decision that very likely cut his life tragically short.

What Should We Do?  We need to embrace new tools and opportunities, but we need to do so with our eyes open.   Loss aversion, and the fear of losing out is a very powerful motivator of human behavior, and so an important driver in the adoption of new technology.  But it can be costly. A lot of people lost out with crypto and NFT’s because they had a fairly concrete idea of what they could miss out on if they didn’t engage, but a much less defined idea of the risk, because they didn’t deeply understand the system. Ironically, in this case, our loss aversion bias caused a significant number of people to lose out!

Similarly with AI, a lot of people are embracing it enthusiastically, in part because they are afraid of being left behind.  That is probably right, but it’s important to balance this enthusiasm with an understanding of its potential limitations.  We may not need to know how to build a car, but it really helps to know how to steer and when to apply the brakes .   Knowing how to ask an AI questions, and when to double check answers are both going to be critical skills.  For big decisions, ‘second opinions’ are going to become extremely important.   And the human ability to interpret answers through a filter of nuance, critical thinking, different perspectives, analogy and appropriate skepticism is going to be a critical element in fully leveraging AI technology, at least for now. 

Today AI is still a tool, not an oracle. It augments our intelligence, but for complex, important or nuanced decisions or information retrieval, I’d be wary of sitting back and letting it replace us.  Its ability to process data in quantity is certainly superior to any human, but we still need humans to interpret, challenge and integrate information.  The winners of this iteration of AI technology will be those who become highly skilled at walking that line, and who are good at managing the trade off between speed and accuracy using AI as a tool.  The good news is that we are naturally good at this, it’s a critical function of the human brain, embodied in the way it balances Kahneman’s System 1 and System 2 thinking. Future iterations may not need us, but for now AI is a powerful partner and tool, but not a replacement

Image credit: Pixabay

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Top 10 Human-Centered Change & Innovation Articles of March 2023

Top 10 Human-Centered Change & Innovation Articles of March 2023Drum 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 March’s ten most popular innovation posts:

  1. Taking Care of Yourself is Not Impossible — by Mike Shipulski
  2. Rise of the Prompt Engineer — by Art Inteligencia
  3. A Guide to Effective Brainstorming — by Diana Porumboiu
  4. What Disruptive Innovation Really Is — by Geoffrey A. Moore
  5. The 6 Building Blocks of Great Teams — by David Burkus
  6. Take Charge of Your Mind to Reclaim Your Potential — by Janet Sernack
  7. Ten Reasons You Must Deliver Amazing Customer Experiences — by Shep Hyken
  8. Deciding You Have Enough Opens Up New Frontiers — by Mike Shipulski
  9. The AI Apocalypse is Here – 3 Reasons You Should Celebrate! — by Robyn Bolton
  10. Artificial Intelligence is Forcing Us to Answer Some Very Human Questions — by Greg Satell

BONUS – Here are five more strong articles published in February 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 three years:

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Top 10 Human-Centered Change & Innovation Articles of January 2023

Top 10 Human-Centered Change & Innovation Articles of January 2023Drum 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 January’s ten most popular innovation posts:

  1. Top 40 Innovation Bloggers of 2022 — Curated by Braden Kelley
  2. Back to Basics: The Innovation Alphabet — by Robyn Bolton
  3. 99.7% of Innovation Processes Miss These 3 Essential Steps — by Robyn Bolton
  4. Top 100 Innovation and Transformation Articles of 2022 — Curated by Braden Kelley
  5. Ten Ways to Make Time for Innovation — by Nick Jain
  6. Agility is the 2023 Success Factor — by Soren Kaplan
  7. Five Questions All Leaders Should Always Be Asking — by David Burkus
  8. 23 Ways in 2023 to Create Amazing Experiences — by Shep Hyken
  9. Startups Must Be Where Their Customers Are — by Steve Blank
  10. Will CHATgpt make us more or less innovative? — by Pete Foley

BONUS – Here are five more strong articles published in December 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 three years:

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






Will CHATgpt make us more or less innovative?

Will CHATgpt make us more or less innovative?

GUEST POST from Pete Foley

The rapid emergence of increasingly sophisticated ‘AI ‘ programs such as CHATgpt will profoundly impact our world in many ways. That will inevitably include Innovation, especially the front end. But will it ultimately help or hurt us? Better access to information should be a huge benefit, and my intuition was to dive in and take full advantage. I still think it has enormous upside, but I also think it needs to be treated with care. At this point at least, it’s still a tool, not an oracle. It’s an excellent source for tapping existing information, but it’s (not yet) a source of new ideas. As with any tool, those who understand deeply how it works, its benefits and its limitations, will get the most from it. And those who use it wrongly could end up doing more harm than good. So below I’ve mapped out a few pros and cons that I see. It’s new, and like everybody else, I’m on a learning curve, so would welcome any and all thoughts on these pros and cons:

What is Innovation?

First a bit of a sidebar. To understand how to use a tool, I at least need to have a reasonably clear of what goals I want it to help me achieve. Obviously ‘what is innovation’ is a somewhat debatable topic, but my working model is that the front end of innovation typically involves taking existing knowledge or technology, and combining it in new, useful ways, or in new contexts, to create something that is new, useful and ideally understandable and accessible. This requires deep knowledge, curiosity and the ability to reframe problems to find new uses of existing assets. A recent illustrative example is Oculus Rift, an innovation that helped to make virtual reality accessible by combining fairly mundane components including a mobile phone screen and a tracking sensor and ski glasses into something new. But innovation comes in many forms, and can also involve serendipity and keen observation, as in Alexander Fleming’s original discovery of penicillin. But even this requires deep domain knowledge to spot the opportunity and reframing undesirable mold into a (very) useful pharmaceutical. So, my start-point is which parts of this can CHATgpt help with?

Another sidebar is that innovation is of course far more than simply discovery or a Eureka moment. Turning an idea into a viable product or service usually requires considerable work, with the development of penicillin being a case in point. I’ve no doubt that CHATgpt and its inevitable ‘progeny’ will be of considerable help in that part of the process too.   But for starters I’ve focused on what it brings to the discovery phase, and the generation of big, game changing ideas.

First the Pros:

1. Staying Current: We all have to strike a balance between keeping up with developments in our own fields, and trying to come up with new ideas. The sheer volume of new information, especially in developing fields, means that keeping pace with even our own area of expertise has become challenging. But spend too much time just keeping up, and we become followers, not innovators, so we have to carve out time to also stretch existing knowledge. But if we don’t get the balance right, and fail to stay current, we risk get leapfrogged by those who more diligently track the latest discoveries. Simultaneous invention has been pervasive at least since the development of calculus, as one discovery often signposts and lays the path for the next. So fail to stay on top of our field, and we potentially miss a relatively easy step to the next big idea. CHATgpt can become an extremely efficient tool for tracking advances without getting buried in them.

2. Pushing Outside of our Comfort Zone: Breakthrough innovation almost by definition requires us to step beyond the boundaries of our existing knowledge. Whether we are Dyson stealing filtration technology from a sawmill for his unique ‘filterless’ vacuum cleaner, physicians combining stem cell innovation with tech to create rejection resistant artificial organs, or the Oculus tech mentioned above, innovation almost always requires tapping resources from outside of the established field. If we don’t do this, then we not only tend towards incremental ideas, but also tend to stay in lock step with other experts in our field. This becomes increasingly the case as an area matures, low hanging fruit is exhausted, and domain knowledge becomes somewhat commoditized. CHATgpt simply allows us to explore beyond our field far more efficiently than we’ve ever been able to before. And as it or related tech evolves, it will inevitably enable ever more sophisticated search. From my experience it already enables some degree of analogous search if you are thoughtful about how to frame questions, thus allowing us to more effectively expand searches for existing solutions to problems that lie beyond the obvious. That is potentially really exciting.

Some Possible Cons:

1. Going Down the Rabbit Hole: CHATgpt is crack cocaine for the curious. Mea culpa, this has probably been the most time consuming blog I’ve ever written. Answers inevitably lead to more questions, and it’s almost impossible to resist playing well beyond the specific goals I initially have. It’s fascinating, it’s fun, you learn a lot of stuff you didn’t know, but I at least struggle with discipline and focus when using it. Hopefully that will wear off, and I will find a balance that uses it efficiently.

2. The Illusion of Understanding: This is a bit more subtle, but a topic inevitably enhances our understanding of it. The act of asking questions is as much a part of learning as reading answers, and often requires deep mechanistic understanding. CHATgpa helps us probe faster, and its explanations may help us to understand concepts more quickly. But it also risks the illusion of understanding. When the heavy loading of searching is shifted away from us, we get quick answers, but may also miss out on the deeper mechanistic understanding we’d have gleaned if we’d been forced to work a bit harder. And that deeper understanding can be critical when we are trying to integrate superficially different domains as part of the innovation process. For example, knowing that we can use a patient’s stem cells to minimize rejection of an artificial organ is quite different from understanding how the immune system differentiates between its own and other stem cells. The risk is that sophisticated search engines will do more heavy lifting, allow us to move faster, but also result in a more superficial understanding, which reduces our ability to spot roadblocks early, or solve problems as we move to the back end of innovation, and reduce an idea to practice.

3. Eureka Moment: That’s the ‘conscious’ watch out, but there is also an unconscious one. It’s no secret that quite often our biggest ideas come when we are not actually trying. Archimedes had his Eureka moment in the bath, and many of my better ideas come when I least expect them, perhaps in the shower, when I first wake up, or am out having dinner. The neuroscience of creativity helps explain this, in that the restructuring of problems that leads to new insight and the integration of ideas works mostly unconsciously, and when we are not consciously focused on a problem. It’s analogous to the ‘tip of the tongue’ effect, where the harder we try to remember something, the harder it gets, but then comes to us later when we are not trying. But the key for the Eureka moment is that we need sufficiently deep knowledge for those integrations to occur. If CHATgpt increases the illusion of understanding, we could see less of those Eureka moments, and the ‘obvious in hindsight ideas’ they create.

Conclusion

I think that ultimately innovation will be accelerated by CHATgpt and what follows, perhaps quite dramatically. But I also think that we as innovators need to try and peel back the layers and understand as much as we can about these tools, as there is potential for us to trip up. We need to constantly reinvent the way we interact with them, leverage them as sophisticated innovation tools, but avoid them becoming oracles. We also need to ensure that we, and future generations use them to extend our thinking skill set, but not become a proxy for it. The calculator has in some ways made us all mathematical geniuses, but in other ways has reduced large swathes of the population’s ability to do basic math. We need to be careful that CHATgpt doesn’t do the same for our need for cognition, and deep mechanistic and/or critical thinking.

Image credit: Pixabay

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Get Social with Your Innovation

Get Social with Your InnovationIf your organization is struggling to sustain its innovation efforts, then I hope you will do the following things.

  • Find the purpose and passion that everyone can rally around.
  • Create the flexibility necessary to deal with the constant change that a focus on innovation requires for both customers and the organization.
  • Make innovation the social activity it truly must be for you to become successful.

If your organization has lost the courage to move innovation to its center and has gotten stuck in a project – focused, reactive innovation approach, then now is your chance to regain the higher ground and to refocus, not on having an innovation success but on building an innovation capability. Are you up to the challenge?

There is a great article “ Passion versus Obsession ” by John Hagel that explores the differences between passion and obsession. This is an important distinction to understand in order to make sure you are hiring people to power your innovation efforts who are passionate and not obsessive. Here are a few key quotes from the article:

“The first significant difference between passion and obsession is the role free will plays in each disposition: passionate people fight their way willingly to the edge to find places where they can pursue their passions more freely, while obsessive people (at best) passively drift there or (at worst) are exiled there.”

“It’s not an accident that we speak of an “object of obsession,” but the “subject of passion.” That’s because obsession tends towards highly specific focal points or goals, whereas passion is oriented toward networked, diversified spaces.”

More quotes from the John Hagel article:

“The subjects of passion invite and even demand connections with others who share the passion.”

“Because passionate people are driven to create as a way to grow and achieve their potential, they are constantly seeking out others who share their passion in a quest for collaboration, friction and inspiration . . . . The key difference between passion and obsession is fundamentally social: passion helps build relationships and obsession inhibits them.”

“It has been a long journey and it is far from over, but it has taught me that obsession confines while passion liberates.”

These quotes from John Hagel’s article are important because they reinforce the notion that innovation is a social activity. While many people give Thomas Edison, Alexander Graham Bell, and the modern-day equivalent, Dean Kamen, credit for being lone inventors, the fact is that the lone inventor myth is just that — a myth, one which caused me to create The Nine Innovation Roles.

The fact is that all of these gentlemen had labs full of people who shared their passion for creative pursuits. Innovation requires collaboration, either publicly or privately, and is realized as an outcome of three social activities.

1. Social Inputs

From the very beginning when an organization is seeking to identify key insights to base an innovation strategy or project on, organizations often use ethnographic research, focus groups, or other very social methods to get at the insights. Great innovators also make connections to other industries and other disciplines to help create the great in sights that inspire great solutions.

2. Social Evolution

We usually have innovation teams in organizations, not sole inventors, and so the activity of transforming the seeds of useful invention into a solution valued above every existing alternative is very social. It takes a village of passionate villagers to transform an idea into an innovation in the marketplace. Great innovators make connections inside the organization to the people who can ask the right questions, uncover the most important weaknesses, help solve the most difficult challenges, and help break down internal barriers within the organization — all in support of creating a better solution.

3. Social Execution

The same customer group that you may have spent time with, seeking to understand, now requires education to show them that they really need the solution that all of their actions and behaviors indicated they needed at the beginning of the process. This social execution includes social outputs like trials, beta programs, trade show booths, and more. Great innovators have the patience to allow a new market space to mature, and they know how to grow the demand while also identifying the key shortcomings with customers who are holding the solution back from mass acceptance.

Conclusion

When it comes to insights, these three activities are not completely discrete. Insights do not expose themselves only in the social inputs phase, but can also expose themselves in other phases — if you’re paying attention.

Flickr famously started out as a company producing a video game in the social inputs phase, but was astute enough during the social execution phase to recognize that the most used feature was one that allowed people to share photos. Recognizing that there was an unmet market need amongst customers for easy sharing of photos, Flickr reoriented its market solution from video game to photo sharing site and reaped millions of dollars in the process when they ultimately sold their site to Yahoo!.

Ultimately, action is more important than intent, and so as an innovator you must always be listening and watching to see what people do and not just what they say. Build your solution on the wrong insight and nobody will be beating a path to your door.

NOTE: This article is an adaptation of some of the great content in my five-star book Stoking Your Innovation Bonfire (available in many local libraries and fine booksellers everywhere).

Build a Common Language of Innovation

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The Eight Change Mindsets

“While there is risk to change, just like with innovation, there is often potentially more risk associated with doing nothing.” – Braden Kelley

The Eight Change MindsetsIf your organization is seeking to create a continuous change capability, it must have a strong focus on increasing its organizational agility.

As you use the Change Planning Toolkit™ to kick off your next project or your next change initiative, keep thinking about what the minimum viable progress (MVP) might be in order to maintain momentum. This is very similar to the idea of a minimum viable product, a key lean startup concept popularized by Eric Ries, author of the bestselling book, The Lean Startup.

Minimum viable progress means that for change initiatives and projects to be successful, it is mandatory to have a successful planning session where strong buy-in is achieved at the start. It is equally important at all stages of the process to show a level of progress sufficient to maintain the momentum and support for the project or change initiative you worked so hard to achieve at the start.

This is where the agile principles highlighted later in this article come into play. The goal of our change or project planning efforts should be not just to prototype what the change might look like, but to also build a plan that breaks up the work into a cadence the organization can cope with and successfully implement into a new standard operating procedure. Many thought leaders extol the virtues of quick wins, but I believe structuring your project or change effort into a series of similarly sized sprints will give you a sustainable flow of wins (and thus momentum) throughout all of the transitions that will lead to success. In the end, momentum wins.

Quick Wins versus Momentum

One of the ways to create sustainable momentum is to take an agile approach to change and to segment your overall change effort into a series of work packages that you can properly staff, execute, and celebrate. Many projects and change efforts get off to a roaring start, achieve a few quick wins, but stall when longer, more substantial pieces of the work must be completed, often with only limited communication and little visible progress.

The change initiative then begins to lose the support of key stakeholders (and potentially resources) as members of the change leadership team begin to lose enthusiasm, break solidarity, and withdraw support. This dooms the effort, preventing it from ever being completed as intended.

Momentum beats quick wins, and engaging in a more visual, collaborative, agile change planning method like the one described in my book Charting Change will lead you to more successful change efforts because these methods can help you maintain momentum. The Agile Change Management Kanban is a useful tool that toolkit buyers can leverage to visualize and track change effort progress.

Building and Maintaining Momentum

There are many different reasons why people will do the right thing to help you build and maintain the momentum for your change initiative and to help you achieve sustained, collective momentum. The key to building and maintaining momentum is to understand and harness the different mindsets that cause people to choose change; these include:

1. Mover ’n’ Shaker

  • give these people the chance to be first

2. Thrill Seeker

  • these people like to try new things and experiment

3. Mission-Driven

  • these people need reasons to believe

4. Action-Oriented

  • these people just want to know what needs to be done

5. Expert-Minded

  • teach these people how to do it, and they will seek mastery

6. Reward-Hungry

  • these people want recognition for adopting the change

7. Team Player

  • these people are happy to help if you show them why the change will be helpful

8. Teacher

  • show these people how to get others to choose change

Change leaders and project managers should read through this list and imagine what might happen if you don’t address any of these mindsets in your change plan. In doing so, you might find yourself quickly identifying eight potential explanations for why people may be resisting your change effort. If any of these mindsets are playing out in the negative, then you must try and identify ways to turn these individuals back toward the positive as you work through the different phases of change.

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Bringing More Elements of Agile to Change

As you begin to move from the widespread chaos-driven change management model (“we do it differently every time”) to using the concepts presented in my book Charting Change and reinforced through the use of the Change Planning Toolkit™ to spread the knowledge of how to use the collaborative, visual change planning process, you will crave a more coordinated approach to change readiness evaluation. Instead of looking at change readiness on a case-by-case basis for each individual project or change initiative, you will quickly find yourself considering the use of a more agile approach to managing change readiness. You may begin asking yourself these ten (10) questions:

  1. Is it possible to have a change backlog?
  2. Do we need a burndown chart to measure how quickly we are burning through our backlog?
  3. Is it necessary to begin prioritizing the change backlog in order to phase in change into different parts of the organization at a pace each part can absorb?
  4. Should we carve up our change initiatives into a predictable series of sprints with a regular cadence?
  5. How long should our change sprints be?
  6. How much of the change initiative can the organization absorb at any one time in order to maintain forward momentum?
  7. Is there a need for periods of settling in (scheduled periods of equilibrium) between change sprints?
  8. Is there a need for the status of various projects and change initiatives to be visible throughout the organization?
  9. Is there a need for a business architect to build a business capability heatmap that highlights the amount of change impacting different business capabilities?
  10. Do you have a business capability map? Do you have business architects in your organization?

If your organization is trying to become more capable of continuous change, then answering many of these questions in the affirmative and taking appropriate action will result in an accelerated change planning capability and faster change absorption.

An Appropriate Pace of Change

For your change effort to be a success you need to find the appropriate pace of change. Finding the right pace of change is very similar to trying to fly an airplane: Go too slow and your change effort will stall. Go too fast and you will face an increasing amount of resistance, potentially depleting the support for your change faster than expected.

In many cases, using up the energy for change too fast may prevent you from reaching your intended destination. One other danger of trying to change too fast, especially if you are trying to run too many change initiatives (or projects) at the same time in the same areas of the company, is that you may run into issues of change saturation.

The key for you as change leader is to identify a regular cadence for your change initiative (or project) that is comfortable for the organization as a whole. That cadence must be slow enough so that the incremental change can be readily adopted and absorbed but fast enough so that your positive forward momentum, executive sponsorship, and overall support are maintained. The pacing and the approach must ultimately help enlist the broader organization in the change effort by reducing feelings of uncertainty, reinforcing that the change is a team effort, and accumulating reasons to believe in the change outcomes and so that people choose change.

Finally, you must have a plan for harnessing each of the eight change mindsets in your organization and leveraging them to advance your change effort, otherwise these mindsets will occupy themselves in negative ways and actively resist your change initiative or project. So, harness these mindsets, leverage the infographic and link back to this article using the embed code, and get yourself a copy of the #2 new release on Amazon for Organizational Change, my new book – Charting Change.

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Eight Change Mindsets to Harness for Success PDF

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Innovation Quotes of the Day – May 28, 2012


“The more successful an organization becomes the bigger it gets. The bigger it gets the more it focuses on optimizing its resources. The more it optimizes it resources the more it eliminates variation. Innovation requires variation. We have seen the enemy and he is us.”

– Jeff DeGraff


“It is in identifying which of The Nine Innovation Roles are vacant (or sub-optimally filled) that you will be able to see some of the areas where your efforts are likely to come up short, and then can take actions to improve your chances of innovation success.”

– Braden Kelley


“It is not enough to simply go through the motions. In order to build our abilities, cognitive or otherwise, we must think about what we’re doing, concentrate while we’re doing it and then review what we have done. Further, we need to seek out mentors and peers who will critique our efforts.”

– Greg Satell


What are some of your favorite innovation quotes?

Add one or more to the comments, listing the quote and who said it, and I’ll share the best of the submissions as future innovation quotes of the day!

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