Tag Archives: change management

Implementing Successful Transformation Initiatives for 2024

Implementing Successful Transformation Initiatives for 2024

GUEST POST from Janet Sernack

Transformation and change initiatives are usually designed as strategic interventions, intending to advance an organization’s growth, deliver increased shareholder value, build competitive advantage, or improve speed and agility to respond to fast-changing industries.  These initiatives typically focus on improving efficiency, and productivity, resolving IT legacy and technological issues, encouraging innovation, or developing high-performance organizational cultures. Yet, according to research conducted over fifteen years by McKinsey & Co., shared in a recent article “Losing from day one: Why even successful transformations fall short” – Organizations have realized only 67 percent of the maximum financial benefits that their transformations could have achieved. By contrast, respondents at all other companies say they captured an average of only 37 percent of the potential benefit, and it’s all due to a lack of human skills, and their inability to adapt, innovate, and thrive in a decade of disruption.

Differences between success and failure

The survey results confirm that “there are no short­cuts to successful transformation and change initiatives. The main differentiator between success and failure was not whether an organization followed a specific subset of actions but rather how many actions it took throughout an organizational transformation’s life cycle” and actions taken by the people involved.

Capacity, confidence, and competence – human skills

What stands out is that thirty-five percent of the value lost occurs in the implementation phase, which involves the unproductive actions taken by the people involved.

The Boston Consulting Group (BCG) supports this in a recent article “How to Create a Transformation That Lasts” – “Transformations are inherently difficult, filled with compressed deadlines and limited resources. Executing them typically requires big changes in processes, product offerings, governance, structure, the operating model itself, and human behavior.

Reinforcing the need for organizations to invest in developing the deep human skills that embed transformation disciplines into business-as-usual structures, processes, and systems, and help shift the culture. Which depends on enhancing people’s capacity, confidence, and competence to implement the “annual business-planning processes and review cycles, from executive-level weekly briefings and monthly or quarterly reviews to individual performance dialogue” that delivers and embeds the desired changes, especially the cultural enablers.

Complex and difficult to navigate – key challenges

As a result of the impact of our VUCA/BANI world, coupled with the global pandemic, current global instability, and geopolitics, many people have had their focus stolen, and are still experiencing dissonance cognitively, emotionally, and viscerally.

This impacts their ability to take intelligent actions and the range of symptoms includes emotional overwhelm, cognitive overload, and change fatigue.

It seems that many people lack the capacity, confidence, and competence, to underpin their balance, well-being, and resilience, which resources their ability and GRIT to engage fully in transformation and change initiatives.

The new normal – restoring our humanity

At ImagineNation™ for the past four years, in our coaching and mentoring practice, we have spent more than 1000 hours partnering with leaders and managers around the world to support them in recovering and re-emerging from a range of uncomfortable, disabling, and disempowering feelings.

Some of these unresourceful states include loneliness, disconnection, a lack of belonging, and varying degrees of burnout, and have caused them to withdraw and, in some cases, even resist returning to the office, or to work generally.

It appears that this is the new normal we all have to deal with, knowing there is no playbook, to take us there because it involves restoring the essence of our humanity and deepening our human skills.

Taking a whole-person approach – develop human skills

By embracing a whole-person approach, in all transformation and change initiatives, that focuses on building people’s capacity, confidence, and competence, and that cultivates their well-being and resilience to:

  • Engage, empower, and enable them to collaborate in setting the targets, business plans, implementation, and follow-up necessary to ensure a successful transformation and change initiative.
  • Safely partner with them through their discomfort, anxiety, fear, and reactive responses.
  • Learn resourceful emotional states, traits, mindsets, behaviors, and human skills to embody, enact and execute the desired changes strategically and systemically.

By then slowing down, to pause, retreat and reflect, and choose to operate systemically and holistically, and cultivate the “deliberate calm” required to operate at the three different human levels outlined in the illustration below:

The Neurological Level – which most transformation and change initiatives fail to comprehend, connect to, and work with. Because people lack the focus, intention, and skills to help people collapse any unconscious RIGIDITY existing in their emotional, cognitive, and visceral states, which means they may be frozen, distracted, withdrawn, or aggressive as a result of their fears and anxiety.

You can build your capacity, confidence, and competence to operate at this level by accepting “what is”:

  • Paying attention and being present with whatever people are experiencing neurologically by attending, allowing, accepting, naming, and acknowledging whatever is going on for them, and by supporting and enabling them to rest, revitalize and recover in their unique way.
  • Operating from an open mind and an open heart and by being empathic and compassionate, in line with their fragility and vulnerability, being kind, appreciative, and considerate of their individual needs.
  • Being intentional in enabling them to become grounded, mindful conscious, and truly connected to what is really going on for them, and rebuild their positivity, optimism, and hope for the future.
  • Creating a collective holding space or container that gives them permission, safety, and trust to pull them towards the benefits and rewards of not knowing, unlearning, and being open to relearning new mental models.
  • Evoking new and multiple perspectives that will help them navigate uncertainty and complexity.

The Emotional Cognition Levels – which most transformation and change initiatives fail to take into account because people need to develop their PLASTICITY and flexibility in regulating and focusing their thoughts, feelings, and actions to adapt and be agile in a world of unknowns, and deliver the outcomes and results they want to have.

You can build your capacity, confidence, and competence to operate at this level by supporting them to open their hearts and minds:

  • Igniting their curiosity, imagination, and playfulness, introducing novel ideas, and allowing play and improvisation into their thinking processes, to allow time out to mind wander and wonder into new and unexplored territories.
  • Exposing, disrupting, and re-framing negative beliefs, ruminations, overthinking and catastrophizing patterns, imposter syndromes, fears of failure, and feelings of hopelessness and helplessness.
  • Evoking mindset shifts, embracing positivity and an optimistic focus on what might be a future possibility and opportunity.
  • Being empathic, compassionate, and appreciative, and engaging in self-care activities and well-being practices.

The Generative Level – which most transformation and change initiatives ignore, because they fail to develop the critical and creative thinking, and problem sensing and solving skills that are required to GENERATE the crucial elastic thinking and human skills that result in change, and innovation.

You can build your capacity, confidence, and competence to operate at this level by:

  • Creating a safe space to help people reason and make sense of the things occurring within, around, and outside of them.
  • Cultivating their emotional and cognitive agility, creative, critical, and associative thinking skills to challenge the status quo and think differently.
  • Developing behavioral flexibility to collaborate, being inclusive to maximize differences and diversity, and safe experimentation to close their knowing-doing gaps.
  • Taking small bets, giving people permission and safety to fail fast to learn quickly, be courageous, be both strategic and systemic in taking smart risks and intelligent actions.

Reigniting our humanity – unlocking human potential  

At the end of the day, we all know that we can’t solve the problem with the same thinking that created it. Yet, so many of us keep on trying to do that, by unconsciously defaulting into a business-as-usual linear thinking process when involved in setting up and implementing a transformation or change initiative.

Ai can only take us so far, because the defining trait of our species, is our human creativity, which is at the heart of all creative problem-solving endeavors, where innovation can be the engine of change, transformation, and growth, no matter what the context. According to Fei-Fei Li, Sequoia Professor of Computer Science at Stanford, and co-director of AI4All, a non-profit organization promoting diversity and inclusion in the field of AI.

“There’s nothing artificial about AI. It’s inspired by people, created by people, and most importantly it has an impact on people”.

  • Develop the human skills

When we have the capacity, confidence, and competence to reignite our humanity, we will unlock human potential, and stop producing results no one wants. By developing human skills that enable people to adapt, be resilient, agile, creative, and innovate, they will grow through disruption in ways that add value to the quality of people’s lives, that are appreciated and cherished, we can truly serve people, deliver profits and perhaps save the planet.

Find out more about our work at ImagineNation™

Find out about our collective, learning products and tools, including The Coach for Innovators, Leaders, and Teams Certified Program, presented by Janet Sernack, is a collaborative, intimate, and deeply personalized innovation coaching and learning program, supported by a global group of peers over 9-weeks, and can be customized as a bespoke corporate learning and coaching program for leadership and team development and change and culture transformation initiatives.

Image Credit: Pixabay

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Change Management Team Dynamics

Change Management Team Dynamics

GUEST POST from Stefan Lindegaard

As the pace of change accelerates and becomes more encompassing, teams stand as the backbone of a successful organization. To stay ahead, teams must not only adapt to change but also leverage it to their advantage.

So, how do we harness change management to ensure our teams remain robust and agile through ongoing transformations and uncertainties?

By integrating team dynamics with change management, we aim to transform not only how teams operate but also how individuals perceive and engage with change.

That’s why I’m developing the Team Dynamics for Change Management Framework, and I invite your feedback and perspectives on it.

Understanding Change Management:

Change Management is the structured approach to transitioning teams or organizations from their current state to a desired future state. It’s about guiding and supporting individuals through this transition to realize lasting benefits. A significant part of this involves understanding people – their perceptions of change and how best to aid them through it.

Defining Team Dynamics:

Team dynamics are the behavioral and psychological forces at play within a group, profoundly influencing its direction and overall performance. These forces spring from individual personalities, relationships, roles, and the environment the team operates within. They mold the team’s interactions, communication patterns, collaborative efforts, and conflict resolutions.

Why a Framework for This Makes Sense

While numerous change management models cater to organizational or individual change, few focus directly on the unique behaviors and interactions within teams.

Given the pivotal role of teams, it’s essential to have an approach that marries the principles of change management with the realities of team dynamics.

Inspiration & Roots:

Two groundbreaking models serve as the foundational inspiration for this approach:

Kotter’s 8-Step Process for Leading Change: Developed by Harvard Business School Professor John Kotter, this model provides a step-by-step strategy for organizational change. Its emphasis on creating urgency, building a guiding coalition, and embedding new approaches makes it a revered guide in change management.

ADKAR Model: Introduced by Prosci, a global leader in change management solutions, this model emphasizes the individual’s journey through change. Its focus on Awareness, Desire, Knowledge, Ability, and Reinforcement captures the stages of personal transition during organizational shifts.

Choosing these models as the foundation is due to their robust, time-tested strategies, which I believe can be tailored to address team dynamics specifically.

Change Curve

Eight (8) Elements for the Team Dynamics for Change Management Framework

1. Assessing Team Dynamics:

Objective: Understand the current state and behaviors within the team.

Rationale: Before any change management strategy can be effectively implemented, there’s a need to understand the present dynamics of the team. This sets the foundation for everything that follows.

2. Understanding Individual Aspirations (WIIFM):

Objective: Recognize and validate the personal drivers and motivations of each team member.

Rationale: Following the assessment of team dynamics, it’s critical to delve deeper into individual motivations. Understanding the “what’s in it for me?” for every team member will influence and enrich subsequent steps, ensuring changes resonate on a personal level.

3. Evaluating Team Change Readiness:

Objective: Gauge the team’s willingness and preparation for change, considering both collective and individual motivations.

Rationale: Once the team dynamics and individual aspirations are clear, it’s pivotal to measure the readiness for change, which will be greatly influenced by the alignment (or lack thereof) between team goals and personal drivers.

4. Formulating a Shared Vision:

Objective: Create a unified direction for the team that also respects individual aspirations.

Rationale: Armed with insights from previous steps, crafting a shared vision becomes more feasible and grounded. This vision will better reflect the aspirations of the team as a whole and its individual members.

5. Enhancing Communication & Collaboration:

Objective: Foster positive and efficient team interactions.

Rationale: With a clear vision in place, the focus can shift to enhancing the ways team members interact, ensuring that individual aspirations and the collective vision are continually in dialogue.

6. Implementing Change & Skill Development:

Objective: Facilitate the smooth adoption of new practices while building necessary skills.

Rationale: Changes can now be introduced and executed, backed by a well-understood team dynamic and vision, and supported by individual motivations.

7. Feedback & Continuous Improvement:

Objective: Monitor the impact of the changes and refine as necessary.

Rationale: As changes are implemented, it’s essential to keep the channels of feedback open. Here, the alignment between team goals and individual motivations will be rechecked and fine-tuned.

8. Celebrating Success & Expanding Impact:

Objective: Recognize achievements and share the team’s journey with a wider audience.

Rationale: Concluding with acknowledgment reinforces the importance of both the collective endeavor and individual contributions. Celebrations serve as reminders of the harmony between team goals and personal aspirations.

What’s in it for Teams:

  • A clearer path through organizational changes.
  • Enhanced trust, teamwork, and collaboration.
  • Fewer conflicts and more transparent communication channels.
  • Readiness for upcoming challenges.
  • Foster an environment where everyone thrives.
  • Provides individuals clarity on their roles, highlighting the unique value they bring to the organization, reducing uncertainty.

Help develop our framework? Get a free e-book!

I’m in the process of refining this framework and would greatly value your perspectives. If you have insights, feedback to offer or questions to ask, please get in touch. Let’s work together to redefine how teams adapt to change. I will soon turn this into a free e-book to share the learning.

Image Credit: Stefan Lindegaard, Unsplash

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AI as an Innovation Tool – How to Work with a Deeply Flawed Genius!

AI as an Innovation Tool - How to Work with a Deeply Flawed Genius!

GUEST POST from Pete Foley

For those of us working in the innovation and change field, it is hard to overstate the value and importance of AI.   It opens doors, that were, for me at least, barely imaginable 10 years ago.  And for someone who views analogy, crossing expertise boundaries, and the reapplication of ideas across domains as central to innovation, it’s hard to imagine a more useful tool.

But it is still a tool.  And as with any tool, leaning it’s limitations, and how to use it skillfully is key.  I make the analogy to an automobile.  We don’t need to know everything about how it works, and we certainly don’t need to understand how to build it.  But we do need to know what it can, and cannot do. We also need to learn how to drive it, and the better our driving skills, the more we get out of it.

AI, the Idiot Savant?  An issue with current AI is that it is both intelligent and stupid at the same time (see Yejin Chois excellent TED talk that is attached). It has phenomenal ‘data intelligence’, but can also fail on even simple logic puzzles. Part of the problem is that AI lacks ‘common sense’ or the implicit framework that filters a great deal of human decision making and behavior.  Chois calls this the  ‘dark matter’ common sense of decision-making. I think of it as the framework of knowledge, morality, biases and common sense that we accumulate over time, and that is foundational to the unconscious ‘System 1’ elements that influence many, if not most of our decisions. But whatever we call it, it’s an important, but sometimes invisible and unintuitive part of human information processing that is can be missing from AI output.    

Of course, AI is far from being unique in having limitations in the quality of its output.   Any information source we use is subject to errors.  We all know not to believe everything we read on the internet. That makes Google searches useful, but also potentially flawed.  Even consulting with human experts has pitfalls.   Not all experts agree, and even to most eminent expert can be subject to biases, or just good old fashioned human error.  But most of us have learned to be appropriately skeptical of these sources of information.  We routinely cross-reference, challenge data, seek second opinions and do not simply ‘parrot’ the data they provide.

But increasingly with AI, I’ve seen a tendency to treat its output with perhaps too much respect.   The reasons for this are multi-faceted, but very human.   Part of it may be the potential for generative AI to provide answers in an apparently definitive form.  Part may simply be awe of its capabilities, and to confuse breadth of knowledge with accuracy.  Another element is the ability it gives us to quickly penetrate areas where we may have little domain knowledge or background.  As I’ve already mentioned, this is fantastic for those of us who value exploring new domains and analogies.  But it comes with inherent challenges, as the further we step away from our own expertise, the easier it is for us to miss even basic mistakes.  

As for AI’s limitations, Chois provides some sobering examples.  It can pass a bar exam, but can fail abysmally on even simple logic problems.  For example, it suggests building a bridge over broken glass and nails is likely to cause punctures!   It has even suggested increasing the efficiency of paperclip manufacture by using humans as raw materials.  Of course, these negative examples are somewhat cherry picked to make a point, but they do show how poor some AI answers can be, and how they can be low in common sense.   Of course, when the errors are this obvious, we should automatically filter them out with our own common sense.  But the challenge comes when we are dealing in areas where we have little experience, and AI delivers superficially plausible but flawed answers. 

Why is this a weak spot for AI?  At the root of this is that implicit knowledge is rarely articulated in the data AI scrapes. For example, a recipe will often say ‘remove the pot from the heat’, but rarely says ‘remove the pot from heat and don’t stick your fingers in the flames’. We’re supposed to know that already. Because it is ‘obvious’, and processed quickly, unconsciously and often automatically by our brains, it is rarely explicitly articulated. AI, however, cannot learn what is not said.  And so because we don’t tend to state the obvious, it can make it challenging for an AI to learn it.  It learns to take the pot off of the heat, but not the more obvious insight, which is to avoid getting burned when we do so.  

This is obviously a known problem, and several strategies are employed to help address it.  These include manually adding crafted examples and direct human input into AI’s training. But this level of human curation creates other potential risks. The minute humans start deciding what content should and should not be incorporated, or highlighted into AI training, the risk of transferring specific human biases to that AI increase.   It also creates the potential for competing AI’s with different ‘viewpoints’, depending upon differences in both human input and the choices around what data-sets are scraped. There is a ‘nature’ component to the development of AI capability, but also a nurture influence. This is of course analogous the influence that parents, teachers and peers have on the values and biases of children as they develop their own frameworks. 

But most humans are exposed to at least some diversity in the influences that shape their decision frameworks.  Parents, peers and teachers provide generational variety, and the gradual and layered process that builds the human implicit decision framework help us to evolve a supporting network of contextual insight.  It’s obvious imperfect, and the current culture wars are testament to some profound differences in end result.  But to a large extent, we evolve similar, if not identical common sense frameworks. With AI, the narrower group contributing to curated ‘education’ increases the risk of both intentional and unintentional bias, and of ‘divergent intelligence’.     

What Can We do?  The most important thing is to be skeptical about AI output.  Just because it sounds plausible, don’t assume it is.  Just as we’d not take the first answer on a Google search as absolute truth, don’t do the same with AI.  Ask it for references, and check them (early iterations were known to make up plausible looking but nonsense references).  And of course, the more important the output is to us, the more important it is to check it.  As I said at the beginning, it can be tempting to take verbatim output from AI, especially if it sounds plausible, or fits our theory or worldview.  But always challenge the illusion of omnipotence that AI creates.  It’s probably correct, but especially if its providing an important or surprising insight, double check it.    

The Sci-Fi Monster!  The concept of a childish super intelligence has been explored by more than one Science Fiction writer.  But in many ways that is what we are dealing with in the case of AI.  It’s informational ‘IQ’ is greater than the contextual or common sense ‘IQ’ , making it a different type of intelligence to those we are used to.   And because so much of the human input side is proprietary and complex, it’s difficult  to determine whether bias or misinformation is included in its output, and if so, how much?   I’m sure these are solvable challenges.  But some bias is probably unavoidable the moment any human intervention or selection invades choice of training materials or their interpretation.   And as we see an increase in copyright law suits and settlements associated with AI, it becomes increasingly plausible that narrowing of sources will result in different AI’s with different ‘experiences’, and hence potentially different answers to questions.  

AI is an incredible gift, but like the three wishes in Aladdin’s lamp, use it wisely and carefully.  A little bit of skepticism, and some human validation is a good idea. Something that can pass the bar, but that lacks common sense is powerful, it could even get elected, but don’t automatically trust everything it says!

Image credits: Pexels

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Las Vegas Formula One

Successful Innovation, Learning Experience or Total Disaster?

GUEST POST from Pete Foley

In Las Vegas, we are now clearing up after the Formula 1 Grand Prix on the Strip.  This extremely complex event required a great deal of executional innovation, and one that I think as innovators, we can learn quite a lot from. 

It was certainly a bumpy ride, both for the multi-million dollar Ferrari that hit an errant drain cover during practice, but also with respect to broader preparation, logistics, pricing and projections of consumer behavior.  Despite this, race itself was exciting and largely issue free, and even won over some of the most skeptical drivers.  In terms of Kahneman’s peak-end effects, there were both memorable lows, but also a triumphant end result.   So did this ultimately amount to success?

Success?:   For now, I think it very much depends upon your perspective and who you talk to.  Perhaps it’s a sign of the times, but in Las Vegas, the race was extremely polarizing, with often heated debates between pro- and anti- F1-ers that were often as competitive as the race.

The reality is that it will be months, or more likely years before the dust settles, and we know the answer.  And I strongly suspect that even then, those who are for and against it will all likely be able to claim support for their point of view.  One insight I think innovators can take from this is that success can be quite subjective in of itself, and greatly depends upon what factors you measure, what period of time you measure over, and often your ingoing biases.  And the bigger and more complex the innovation, often the harder it is to define and measure success.  

Compromise Effects:  When you launch a new product, it is often simpler and cheaper to measure its success narrowly in terms of specific dollar contribution to your business. But this often misses its holistic impact.   Premium products can elevate an entire category or brand, while poorly executed innovations can do the opposite.  For example, the compromise effect from Behavioral Economics suggests that a premium addition to a brand line up can shift the ‘Good, Better, Best’ spectrum of a category upwards.  This can boost dollar sales across a line up, even if the new premium product itself has only moderate sales.   For example, the addition of high priced wines to a menu can often increase the average dollars per bottle spent by diners, even if the expensive wine itself doesn’t sell.  The expensive wines shift the ‘safe middle’ of the consideration set upwards, and thus increase revenue, and hopefully profit.      

Money, Scope and Intangibles:  In the case of F1, how far can and should we cast the net when trying to measure success?  Can we look just at the bottom line?  Did this specific weekend bring in more than the same weekend the previous year in sports betting, rooms and entertainment?  Did that difference exceed the investments? 

Or is that too narrow?  What about the $$ impact on the weeks surrounding the event?  We know that some people stayed away because of the construction and congestion in the lead up to the race.  That should probably be added into, or subtracted from the equation. 

And then there’s the ‘who won and who lost question’? The benefits and losses were certainly not homogeneous across stakeholders.  The big casinos benefited disproportionately in comparison to the smaller restaurants that lost business due to construction, some to a degree that almost rivaled Covid.  Gig workers also fared differently. I have friends who gained business from the event, and friends who lost.  Many Uber drivers simply gave up and stopped working. But those who stayed, or the high-end limo drivers likely had bumper weekends.   Entertainers working shows that were disrupted by F1 lost out, but the plethora of special events that came with F1 also provided a major uptick in business for many performers and entertainers.

There is also substantial public investment to consider.  Somewhat bizarrely, the contribution of public funds was not agreed prior to the race, and the public-private cost sharing of tens of millions is still being negotiated.  But even facing that moving target, did increased (or decreased) tax income before, during and after the race offset those still to be determined costs?

Intangibles:  And then there’s the intangibles.  While Vegas is not exactly an unknown entity, F1 certainly upped its exposure, or in marketing terms, it’s mental availability.   It brought Vegas into the news, but was that in a positive or negative light?  Or is all publicity good publicity in this context? News coverage was mixed, with a lot of negative focus on the logistic issues, but also global coverage of what was generally regarded as an exciting race.   And of course, that media coverage also by definition marketed other businesses, including the spectacular Sphere. 

Logistics:  Traffic has been a nightmare with many who work on the strip facing unprecedented delays in their commutes for many weeks, with many commutes going from minutes to hours.   This reached a point where casinos were raffling substantial prizes, including a Tesla, just to persuade people to not call in sick.  Longer term, it’s hard to determine the impact on employee morale and retention, but its hard to imagine that it will be zero, and that brings costs of its own that go well beyond a raffled Tesla

Measuring Success?  In conclusion, this was a huge operation, and its impact by definition is going to be multidimensional.  The outcome was, not surprisingly, a mixed bag.  It could have been a lot better, or a lot worse. And even as the dust settles, it’s likely that different groups will be able to cherry pick data to support their current opinions and biases. 

Innovation Insights:  So what are some of the more generalized innovation insights we can draw?

(a) Innovation is rarely a one and done process.   We rarely get it right first time, and the bigger and more complex an innovation is, the more we usually have to learn.  F1 is the poster child for this, and the organization is going to have an enormous amount of data to plough through. The value of this will greatly depend on F1’s internal innovation culture.  Is it a learning organization?  In a situation like this, where billions of dollars, and careers are on the line, will it be open or defensive?  Great innovation organizations mostly put defensiveness aside, actively learn from mistakes, and adopt Devils Advocate approaches to learn from hard earned data. But culture is deeply embedded, and difficult to change, so much depends on the current culture of the organizations involved.  

(b) Going Fast versus Going Slow:  This project moved very, very quickly.  Turning a city like Las Vegas from scratch into a top of the line race track in less than a year was a massive challenge.  The upside is that if you go fast, you learn fast.  And the complexity of the task meant much of the insight could pragmatically only be achieved ‘on the ground’.  But conversely, better scenario planning might have helped anticipate some of the biggest issues, especially around traffic disruption, loss of business to smaller organizations, commuting issues and community outreach.  And things like not finalizing public-private contracts prior to execution will likely end up prolonging the agony.  Whatever our innovation is, big or small, hitting that sweet spot between winging it and over-thinking is key. 

(c) Understanding Real Consumer Behavior.  The casinos got pricing horribly wrong.  When the race was announced, hotel prices and race packages for the F1 weekend went through the roof.  But in the final run up to the race, prices for both rooms and the race itself plummeted.  One news article reported a hotel room on the strip as low as $18!  Tickets for the race that the previous month had cost $1600 had dropped to $800 or less on race day.  Visitors who had earlier paid top dollar for rooms were reported to be cancelling and rebooking, while those locked into rates were frustrated.  There is even a major lawsuit in progress around a cancelled practice.  I don’t know any details around how pricing was researched, and predicting the market for a new product or innovation is always a challenge.  In addition, the bigger the innovation, the more challenging the prediction game is, as there are less relevant anchors for consumers or the business to work from.   But I think the generalizable lesson for all innovators is to be humble.  Assume you don’t know, that your models are approximate, do as much research as you can in contexts that are a close to realistic as possible, don’t squeeze margins based on unrealistic expectations for the accuracy of business models, and build as much agility into innovation launches as possible.  Easier said than done I know, but one of the most consistent reasons for new product failure is over confidence in understanding real consumer response when the rubber hits the road (pun intended), and how it can differ from articulated consumer response derived in unrealistic contexts. Focus groups and on-line surveys can be quite misleading when it comes down to the reality of handing over hard cash, opportunity cost, or how we value ur precious time short versus long-term term.

Conclusion: Full disclosure, I’ve personally gone through the full spectrum with Formula One in Vegas.  I loved the idea when it was announced, but 6 months of construction, disruption, and the prospect of another two months of tear down have severely dented my enthusiasm.  Ultimately I went from coveting tickets to avoiding the event altogether.  People I know range from ecstatic to furious, and everything in between.  Did I mention it was polarizing? 

The reality is that this is an ongoing innovation process.   There is a 3-year contract with options to extend to 10 years.  How successful it ultimately is will likely be very dependent upon how good a learning and innovation culture Formula One and its partners are, or can become.  It’s a steep and expensive learning curve, and how it moves forward is going to be interesting if nothing else.  And being Vegas, we have both CES and the Super Bowl to distract us in the next few months, before we start preparing again for next year. 

Image credits: Pexels

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AI and Human Creativity Solving Complex Problems Together

AI and Human Creativity Solving Complex Problems Together

GUEST POST from Janet Sernack

A recent McKinsey Leading Off – Essentials for leaders and those they lead email newsletter, referred to an article “The organization of the future: Enabled by gen AI, driven by people” which stated that digitization, automation, and AI will reshape whole industries and every enterprise. The article elaborated further by saying that, in terms of magnitude, the challenge is akin to coping with the large-scale shift from agricultural work to manufacturing that occurred in the early 20th century in North America and Europe, and more recently in China. This shift was powered by the defining trait of our species, our human creativity, which is at the heart of all creative problem-solving endeavors, where innovation is the engine of growth, no matter, what the context.

Moving into Unchartered Job and Skills Territory

We don’t yet know what exact technological, or soft skills, new occupations, or jobs will be required in this fast-moving transformation, or how we might further advance generative AI, digitization, and automation.

We also don’t know how AI will impact the need for humans to tap even more into the defining trait of our species, our human creativity. To enable us to become more imaginative, curious, and creative in the way we solve some of the world’s greatest challenges and most complex and pressing problems, and transform them into innovative solutions.

We can be proactive by asking these two generative questions:

  • What if the true potential of AI lies in embracing its ability to augment human creativity and aid innovation, especially in enhancing creative problem solving, at all levels of civil society, instead of avoiding it? (Ideascale)
  • How might we develop AI as a creative thinking partner to effect profound change, and create innovative solutions that help us build a more equitable and sustainable planet for all humanity? (Hal Gregersen)

Because our human creativity is at the heart of creative problem-solving, and innovation is the engine of growth, competitiveness, and profound and positive change.

Developing a Co-Creative Thinking Partnership

In a recent article in the Harvard Business Review “AI Can Help You Ask Better Questions – and Solve Bigger Problems” by Hal Gregersen and Nicola Morini Bianzino, they state:

“Artificial intelligence may be superhuman in some ways, but it also has considerable weaknesses. For starters, the technology is fundamentally backward-looking, trained on yesterday’s data – and the future might not look anything like the past. What’s more, inaccurate or otherwise flawed training data (for instance, data skewed by inherent biases) produces poor outcomes.”

The authors say that dealing with this issue requires people to manage this limitation if they are going to treat AI as a creative-thinking partner in solving complex problems, that enable people to live healthy and happy lives and to co-create an equitable and sustainable planet.

We can achieve this by focusing on specific areas where the human brain and machines might possibly complement one another to co-create the systemic changes the world badly needs through creative problem-solving.

  • A double-edged sword

This perspective is further complimented by a recent Boston Consulting Group article  “How people can create-and destroy value- with generative AI” where they found that the adoption of generative AI is, in fact, a double-edged sword.

In an experiment, participants using GPT-4 for creative product innovation outperformed the control group (those who completed the task without using GPT-4) by 40%. But for business problem solving, using GPT-4 resulted in performance that was 23% lower than that of the control group.

“Perhaps somewhat counterintuitively, current GenAI models tend to do better on the first type of task; it is easier for LLMs to come up with creative, novel, or useful ideas based on the vast amounts of data on which they have been trained. Where there’s more room for error is when LLMs are asked to weigh nuanced qualitative and quantitative data to answer a complex question. Given this shortcoming, we as researchers knew that GPT-4 was likely to mislead participants if they relied completely on the tool, and not also on their own judgment, to arrive at the solution to the business problem-solving task (this task had a “right” answer)”.

  • Taking the path of least resistance

In McKinsey’s Top Ten Reports This Quarter blog, seven out of the ten articles relate specifically to generative AI: technology trends, state of AI, future of work, future of AI, the new AI playbook, questions to ask about AI and healthcare and AI.

As it is the most dominant topic across the board globally, if we are not both vigilant and intentional, a myopic focus on this one significant technology will take us all down the path of least resistance – where our energy will move to where it is easiest to go.  Rather than being like a river, which takes the path of least resistance to its surrounding terrain, and not by taking a strategic and systemic perspective, we will always go, and end up, where we have always gone.

  • Living our lives forwards

According to the Boston Consulting Group article:

“The primary locus of human-driven value creation lies not in enhancing generative AI where it is already great, but in focusing on tasks beyond the frontier of the technology’s core competencies.”

This means that a whole lot of other variables need to be at play, and a newly emerging set of human skills, especially in creative problem solving, need to be developed to maximize the most value from generative AI, to generate the most imaginative, novel and value adding landing strips of the future.

Creative Problem Solving

In my previous blog posts “Imagination versus Knowledge” and “Why Successful Innovators Are Curious Like Cats” we shared that we are in the midst of a “Sputnik Moment” where we have the opportunity to advance our human creativity.

This human creativity is inside all of us, it involves the process of bringing something new into being, that is original, surprising useful, or desirable, in ways that add value to the quality of people’s lives, in ways they appreciate and cherish.

  • Taking a both/and approach

Our human creativity will be paralysed, if we focus our attention and intention only on the technology, and on the financial gains or potential profits we will get from it, and if we exclude the possibilities of a co-creative thinking partnership with the technology.

To deeply engage people in true creative problem solving – and involving them in impacting positively on our crucial relationships and connectedness, with one another and with the natural world, and the planet.

  • A marriage between creatives, technologists, and humanities

In a recent Fast Company video presentation, “Innovating Imagination: How Airbnb Is Using AI to Foster Creativity” Brian Chesky CEO of Airbnb, states that we need to consider and focus our attention and intention on discovering what is good for people.

To develop a “marriage between creatives, technologists, and the humanities” that brings the human out and doesn’t let technology overtake our human element.

Developing Creative Problem-Solving Skills

At ImagineNation, we teach, mentor, and coach clients in creative problem-solving, through developing their Generative Discovery skills.

This involves developing an open and active mind and heart, by becoming flexible, adaptive, and playful in the ways we engage and focus our human creativity in the four stages of creative problem-solving.

Including sensing, perceiving, and enabling people to deeply listen, inquire, question, and debate from the edges of temporarily hidden or emerging fields of the future.

To know how to emerge, diverge, and converge creative insights, collective breakthroughs, an ideation process, and cognitive and emotional agility shifts to:

  • Deepen our attending, observing, and discerning capabilities to consciously connect with, explore, and discover possibilities that create tension and cognitive dissonance to disrupt and challenge the status quo, and other conventional thinking and feeling processes.
  • Create cracks, openings, and creative thresholds by asking generative questions to push the boundaries, and challenge assumptions and mental and emotional models to pull people towards evoking, provoking, and generating boldly creative ideas.
  • Unleash possibilities, and opportunities for creative problem solving to contribute towards generating innovative solutions to complex problems, and pressing challenges, that may not have been previously imagined.

Experimenting with the generative discovery skill set enables us to juggle multiple theories, models, and strategies to create and plan in an emergent, and non-linear way through creative problem-solving.

As stated by Hal Gregersen:

“Partnering with the technology in this way can help people ask smarter questions, making them better problem solvers and breakthrough innovators.”

Succeeding in the Age of AI

We know that Generative AI will change much of what we do and how we do it, in ways that we cannot yet anticipate.

Success in the age of AI will largely depend on our ability to learn and change faster than we ever have before, in ways that preserve our well-being, connectedness, imagination, curiosity, human creativity, and our collective humanity through partnering with generative AI in the creative problem-solving process.

Find Out More About Our Work at ImagineNation™

Find out about our collective, learning products and tools, including The Coach for Innovators, Leaders, and Teams Certified Program, presented by Janet Sernack, is a collaborative, intimate, and deeply personalized innovation coaching and learning program, supported by a global group of peers over 9-weeks, which can be customised as a bespoke corporate learning program.

It is a blended and transformational change and learning program that will give you a deep understanding of the language, principles, and applications of an ecosystem focus, human-centric approach, and emergent structure (Theory U) to innovation, and upskill people and teams and develop their future fitness, within your unique innovation context. Find out more about our products and tools.

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LEGO Knows Why Companies Don’t Innovate

LEGO Knows Why Companies Don't Innovate

GUEST POST from Robyn Bolton

“Lego’s Latest Effort to Avoid Oil-Based Plastic Hits Brick Wall” – WSJ

“Lego axes plans to make bricks from recycled bottles” – BBC

“Lego ditches oil-free brick in sustainability setback” – The Financial Times

Recently, LEGO found itself doing the Walk of Atonement (see video below) after announcing to The Financial Times that it was scrapping plans to make bricks from recycled bottles, and media outlets from The Wall Street Journal to Fast Company to WIRED were more than happy to play the Shame Nun.

And it wasn’t just media outlets ringing the Shame Bell:

  • In the future, they should not make these kinds of announcements (prototype made from recyclable plastic) until they actually do it,” Judith Enck, President of Beyond Plastics
  • They are not going to survive as an organization if they don’t find a solution,” Paolo Taticchi, corporate sustainability expert at University College London.
  • “Lego undoubtedly had good intentions, but if you’re going to to (sic) announce a major environmental initiative like this—one that affects the core of your company—good intentions aren’t enough. And in this instance, it can even undermine progress.” Jesus Diaz, creative director, screenwriter, and producer at The Magic Sauce, writing forFast Company

As a LEGO lover, I am not unbiased, but WOW, the amount of hypocritical, self-righteous judgment is astounding!  All these publications and pundits espouse the need for innovation, yet when a company falls even the tiniest bit short of aspirations, it’s just SHAME (clang) SHAME (clang) SHAME.

LEGO Atlantis 8073 Manta Warrior (i.e., tiny) bit of context

In 1946, LEGO founder Ole Kirk Christiansen purchased Denmark’s first plastic injection molding machine.  Today, 95% of the company’s 4,400 different bricks are made using acrylonitrile butadiene styrene (ABS), a plastic that requires 4.4 pounds of oil to produce 2.2 pounds of brick.  Admittedly, it’s not a great ratio, and it gets worse.  The material isn’t biodegradable or easily recyclable, so when the 3% of bricks not handed down to the next generation end up in a landfill, they’ll break down into highly polluting microplastics.

With this context, it’s easy to understand why LEGO’s 2018 announcement that it will move to all non-plastic or recycled materials by 2030 and reduce its carbon emissions by 37% (from 2019’s 1.2 million tons) by 2032 was such big news.

Three years later, in 2021, LEGO announced that its prototype bricks made from polyethylene terephthalate (PET) bottles offered a promising alternative to its oil-based plastic bricks. 

But last Monday, after two years of testing, the company shared that what was promising as a prototype isn’t possible at scale because the process required to produce PET-based bricks actually increases carbon emissions.

SHAME!

LEGO Art World Map (i.e. massive) amount of praise for LEGO

LEGO is doing everything that innovation theorists, consultants, and practitioners recommend:

  • Setting a clear vision and measurable goals so that people know what the priorities are (reduce carbon emissions), why they’re important (“playing our part in building a sustainable future and creating a better world for our children to inherit”), and the magnitude of change required
  • Defining what is on and off the table in terms of innovation, specifically that they are not willing to compromise the quality, durability, or “clutch power” of bricks to improve sustainability
  • Developing a portfolio of bets that includes new materials for products and packaging, new services to keep bricks out of landfills and in kids’ hands, new building and production processes, and active partnerships with suppliers to reduce their climate footprint
  • Prototyping and learning before committing to scale because what is possible at a prototype level is different than what’s possible at pilot, which is different from what’s possible at scale.
  • Focusing on the big picture and the long-term by not going for the near-term myopic win of declaring “we’re making bricks from more sustainable materials” and instead deciding “not to progress” with something that, when taken as a whole process, moves the company further away from its 2032 goal.

Just one minifig’s opinion

If we want companies to innovate (and we do), shaming them for falling short of perfection is the absolute wrong way to do it.

Is it disappointing that something that seemed promising didn’t work out?  Of course.  But it’s just one of many avenues and experiments being pursued.  This project ended, but the pursuit of the goal hasn’t.

Is 2 years a long time to figure out that you can’t scale a prototype and still meet your goals?  Maybe.  But, then again, it took P&G 10 years to figure out how to develop and scale a perforation that improved one-handed toilet paper tearing.

Should LEGO have kept all its efforts and success a secret until everything was perfect and ready to launch?  Absolutely not.  Sharing its goals and priorities, experiments and results, learnings and decisions shows employees, partners, and other companies what it means to innovate and lead.

Is LEGO perfect? No.

Is it trying to be better? Yes.

Isn’t that what we want?

Image Credit: Pixabay

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The Comforter Cold War of 2006

(or How Assumptions Stifle Innovation)

The Comforter Cold War of 2006

GUEST POST from Robyn Bolton

In the room were two single beds, each with a fluffy white comforter folded neatly on top.

“Yeah, this is not gonna work.”

I had just entered my one-bedroom corporate apartment in Copenhagen, and while everything else was pleasantly light and spacious, there was no way I would spend the next six months sleeping in a single bed.

So, I set down my suitcases and immediately pushed the two beds together, using the two nightstands to secure them. The two comforters would work since there was just one of me, and I made a mental note to request a king-sized comforter from the desk when I left for work in the morning.

Thus began the great Comforter Cold War of 2006/2007.

Every few days, I would request a king-sized comforter for my jerry-rigged king-sized bed.  I would return to find one queen-sized comforter.  The luxury of a larger comforter would diminish the disappointment of not getting an appropriately sized one, and I would bask in the warmth of fully covered sleep.  For one night. The next day, I would return to my room only to find that the two single comforters had returned.

This went on for nine months.

I shared this story of passive-aggressive housekeeping at my going away party with my colleagues. Midway through the story, I noticed the absolutely baffled looks on their faces.

“What?”

“Why did you want one comforter?”

“Because I have one bed.  A comforter should cover the bed.”

“Why?  A bed doesn’t need a comforter.  A person does.  You just need a comforter to cover you.”

[extended silence while we try to process each other’s points]

“So, does that mean that in Denmark, if a couple sleeps together, they each have their own comforter?”

“Yes, of course!  Why would we share?  Each person has their own temperature preferences, and there’s no worry about someone stealing your covers.”

My mind.  Was.  Blown.

This made so much sense. A comforter covers a person, so the 1:1 ratio of comforter to people is far more logical than a 1:1 ratio of comforter to bed (and often a 1:2 ratio of comforter to people).  Seriously, how many relationships would be saved by simply having separate comforters?

Yet, for nine months, it made more sense to me to battle for a comforter size that apparently doesn’t exist in the country without ever asking why I couldn’t get what I was so clearly and reasonably (in my mind) requesting.

I assumed the apartment building didn’t have king-sized comforters or only enough for the actual king-sized beds.  I assumed housekeeping was on automatic pilot, not realizing they were replacing a queen-sized comforter with two single ones.  I assumed that communication amongst the staff was poor, so my request wasn’t being shared.  I assumed a lot.

But I never assumed that I was wrong and that the root of the problem was a cultural difference so deeply ingrained and subtle that it never occurred to anyone to question it.

Question your assumptions.

Assumptions are a shortcut to understanding our world.  Based on culture, experiences, and even stereotypes, we make assumptions about what came before, who we’re interacting with, what’s happening now, and what will happen next.

Most of the time, we’re right (or at least more right than wrong), so we keep making assumptions. It’s also why, when our assumptions are wrong, we tend to question everything but our assumptions.

And that kills innovation because it limits our curiosity and imagination, our perception of what’s possible, and our willingness to engage with and learn from others.

We all cling to assumptions that lead to Cold Wars. 

What’s yours?

Image Credit: Pixabay

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What Einstein Got Wrong

Defining Design

What Einstein Got Wrong - Defining Design

GUEST POST from Robyn Bolton

“If you can’t explain something simply, you don’t understand it well enough.”Albert Einstein (supposedly)

This is one of my favorite quotes because it’s an absolute gut punch.  You think you know something, probably because you’ve been saying and doing it for years.  Then someone comes along and asks you to explain it, and suddenly, you’re just standing there, mouth agape, gesturing, hoping that this wacky game of charades produces an answer.

This happened to me last Monday.

While preparing to teach a course titled “Design Innovation Lab,” I thought it would be a good idea to define “design” and “innovation.”  I already had a slide with the definition of “innovation” – something new that creates value – but when I had to make one for “design,” my stomach sank.

My first definition was “pretty pictures,” which is both wrong and slightly demeaning because designers do that and so much more.  My second definition, I know it when I see it, was worse.

So, I Googled the definition.

Then I asked ChatGPT.

Then I asked some designer friends.

No one had a simple definition of Design.

As the clock ticked closer to 6:00 pm, I defaulted to a definition from the International Council of Design:

“Design is a discipline of study and practice focused on the interaction between a person – a “user” – and the man-made environment, taking into account aesthetic, functional, contextual, cultural, and societal considerations.  As a formalized discipline, design is a modern construct.”

Before unveiling this definition to a classroom full of degreed designers pursuing their Master’s in Design, I asked them to define “design.”

It went as well as all my previous attempts.  Lots of thoughts and ideas.  Lots of “it’s this but not that.”  Lots of debate about whether it needs to have a purpose for it to be distinct from art.

Absolutely no simple explanations or punchy definitions.

So, when I unveiled the definition from the very official-sounding International Council of Design, we all just stared at it.

“Yes, but it’s not quite right.”

“It is all those things, but it’s more than just those things.”

“I guess it is a ‘modern construct’ when you think of it as a job, but we’ve done it forever.”

As we squinted and puzzled, what was missing slowly dawned on us. 

There was nothing human in this definition. There was no mention of feelings or empathy, life or nature, connection or community, aspirations or dreams.

In this definition, designers consider multiple aspects of an unnatural environment in creating something to be used. Designers are simply the step before mass production begins.

Who wants to do that?

Who wants to be a stop, however necessary, on a conveyor belt of sameness?

Yet that’s what we become when we strip the humanness out of our work.

Humans are messy, emotional, unpredictable, irrational, challenging, and infuriating.

We’re also interesting, creative, imaginative, hopeful, kind, curious, hard-working, and resilient.

When we try to strip away human messiness to create MECE (mutually exclusive, collectively exhaustive) target markets and customer personas, we strip away the human we’re creating for.

When we ignore unpredictable and irrational feedback on our ideas, we ignore the creative and imaginative answers that could improve our ideas.

When we give up on a challenge because it’s more difficult than expected and doesn’t produce immediate results, we give up hope, resiliency, and the opportunity to improve things.

I still don’t have a simple definition of design, but I know that one that doesn’t acknowledge all the aspects of a human beyond just being a “user” isn’t correct.

Even if you explain something simply, you may not understand it well enough.

Image Credit: Misterinnovation.com

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An Innovation Rant: Just Because You Can Doesn’t Mean You Should

An Innovation Rant: Just Because You Can Doesn’t Mean You Should

GUEST POST from Robyn Bolton

Why are people so concerned about, afraid of, or resistant to new things?

Innovation, by its very nature, is good.  It is something new that creates value.

Naturally, the answer has nothing to do with innovation.

It has everything to do with how we experience it. 

And innovation without humanity is a very bad experience.

Over the last several weeks, I’ve heard so many stories of inhuman innovation that I have said, “I hate innovation” more than once.

Of course, I don’t mean that (I would be at an extraordinary career crossroads if I did).  What I mean is that I hate the choices we make about how to use innovation. 

Just because AI can filter resumes doesn’t mean you should remove humans from the process.

Years ago, I oversaw recruiting for a small consulting firm of about 50 people.  I was a full-time project manager, but given our size, everyone was expected to pitch in and take on extra responsibilities.  Because of our founder, we received more resumes than most firms our size, so I usually spent 2 to 3 hours a week reviewing them and responding to applicants.  It was usually boring, sometimes hilarious, and always essential because of our people-based business.

Would I have loved to have an AI system sort through the resumes for me?  Absolutely!

Would we have missed out on incredible talent because they weren’t out “type?”  Absolutely!

AI judges a resume based on keywords and other factors you program in.  This probably means that it filters out people who worked in multiple industries, aren’t following a traditional career path, or don’t have the right degree.

This also means that you are not accessing people who bring a new perspective to your business, who can make the non-obvious connections that drive innovation and growth, and who bring unique skills and experiences to your team and its ideas.

If you permit AI to find all your talent, pretty soon, the only talent you’ll have is AI.

Just because you can ghost people doesn’t mean you should.

Rejection sucks.  When you reject someone, and they take it well, you still feel a bit icky and sad.  When they don’t take it well, as one of my colleagues said when viewing a response from a candidate who did not take the decision well, “I feel like I was just assaulted by a bag of feathers.  I’m not hurt.  I’m just shocked.”

So, I understand ghosting feels like the better option.  It’s not.  At best, it’s lazy, and at worst, it’s selfish.  Especially if you’re a big company using AI to screen resumes. 

It’s not hard to add a function that triggers a standard rejection email when the AI filters someone out.  It’s not that hard to have a pre-programmed email that can quickly be clicked and sent when a human makes a decision.

The Golden Rule – do unto others as you would have done unto you – doesn’t apply to AI.  It does apply to you.

Just because you can stack bots on bots doesn’t mean you should.

At this point, we all know that our first interaction with customer service will be with a bot.  Whether it’s an online chatbot or an automated phone tree, the journey to a human is often long and frustrating. Fine.  We don’t like it, but we don’t have a choice.

But when a bot transfers us to a bot masquerading as a person?  Do you hate your customers that much?

Some companies do, as my husband and I discovered.  I was on the phone with one company trying to resolve a problem, and he was in a completely different part of the house on the phone with another company trying to fix a separate issue.  When I wandered to the room where my husband was to get information that the “person” I was talking to needed, I noticed he was on hold.  Then he started staring at me funny (not as unusual as you might think).  Then he asked me to put my call on speaker (that was unusual).  After listening for a few minutes, he said, “I’m talking to the same woman.”

He was right.  As we listened to each other’s calls, we heard the same “woman” with the same tenor of voice, unusual cadence of speech, and indecipherable accent.  We were talking to a bot.  It was not helpful.  It took each of us several days and several more calls to finally reach humans.  When that happened, our issues were resolved in minutes.

Just because innovation can doesn’t mean you should allow it to.

You are a human.  You know more than the machine knows (for now).

You are interacting with other humans who, like you, have a right to be treated with respect.

If you forget these things – how important you and your choices are and how you want to be treated – you won’t have to worry about AI taking your job.  You already gave it away.

Image Credit: Pexels

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Your Innovation is Dictated by Who You Are & What You Do

Your Innovation is Dictated by Who You Are & What You Do

GUEST POST from Robyn Bolton

Using only three words, how would you describe your company?

Better yet, what three words would your customers use to describe your company?

These three words capture your company’s identity. They answer, “who we are” and “what business we’re in.”  They capture a shared understanding of where customers allow you to play and how you take action to win. 

Everything consistent with this identity is normal, safe, and comfortable.

Everything inconsistent with this identity is weird, risky, and scary.

Your identity is killing innovation.

Innovation is something new that creates value.

Identity is carefully constructed, enduring, and fiercely protected and reinforced.

When innovation and identity conflict, innovation usually loses.

Whether the innovation is incremental, adjacent, or radical doesn’t matter. If it conflicts with the company’s identity, it will join the 99.9% of innovations that are canceled before they ever launch.

Your identity can supercharge innovation.

When innovation and identity guide and reinforce each other, it doesn’t matter if the innovation is incremental, adjacent, or radical.  It can win.

Identity-based Innovation changes your perspective. 

We typically think about innovation as falling into three types based on the scope of change to the business model:

  1. Incremental innovations that make existing offerings better, faster, and cheaper for existing customers and use our existing business model
  2. Adjacent innovations are new offerings in new categories, appeal to new customers, require new processes and activities to create or use new revenue models
  3. Radical innovations that change everything – offerings, customers, processes and activities, and revenue models

These types make sense IF we’re perfectly logical and rational beings capable of dispassionately evaluating data and making decisions.  SPOILER ALERT: We’re not.  We decide with our hearts (emotions, values, fears, and desires) and justify those decisions with our heads (logic and data).

So, why not use an innovation-typing scheme that reflects our humanity and reality?

That’s where Identity-based Innovation categories come in:

  1. Identity-enhancing innovations reinforce and strengthen people’s comfort and certainty in who they are and what they do relative to the organization.  “Organizational members all ‘know’ what actions are acceptable based on a shared understanding of what the organization represents, and this knowledge becomes codified u a set of heuristics about which innovative activities should be pursued and which should be dismissed.”
  2. Identity-stretching innovations enable and stretch people’s understanding of who they are and what they do in an additive, not threatening, way to their current identities.
  3. Identity-challenging innovations are threats and tend to occur in one of two contexts:
    • Extreme technological change that “results in the obsolescence of a product market or the convergence of multiple product markets.” (challenges “who we are”)
    • Competitors or new entrants that launch new offerings or change the basis of competition (challenges “what we do”)

By looking at your innovations through the lens of identity (and, therefore, people’s decision-making hearts), you can more easily identify the ones that will be supported and those that will be axed.

It also changes your results.

“Ok, nerd,” you’re probably thinking.  “Thanks for dragging me into your innovation portfolio geek-out.”

Fair, but let me illustrate the power of this perspective using some examples from P&G.

OfferingBusiness-Model TypesIdentity-based Categories
Charmin Smooth TearIncremental
Made Charmin easier to tear
Identity-enhancing
Reinforced Charmin’s premium experience
SwifferAdjacent
New durable product in an existing category (floor cleaning)
Identity-enhancing
Reinforced P&G’s identity as a provider of best-in-class cleaning products
Tide Dry CleanersRadical
Moved P&G into services and uses a franchise model
Identity-stretching
Dry cleaning service is consistent with P&G’s identity but stretches into providing services vs. just products

Do you see what happened on that third line?  A Radical Innovation was identity-stretching (not challenging), and it’s in the 0.1% of corporate innovations that launched!  It’s in 22 states!

The Bottom Line

If you look at innovation in the same way you always have, through the lens of changes to your business model, you’ll get the same innovation results you always have.

If you look at innovation differently, through the lens of how it affects personal and organizational identity, you’ll get different results.  You may even get radical results.

Image Credit: Unsplash

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