Tag Archives: AI

Will Innovation Management Leverage AI in the Future?

Will Innovation Management Leverage AI in the Future?

GUEST POST from Jesse Nieminen

What role can AI play in innovation management, and how can we unlock its true potential?

Unless you’ve been living under a rock, you’ve probably heard a thing or two about AI in the last year. The launch of ChatGPT has supercharged the hype around AI, and now we’re seeing dramatic progress at a pace unlike anything that’s come before.

For those of us into innovation, it’s an exciting time.

Much has been said about the topic at large so I won’t go over the details here. At HYPE, what we’re most excited about is what AI can do for innovation management specifically. We’ve had AI capabilities for years, and have been looking into the topic at large for quite some time.

Here, I share HYPE’s current thinking and answer some key questions:

  • What can AI do for innovation management?
  • What are some common use cases?
  • How can you operationalize AI’s use in innovation management?

The Current State of Innovation Management

Before we answer those questions, let’s review how most organizations carry out innovation management.

We’re all familiar with the innovation funnel.

Hype Innovation Image 1

To oversimplify, you gather ideas, review them, and then select the best ones to move forward to the pilot stage and eventual implementation. After each phase, poor ideas get weeded out.

It’s systematic, it’s conceptually simple, and investment is tiered so that you don’t spend too much time or money before an idea has shown its potential. What’s not to love?

Well, there are a few key challenges: the process is slow, linear, and is usually biased due to the evaluation criteria selected for the gates or decision points (if you use a Phase-Gate model).

Each of these challenges can be mitigated with smart adaptations of the process, but the funnel has another fundamental limitation: It’s generally built for a world where innovation requires significant capital expenditures and vast amounts of proprietary information.

But, regardless of your industry, that just isn’t the case anymore. Now most information is freely available, and technology has come a long way, in many cases because of AI. For example, pharmaceutical companies use AI to accelerate drug discovery while infrastructure and manufacturing companies use advanced simulation techniques, digital twins (virtual replicas of physical objects or systems), and rapid prototyping.

It’s now possible to innovate, test, and validate ideas faster than ever with minimal investment. With the right guidance, these tasks don’t have to be limited to innovation experts like you anymore. That can be an intimidating thought, but it’s also an empowering one. Soon, thanks to AI, you’ll be able to scale your expertise and make an impact significantly bigger than before.

For more than 20 years, we’ve been helping our customers succeed in this era of systematic innovation management. Today, countless organizations manage trends at scale, collect insights and ideas from a wide and diverse audience, and then manage that funnel highly effectively.

Yet, despite, or maybe because of this, more and more seemingly well-run organizations are struggling to keep up and adapt to the future.

What gives?

Some say that innovation is decelerating. Research reveals that as technology gets more complex, coming up with the next big scientific breakthrough is likely to require more and more investment, which makes intuitive sense. This type of research is actually about invention, not innovation per se.

Innovation is using those inventions to drive measurable value. The economic impact of these inventions has always come and gone in waves, as highlighted in ARK Investment’s research, illustrated below.

Throughout history, significant inventions have created platforms that enable dramatic progress through their practical application or, in other words, through innovation. ARK firmly believes that we’re on the precipice of another such wave and one that is likely to be bigger than any that has come before. AI is probably the most important of these platforms, but it’s not the only one.

Mckinsey Hype Innovation Image 2

Whether that will be the case remains to be seen, but regardless, the economic impact of innovation typically derives from the creative combination of existing “building blocks,” be they technologies, processes, or experiences.

Famously, the more such building blocks, or types of innovation, you combine to solve a specific pain point or challenge holistically, the more successful you’re likely to be. Thanks to more and more information and technology becoming free or highly affordable worldwide, change has accelerated rapidly in most industries.

That’s why, despite the evident deceleration of scientific progress in many industries, companies have to fight harder to stay relevant and change dramatically more quickly, as evidenced by the average tenure of S&P500 companies dropping like a stone.

Hype Innovation 3

In most industries, sustainable competitive advantages are a thing of the past. Now, it’s all about strategically planning for, as well as adapting to, change. This is what’s known as transient advantage, and it’s already a reality for most organizations.

How Innovation Management Needs to Change

In this landscape, the traditional innovation funnel isn’t cutting it anymore. Organizations can’t just focus on research and then turn that into new products and expect to do well.

To be clear, that doesn’t mean that the funnel no longer works, just that managing it well is no longer enough. It’s now table stakes. With that approach, innovating better than the next company is getting harder and more expensive.

When we look at our most successful customers and the most successful companies in the world in general, they have several things in common:

  • They have significantly faster cycle times than the competition at every step of the innovation process, i.e., they simply move faster.
  • For them, innovation is not a team, department, or process. It’s an activity the entire organization undertakes.
  • As such, they innovate everything, not just their products but also processes, experiences, business models, and more.

When you put these together, the pace of innovation leaves the competition in the dust.

How can you then maximize the pace of innovation at your organization? In a nutshell, it comes down to having:

  • A well-structured and streamlined set of processes for different kinds of innovation;
  • Appropriate tools, techniques, capabilities, and structures to support each of these processes;
  • A strategy and culture that values innovation;
  • A network of partners to accelerate learning and progress.

With these components in place, you’ll empower most people in the organization to deliver innovation, not just come up with ideas, and that makes all the difference in the world.

Hype Innovation 4

What Role Does AI Play in Innovation Management?

In the last couple of years, we’ve seen massive advancements not just in the quality of AI models and tools, but especially in the affordability and ease of their application. What used to be feasible for just a handful of the biggest and wealthiest companies out there is now quickly commoditizing. Generative AI, which has attracted most of the buzz, is merely the tip of the iceberg.

In just a few years, AI is likely to play a transformative role in the products and services most organizations provide.

For innovation managers too, AI will have dramatic and widely applicable benefits by speeding up and improving the way you work and innovate.

Let’s dive a bit deeper.

AI as an Accelerator

At HYPE, because we believe that using AI as a tool is something every organization that wants to innovate needs to do, we’ve been focusing on applying it to innovation management for some time. For example, we’ve identified and built a plethora of use cases where AI can be helpful, and it’s not just about generative AI. Other types of models and approaches still have their place as well.

There are too many use cases to cover here in detail, but we generally view AI’s use as falling into three buckets:

  • Augmenting: AI can augment human creativity, uncover new perspectives, kickstart work, help alleviate some of the inevitable biases, and make top-notch coaching available for everyone.
  • Assisting: AI-powered tools can assist innovators in research and ideation, summarize large amounts of information quickly, provide feedback, and help find, analyze, and make the most of vast quantities of structured or unstructured information.
  • Automating: AI can automate both routine and challenging work, to improve the speed and efficiency at which you can operate and save time so that you can focus on the value-added tasks at the heart of innovation.

In a nutshell, with the right AI tools, you can move faster, make smarter decisions, and operate more efficiently across virtually every part of the innovation management process.

While effective on their own, it’s only by putting the “three As” together and operationalizing them across the organization that you can unlock the full power of AI and take your innovation work to the next level.

In a nutshell, with the right AI tools, you can move faster, make smarter decisions, and operate more efficiently across virtually every part of the innovation management process.

While effective on their own, it’s only by putting the “three As” together and operationalizing them across the organization that you can unlock the full power of AI and take your innovation work to the next level.

Putting AI Into Practice

So, what’s the key to success with AI?

At HYPE, we think the key is understanding that AI is not just one “big thing.” It’s a versatile and powerful enabling technology that has become considerably cheaper and will likely continue on the same trajectory.

There are significant opportunities for using AI to deliver more value for customers, but organizations need the right data and talent to maximize the opportunities and to enable AI to support how their business operates, not least in the field of innovation management. It’s essential to find the right ways to apply AI to specific business needs; just asking everybody to use ChatGPT won’t cut it.

The anecdotal evidence we’re hearing highlights that learning to use a plethora of different AI tools and operationalizing these across an organization can often become challenging, time-consuming, and expensive.

To overcome these issues, there’s a real benefit in finding ways to operationalize AI as a part of the tools and processes you already use. And that’s where we believe The HYPE Suite with its built-in AI capabilities can make a big difference for our customers.

Final Thoughts

At the start of this article, we asked “Is AI the future of innovation management?”

In short, we think the answer is yes. But the question misses the real point.

Almost everyone is already using AI in at least some way, and over time, it will be everywhere. As an enabling technology, it’s a bit like computers or the Internet: Sure, you can innovate without them, but if everyone else uses them and you don’t, you’ll be slower and end up with a worse outcome.

The real question is how well you use and operationalize AI to support your innovation ambitions, whatever they may be. Using AI in combination with the right tools and processes, you can innovate better and faster than the competition.

At HYPE, we have many AI features in our development roadmap that will complement the software solutions we already have in place. Please reach out to us if you’d like to get an early sneak peek into what’s coming up!

Originally published at https://www.hypeinnovation.com.

Image credits: Pixabay, Hype, McKinsey

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

Top 10 Human-Centered Change & Innovation Articles of January 2024Drum roll please…

At the beginning of each month, we will profile the ten articles from the previous month that generated the most traffic to Human-Centered Change & Innovation. Did your favorite make the cut?

But enough delay, here are January’s ten most popular innovation posts:

  1. Top 40 Innovation Bloggers of 2023 — Curated by Braden Kelley
  2. Creating Organizational Agility — by Howard Tiersky
  3. 5 Simple Steps to Team Alignment — by David Burkus
  4. 5 Essential Customer Experience Tools to Master — by Braden Kelley
  5. Four Ways To Empower Change In Your Organization — by Greg Satell
  6. AI as an Innovation Tool – How to Work with a Deeply Flawed Genius! — by Pete Foley
  7. Top 100 Innovation and Transformation Articles of 2023 — Curated by Braden Kelley
  8. 80% of Psychological Safety Has Nothing to Do With Psychology — by Robyn Bolton
  9. How will you allocate your time differently in 2024? — by Mike Shipulski
  10. Leadership Development Fundamentals – Work Products — by Mike Shipulski

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 four years:

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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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Is AI Saving Corporate Innovation or Killing It?

Is AI Saving Corporate Innovation or Killing It?

GUEST POST from Robyn Bolton

AI is killing Corporate Innovation.

Last Friday, the brilliant minds of Scott Kirsner, Rita McGrath, and Alex Osterwalder (plus a few guest stars like me, no big deal) gathered to debate the truth of this statement.

Honestly, it was one of the smartest and most thoughtful debates on AI that I’ve heard (biased but right, as my husband would say), and you should definitely listen to the whole thing.

But if you don’t have time for the deep dive over your morning coffee, then here are the highlights (in my humble opinion)

Why this debate is important

Every quarter, InnoLead fields a survey to understand the issues and challenges facing corporate innovators.  The results from their Q2 survey and anecdotal follow-on conversations were eye-opening:

  • Resources are shifting from Innovation to AI: 61.5% of companies are increasing the resources allocated to AI, while 63.9% of companies are maintaining or decreasing their innovation investments
  • IT is more likely to own AI than innovation: 61.5% of companies put IT in charge of exploring potential AI use cases, compared to 53.9% of Innovation departments (percentages sum to greater than 0 because multiple departments may have responsibility)
  • Innovation departments are becoming AI departments.  In fact, some former VPs and Directors of Innovation have been retitled to VPs or Directors of AI

So when Scott asked if AI was killing Corporate Innovation, the data said YES.

The people said NO.

What’s killing corporate innovation isn’t technology.  It’s leadership.

Alex Osterwalder didn’t pull his punches and delivered a truth bomb right at the start. Like all the innovation tools and technologies that came before, the impact of AI on innovation isn’t about the technology itself—it’s about the leaders driving it.

If executives take the time to understand AI as a tool that enables successful outcomes and accelerates the accomplishment of key strategies, then there is no reason for it to threaten, let alone supplant, innovation. 

But if they treat it like a shiny new toy or a silver bullet to solve all their growth needs, then it’s just “innovation theater” all over again.

AI is an Inflection Point that leaders need to approach strategically

As Rita wrote in her book Seeing Around Corners, an inflection point has a 10x impact on business, for example, 10x cheaper, 10x faster, or 10x easier.  The emergence and large-scale adoption of AI is, without doubt, an inflection point for business.

Just like the internet and Netscape shook things up and changed the game, AI has the power to do the same—maybe even more. But, to Osterwalder’s point, leaders need to recognize AI as a strategic inflection point and proceed accordingly. 

Leaders don’t need to have it all figured out yet, but they need a plan, and that’s where we come in.

This inflection point is our time to shine

From what I’ve seen, AI isn’t killing corporate innovation. It’s creating the biggest corporate innovation opportunity in decades.  But it’s up to us, as corporate innovators, to seize the moment.

Unlike our colleagues in the core business, we are comfortable navigating ambiguity and uncertainty.  We have experience creating order from what seems like chaos and using innovation to grow today’s business and create tomorrow’s.

We can do this because we’ve done it before.  It’s exactly what we do,

AI is not a problem.  It’s an opportunity.  But only if we make it one.

AI is not the end of corporate innovation —it’s a tool, a powerful one at that.

As corporate innovators, we have the skills and knowledge required to steer businesses through uncertainty and drive meaningful change. So, let’s embrace AI strategically and unlock its full potential.

The path forward may not always be crystal clear, but that’s what makes it exciting. So, let’s seize the moment, navigate the chaos, and embrace AI as the innovation accelerant that it is.

Image Credit: Pixabay

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Top 10 Thanksgiving Innovations of All Time

According to OpenAI, ChatGPT, and Tom the Turkey

Top 10 Thanksgiving Innovations of All Time

by Braden Kelley

Call me crazy, but I’ve always been a big believer in fun as a key component to the successful pursuit of innovation.

In the spirit of this belief and to celebrate this year’s Thanksgiving Holiday I thought I would ask OpenAI to make a list of the top ten Thanksgiving innovations of all time because I honestly have no idea what to put on such a list. So, in honor of the holiday, here is what it came up with:

1. Turkey Fryer: The invention of the turkey fryer revolutionized Thanksgiving cooking, providing a faster and delicious alternative to traditional roasting methods.

2. Canned Cranberry Sauce: The introduction of canned cranberry sauce made it easier to serve this beloved Thanksgiving side dish, eliminating the need to cook fresh cranberries.

3. Instant Pot: This versatile kitchen appliance has made Thanksgiving meal preparation much quicker and hassle-free, reducing cooking time for dishes like mashed potatoes and soups.

4. Green Bean Casserole: Created in 1955 by a staff member of Campbell Soup Company, the green bean casserole quickly became a Thanksgiving staple, providing a tasty and easy-to-make side dish.

5. Thanksgiving Parades: Starting with the Macy’s Thanksgiving Day Parade in 1924, Thanksgiving parades have become a cherished tradition, bringing joy and entertainment to families across the country.

6. Stuffing Mixes: Pre-packaged stuffing mixes have made it simpler to prepare the traditional stuffing, saving time and effort while still delivering the traditional flavors.

7. Pumpkin Pie Filling: Canned pumpkin pie filling has made it convenient for home cooks to make the quintessential Thanksgiving dessert, allowing everyone to enjoy a delicious pumpkin pie without the hassle of preparing fresh puree.

8. Thanksgiving Football: Thanksgiving football games have been a long-standing tradition for many families, providing entertainment and friendly competition after the big feast.

9. Online Recipe Resources: The availability of online recipe resources has made it easier than ever to find new and creative recipes for Thanksgiving dishes, expanding our culinary horizons and inspiring unique menu ideas.

10. Pop-Up Turkey Timer: This simple but effective innovation takes the guesswork out of roasting a turkey by indicating when it is done, ensuring a perfectly cooked bird.

Maybe I have been living in a cave, but I had never heard of Instant Pot so I had to Bing it. ChatGPT also suggested “Thanksgiving Themed Decor” which I thought was a bad suggestion, so I asked it for three more options to replace that one and ended up swapping it out for the beloved “Pop-Up Turkey Timer.”

I hope you enjoyed the list, have great holiday festivities (however you choose to celebrate) and finally – I am grateful for all of you!

What is your favorite Thanksgiving innovation that you’ve seen or experienced recently?

SPECIAL BONUS: My publisher is having a Thanksgiving sale that will allow you to get the hardcover or the digital version (eBook) of my latest best-selling book Charting Change for 55% off using code CYB23 only until November 30, 2023!

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Framing Your 2024 Strategy

Framing Your 2024 Strategy

GUEST POST from Geoffrey A. Moore

Fall is in the air, which brings to mind the season’s favorite sport—no, not football, strategic planning! Let’s face it, 2023 has been a tough year for most of us, with few annual plans surviving first contact with an economy that was not so much sluggish as simply hesitant. With the exception of generative AI’s burst onto the scene, most technology sectors have been more or less trudging along, and that begs the question, what do we think we can do in 2024? Time to bring out the strategy frameworks, polish up those crystal balls that have been a bit murky of late, and chart our course forward.

This post will kick off a series of blogs about framing strategy, all organized around a meta-model we call the Hierarchy of Powers:

Geoffrey Moore Strategy Framework

The inspiration for this model came from looking at how investors prioritize their portfolios. The first thing they do is allocate by sector, based primarily on category power, referring both to the growth rate of the category as well as its potential size. Rising tides float all boats, and one of the toughest challenges in business is how to manage a premier franchise when category growth is negative. In conjunction with assessing our current portfolio’s category power, this is also a time to look at adjacent categories, whether as threats or as opportunities, to see if there are any transformative acquisitions that deserve our immediate attention.

Returning to our current set of assets, within each category the next question to answer is, what is our company power within that category? This is largely a factor of market share. The more share a company has of a given category, the more likely the ecosystem of partners that supports the category will focus first on that company’s installed base, adding more value to its offers, as well as to recommend that company’s products first, again because of the added leverage from partner engagement. Marketplaces, in other words, self-organize around category leaders, accelerating the sales and offloading the support costs of the market share leaders.

But what do you do when you don’t have company power? That’s when you turn your attention to market power. Marketplaces destabilize around problematic use cases that the incumbent vendors do not handle well. This creates openings for new entrants, provided they can authentically address the customer’s problems. The key is to focus product management on the whole product (not just what your enterprise supplies, but rather, everything the customer needs to be successful) and to focus your go-to-market engine on the target market segment. This is the playbook that has kept Crossing the Chasm on entrepreneur’s book lists some thirty years in, but it is a different matter to execute it in a large enterprise where sales and marketing are organized for global coverage, not rifle-shot initiatives. Nonetheless, when properly executed, it is the most reliable play in all of high-tech market development.

If market power is key to taking market share, offer power is key to maintaining it, both in high-growth categories as well as mature ones. Offer power is a function of three disciplines—differentiation to create customer preference, neutralization to catch up to and reduce a competitor’s differentiation, and optimization to eliminate non-value-adding costs. Anything that does not contribute materially to one of these three outcomes is waste.

Finally, execution power is the ability to take advantage of one’s inertial momentum rather than having it take advantage of you. Here the discipline of zone management has proved particularly valuable to enterprises who are seeking to balance investment in their existing lines of business, typically in mature categories, with forays into new categories that promise higher growth.

In upcoming blog posts I am going to dive deeper into each of the five powers outlined above to share specific frameworks that clarify what decisions need to be made during the strategic planning process and what principles can best guide them. In the meantime, there is still one more quarter in 2023 to make, and we all must do our best to make the most of it.

That’s what I think. What do you think?

Image Credit: Pixabay, Geoffrey A. Moore

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Innovation Evolution in the Era of AI

Innovation Evolution in the Era of AI

GUEST POST from Stefan Lindegaard

Half a decade ago, I laid out a perspective on the evolution of innovation. Now, I return to these reflections with a sentiment of both awe and unease as I observe the profound impacts of AI on innovation and business at large. The transformation unfolding before us presents a remarkable panorama of opportunities, yet it also carries with it the potential for disruption, hence the mixed feelings.

1. The Reign of R&D (1970-2015): There was a time when the Chief Technology Officer (CTO) held the reins. The focus was almost exclusively on Research and Development (R&D), with the power of the CTO often towering over the innovative impulses of the organization. Technology drove progress, but a tech-exclusive vision could sometimes be a hidden pitfall.

2. Era of Innovation Management (1990-2001): A shift towards understanding innovation as a strategic force began to emerge in the ’90s. The concept of managing innovation, previously only a flicker in the business landscape, began its journey towards being a guiding light. Pioneers like Christensen brought innovation into the educational mainstream, marking a paradigm shift in the mindsets of future business leaders.

3. Business Models & Customer Experience (2001-2008): The millennium ushered in an era where simply possessing superior technology wasn’t a winning card anymore. Process refinement, service quality, and most critically, innovative business models became the new mantra. Firms like Microsoft demonstrated this shift, evolving their strategies to stay competitive in this new game.

4. Ecosystems & Platforms (2008-2018): This phase saw the rise of ecosystems and platforms, representing a shift from isolated competition to interconnected collaboration. The lines that once defined industries began to blur. Companies from emerging markets, particularly China, became global players, and we saw industries morphing and intermingling. Case in point: was it still the automotive industry, or had the mobility industry arrived?

5. Corporate Transformation (2019-2025): With the onslaught of digital technologies, corporations faced the need to transform from within. Technological adoption wasn’t a mere surface-level change anymore; it demanded a thorough, comprehensive rethinking of strategies, structures, and processes. Anything less was simply insufficient to weather the storm of this digital revolution.

6. Comborg Transformation (2025-??): As we gaze into the future, the ‘Comborg’ era comes into view. This era sees organizations fusing human elements and digital capabilities into a harmonious whole. In this stage, the equilibrium between human creativity and AI-driven efficiency will be crucial, an exciting but challenging frontier to explore.

I believe that revisiting this timeline of innovation’s evolution highlights the remarkable journey we’ve undertaken. As we now figure out the role of AI in innovation and business, it’s an exciting but also challenging time. Even though it can be a bit scary, I believe we can create a successful future if we use AI in a responsible and thoughtful way.

Stefan Lindegaard Evolution of Innovation

Image Credit: Stefan Lindegaard, Unsplash

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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.

Image Credit: Pixabay

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AI and the Productivity Paradox

AI and the Productivity Paradox

GUEST POST from Greg Satell

In the 1970’s and 80’s, business investment in computer technology were increasing by more than twenty percent per year. Strangely though, productivity growth had decreased during the same period. Economists found this turn of events so strange that they called it the productivity paradox to underline their confusion.

Productivity growth would take off in the late 1990s, but then mysteriously drop again during the mid-aughts. At each juncture, experts would debate whether digital technology produced real value or if it was all merely a mirage. The debate would continue even as industry after industry was disrupted.

Today, that debate is over, but a new one is likely to begin over artificial intelligence. Much like in the early 1970s, we have increasing investment in a new technology, diminished productivity growth and “experts” predicting massive worker displacement . Yet now we have history and experience to guide us and can avoid making the same mistakes.

You Can’t Manage (Or Evaluate) What You Can’t Measure

The productivity paradox dumbfounded economists because it violated a basic principle of how a free market economy is supposed to work. If profit seeking businesses continue to make substantial investments, you expect to see a return. Yet with IT investment in the 70s and 80s, firms continued to increase their investment with negligible measurable benefit.

A paper by researchers at the University of Sheffield sheds some light on what happened. First, productivity measures were largely developed for an industrial economy, not an information economy. Second, the value of those investments, while substantial, were a small portion of total capital investment. Third, the aggregate productivity numbers didn’t reflect differences in management performance.

Consider a widget company in the 1970s that invested in IT to improve service so that it could ship out products in less time. That would improve its competitive position and increase customer satisfaction, but it wouldn’t produce any more widgets. So, from an economic point of view, it wouldn’t be a productive investment. Rival firms might then invest in similar systems to stay competitive but, again, widget production would stay flat.

So firms weren’t investing in IT to increase productivity, but to stay competitive. Perhaps even more importantly, investment in digital technology in the 70s and 80s was focused on supporting existing business models. It wasn’t until the late 90s that we began to see significant new business models being created.

The Greatest Value Comes From New Business Models—Not Cost Savings

Things began to change when firms began to see the possibilities to shift their approach. As Josh Sutton, CEO of Agorai, an AI marketplace, explained to me, “The businesses that won in the digital age weren’t necessarily the ones who implemented systems the best, but those who took a ‘digital first’ mindset to imagine completely new business models.”

He gives the example of the entertainment industry. Sure, digital technology revolutionized distribution, but merely putting your programming online is of limited value. The ones who are winning are reimagining storytelling and optimizing the experience for binge watching. That’s the real paradigm shift.

“One of the things that digital technology did was to focus companies on their customers,” Sutton continues. “When switching costs are greatly reduced, you have to make sure your customers are being really well served. Because so much friction was taken out of the system, value shifted to who could create the best experience.”

So while many companies today are attempting to leverage AI to provide similar service more cheaply, the really smart players are exploring how AI can empower employees to provide a much better service or even to imagine something that never existed before. “AI will make it possible to put powerful intelligence tools in the hands of consumers, so that businesses can become collaborators and trusted advisors, rather than mere service providers,” Sutton says.

It Takes An Ecosystem To Drive Impact

Another aspect of digital technology in the 1970s and 80s was that it was largely made up of standalone systems. You could buy, say, a mainframe from IBM to automate back office systems or, later, Macintoshes or a PCs with some basic software to sit on employees desks, but that did little more than automate basic clerical tasks.

However, value creation began to explode in the mid-90s when the industry shifted from systems to ecosystems. Open source software, such as Apache and Linux, helped democratize development. Application developers began offering industry and process specific software and a whole cadre of systems integrators arose to design integrated systems for their customers.

We can see a similar process unfolding today in AI, as the industry shifts from one-size-fits-all systems like IBM’s Watson to a modular ecosystem of firms that provide data, hardware, software and applications. As the quality and specificity of the tools continues to increase, we can expect the impact of AI to increase as well.

In 1987, Robert Solow quipped that, “ You can see the computer age everywhere but in the productivity statistics,” and we’re at a similar point today. AI permeates our phones, smart speakers in our homes and, increasingly, the systems we use at work. However, we’ve yet to see a measurable economic impact from the technology. Much like in the 70s and 80s, productivity growth remains depressed. But the technology is still in its infancy.

We’re Just Getting Started

One of the most salient, but least discussed aspects of artificial intelligence is that it’s not an inherently digital technology. Applications like voice recognition and machine vision are, in fact, inherently analog. The fact that we use digital technology to execute machine learning algorithms is actually often a bottleneck.

Yet we can expect that to change over the next decade as new computing architectures, such as quantum computers and neuromorphic chips, rise to the fore. As these more powerful technologies replace silicon chips computing in ones and zeroes, value will shift from bits to atoms and artificial intelligence will be applied to the physical world.

“The digital technology revolutionized business processes, so it shouldn’t be a surprise that cognitive technologies are starting from the same place, but that’s not where they will end up. The real potential is driving processes that we can’t manage well today, such as in synthetic biology, materials science and other things in the physical world,” Agorai’s Sutton told me.

In 1987, when Solow made his famous quip, there was no consumer Internet, no World Wide Web and no social media. Artificial intelligence was largely science fiction. We’re at a similar point today, at the beginning of a new era. There’s still so much we don’t yet see, for the simple reason that so much has yet to happen.

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

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The Hard Problem of Consciousness is Not That Hard

The Hard Problem of Consciousness is Not That Hard

GUEST POST from Geoffrey A. Moore

We human beings like to believe we are special—and we are, but not as special as we might like to think. One manifestation of our need to be exceptional is the way we privilege our experience of consciousness. This has led to a raft of philosophizing which can be organized around David Chalmers’ formulation of “the hard problem.”

In case this is a new phrase for you, here is some context from our friends at Wikipedia:

“… even when we have explained the performance of all the cognitive and behavioral functions in the vicinity of experience—perceptual discrimination, categorization, internal access, verbal report—there may still remain a further unanswered question: Why is the performance of these functions accompanied by experience?”

— David Chalmers, Facing up to the problem of consciousness

The problem of consciousness, Chalmers argues, is two problems: the easy problems and the hard problem. The easy problems may include how sensory systems work, how such data is processed in the brain, how that data influences behavior or verbal reports, the neural basis of thought and emotion, and so on. The hard problem is the problem of why and how those processes are accompanied by experience.3 It may further include the question of why these processes are accompanied by that particular experience rather than another experience.

The key word here is experience. It emerges out of cognitive processes, but it is not completely reducible to them. For anyone who has read much in the field of complexity, this should not come as a surprise. All complex systems share the phenomenon of higher orders of organization emerging out of lower orders, as seen in the frequently used example of how cells, tissues, organs, and organisms all interrelate. Experience is just the next level.

The notion that explaining experience is a hard problem comes from locating it at the wrong level of emergence. Materialists place it too low—they argue it is reducible to physical phenomena, which is simply another way of denying that emergence is a meaningful construct. Shakespeare is reducible to quantum effects? Good luck with that.

Most people’s problems with explaining experience, on the other hand, is that they place it too high. They want to use their own personal experience as a grounding point. The problem is that our personal experience of consciousness is deeply inflected by our immersion in language, but it is clear that experience precedes language acquisition, as we see in our infants as well as our pets. Philosophers call such experiences qualia, and they attribute all sorts of ineluctable and mysterious qualities to them. But there is a much better way to understand what qualia really are—namely, the pre-linguistic mind’s predecessor to ideas. That is, they are representations of reality that confer strategic advantage to the organism that can host and act upon them.

Experience in this context is the ability to detect, attend to, learn from, and respond to signals from our environment, whether they be externally or internally generated. Experiences are what we remember. That is why they are so important to us.

Now, as language-enabled humans, we verbalize these experiences constantly, which is what leads us to locate them higher up in the order of emergence, after language itself has emerged. Of course, we do have experiences with language directly—lots of them. But we need to acknowledge that our identity as experiencers is not dependent upon, indeed precedes our acquisition of, language capability.

With this framework in mind, let’s revisit some of the formulations of the hard problem to see if we can’t nip them in the bud.

  • The hard problem of consciousness is the problem of explaining why and how we have qualia or phenomenal experiences. Our explanation is that qualia are mental abstractions of phenomenal experiences that, when remembered and acted upon, confer strategic advantage to organisms under conditions of natural and sexual selection. Prior to the emergence of brains, “remembering and acting upon” is a function of chemical signals activating organisms to alter their behavior and, over time, to privilege tendencies that reinforce survival. Once brain emerges, chemical signaling is supplemented by electrical signaling to the same ends. There is no magic here, only a change of medium.
  • Annaka Harris poses the hard problem as the question of “how experience arise[s] out of non-sentient matter.” The answer to this question is, “level by level.” First sentience has to emerge from non-sentience. That happens with the emergence of life at the cellular level. Then sentience has to spread beyond the cell. That happens when chemical signaling enables cellular communication. Then sentience has to speed up to enable mobile life. That happens when electrical signaling enabled by nerves supplements chemical signaling enabled by circulatory systems. Then signaling has to complexify into meta-signaling, the aggregation of signals into qualia, remembered as experiences. Again, no miracles required.
  • Others, such as Daniel Dennett and Patricia Churchland believe that the hard problem is really more of a collection of easy problems, and will be solved through further analysis of the brain and behavior. If so, it will be through the lens of emergence, not through the mechanics of reductive materialism.
  • Consciousness is an ambiguous term. It can be used to mean self-consciousness, awareness, the state of being awake, and so on. Chalmers uses Thomas Nagel’s definition of consciousness: the feeling of what it is like to be something. Consciousness, in this sense, is synonymous with experience. Now we are in the language-inflected zone where we are going to get consciousness wrong because we are entangling it in levels of emergence that come later. Specifically, to experience anything as like anything else is not possible without the intervention of language. That is, likeness is not a qualia, it is a language-enabled idea. Thus, when Thomas Nagel famously asked, “What is it like to be a bat?” he is posing a question that has meaning only for humans, never for bats.

Going back to the first sentence above, self-consciousness is another concept that has been language-inflected in that only human beings have selves. Selves, in other words, are creations of language. More specifically, our selves are characters embedded in narratives, and use both the narratives and the character profiles to organize our lives. This is a completely language-dependent undertaking and thus not available to pets or infants. Our infants are self-sentient, but it is not until the little darlings learn language, hear stories, then hear stories about themselves, that they become conscious of their own selves as separate and distinct from other selves.

On the other hand, if we use the definitions of consciousness as synonymous with awareness or being awake, then we are exactly at the right level because both those capabilities are the symptoms of, and thus synonymous with, the emergence of consciousness.

  • Chalmers argues that experience is more than the sum of its parts. In other words, experience is irreducible. Yes, but let’s not be mysterious here. Experience emerges from the sum of its parts, just like any other layer of reality emergences from its component elements. To say something is irreducible does not mean that it is unexplainable.
  • Wolfgang Fasching argues that the hard problem is not about qualia, but about pure what-it-is-like-ness of experience in Nagel’s sense, about the very givenness of any phenomenal contents itself:

Today there is a strong tendency to simply equate consciousness with qualia. Yet there is clearly something not quite right about this. The “itchiness of itches” and the “hurtfulness of pain” are qualities we are conscious of. So, philosophy of mind tends to treat consciousness as if it consisted simply of the contents of consciousness (the phenomenal qualities), while it really is precisely consciousness of contents, the very givenness of whatever is subjectively given. And therefore, the problem of consciousness does not pertain so much to some alleged “mysterious, nonpublic objects”, i.e. objects that seem to be only “visible” to the respective subject, but rather to the nature of “seeing” itself (and in today’s philosophy of mind astonishingly little is said about the latter).

Once again, we are melding consciousness and language together when, to be accurate, we must continue to keep them separate. In this case, the dangerous phrase is “the nature of seeing.” There is nothing mysterious about seeing in the non-metaphorical sense, but that is not how the word is being used here. Instead, “seeing” is standing for “understanding” or “getting” or “grokking” (if you are nerdy enough to know Robert Heinlein’s Stranger in a Strange Land). Now, I think it is reasonable to assert that animals “grok” if by that we mean that they can reliably respond to environmental signals with strategic behaviors. But anything more than that requires the intervention of language, and that ends up locating consciousness per se at the wrong level of emergence.

OK, that’s enough from me. I don’t think I’ve exhausted the topic, so let me close by saying…

That’s what I think, what do you think?

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