Category Archives: Technology

Powering the Google Innovation Machine with the World’s Top Minds

Powering the Google Innovation Machine with the World's Top Minds

GUEST POST from Greg Satell

It’s no secret that Google is one of the most innovative companies on the planet. Besides pioneering and then dominating the search industry, it has also become a leader in developing futuristic technologies such as artificial intelligence, driverless cars and quantum computing. It has even launched a life science company.

What makes Google so successful is not one particular process, but how it integrates multiple strategies into a seamless whole. For example, Google Brain started out as a 20% time project, then migrated out to its “X” Division to accelerate development and finally came back to the mothership, where it now collaborates closely with engineering teams to build new products.

Yet perhaps its most important strategy, in fact the one that makes much of the rest possible, is how it partners with top scientists in the academic world. This is no “quick hit,” but a well thought out, long-term game plan designed to establish deep relationships based on cutting edge science and embed that knowledge deeply into just about everything Google does.

Building Deep Relationships to the Academic Community

“We design a variety programs that widen and deepen our relationships with academic scientists,” Maggie Johnson, who heads up University Relations at Google, told me. In fact, there are three distinct ways that Google engages directly with scientists beyond the typical research partnerships with universities.

The first is its Faculty Research Awards program, which are small one-year grants, usually to graduate students or postdocs whose work may be of interest to Google. These are unrestricted gifts, although recipients are highly encouraged to publish their work publicly, that allow the company to develop relationships with young talent at the beginning of their careers.

While anybody can apply for a Faculty Research Award, Focused Research Awards are only available by invitation. Typically, these are awarded to more senior researchers that Google has already had some contact with and last two to three years. However, they are also unrestricted grants that researchers can use as they see fit.

The third way that Google engages with scientists to to proactively engage leaders in a particular field of interest. Geoffrey Hinton, for example, is a pioneer in neural networks and widely considered one of the top AI experts in the world. He splits his time between his faculty position at the University of Toronto and working on Google Brain.

“Spinning In” World Class Scientists

The academic research programs provide many benefits to Google as a company. They give access to the most promising students for recruiting, allow it to help shape university curriculums and keep it connected to breakthrough research in important fields. However, the most direct benefits probably come inviting researchers to spend a sabbatical year at Google, which it calls its Visiting Faculty Program.

For example, Andrew Ng, a top AI researcher, decided to spend a year working at Google and quickly formed a close working relationship with two of the company’s brightest minds, Greg Corrado and Jeff Dean, who were interested in what was then a new brand of artificial intelligence called deep learning. Their collaboration became the Google Brain project.

The Visiting Faculty Program touches on everything Google does. Recently they’ve had people visiting the company like John Canny at UC Berkeley, who helped with the development of TPU’s, chips specialized to run Google’s AI algorithms and Michael Rabin, a Turing Award winning mathematician who was working on auction algorithms. For every Google priority, at least one of the world’s top minds is working with the company on it.

What makes the sabbatical program unusual is how deeply it is integrated into everyday work at the company. “In most cases, these scientists have already been working with our teams through one of our other programs, so the groundwork for a productive relationship has already been laid,” Maggie Johnson told me.

Developing “Win-Win” Relationships

One of the things that makes Google’s outreach to researchers work so well is that it is truly a win-win arrangement. Yes, the company gets top experts in important fields to work on its problems, but the researchers themselves get to work with unparalleled tools and data sets. They also get a much better sense of what problems are considered important in a commercial environment.

Katya Scheinberg, a Professor at Lehigh University who focuses on optimization problems, found working at Google to be a logical extension of her earlier collaboration with the company. “I had been working on large-scale machine learning problems and had some connections with Google scientists. So spending part of my sabbatical year at the company seemed fairly natural. I learned a lot about the practical problems that private sector researchers are working on,” she told me.

Since leaving Google, she’s found that her time at the company has shifted the focus of her research. “Working at Google got me interested in some different problems and alerted me to the possibility of applying some approaches I had worked on before to different fields of application.”

Sometimes scholars stay for longer and can have a transformative impact on the company. As noted above, Andrew Ng spent several years at the company. Andrew Moore, a renowned computer scientist and a former Dean of Carnegie Mellon’s computer program, took a leave of absence from his university to set up Google’s Research Center in Pittsburgh. Lasting relationships like these are rare in industry, but incredibly valuable.

Connecting to Discovery Is Something Anyone Can Do, But You Have to Make the Effort

Clearly, Google is an unusual company. There’s not many places that can attract the type of talent that it can. However, just about any business can, for example, support the work of a young graduate student or postdoc at a local university. In much the same way, inviting even a senior researcher to come for a short time is not prohibitively expensive.

Innovation is never a single event, but a process of discovery, engineering and transformation. It is by connecting to discovery that businesses can truly see into the future and develop the next generation of breakthrough products. Unfortunately, few businesses realize the importance of connecting with the academic world.

Make no mistake, if you don’t discover, you won’t invent and if you don’t invent you will be disrupted eventually. It’s just a matter of time. However, you can’t just show up one day and decide you want to work with the world’s greatest minds. Even Google, with all its resources and acumen, has had to work really hard at it.

It’s made these investments in time, focus and resources because it understands that the search business, as great as it is, won’t deliver outsized profits forever. Today, we no longer have the luxury to manage for stability, but must prepare for disruption.

— Article courtesy of the Digital Tonto blog and previously appeared on Inc.com
— Image credit: Dall-E on Bing

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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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Innovation the Star of the 2024 NBA All-Star Game

Innovation the Star of the 2024 NBA All-Star Game

by Braden Kelley

Eight years ago, back in 2016, I wrote an article titled What April Fool’s Day Teaches Us About Innovation about an April Fool’s prank played my alma mater, the University of Oregon involving an announcement that football games at Autzen Stadium would no longer played on artificial turf, but would be played on a giant digital screen instead. Here is the video:

It seemed preposterous at the time (2016) during the era of the technologically ancient Apple iPhone 7 and Samsung Galaxy S7 when the average LCD TV size according to Statista was only 43 inches.

Fast forward to February 16, 2024 and NBA All-Star Weekend in Indianapolis, Indiana and we saw the first ever basketball game of note held on a glass basketball court. But isn’t glass slippery when wet? Yes, but so is a heavily lacquered hardwood court – believe me I know from repeated spills during pickup basketball games. To help give it the traction of a hardwood court they’ve engineered thousands (or maybe millions) of tiny raised dots onto the glass surface.

Sports are always experimenting with various technologies, some of which don’t work out (like the tail following the puck on hockey broadcasts), and others which are executed so well that they enhance the viewing experience (first down yardage line in American football) or that most people don’t even know that they exist (advertisements projected onto the court in basketball television broadcasts that aren’t actually on the court but look as if they are).

So, how has this 2016 April Fool’s prank visualization evolved into a 2024 reality? What does it look like? Here is a video that will give you a sense of its capabilities:

First let me say this a pretty incredible technology that has definitely added to the excitement of this year’s NBA All-Star Weekend, but second I must also say that I would NEVER want to watch a regular NBA, college or international game played on a court like this because for me, sporting events are a time to unplug from technology, not be over-stimulated by it. But, for a special event like NBA All-Star Game Weekend or maybe the Harlem Globetrotters I think it makes sense.

How does this court make the leap from invention to innovation you might ask?

How does this court not find itself in the digital trash can next to the tail on the hockey puck?

The short answer is that scores well on my Innovation is All About Value framework. It creates value by adding value to the contest (skills challenge, celebrity all-star game), translates that value very quickly because it’s all visual, and the barriers to value access are non-existent for all but the visually impaired.

The court allowed the NBA to hold different games with different rules and lines on the same court without changing courts or making physical modifications. For example, the celebrity all-star game had a four point line (sponsored by Frito Lay) and at times the three point and four point lines even were actively moving. There was also a micro competition in game where three people ran to stars that appeared on the floor and shot and when they made a shot there a new star appeared and you could see over time which side of the court was winning because you could see which side had more stars. There was another moment where for a limited time the coaches faces appeared on the court and six points were awarded for each shot made from that spot. The dynamic nature of the game meant that you almost didn’t know what might come next – which was kind of exciting.

The integration of the court into the competition occurring upon it is what helps this technology make the leap from invention to innovation. But again, for me, only in special use cases like an All-Star game, an entertainment-based event or skills competition, but NOT for a pure competition use case where in my mind it distracts from the sport.

Here is a video of the skills challenge relay race – notice that the floor shows the player which way to go, but despite that Tyrese Maxey still goes the wrong way and has to double back. 😉

Then in the skills challenge passing team event the floor showed players where to stand and how many points had been scored at each of three targets. Again, it felt like part of the event and it allows the court to be instantly and uniquely re-configured.

And here are video highlights of the celebrity all-star game where you can see some of what I mentioned above:

So, what do you think? Innovation or not?

Image credit: NBA.com

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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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Resistance to Innovation – What if electric cars came first?

Resistance to Innovation - What if electric cars came first?

GUEST POST from Dennis Stauffer

In his acclaimed book the The Diffusion of Innovations—the most-cited work in all the social sciences—Everett Rogers explained how innovations frequently meet resistance. Resistance that isn’t always rational. How all-too-often we’re willing to accept the status quo despite its flaws and reject new options despite their benefits.

We’re seeing exactly this phenomenon with electric vehicles. Demand from what Rogers identified as the early adopters—wealthy buyers who can pay a premium for the newest technology—has largely been met. The challenge now is to reach a broader market of buyers with more practical concerns about cost, range, reliability, and safety. News articles and commentary are popping up noting those concerns and expressing doubts about just how useful electric cars really are. The lack of charging stations, the environmental impact of mining lithium, the danger of battery fires, and potential strains to the electrical grid. There are some legitimate concerns, but how much of that skepticism is grounded in the reality of electrification and how much is good old-fashioned resistance to change?

To answer that question, let’s turn the tables. What if electric cars came first, and we’re trying to introduce internal combustion engines? Here are some predictable—and quite similar—objections.

  • How can we possibly build all the gas stations we’re going to need, and should we? (If electrification is the entrenched technology, we’d have plenty of charging stations everywhere.)
  • Do you really want trucks carrying 10,000 gallons of highly explosive gasoline driving down the highway next to you? Accidents happen! Do you want 20 gallons of it parked in your garage, waiting for just one spark to set it off—taking your house with it?
  • You can charge your electric car at home while you sleep, or at a charging station while at work. You can’t do that with a gasoline engine. You must go somewhere to buy gas, take time to get there, and then stand next to a hose pumping one of the most flammable liquids we know of.
  • We’re going to need a lot of that gasoline. Where will we find it, and at what environmental cost? Are we going to start drilling everywhere? Even in the ocean, the arctic, and in fragile ecosystems?  Are we going to have massive tankers crisscrossing the oceans? What if there’s a leak or a spill?
  • How are we going to build all the refining capacity we’ll need to process and transport all that gas? That’s a massive investment. Who’s going to pay for it?
  • What if we need to get that gas from countries that don’t like us? Will they refuse to sell to us or charge exorbitant prices? Will we make our enemies rich?
  • Gasoline is more expensive per mile driven than electricity, and because it’s a commodity, its price fluctuates—sometimes a lot. You never know what you may have to pay.
  • Gasoline engines are a lot more expensive than electric motors. They’re much more complex and since we’re building them in smaller numbers at first, carmakers don’t have the same economies of scale.
  • Internal combustion engines are more complex to repair. How often will your car need to be fixed? Will your mechanic know how?
  • What about air pollution? Just one internal combustion car emits 4.6 metric tons of carbon dioxide each year. Multiply that by all the cars on the road!
  • Would you like a car that’s slower? The most powerful—and most expensive—internal combustion cars on the road have less torque than a typical electric vehicle. That means less acceleration when you need to pass someone.

Some of these concerns are a bit overblown — just like some of the concerns about electric cars. But others are entirely valid. Yet too often we shrug them off because we’ve already accepted those costs, inconveniences, and dangers.

What we’re seeing with electric cars is the same progression we saw with early automobiles, airplanes, hybrid crops, personal computers, and many other now widely popular innovations. We’ll get there, but not without some pushback.

Image Credit: Pixabay

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Customer Journeys and the Technology Adoption Lifecycle

Customer Journeys and the Technology Adoption Lifecycle

GUEST POST from Geoffrey A. Moore

Like everything else in this Darwinian world of ours, customer journeys evolve with changes in the environment. Ever since the advent of the semiconductor, a compelling source of such changes has been disruptive digital technology. Although we are all eager to embrace its benefits, markets must first work through their adoption life cycles, during which different buying personas come to the fore at different stages, with each one on a very different kind of journey.

So, if you plan to catch the next wave and sell the next big thing, you’re going to need to adjust your customer journey playbook as you go along. Here’s a recap of what is in store for you.

Customer Journeys in the Early Market

The early market buying personas are the visionary and the technology enthusiast, the former eager to leverage disruption to gain first-mover competitive advantage, the latter excited to participate in the latest and greatest thing. Both are on a journey of discovery.

Technology enthusiasts need to get as close to the product as possible, seeing demos and alpha-testing prototypes as soon as they are released. They are not looking to be sold (for one thing, they have no money)—they are looking to educate themselves in order to be a reliable advisor to their visionary colleague. The key is to garner them privileged access to the technical whizzes in your own enterprise and, once under NDA, to share with them the wondrous roadmap you have in mind.

Visionaries are on a different path. They want to get as clear an understanding as possible of what makes the disruptive technology so different, to see whether such a difference could be a game changer in their circumstances. This is an exercise in imagineering. It will involve discussing hypothetical use cases, and applying first principles, which means you need to bring the smartest people in your company to the table, people who can not only communicate the magic of what you have but who can also keep up with the visionary’s vision as well.

Once this journey is started, you need to guide it toward a project, not a product sale. It is simply too early to make any kind of product promise that you can reliably keep. Not only is the paint not yet dry on your own offer, but also the partner ecosystem is as yet non-existent, so the only way a whole product can be delivered is via a dedicated project team. To up the stakes even further, visionaries aren’t interested in any normal productivity improvements, they are looking to leapfrog the competition with something astounding, so a huge amount of custom work will be required. This is all well and good provided you have a project-centric contract that doesn’t leave you on the hook for all the extra labor involved.

Customer Journeys to Cross the Chasm

The buying personas on the other side of the chasm are neither visionaries nor technology enthusiasts. Rather, they are pragmatists, and to be really specific, they are pragmatists in pain. Unlike early market customers, they are not trying to get ahead, they are trying to get themselves out of a jam. In such a state, they could care less about your product, and they do not want to meet your engineers or engage in any pie-in-the-sky discussions of what the future may hold. All they want to do is find a way out of their pain.

This is a journey of diagnosis and prescription. They have a problem which, given conventional remedies, is not really solvable. They are making do with patchwork solutions, but the overall situation is deteriorating, and they know they need help. Sadly, their incumbent vendors are not able to provide it, so despite their normal pragmatist hesitation about committing to a vendor they don’t know and a solution that has yet to be proven, they are willing to take a chance—provided, that is, that:

  • you demonstrate that you understand their problem in sufficient depth to be credible as a solution provider, and
  • that you commit to bringing the entire solution to the table, even when it involves orchestrating with partners to do so.

To do so, your first job is to engage with the owner of the problem process in a dialog about what is going on. During these conversations, you demonstrate your credibility by anticipating the prospective customer’s issues and referencing other customers who have faced similar challenges. Once prospects have assured themselves that you appreciate the magnitude of their problem and that you have expertise to address its challenges, then (and only then) will they want to hear about your products and services.

As the vendor, therefore, you are differentiating on experience and domain expertise, ideally by bringing someone to the table who has worked in the target market segment and walked in your prospective customer’s shoes. Once you have established credibility by so doing, then you must show how you have positioned the full force of your disruptive product to address the very problem that besets your target market. Of course, you know that your product is far more capable than this, and you also know you have promised your investors global domination, not a niche market solution. But for right now, to cross the chasm, you forsake all that and become laser-focused on demolishing the problem at hand. Do that for the first customer, and they will tell others. Do that for the next, and they will tell more. By the time you have done this four or five times, your phone will start ringing. But to get to this point, you need to be customer-led, not product-led.

Customer Journeys Inside the Tornado.

The tornado is that point in the technology adoption life cycle when the pragmatist community shifts from fear of going too soon to fear of missing out. As a consequence, they all rush to catch up. Even without a compelling first use case, they commit resources to the new category. Thus, for the first time in the history of the category, prospective customers have budget allocated before the salesperson calls. (In the early market, there was no budget at all—the visionary had to create it. In the chasm-crossing scenario, there is budget, but it is being spent on patchwork fixes with legacy solutions and needs to get reallocated before a deal can be closed.)
Budget is allocated to the department that will purchase and support the new offer, not the ones who will actually use it (although they will no doubt get chargebacks at some point). That means for IT offerings the target customer is the technical buyer and the CIO, the former who will make the product decision, the latter who will make the vendor decision. Ideally, the two will coincide, but when they don’t, the vendor choice usually prevails.

Now, one thing we know about budgets is that once they have been allocated they will get spent. These customers are on a buying mission journey. They produce RFPs to let them compare products and vet companies, and they don’t want any vendor to get too close to them during the process. Sales cycles are super-competitive, and product bake-offs are not uncommon. This means you need to bring your best systems engineers to the table, armed with killer demos, supported by sales teams, armed with battle cards that highlight competitor strengths and weaknesses and how to cope with the former and exploit the latter. There is no customer intimacy involved.

What is at stake, instead, is simply winning the deal. Here account mapping can make a big difference. Who is the decision maker really? Who are the influencers? Who has the inside track? You need a champion on the inside who can give you the real scoop. And at the end of the sales cycle, you can expect a major objection to your proposal, a real potential showstopper, where you will have to find some very creative way to close the deal and get it off the table. That is how market share battles are won.

Customer Journeys on Main Street

On Main Street, you are either the incumbent or a challenger. If the latter, your best bet is to follow a variation on the chasm-crossing playbook, searching out a use case where the incumbent is not well positioned and the process owner is getting frustrated—as discussed above. For incumbents, on the other hand, it is a completely different playbook.

The persona that matters most on Main Street is the end user, regardless of whether they have budget or buying authority. Increasing their productivity is what creates the ROI that justifies any additional purchases, not to mention retaining the current subscription. This calls for a journey of continuous improvement.

Such a journey rewards two value disciplines on the vendor’s part—customer intimacy and operational excellence. The first is much aided by the advent of telemetry which can track product usage by user and identify opportunities for improvement. Telemetric data can feed a customer health score which allows the support team to see where additional attention is most needed. Supplying the attention requires operational excellence, and once again technology innovation is changing the game, this time through product-led prompts, now amplified by generative AI commentary. Finally, sitting atop such infrastructure is the increasingly powerful customer success function whose role is to connect with the middle management in charge, discuss with them current health score issues and their remediation, and explore opportunities for adding users, incorporating product extensions, and automating adjacent use cases.

Summing up

The whole point of customer journeys done right is to start with the customer, not with the sales plan. That said, where the customer is in their adoption life cycle defines the kind of journey they are most likely to be on. One size does not fit all, so it behooves the account team to place its bets as best it can and then course correct from there.
That’s what I think. What do you think?

Image Credit: Pixabay

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Top 100 Innovation and Transformation Articles of 2023

Top 100 Innovation and Transformation Articles of 2023

2021 marked the re-birth of my original Blogging Innovation blog as a new blog called Human-Centered Change and Innovation.

Many of you may know that Blogging Innovation grew into the world’s most popular global innovation community before being re-branded as InnovationExcellence.com and being ultimately sold to DisruptorLeague.com.

Thanks to an outpouring of support I’ve ignited the fuse of this new multiple author blog around the topics of human-centered change, innovation, transformation and design.

I feel blessed that the global innovation and change professional communities have responded with a growing roster of contributing authors and more than 17,000 newsletter subscribers.

To celebrate we’ve pulled together the Top 100 Innovation and Transformation Articles of 2023 from our archive of over 1,800 articles on these topics.

We do some other rankings too.

We just published the Top 40 Innovation Bloggers of 2023 and as the volume of this blog has grown we have brought back our monthly article ranking to complement this annual one.

But enough delay, here are the 100 most popular innovation and transformation posts of 2023.

Did your favorite make the cut?

1. Fear is a Leading Indicator of Personal Growth – by Mike Shipulski

2. The Education Business Model Canvas – by Arlen Meyers

3. Act Like an Owner – Revisited! – by Shep Hyken

4. Free Innovation Maturity Assessment – by Braden Kelley

5. The Role of Stakeholder Analysis in Change Management – by Art Inteligencia

6. What is Human-Centered Change? – by Braden Kelley

7. Sustaining Imagination is Hard – by Braden Kelley

8. The One Movie All Electric Car Designers Should Watch – by Braden Kelley

9. 50 Cognitive Biases Reference – Free Download – by Braden Kelley

10. A 90% Project Failure Rate Means You’re Doing it Wrong – by Mike Shipulski

11. No Regret Decisions: The First Steps of Leading through Hyper-Change – by Phil Buckley

12. Reversible versus Irreversible Decisions – by Farnham Street

13. Three Maps to Innovation Success – by Robyn Bolton

14. Why Most Corporate Innovation Programs Fail (And How To Make Them Succeed) – by Greg Satell

15. The Paradox of Innovation Leadership – by Janet Sernack

16. Innovation Management ISO 56000 Series Explained – by Diana Porumboiu

17. An Introduction to Journey Maps – by Braden Kelley

18. Sprint Toward the Innovation Action – by Mike Shipulski

19. Marriott’s Approach to Customer Service – by Shep Hyken

20. Should a Bad Grade in Organic Chemistry be a Doctor Killer? – NYU Professor Fired for Giving Students Bad Grades – by Arlen Meyers, M.D.

21. How Networks Power Transformation – by Greg Satell

22. Are We Abandoning Science? – by Greg Satell

23. A Tipping Point for Organizational Culture – by Janet Sernack

24. Latest Interview with the What’s Next? Podcast – with Braden Kelley

25. Scale Your Innovation by Mapping Your Value Network – by John Bessant

26. Leveraging Emotional Intelligence in Change Leadership – by Art Inteligencia

27. Visual Project Charter™ – 35″ x 56″ (Poster Size) and JPG for Online Whiteboarding – by Braden Kelley

28. Unintended Consequences. The Hidden Risk of Fast-Paced Innovation – by Pete Foley

29. A Shortcut to Making Strategic Trade-Offs – by Geoffrey A. Moore

30. 95% of Work is Noise – by Mike Shipulski


Build a common language of innovation on your team


31. 8 Strategies to Future-Proofing Your Business & Gaining Competitive Advantage – by Teresa Spangler

32. The Nine Innovation Roles – by Braden Kelley

33. The Fail Fast Fallacy – by Rachel Audige

34. What is the Difference Between Signals and Trends? – by Art Inteligencia

35. A Top-Down Open Innovation Approach – by Geoffrey A. Moore

36. FutureHacking – Be Your Own Futurist – by Braden Kelley

37. Five Key Digital Transformation Barriers – by Howard Tiersky

38. The Malcolm Gladwell Trap – by Greg Satell

39. Four Characteristics of High Performing Teams – by David Burkus

40. ACMP Standard for Change Management® Visualization – 35″ x 56″ (Poster Size) – Association of Change Management Professionals – by Braden Kelley

41. 39 Digital Transformation Hacks – by Stefan Lindegaard

42. The Impact of Artificial Intelligence on Future Employment – by Chateau G Pato

43. A Triumph of Artificial Intelligence Rhetoric – Understanding ChatGPT – by Geoffrey A. Moore

44. Imagination versus Knowledge – Is imagination really more important? – by Janet Sernack

45. A New Innovation Sphere – by Pete Foley

46. The Pyramid of Results, Motivation and Ability – Changing Outcomes, Changing Behavior – by Braden Kelley

47. Three HOW MIGHT WE Alternatives That Actually Spark Creative Ideas – by Robyn Bolton

48. Innovation vs. Invention vs. Creativity – by Braden Kelley

49. Where People Go Wrong with Minimum Viable Products – by Greg Satell

50. Will Artificial Intelligence Make Us Stupid? – by Shep Hyken


Accelerate your change and transformation success


51. A Global Perspective on Psychological Safety – by Stefan Lindegaard

52. Customer Service is a Team Sport – by Shep Hyken

53. Top 40 Innovation Bloggers of 2022 – Curated by Braden Kelley

54. A Flop is Not a Failure – by John Bessant

55. Generation AI Replacing Generation Z – by Braden Kelley

56. ‘Innovation’ is Killing Innovation. How Do We Save It? – by Robyn Bolton

57. Ten Ways to Make Time for Innovation – by Nick Jain

58. The Five Keys to Successful Change – by Braden Kelley

59. Back to Basics: The Innovation Alphabet – by Robyn Bolton

60. The Role of Stakeholder Analysis in Change Management – by Art Inteligencia

61. Will CHATgpt make us more or less innovative? – by Pete Foley

62. 99.7% of Innovation Processes Miss These 3 Essential Steps – by Robyn Bolton

63. Rethinking Customer Journeys – by Geoffrey A. Moore

64. Reasons Change Management Frequently Fails – by Greg Satell

65. The Experiment Canvas™ – 35″ x 56″ (Poster Size) – by Braden Kelley

66. AI Has Already Taken Over the World – by Braden Kelley

67. How to Lead Innovation and Embrace Innovative Leadership – by Diana Porumboiu

68. Five Questions All Leaders Should Always Be Asking – by David Burkus

69. Latest Innovation Management Research Revealed – by Braden Kelley

70. A Guide to Effective Brainstorming – by Diana Porumboiu

71. Unlocking the Power of Imagination – How Humans and AI Can Collaborate for Innovation and Creativity – by Teresa Spangler

72. Rise of the Prompt Engineer – by Art Inteligencia

73. Taking Care of Yourself is Not Impossible – by Mike Shipulski

74. Design Thinking Facilitator Guide – A Crash Course in the Basics – by Douglas Ferguson

75. What Have We Learned About Digital Transformation Thus Far? – by Geoffrey A. Moore

76. Building a Better Change Communication Plan – by Braden Kelley

77. How to Determine if Your Problem is Worth Solving – by Mike Shipulski

78. Increasing Organizational Agility – by Braden Kelley

79. Mystery of Stonehenge Solved – by Braden Kelley

80. Agility is the 2023 Success Factor – by Soren Kaplan


Get the Change Planning Toolkit


81. The Five Gifts of Uncertainty – by Robyn Bolton

82. 3 Innovation Types Not What You Think They Are – by Robyn Bolton

83. Using Limits to Become Limitless – by Rachel Audige

84. What Disruptive Innovation Really Is – by Geoffrey A. Moore

85. Today’s Customer Wants to Go Fast – by Shep Hyken

86. The 6 Building Blocks of Great Teams – by David Burkus

87. Unlock Hundreds of Ideas by Doing This One Thing – Inspired by Hollywood – by Robyn Bolton

88. Moneyball and the Beginning, Middle, and End of Innovation – by Robyn Bolton

89. There are Only 3 Reasons to Innovate – Which One is Yours? – by Robyn Bolton

90. A Shortcut to Making Strategic Trade-Offs – by Geoffrey A. Moore

91. Customer Experience Personified – by Braden Kelley

92. 3 Steps to a Truly Terrific Innovation Team – by Robyn Bolton

93. Building a Positive Team Culture – by David Burkus

94. Apple Watch Must Die – by Braden Kelley

95. Kickstarting Change and Innovation in Uncertain Times – by Janet Sernack

96. Take Charge of Your Mind to Reclaim Your Potential – by Janet Sernack

97. Psychological Safety, Growth Mindset and Difficult Conversations to Shape the Future – by Stefan Lindegaard

98. 10 Ways to Rock the Customer Experience In 2023 – by Shep Hyken

99. Artificial Intelligence is Forcing Us to Answer Some Very Human Questions – by Greg Satell

100. 23 Ways in 2023 to Create Amazing Experiences – by Shep Hyken

Curious which article just missed the cut? Well, here it is just for fun:

101. Why Business Strategies Should Not Be Scientific – by Greg Satell

These are the Top 100 innovation and transformation articles of 2023 based on the number of page views. If your favorite Human-Centered Change & Innovation article didn’t make the cut, then send a tweet to @innovate and maybe we’ll consider doing a People’s Choice List for 2023.

If you’re not familiar with Human-Centered Change & Innovation, we publish 1-6 new articles every week focused on human-centered change, innovation, transformation and design insights from our roster of contributing authors and ad hoc submissions from community members. Get the articles right in your Facebook feed or on Twitter or LinkedIn too!

Editor’s Note: Human-Centered Change & Innovation is open to contributions from any and all the innovation & transformation professionals out there (practitioners, professors, researchers, consultants, authors, etc.) who have a valuable insight to share with everyone for the greater good. If you’d like to contribute, contact us.

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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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What’s Next?

What's Next?

GUEST POST from Mike Shipulski

Anonymous: What do you think we should do next?

Me: It depends. How did you get here?

Anonymous: Well, we’ve had great success improving on what we did last time.

Me: Well, then you’ll likely do that again.

Anonymous: Do you think we’ll be successful this time?

Me: It depends. If the performance/goodness has been flat over your last offerings, then no. When performance has been constant over the last several offerings it means your technology is mature and it’s time for a new one. Has performance been flat over the years?

Anon: Yes, but we’ve been successful with our tried-and-true recipe and the idea of creating a new technology is risky.

Me: All things have a half-life, including successful business models and long-in-the-tooth technologies, and your success has blinded you to the fact that yours are on life support. Developing a new technology isn’t risky. What’s risk is grasping tightly to a business model that’s out of gas.

Anon: That’s harsh.

Me: I prefer “truthful.”

Anon: So, we should start from scratch and create something altogether new?

Me: Heavens no. That would be a disaster. Figure out which elements are blocking new functionality and reinvent those. Hint: look for the system elements that haven’t changed in a dog’s age and that are shared by all your competitors.

Anon: So, I only have to reinvent several elements?

Me: Yes, but probably fewer than several. Probably just one.

Anon: What if we don’t do that?

Me: Over the next five years, you’ll be successful. And then in year six, the wheels will fall off.

Anon: Are you sure?

Me: No, they could fall off sooner.

Anon: How do you know it will go down like that?

Me: I’ve studied systems and technologies for more than three decades and I’ve made a lot of mistakes. Have you heard of The Voice of Technology?

Anon: No.

Me: Well, take a bite of this – The Voice of Technology. Kevin Kelly has talked about this stuff at great length. Have you read him?

Anon: No.

Me: Here’s a beauty from Kevin – What Technology Wants. How about S-curves?

Anon: Nope.

Me: Here’s a little primer – Beyond Dead Reckoning. How about Technology Forecasting?

Anon: Hmm. I don’t think so.

Me: Here’s something from Victor Fey, my teacher. He worked with Altshuller, the creator of TRIZ – Guided Technology Evolution. I’ve used this method to predict several industry-changing technologies.

Anon: Yikes! There’s a lot here. I’m overwhelmed.

Me: That’s good! Overwhelmed is a sign you realize there’s a lot you don’t know. You could be ready to become a student of the game.

Anon: But where do I start?

Me: I’d start Wardley Maps for situation analysis and LEANSTACK to figure out if customers will pay for your new offering.

Anon: With those two I’m good to go?

Me: Hell no!

Anon: What do you mean?

Me: There’s a whole body of work to learn about. Then you’ve got to build the organization, create the right mindset, select the right projects, train on the right tools, and run the projects.

Anon: That sounds like a lot of work.

Me: Well, you can always do what you did last time. END.

Image credit: Unsplash

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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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