Category Archives: Innovation

Five Things Most Managers Don’t Know About Innovation

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Five Things Most Managers Don't Know About Innovation

GUEST POST from Greg Satell

Every business knows it needs to innovate. What isn’t so clear is how to go about it. There is no shortage of pundits, blogs and conferences that preach the gospel of agility, disruptive innovation, open innovation, lean startups or whatever else is currently in vogue. It can all be overwhelming.

The reality is that there is no one ‘true’ path to innovation. In researching my book, Mapping Innovation, I found that organizations of all shapes and sizes can be great innovators. Some are lean and nimble, while others are large and bureaucratic. Some have visionary leaders, others don’t. No one model prevails.

However, there are common principles that we can apply. While there is no “right way” to innovate, there are plenty of wrong ways. So perhaps the best way forward is to avoid the pitfalls that can undermine innovative efforts in your organization and kill promising new solutions. Here are five things every business should know about innovation.

1. Every Square-Peg Business Eventually Meets Its Round-Hole World

IBM is many peoples’ definition of a dinosaur. Not too long ago, it announced its 22nd consecutive quarter of declining revenues. Nevertheless, it seems to be turning a corner. What’s going on? How can a century-old technology company survive against the onslaught of the 21st century phenoms like Google, Amazon, Apple and Facebook?

The truth is that this is nothing new for IBM. Today, its business of providing installed solutions for large enterprises is collapsing due to the rise of the cloud. In the 90s it was near bankruptcy. In the 50s, its tabulating machine business was surpassed by digital technology. Each time eulogies are paraded around for Big Blue it seems to come back even stronger.

What IBM seems to understand better than just about anybody else is that every square-peg business eventually meets its round-hole world. Changes in technology, customer preferences and competitive environment eventually render every business model irrelevant. That’s just reality and there really is no changing it.

IBM’s secret weapon is its research division, which explores pathbreaking technologies long before they have a clear path to profitability. So when one business dies they have something to replace it with. Despite those 22 quarters of declining revenues it has a bright future with things like Watson, quantum computing and neuromorphic chips.

It’s better to prepare than adapt.

2. Innovation Isn’t About Ideas, It’s About Solving Problems

Probably the biggest misconception about innovation is that it’s about ideas. So there is tons of useless advice about brainstorming methods, standing meetings and word games, such as replacing “can’t” with “can if.” If these things help you work more productively, great, but they will not make you an innovator.

In my work, I speak to top executives, amazingly successful entrepreneurs and world class scientists. Some of these have discovered or created things that truly changed the world. Yet not once did anyone tell me that a brainstorming session or “productivity hack” set them on the road to success. They were simply trying to solve a problem that was meaningful to them.

What I do hear a lot from mid-level and junior executives is that they are not given “permission” to innovate and that nobody wants to hear about their ideas. That’s right. Nobody wants to hear about your ideas. People are busy with their own ideas.

So stop trying to come up with some earth shattering idea. Go out and find a good problem and start figuring out how to solve it. Nobody needs an idea, but everybody has a problem they need solved.

3. You Don’t Hire Or Buy Innovation, You Empower It

One of the questions I always get asked when I advise organizations is how to recruit and retain more innovative people. I know the type they have in mind. Someone fashionably dressed, probably with some tasteful piercings and some well placed ink, that spouts off a never-ending stream of ideas.

Yet that’s exactly what you don’t want. That’s exactly the type of unproductive hotshot that can stop innovation in its tracks. They talk over other people, which discourages new ideas from being voiced and their constant interruptions kill collaboration.

The way you create innovation is by empowering an innovative culture. That means creating a safe space for ideas, fostering networks inside and outside the organization, promoting collaboration and instilling a passion for solving problems. That’s how you promote creativity.

So if you feel that your people are not innovating, ask yourself what you’re doing to get in their way.

4. If Something Is Truly New And Different, You Need a “Hair On Fire” Use Case

As a general operational rule, you should seek out the largest addressable market you can find. Larger markets not only have more money, they are more stable and usually more diverse. Identifying even a small niche in a big market can make for a very profitable business.

Unfortunately, what thrives in operations can often fail for innovation. When you have an idea that’s truly new and different, you don’t want to start with a large addressable market. You want to find a hair-on-fire use case — somebody that needs a problem solved so badly that they either already have a budget for it or have scotched-taped together some half solution.

The reason you want to find a hair-on-fire use case is that when something is truly new and different, it is untested and poorly understood. But someone who needs a problem solved really badly will be willing to work with you to find flaws, fix them and improve your offer. From there you can begin to scale up and hunt larger game.

5. You Need To Seek Out A Grand Challenge

Most of the problems we deal with are relatively small. We cater to changing customer tastes, respond to competitive threats and fix things that are broken. Sometimes we go a bit further afield and enter a new market or develop a new capability. These are the bread and butter of a good business. That’s how you win in the marketplace.

Yet every business is ultimately disrupted. When that happens, normal operating practice will only make you better and better at things people care less and less about. You can’t build the future by looking to the past. You build the future by creating something that’s new and important, that solves problems that are currently unsolvable.

That’s why every organization needs to seek out grand challenges. These are long, sustainable efforts that solve a fundamental problem in your industry or field that change the realm of what’s considered possible. They are not “bet the company” initiatives and shouldn’t present a material risk to the business if they fail, but have a transformational impact if they succeed.

As I noted above, there is no one “true” path to innovation. Everybody needs to find their own way. Still, there are common principles and by applying them, every business can up their innovation game.

— Article courtesy of the Digital Tonto blog and previously appeared on Harvard Business Review
— Image credits: Pexels

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Bringing Emotional Energy and Creative Thinking to AI

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Bringing Emotional Energy and Creative Thinking to AI

GUEST POST from Janet Sernack

The impact of disruption, hyper-connectivity, and uncertainty, coupled with the pace of change, is causing many people to feel fearful and anxious. They become defensive and reactive and ‘go under’ emotionally and ‘go inwards’ cognitively by ruminating about their past and what bad things may happen in the future.  Dwelling on past mistakes, failures, and poor performance also causes them to disengage emotionally, take flight and move away, avoid taking action, fight, or freeze and become inert, paralyzed, and immobilized. The outcome is resistance to the possibilities and creative changes using Generative AI might bring. Because they lack the vital creative and emotional energy to generate creative thinking in partnership with AI, they will resist innovation-led change and stay ‘stuck’ in their habitual, safe and conventional roles, capabilities and identities.

Emotional energy is the catalyst that fuels the creative process. Understanding and harnessing this energy inspires and motivates individuals to explore and embrace creative thinking strategies in partnership with AI.

When a person’s emotional energy has contracted, it results in constrained, negative, pessimistic, and even catastrophic thinking habits.

Where there is no space, doorway, or threshold to take on anything new, novel, or different or to imagine what might be possible in an uncertain future to evolve, advance, or transform their personal or professional lives.

Emotional energy catalyzes people’s hope, positivity and optimism to approach their worlds differently.

When people are constrained from becoming hopeful, positive, and optimistic, they cannot apply foresight to explore future possibilities and opportunities at the accelerating pace that Generative AI tools offer in unleashing the human ingenuity and generating creative thinking required to solve challenges and increasingly complex problems.

Augmenting human creativity

Generative AI, as highlighted in a recent Harvard Business Review article, How Generative AI can Augment Human Creativity, has the potential to assist humans in creating innovative solutions. Its role is not to replace humans but to augment their creativity, helping them generate and identify novel ideas and improve the quality of raw ideas.

To empower individuals to make intelligent decisions and solve complex problems, it is crucial to notice, disrupt, dispute and deviate from their unresourceful default patterns or habitual ways of doing things.

Because emotional energy is the catalyst that fuels the creative process, it is crucial to help people find ways to re-ignite their emotional energy.

Empowering, enabling, and equipping them to embody and take on new, more resourceful emotional states and traits that allow them to break free from the constraints by identifying and letting go of old, irrelevant roles, capabilities, and identities. To take on new ones to facilitate positive changes, solve challenges, and deliver highly valued innovative solutions in partnership with Generative AI to generate creative thinking.

Generating the power of questions in problem-solving

I applied and implemented three key strategies to partner with Generative AI during the six-month coaching partnership. I used creative thinking strategies to develop a comprehensive life-coaching plan for a coaching client that serendipitously co-created a range of transformational outcomes.

Identify the key challenges, strengths, and systemic nature of the core problem and set a goal for change.

Encouraged to experiment with coaching in partnership with Generative AI, I created a comprehensive summary of what my client and I agreed her core problem was.  We defined a goal for effecting positive and constructive change and outlined evidence of achieving a successful outcome. I incorporated these elements into a descriptive paragraph and uploaded it into the Generative AI platform.

Develop a range of catalytic questions.

I focused on designing four key catalytic questions, to evoke and provoke creative thinking strategies. I requested the platform to design and develop a life-coaching plan to achieve our goal and solve her unique problem:   

Integration involves showing that two things which appear to be different are actually the same:

  • What might be some key existing transformational coaching elements that can be integrated into the new life-coaching plan I am trying to create to solve this problem?
  • Splitting involves seeing how two things that look the same might actually be different and can be divided into useful parts, like an assembly line:
  • What might be some key components of transformational coaching plans that can be combined to connect with a life-coaching plan to help solve this problem?
  • Figure-ground reversal involves realizing that what is crucial is in the background and not in the foreground, like the invention of Slack.
  • What might be some of the missing parts in the transformational and life coaching processes that might be included to help solve this problem?
  • Distal thinking involves imagining things different from the present, like the Tesla electric car.
  • What could be possible without boundaries, rules or limitations in harnessing the emotional energy required to partner with my client in our coaching relationship?
  • How might I create value for my client? What key constraints in her whole system relate to life coaching, and how might I leverage these to solve the problem differently?

It took less than a minute and consisted of a comprehensive, step-by-step, detailed plan that would have taken me at least half a day to consider and construct.

I was delighted to have an evidence-based example of successfully augmenting human creativity, partnering with Generative AI to generate creative thinking to advance my coaching partnership.

Partner with applying a transformational process.

It took less than a minute and consisted of a comprehensive, step-by-step, detailed plan that would have taken me at least half a day of using my pause-power to construct. I was delighted to have an evidence-based example of successfully augmenting human creativity to experiment with when partnering with Generative AI to generate creative thinking in my coaching partnership:

  • Generating and identifying a range of novel ideas towards improving her well-being.
  • Exploring and improving the range and quality of the initial raw ideas by applying pause power to incubate, illuminate, and generate creative thinking.
  • Identifying and developing a range of options for my client to choose from, allowing her to let go of what was depleting her emotional energy and retain her hopefulness, positivity, and optimism.
  • Identifying and developing a range of options for my client to choose from, to take on to manifest the desired future state of well-being and re-energize her emotional energy.

What was the outcome?

By co-creating a safe and collective holding space with my client, we supported her in re-energizing emotionally and applying future-oriented creative thinking strategies. We partnered with Generative AI to innovate my coaching approach and maximize our intelligence.

The outcome was personally transformative and sustained by:

  • Ensuring she re-ignited and identified strategies and new habits to sustain her emotional energy and make the necessary changes and future choices.
  • Applying circuit breakers and divergent thinking strategies to disrupt and dispute unresourceful beliefs, biases and behavior patterns.
  • Creating a safe space allowed her to deviate from her feelings, thoughts, and mindset to identify what new roles, capabilities, and identities to take on in the future and how they could benefit her and add value to the quality of her life.
  • Assisting in creating various ideas and options to refine when making significant lifestyle change choices.

It was a powerful learning experience for both my client and myself, reinforcing and validating that “Generative AI’s greatest potential is not replacing humans; it is to assist humans in their individual and collective efforts to create hitherto unimaginable solutions. It can truly democratize innovation.”

Please find out more about our work at ImagineNation™.

Check out our learning products and tools, including The Coach for Innovators, Leaders, and Teams Certified Program, presented by Janet Sernack. It is a collaborative, intimate, and profoundly personalised innovation coaching and learning program supported by a global group of peers over nine weeks and can be customised as a bespoke corporate learning program. Please find out more about our products and tools.

Image Credit: Pixabay

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Delivering Customer Value is the Key to Success

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Delivering Customer Value is the Key to Success

GUEST POST from Mike Shipulski

Whatever your initiative, start with customer value. Whatever your project, base it on customer value. And whatever your new technology, you guessed it, customer value should be front and center.

Whenever the discussion turns to customer value, expect confusion, disagreement, and, likely, anger. To help things move forward, here’s an operational definition I’ve found helpful:

When they buy it for more than your cost to make it, you have customer value.

And when there’s no way to pull out of the death spiral of disagreement, use this operational definition to avoid (or stop) bad projects:

When no one will buy it, you don’t have customer value and it’s a bad project.

As two words, customer and value don’t seem all that special. But, when you put them together, they become words to live by. But, also, when you do put them together, things get complicated. Here’s why.

To provide customer value, you’ve got to know (and name) the customer. When you asked “Who is the customer?” the wheels fall off. Here are some wrong answers to that tricky question. The Board of Directors is the customer. The shareholders are the customers. The distributor is the customer. The OEM that integrates your product is the customer. And the people that use the product are the customer. Here’s an operational definition that will set you free:

When someone buys it, they are the customer.

When the discussions get sticky, hold onto that definition. Others will try to bait you into thinking differently, but don’t bite. It will be difficult to stand your ground. And if you feel the group is headed in the wrong direction, try to set things right with this operational definition:

When you’ve found the person who opens their wallet, you’ve found the customer.

Now, let’s talk about value. Isn’t value subjective? Yes, it is. And the only opinion that matters is the customer’s. And here’s an operational definition to help you create customer value:

When you solve an important customer problem, they find it valuable.

And there you have it. Putting it all together, here’s the recipe for customer value:

  • Understand who will buy it.
  • Understand their work and identify their biggest problem.
  • Solve their problem and embed it in your offering.
  • Sell it for more than it costs you to make it.

Image credit: Unsplash

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Adjacent Innovation is the Key to Growth and Risk

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Adjacent Innovation is the Key to Growth and Risk

GUEST POST from Robyn Bolton

It’s not easy leading innovation.  Especially these days.  You need to do more with less.  Take risks while guaranteeing results.  Keep up with competition through incremental innovation and redefine the industry with radical and disruptive innovation.  It’s maddening.  Until you find the Goldilocks Zone of adjacent innovation.

Adjacent Innovation: From Middle Child to Just Right

As HBS Professor Regina E. Herzlinger and her co-authors point out in a recent HBR article, the US is in the midst of an innovation crisis. The cost of lost productivity, estimated at over $10 trillion between 2006 and 2018, is a stark reminder of the economic consequences of a lack of innovation. This figure, equivalent to $95,000 per US worker, should serve as a wake-up call to the importance of innovation in driving economic growth.

The authors identify the root cause of this loss as the ‘polarized approach companies take to innovation.’ While companies focus on incremental innovation, the safe and reliable oldest child of the innovation family, the Venture Captialists chase after radical, transformative innovations, the wild, charismatic, free-spirited youngest child.  Meanwhile, adjacent innovation – new offerings and business models for existing customers or new customers for existing offerings and business models – is, like the middle child, too often overlooked.

It’s time to rediscover it.  In fact, it’s also time to embrace and pursue it as the most promising path back to growth.   While incremental innovation is safe and reliable, it’s also the equivalent of cold porridge. Radical or transformative innovation is sexy, but, like hot porridge, it’s more likely to scorch than sustain you. Adjacent innovation, however, is just right – daring enough to change the game and leapfrog the competition and safe enough to merit investment and generate short-term growth.

Proof in the Porridge: 4x the returns in HALF the time

Last year, I worked with an industrial goods company. Their products aren’t sexy, and their brands are far from household names, but they make the things that make America run and keep workers (and the public) safe. The pandemic’s supply chain disruptions battered their business, and their backlog ballooned from weeks to months and even years.  Yet amidst these challenges, they continued to look ahead, and what they saw was a $6M revenue cliff that had to be filled in three years and a product and innovation pipeline covered in dust and cobwebs.

From Day 1, we agreed to focus on adjacent innovation.  For four weeks, we brainstormed, interviewed customers, and analyzed their existing offerings and capabilities, ultimately developing three concepts – two new products for existing customers and one existing product repositioned to serve a new customer.  After eight more weeks of work, we had gathered enough data to reject one of the concepts and double down on the other two.  Three months later, the teams had developed business cases to support piloting two of the concepts.

It took six months to go from a blank piece of paper to pilot approval.

It took just another 12 months to record nearly $25M in new revenue.

Those results are more than “just right.”

Be Goldilocks. Pursue Adjacent Innovation

Every organization can pursue adjacent innovation.  In fact, most of the companies we consider amongst the world’s “Most Innovative” have that reputation because of adjacent innovation. 

How will you become your organization’s Innovation Goldilocks and use adjacent innovation to create “just right” growth?

Image credit: Pixabay

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How to Avoid AI Project Failures

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How To Avoid AI Project Failures

GUEST POST from Greg Satell

A survey a few years ago by Deloitte of “aggressive adopters” of cognitive technologies found that 76% believe that they will “substantially transform” their companies within the next three years. There probably hasn’t been this much excitement about a new technology since the dotcom boom years in the late 1990s.

The possibilities would seem to justify the hype. AI isn’t just one technology, but a wide array of tools, including a number of different algorithmic approaches, an abundance of new data sources and advancement in hardware. In the future, we will see new computing architectures, like quantum computing and neuromorphic chips, propel capabilities even further.

Still, there remains a large gap between aspiration and reality. Gartner estimated that 85% of big data projects fail. There have also been embarrassing snafus, such as when Dow Jones reported that Google was buying Apple for $9 billion and the bots fell for it or Microsoft’s Tay chatbot went berserk on Twitter. Here’s how to transform the potential of AI into real results.

Make Your Purpose Clear

AI does not exist in a vacuum, but in the context of your business model, processes and culture. Just as you wouldn’t hire a human employee without an understanding of how he or she would fit into your organization, you need to think clearly about how an artificial intelligence application will drive actual business results.

“The first question you have to ask is what business outcome you are trying to drive,” Roman Stanek, CEO at GoodData, told me. “All too often, projects start by trying to implement a particular technical approach and not surprisingly, front-line managers and employees don’t find it useful. There’s no real adoption and no ROI.”

While change always has to be driven from the top, implementation is always driven lower down. So it’s important to communicate a sense of purpose clearly. If front-line managers and employees believe that artificial intelligence will help them do their jobs better, they will be much more enthusiastic and effective in making the project successful.

“Those who are able to focus on business outcomes are finding that AI is driving bottom-line results at a rate few had anticipated,” Josh Sutton, CEO of Agorai.ai, told me. He pointed to a McKinsey study from a few years ago that pegs the potential economic value of cognitive tools at between $3.5 trillion and $5.8 trillion as just one indication of the possible impact.

Choose The Tasks You Automate Wisely

While many worry that cognitive technologies will take human jobs, David Autor, an economist at MIT, sees the the primary shift as one of between routine and nonroutine work. In other words, artificial intelligence is quickly automating routine cognitive processes much like industrial era machines automated physical labor.

To understand how this can work, just go to an Apple store. Clearly, Apple is a company that clearly understands how to automate processes, but the first thing you see when you walk into an Apple store you see is a number employees waiting to help you. That’s because it has chosen to automate background tasks, not customer interactions.

However, AI can greatly expand the effectiveness of human employees. For example, one study cited by a White House report during the Obama Administration found that while machines had a 7.5 percent error rate in reading radiology images and humans had a 3.5% error rate, when humans combined their work with machines the error rate dropped to 0.5%.

Perhaps most importantly, this approach can actually improve morale. Factory workers actively collaborate with robots they program themselves to do low-level tasks. In some cases, soldiers build such strong ties with robots that do dangerous jobs that they hold funerals for them when they “die.”

Data Is Not Just An Asset, It Can Also Be A Liability

For a long time more data was considered better. Firms would scoop up as much of it as they could and then feed it into sophisticated algorithms to create predictive models with a high degree of accuracy. Yet it’s become clear that’s not a great approach.

As Cathy O’Neil explains in Weapons of Math Destruction, we often don’t understand the data we feed into our systems and data bias is becoming a massive problem. A related problem is that of over-fitting. It may sound impressive to have a model that is 99% accurate, but if it is not robust to changing conditions, you might be better off with one that is 70% accurate and simpler.

Finally, with the implementation of GDPR in Europe and the likelihood that similar legislation will be adopted elsewhere, data is becoming a liability as well as an asset. So you should think through which data sources you are using and create models that humans can understand and verify. “Black boxes” serve no one.

Shift Humans To Higher Value Tasks

One often overlooked fact about automation is that once you automate a task, it becomes largely commoditized and value shifts somewhere else. So if you are merely looking to use cognitive technologies to replace human labor and cut costs, you are most probably on the wrong track.

One surprising example of this principle comes from the highly technical field of materials science. A year ago, I was speaking to Jim Warren of the Materials Genome Initiative about the exciting possibility of applying machine learning algorithms to materials research. More recently, he told me that this approach has increasingly become a focus of materials research.

That’s an extraordinary shift in one year. So should we be expecting to see a lot of materials scientists at the unemployment office? Hardly. In fact, because much of the grunt work of research is being outsourced to algorithms, the scientists themselves are able to collaborate more effectively. As George Crabtree, Director of the Joint Center for Energy Storage Research, which has been a pioneer in automating materials research put it to me, “We used to advance at the speed of publication. Now we advance at the speed of the next coffee break.”

And that is the key to understanding how to implement cognitive technologies effectively. Robots are not taking our jobs, but rather taking over tasks. That means that we will increasingly see a shift in value from cognitive skills to social skills. The future of artificial intelligence, it seems, is all too human.

— Article courtesy of the Digital Tonto blog and previously appeared on Harvard Business Review
— Image credits: Pexels

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Time to Make a Difference

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Time to Make a Difference

GUEST POST from Mike Shipulski

When it’s time to make meaningful change, there’s no time for consensus.

When the worn path of success must be violated, use a small team.

When it’s time for new thinking, create an unreasonable deadline, and get out of the way.

The best people don’t want the credit, they want to be stretched just short of their breaking point.

When company leadership wants you to build consensus before moving forward, they don’t think the problem is all that important or they don’t trust you.

When it’s time to make unrealistic progress, it’s time for fierce decision making.

When there’s no time for consensus, people’s feelings will be hurt. But there’s no time for that either.

When you’re pissed off because there’s been no progress for three years, do it yourself.

When it’s time to make a difference, permission is not required. Make a difference.

The best people must be given the responsibility to use their judgment.

When it’s time to break the rules, break them.

When the wheels fall off, regardless of the consequences, put them back on.

When you turn no into yes and catch hell for violating protocol, you’re working for the wrong company.

When everyone else has failed, it’s time to use your discretion and do as you see fit.

When you ask the team to make rain and they balk, you didn’t build the right team.

When it’s important and everyone’s afraid of getting it wrong, do it yourself and give them the credit.

The best people crave ridiculous challenges.

When the work must be different, create an environment that demands the team acts differently.

When it’s time for magic, keep the scope tight and the timeline tighter.

When the situation is dire and you use your discretion, to hell with anyone who has a problem with it.

When it’s time to pull a rabbit out of the hat, you get to decide what gets done and your special team member gets to decide how to go about it. Oh, and you also get to set an unreasonable time constraint.

When it’s important, to hell with efficiency. All that matters is effectiveness.

The best people want you to push them to the limit.

When you think you might get fired for making a difference, why the hell would you want to work for a company like that?

When it’s time to disrespect the successful business model, it’s time to create harsh conditions that leave the team no alternative.

The best people want to live where they want to live and do impossible work.

Image credit: Unsplash

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Breaking Free From Stagnation

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Breaking Free From Stagnation

GUEST POST from Robyn Bolton

As a leader in your organization, you’re under tremendous stress. Not only do you need to deliver against a “growth strategy” that demands constant increases in revenue and profit, but you also need to cut costs and support employees who are more disengaged and burned out than ever before.  If it feels like you’re working harder and running faster than ever to maintain the status quo, then I have good and bad news for you.

Bad news: You’re right. 

The feeling of working harder or moving faster simply to stay in the same place is called the Red Queen effect or hypothesis.  The hypothesis asserts “that species must constantly adapt, evolve, and proliferate in order to survive while pitted against ever-evolving opposing species.”  Its name is inspired by the Red Queen in Lewis Carroll’s Through the Looking Glass, who explains to Alice, “here, you see, it takes all the running you can do, to keep in the same place.”

You probably feel the same need to adapt to survive “while pitted against ever-evolving opposing species” every time you see new technologies, read about another new management framework, or hear news from your competitors. You also understand that your organization needs to grow and often hear that it needs to do so at all costs, so you buckle down, work hard, and pull off quarterly miracles.

Good for you! You’re reward?  You get to do it all over again, and faster, this quarter.  And, to add insult to injury, all that growth you’re working harder and harder to achieve is a mirage.

75% of companies do not grow.

HBS professor Gary P. Pisano examined the growth rate of 10,897 publicly held US companies between 1976 and 2019.  When adjusted for inflation, the top quartile grew 11.8% yearly, but the other 75% showed little to negative growth. 

Being in that top quartile was no guarantee of success, as only 15% (3% of the total sample) were able to sustain a growth rate of 0.3%+ for 30 years. In fact, only SEVEN companies—Walmart, UPS, Southwest, Publix, Johnson & Johnson, Danaher, and Berkshire Hathaway—were top-quartile growth companies throughout the thirty years studied.

If you worked at one of those 7 companies, congrats!  Your hard work delivered real and repeatable growth.  If you worked at any of the other 10,890, I hope they offer great benefits?

We know why.

Every good academic knows you can’t just throw out some data without trying to find a causal link, and Professor Pisano is a good academic

“I have found that while the usual explanations for slow or minimal growth—market forces and technological changes such as disruptive innovation—play a role, many companies’ growth problems are self-inflicted. Specifically, firms approach growth in a highly reactive, opportunistic manner. When market demand is booming, they go on hiring binges, throw resources at developing new capacity, and build out organizational infrastructure without thinking through the implications… In the process of chasing growth, companies can easily destroy the things that made them successful in the first place, such as their capacity for innovation, their agility, their great customer service, or their unique cultures. When demand slows, pressures to maintain historical growth rates can lead to quick-fix solutions such as costly acquisitions or drastic cuts in R&D, other capabilities, and training. The damage caused by these moves only exacerbates the growth problems.”

(Bold text added by me)

Good news: You Can Do Something About It

In fact, as a leader in your organization, you’re among the few who have any prayer of pulling your organization out of the Red Queen’s race and putting it on track to real and sustainable growth. Achieving this incredible success requires you (and your colleagues) to decide three things:

  1. How fast to grow (target rate of growth)
  2. Where to find sources of new demand (direction of growth)
  3. How to assemble the resources required to grow (method of growth)

Together, these three decisions comprise your growth strategy and enable your organization to achieve the “delicate balance” between demand and supply required to sustain profitable growth.

Getting to these decisions isn’t easy, but neither is slaying the Jabberwocky.  So, as this brief rest stop in your race comes to an end, who do you choose to be – Alice, who works hard and deals with a bit of nonsense to progress, or the Red Queen, content to work harder to stay in the same place?

Image credit: Unsplash

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Only One Type of Innovation Will Win the Future

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Only One Type of Innovation Will Win the Future

GUEST POST from Greg Satell

Very few businesses last. While we like to think we live in a particularly disruptive era, this has always been true. Entrepreneurs start businesses because they see opportunity and build skills, practices and processes to leverage it. Yet as the world changes, these strengths often become vulnerabilities.

The problem is that the past is not always a good guide to the future. Business models, even the successful ones, are designed for inertia. They are great for leveraging past insights, but are often resistant to change. Success does not, in fact, always breed more success, sometimes it breeds failure.

That’s why every business needs to innovate. Yet innovation is not, as some would have us believe, just about moving fast and breaking things. It’s about solving the problems you need to create a better future. What most fail to grasp is that a key factor of success is how you source problems, build a pipeline and, ultimately, choose which ones you will work on.

1. Getting Better At What You Already Do

Every year, Apple comes up with a new iPhone. That’s not as exciting as it used to be, but it’s still key to the company maintaining its competitive edge. Every model is a bit faster, more secure and has new features that make it more capable. It’s still an iPhone, but better.

Some self-appointed ‘innovation gurus” often scoff at this type of innovation as “incremental” and favor new technologies that are more “radical” or “disruptive,” but the truth is that this is where you derive the most value from innovation — getting better at what you already do and selling to customers what you already know.

So the first line of defense against irrelevance is to identify ways to improve performance in current practices and processes. The challenge, of course, with this type of innovation is that your competitors will be working on the same problems you are and it takes no small amount of agility and iteration to stay ahead. Even then, any victory is short-lived.

Still, most technologies can be improved for a long time. Moore’s Law, for example, has been around for almost 50 years and is just ending now.

2. Applying What You’re Already Good At To A Different Context

Amazon started out selling books online. It then applied its approach to other categories, such as electronics and toys. That took enormous investments in technology, which it then used to create new businesses, such as Amazon Web Services (AWS), Kindle tablets and its Echo line of smart speakers.

In each case, the company took what it already did well and expanded to an adjacent set of markets or capabilities, often with great success. The Kindle helped the company dominate e-books and strengthened its core business. AWS is far more profitable than online retail and accounts for virtually all of Amazon’s operating income.

Still, adjacent opportunities are can be risky. Amazon, despite its huge successes, has had its share of flops too. Whenever you go into a new business you are, to a greater or lesser extent, charting a course into the unknown. So you need to proceed with some caution. When you launch a new business into an adjacency, you are basically launching a startup and most of those fail.

3. Finding A Completely New Problem To Solve

Besides getting better at what you already do and applying things you already know to a different market or capability, you can also look for a new problem to solve. Clearly, this the most uncertain type of opportunity, because no one knows what a good solution will look like.

To return to the Moore’s law example, everybody knows what a 20% performance improvement in computer chips looks like. Metrics for speed and power consumption have long been established, so there is little ambiguity around what would constitute success. Customers will instantly recognize the improvement as having a specific market value.

On the other hand, no one knows what the value of a quantum computer will be. It’s a fundamentally new kind of technology that will solve new types of problems. So customers will have to explore the technology and figure out how to use it to create better products and services.

Despite the uncertainty though, I found in the research that led to my book, Mapping Innovation, that this type of exploration is probably the closest thing to a sure bet that you’re going to find. Every single organization I studied that invested in exploration found that it paid off big, with extremely high returns even accounting for the inevitable wrong turns and blind alleys.

The 70-20-10 Rule

Go to any innovation conference and you will find no shortage of debates about what type of approach creates the most value, usually ending with no satisfying conclusion. The truth is that every organization needs to improve what they already do, search for opportunities in adjacencies and explore new problems. The key is how you manage resources.

One popular approach is the 70-20-10 rule, which prescribes investing 70% of your innovation resources in improving existing technologies, 20% in adjacent markets and capabilities and 10% in markets and capabilities that don’t exist yet. That’s more of a rule of thumb than a physical law and should be taken with a grain of salt, but it’s a good guide.

Practically speaking, however, I have found that the exploration piece is the most neglected. All too often, in our over-optimized business environment, any business opportunity that can’t be immediately quantified in considered a non-starter. So we fail to begin to explore new problems until their market value has been unlocked by someone else. By that point, we are already behind the curve.

Make no mistake. The next big thing always starts out looking like nothing at all. Things that change the world always arrive out of context for the simple reason that the world hasn’t changed yet. But if you do not explore, you will not discover. If you do not discover, you will not invent. And if you do not invent, you will be disrupted. It’s just a matter of time.

— Article courtesy of the Digital Tonto blog and previously appeared on Inc.com
— Image credits: Pixabay

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Are We Doing Social Innovation Wrong?

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Are We Doing Social Innovation Wrong?

GUEST POST from Geoffrey A. Moore

The Volume Operations business model kicks in when you have hundreds of thousands of users and goes up from there. 100,000, for those of us who are not math majors, is 10 to the power of 5. Uber-successful volume ops businesses operate at 10 to the power of 9 and up—millions of users or customers. But if you are a start-up, you are looking at 10 or maybe 100. How do you get from here to there?

The key thought to keep in mind is the old chestnut “what got you here won’t get you there.” That is, whatever operating model you have, keep in mind it can scale to two exponents but never to three. That means for every two exponents you have to change operating models, which likely means you have to change executive leadership in order to go forward.

To illustrate this idea, I’d like to focus on the non-profit sector and ask the question, what would it take to really solve for any widespread social problem? Homelessness was the first one that came to mind, but hunger is another obvious one, drug addiction a second, street crime a third. They are all seemingly intractable issues that, despite the best intentions of a whole raft of people, and regardless of how much funding is supplied, stubbornly resist any sustainable improvement.

The question I want to address is not what programs would work—because I actually think a whole lot of programs would work—but rather, how could we organize to deploy these programs successfully at scale.

Following our principle of what got you here won’t get you there, we need a ladder of operating models that can take us, exponent by exponent, from 10 to the power of 1 to, say, 10 to the power of 7. What might that look like?

Scaling Social Innovation

Consider this a straw man, a place to start, something to edit. It conveys a key lesson from the high-tech sector, namely that the fastest way to kill a disruptive innovation is to race to scale by skipping over one or more of these “exponential steps.” It just doesn’t work. There are too many emergent factors at each new level you must learn to cope with in order to succeed. The only reliable way to scale is to ratchet your way up this staircase, adapting your systems and operations as you go.

Unfortunately, that’s not what politicians do. They want to make a big impact right away. That means they start everything on one of the upper stairs. Driven by impatience, they ignore the dynamics of adoption and demand mass deployment from the get-go. They think the problem is simply one of getting enough funding. It’s not. It’s one of operational innovation. Scaling prematurely simply wastes the funding. And then when programs do flounder, as they inevitably will, they blame it on execution when in reality they simply did not do the hard, time-consuming work of building up their foundation step by step from below.

One of the implications of this framework is that social services should be incubated in the private sector where freedom from regulatory constraints supports agile innovation. But as they scale, the importance of regulatory oversight increases and more communal engagement is required. The goal should be to keep this oversight as local as possible as long as possible, doing as much as we can to empower the people delivering the service itself. Once that operating model solidifies, then, and only then, is there a proper foundation for scaling to state and federal programs.

Today, we do not lack the empathy to support social services. Nor do we lack the funding. But we are failing nonetheless. We can do better. We need to do better.

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

Image Credit: Pexels

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

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

  1. Organizational Debt Syndrome Poses a Threat — by Stefan Lindegaard
  2. Do Nothing More Often — by Robyn Bolton
  3. Is Disruption About to Claim a New Victim? — by Robyn Bolton
  4. What Top Innovators Do Differently — by Greg Satell
  5. Four Hidden Secrets of Innovation — by Greg Gatell
  6. Rise of the Atomic Consultant — by Braden Kelley
  7. Do You Bring Your Whole Self to Work? — by Mike Shipulski
  8. Giving Your Team a Sense of Shared Purpose — by David Burkus
  9. Creating Effective Digital Teams — by Howard Tiersky
  10. Smarter Risk Taking — by Janet Sernack

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

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P.S. Here are our Top 40 Innovation Bloggers lists from the last four years:

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