Tag Archives: Innovation

How to Fail Your Way to Success

How to Fail Your Way To Success

GUEST POST from Robyn Bolton

“Rapid Unscheduled Disassembly”

It’s a meme and my new favorite euphemism for getting dumped/fired (as in, “There was a rapid unscheduled disassembly of our relationship.” Thank you, social media, for this gem)

It’s also spurred dozens of conversations with corporate leaders and innovation teams about the importance of defining success, the purpose of experiments, and the necessity of risk.

Define Success so You Can Identify Failure

The dictionary defines “fail (verb)” as “be unsuccessful in achieving one’s goal.”

But, as I wrote last week, using your definition of success to classify something as a failure assumes you defined success correctly.

Space X didn’t define success as carrying “two astronauts from lunar orbit to the surface of the moon,” Starship’s ultimate goal.

It defined success in 3 ways:

Big picture (but a bit general) – Validating “whether the design of the rocket system is sound.”
Ideal outcome – “Reach an altitude of 150 miles before splashing down in the Pacific Ocean near Hawaii 90 minutes [after take-off].”
Base Case – Fly far enough from the launchpad and long enough to generate “data for engineers to understand how the vehicle performed.”

By defining multiple and internally consistent types of success, SpaceX inspired hope for the best and set realistic expectations. And, if the rocket exploded on the launchpad? That would be a failure.

Know What You Need to Learn so You Know What You Need to Do

This was not the first experiment SpaceX ran to determine “whether the design of the rocket system was sound.” But this probably was the only experiment they could run to get the data they needed at this point in the process.

You can learn a lot from lab tests, paper prototypes, and small-scale experiments. But you can’t learn everything. Sometimes, you need to test your idea in the wild.

And this scares the heck out of executives.

As the NYT pointed out, “Big NASA programs like the Space Launch System…are generally not afforded the same luxury of explode-as-you-learn. There tends to be much more testing and analysis on the ground — which slows development and increases costs — to avoid embarrassing public failures.”

Avoiding public failure is good. Not learning because you’re afraid of public failure is not.

So be clear about what you need to learn, all the ways you could learn it, and the trade-offs of private, small-scale experiments vs. large-scale public ones. Then make your choice and move forward.

Have Courage. Take a Risk

“Every great achievement throughout history has demanded some level of calculated risk, because with great risk comes great reward,” Bill Nelson, NASA Administrator.

“Great risk” is scary. Companies do not want to take great risks (see embarrassing public failure).

“Calculated risk” is smart. It’s necessary. It’s also a bit scary.

You take a risk to gain something – knowledge, money, recognition. But you also create the opportunity to lose something. And since the psychological pain of losing is twice as powerful as the pleasure of gaining, we tend to avoid risk.

But to make progress, you must take a risk. To take a risk, you need courage.

And courage is a skill you can learn and build. For many of us, it starts with remembering that courage is not the absence of fear. It is the choice to take action despite fear.

When faced with a risk, face it. Acknowledge it and how you feel. Assess it by determining the best, worst, and most likely scenarios. Ask for input and see it from other people’s perspectives. Then make your choice and move forward.

How to know when you’ve successfully failed

Two quotes perfectly sum up what failure en route to success is:

“It may look that way to some people, but it’s not a failure. It’s a learning experience.”- Daniel Dumbacher, executive director of the American Institute of Aeronautics and Astronautics and a former high-level NASA official.

“Would it have been awesome if it didn’t explode? Yeah. But it was still awesome.” – Launch viewer Lauren Posey, 34.

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What is Failure?

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What is Failure?

GUEST POST from Robyn Bolton

A couple of weeks ago, I wrote about my hatred of failure while acknowledging that there are things I hate more (inertia, blind allegiance to the status quo, unwillingness to try) that motivate me to risk it.

In response, I received this email from my friend and former colleague Daymara, now the Founder & CEO of Rockin’ Baker in Fayetteville, AR (shared here with her permission)

I’m the opposite. I love failing! That’s when I learn the most, that I question what and how I could better, question more and more. It triggers my brain to look back, re-evaluate, assess and spring forward. I wouldn’t be here today if I had not risked. I don’t think anyone starts anything thinking when they’d fail. But some of us aren’t afraid or hate it. I wouldn’t be here if I hate failing, wouldn’t have left my country looking for a safer place, wouldn’t have launched RBI because I didn’t have any entrepreneurial experience not even in the hospitality industry, wouldn’t have switched to focus on neurodiversity and so much more.

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Because I came to the US, I got to meet you. Yes, I failed at seeing the signs & lost over 60% of my savings just 2 weeks before leaving Venezuela. I could’ve decided to stay because maybe it was going to be harder and the risk of failing in a country I didn’t know higher. I had a plan. If it didn’t work, come back home & start all over again.

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I started RBI understanding that I could fail. I told myself, if I did, at least I would have an answer. Yes, I’m failing terribly at making this social enterprise work. Yet, I’ve gained so much knowledge about humanity, our differences, the unfairness that neurodivergents have to live daily, running a social enterprise and so much more. If I had hated failing, I wouldn’t be sharing my experience with other entrepreneurs so they don’t make the same mistakes I made. I wouldn’t be advocating for more equitable places for all, including women.

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Failing feeds me to do better, to ask more questions, to explore more, to lead me to become better. I don’t love failing, I welcome it.

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My first thought was, “Wow, this is so healthy! I wish more people felt and acted this way!”

My second thought was, “I wouldn’t apply the word ‘fail’ to any of these situations. You’re trying, learning, changing, and trying again.:

Just because you don’t get the expected outcome the first time doesn’t mean you failed.

Or does it?

What the Dictionary Says

According to Oxford Languages, “fail” (verb) means

Be unsuccessful in achieving one’s goal, “he failed in his attempt to secure election.”
Neglect to do something, “the firm failed to give adequate risk warnings.”
Break down; cease to work well, “a truck whose brakes had failed.”

True but contextual:

If success is defined as launching a new product, but customer feedback proves there’s no demand or willingness to pay, is shutting it down a failure?
If you neglect something that isn’t important or doesn’t have significant ramifications, like not eating breakfast, did you fail or simply forget, run out of time, or make a mistake?
If something works but not well, like an expense reporting system, is it a failure or just burdensome, a pain, or a necessary evil?

Also, incomplete.

What People Say

“Fail” has so many definitions and meanings in Daymara’s telling of her story. In addition to some of the dictionary’s definitions, she also uses “Fail” to mean:

Take smart risks, “I could’ve decided to stay because maybe it was going to be harder and the risk of failing in a country I didn’t know higher. I had a plan. If it didn’t work, come back home & start all over again.”
Get new information to facilitate learning,
- “I’m the opposite. I love failing! That’s when I learn the most, that I question what and how I could better, question more and more. It triggers my brain to look back, re-evaluate, assess and spring forward.”
- I started RBI understanding that I could fail. I told myself, if I did, at least I would have an answer.
Adapt and change based on learning, “wouldn’t have switched to focus on neurodiversity”
Grow, improve, evolve, “Failing feeds me to do better, to ask more questions, to explore more, to lead me to become better. I don’t love failing, I welcome it.”

What Do You Say?

Like “Innovation,” “Failure” is a word we all use A LOT that no longer has a common definition. In the dictionary, failure is bad and to be avoided. To Daymara and scores of entrepreneurs and innovators, failure is wonderful and welcome.

Progress, either towards or away from failure, requires us to define “Failure” for ourselves and our work and agree on a definition with our teammates.

So, tell me:

What is failure to you?
To your team?
To your boss?

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When Innovation Becomes Magic

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When Innovation Becomes Magic

GUEST POST from Pete Foley

Arthur C Clarke’s 3rd Law famously stated:

“Any sufficiently advanced technology is indistinguishable from magic”

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

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

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

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

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

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

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

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

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

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

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

Image credit: Pixabay

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3 Innovation Types Not What You Think They Are

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But They Do Determine Your Success

3 Innovation Types Not What You Think They Are

GUEST POST from Robyn Bolton

The Official Story

When discussing innovation, you must be specific so people know what you expect. This is why so many thought leaders, consultants, and practitioners preach the importance of defining different types of innovation.

Clayton Christensen encourages focusing on WHY innovation is happening – improve performance, improve efficiency, or create markets – in his 2014 HBR article.
The classic Core/Adjacent/Transformational model focuses on WHAT is changing – target customer, offering, financial model, and resources and processes.
McKinsey’s 3 Horizons focus on WHEN the results are achieved – this year, 2-3 years, 3-6 years.

It’s easy to get overwhelmed by the options and worry about which approach is “best.” But, like all frameworks, they’re all a little bit right and a little bit wrong, and the best one is the one that will be used and get results in your organization.

The REAL story

Everything in the official story is true, but not the whole truth.

“Innovation” is not peanut butter.

You can’t smear it all over everything and expect deliciousness.

When doing innovation, you must remember your customer – the executives who make decisions, allocate resources, and can accelerate or decimate your efforts.

More importantly, you need to remember their Jobs to be Done (JTBD) – keep my job, feel safe and respected, and be perceived as competent/a rising star – because these jobs define the innovations that will get to market.

Three (3) REAL types of innovation

SAFE – The delightful solution to decision-makers’ JTBD

Most closely aligned with Core innovation, improving performance or efficiency, and Horizon 1 because the focus is on improving what exists in a way that will generate revenue this year or next. Decision-makers feel confident because they’ve “been there and done that” (heck, doing “that” is probably what got them promoted in the first place). In fact, they’re more likely to get in trouble for NOT investing in these types of innovations than they are for investing in them.

STRETCH – The Good Enough solution

Most like Adjacent innovation because they allow decision-makers to keep one foot in the known while “stretching” their other foot into a new (to them) area. This type of innovation makes decision-makers nervous because they don’t have all the answers, but they feel like they at least know what questions to ask. Progress will require more data, and decisions will take longer than most intrapreneurs want. But eventually, enough time and resources (and ego/reputation) will be invested that, unless the team recommends killing it, the project will launch.

SPLATTER – The Terrible solution

No matter what you call them – transformational, radical, breakthrough, disruptive, or moonshots – these innovations make everyone’s eyes light up before reality kicks in and crushes our dreams. These innovations “define the next chapter of our business” and “disrupt ourselves before we’re disrupted.” These innovations also require decision-makers to let go of everything they know and wander entirely into the unknown. To invest resources in the hope of seeing the return (and reward) come back to their successor (or successor’s successor). To defend their decisions, their team, and themselves when things don’t go exactly as planned.

How to find the REAL type that will get real results.

“You said you want X. Would you describe that for me?” (you may need to give examples). When I worked at Clayton Christensen’s firm, executives would always call and ask for our help to create a disruptive innovation. When I would explain what they were actually asking for (something with “good enough” performance and a low selling price that appeals to non-consumers), they would back away from the table, wave their hands, and say, “Oh, not that. We don’t want that.”
“How much are you willing to risk?” If they’re willing to go to their boss to ask for resources, they’re willing to Stretch. If they’re willing to get fired, they’re willing to Splatter. If everything needs to stay within their signing authority, it’s all about staying Safe.
“What would you need to see to risk more?” As an innovator, you’ll always want more freedom to push boundaries and feel confident that you can convince others to see things your way. But before you pitch Stretch to a boss that wants Safe, or Splatter to a boss barely willing to Stretch, learn what they need to change their minds. Maybe it will be worth your effort, maybe it won’t. Better to know sooner rather than later.

Image credits: Pixabay

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Moneyball and the Beginning, Middle, and End of Innovation

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Moneyball and the Beginning, Middle, and End of Innovation

GUEST POST from Robyn Bolton

Recently, pitchers and catchers reported to MLB Spring Training facilities in Florida and Arizona. For baseball fans, this is the first sign of Spring, an occasion that heralds months of warmth and sunshine, ballparks filled (hopefully) with cheering fans, dinners of beers and brats, and the undying belief that this year will be the year.

Of course, there was still a lot of dark, dreary cold between then and Opening Day. Perfect weather for watching baseball movies – Bull Durham, Major League, The Natural, Field of Dreams, and, of course, Moneyball.

Moneyball is based on the book of the same name by Michael Lewis and chronicles the 2002 Oakland Athletics season. The ’02 Oakland A’s, led by General Manager Billy Beane (played by Brad Pitt), forever changed baseball by adopting an approach that valued rigorous statistical analysis over the collective wisdom of baseball insiders (coaches, scouts, front office personnel) when building a team. This approach, termed “Moneyball,” enabled the A’s to reach the postseason with a team that cost only $44M in salary, compared to the NY Yankees that spent $125M to achieve the same outcome.

While the whole movie (and book) is a testament to the courage and perseverance required to challenge and change the status quo, time and again I come back to three lines that perfectly sum up the journey of every successful intrapreneur I’ve ever met.

The Beginning

“I know you’ve taken it in the teeth out there, but the first guy through the wall…he always gets bloody…always always gets bloody. This is threatening not just a way of doing business… but in their minds, it’s threatening the game. Really what it’s threatening is their livelihood, their jobs. It’s threatening the way they do things… and every time that happens, whether it’s the government, a way of doing business, whatever, the people who are holding the reins – they have their hands on the switch – they go batshit crazy.”

John Henry, Owner of the Boston Red Sox

Context

The 2002 season is over, and the A’s were eliminated in the first round of the playoffs. John Henry, an owner of the Boston Red Sox, has invited Bill Beane to Boston to offer him the Red Sox GM job.

Lesson

This is what you sign up for when you decide to be an Intrapreneur. The more you challenge the status quo, the more you question how business is done, the more you ask Why and demand an answer, the closer you get to “tak(ing) it in the teeth.”

This is why courage, perseverance, and an unshakeable belief that things can and should be better are absolutely essential for intrapreneurs. Your job is to run at the wall over and over until you get through it.

People will follow. The Red Sox did. They won the World Series in 2004, breaking an 84-year-old curse.

The Middle

“It’s a process, it’s a process, it’s a process”

Bill Beane

Context

Billy has to convince the ballplayers to forget all the habits that made them great and embrace the philosophy of Moneyball. To stop stealing bases, turning double plays on bunts, and swinging for the fences and to start taking walks, throwing to first for the easy out, and prioritize getting on base over hitting a home run.

The players are confused and frustrated. Suddenly, everything that they once did right is wrong and what was not valued is deeply prized.

Lesson

Innovation is something new that creates value. Something new doesn’t just require change, it requires people to stop doing things that work and start doing things that seem strange or even wrong.

Change doesn’t happen overnight. It’s not a switch to be flipped. It’s a process to be learned. It takes time, practice, reminders, and patience.

The End

“When you get an answer you’re looking for, hang up.”

Billy Beane

Context

In this scene, Billy has offered one of his players to multiple teams, searching for the best deal. When the phone rings with a deal he likes, he and the other General Manager (GM) agree to it, Billy hangs up. Even though the other GM was in the middle of a sentence. When Peter Brand, the Assistant GM played by Jonah Hill, points out that Billy had just hung up on the other GM, Billy responds with this nugget of wisdom.

Lesson

It’s advice intrapreneurs should take very much to heart. I often see Innovation teams walk into management presentations with long presentations, full of data and projections, anxious to share their progress, and hoping for continued funding and support. When the meeting starts, a senior exec will say something like, “We’re excited by the progress we’re hearing about and what it will take to continue.”

That’s the cue to “hang up.”

Instead of starting the presentation from the beginning, start with “what it will take to continue.” You got the answer you’re looking for – they’re excited about the progress you’ve made – don’t spend time giving them the info they already have or, worse, could raise questions and dim their enthusiasm. Hang up on the conversation you want to have and have the conversation they want to have.

In closing

Moneyball was an innovation that fundamentally changed one of the most tradition-bound businesses in sports. To be successful, it required someone willing to take it in the teeth, to coach people through a process, and to hang up when they got the answer they wanted. It wasn’t easy but real change rarely is.

The same is true in corporations. They need their own Bill Beanes.

Are you willing to step up to the plate?

Image credits: Pixabay

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Unintended Consequences. The Hidden Risk of Fast-Paced Innovation

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Unintended Consequences. The Hidden Risk of Fast-Paced Innovation

GUEST POST from Pete Foley

Most innovations go through a similar cycle, often represented as an s-curve.

We start with something potentially game changing. It’s inevitably a rough-cut diamond; un-optimized and not fully understood. But we then optimize it. This usually starts with a fairly steep leaning curve as we address ‘low hanging fruit’ but then evolves into a fine-tuning stage. Eventually we squeeze efficiency from it to the point where the incremental cost of improving it becomes inefficient. We then either commoditize it, or jump to another s-curve.

This is certainly not a new model, and there are multiple variations on the theme. But as the pace of innovation accelerates, something fundamentally new is happening with this s-curve pattern. S-curves are getting closer together. Increasingly we are jumping to new s-curves before we’ve fully optimized the previous one. This means that we are innovating quickly, but also that we are often taking more ‘leaps into the dark’ than ever before.

This has some unintended consequences of its own:

1. Cumulative Unanticipated Consequences. No matter how much we try to anticipate how a new technology will fare in the real world, there are always surprises. Many surprises emerge soon after we hit the market, and create fires than have to be put out quite quickly (and literally in the cases of some battery technologies). But other unanticipated effects can be slower burn (pun intended). The most pertinent example of this is of course greenhouse gasses from Industrialization, and their impact on our climate. This of course took us years to recognize. But there are many more examples, including the rise of antibiotic resistance, plastic pollution, hidden carcinogens, the rising cost of healthcare and the mental health issues associated with social media. Just as the killer application for a new innovation is often missed at its inception, it’s killer flaws can be too. And if the causal relationship between these issues and the innovation are indirect, they can accumulate across multiple s-curves before we notice them. By the time we do, technology is often so entrenched it can be a huge challenge to extract ourselves from it.

2. Poorly understood complex network effects. The impact of new innovation is very hard to predict when it is introduced into a complex, multivariable system. A butterfly flapping its wings can cascade and amplify through a system, and when the butterfly is transformative technology, the effect can be profound. We usually have line of sight of first generation causal effects: For example, we know that electric cars use an existing electric grid, as do solar energy farms. But in today’s complex, interconnected world, it’s difficult to predict second, third or fourth generation network effects, and likely not cost effective or efficient for an innovator to try and do so. For example, the supply-demand interdependency of solar and electric cars is a second-generation network effect that we are aware of, but that is already challenging to fully predict. More causally distant effects can be even more challenging. For example, funding for the road network without gas tax, the interdependency of gas and electric cost and supply as we transition, the impact that will have on broader on global energy costs and socio political stability. Then add in complexities supply of new raw materials needed to support the new battery technologies. These are pretty challenging to model, and of course, are the challenges we are at least aware of. The unanticipated consequences of such a major change are, by definition, unanticipated!

3. Fragile Foundations. In many cases, one s-curve forms the foundation of the next. So if we have not optimized the previous s-curve sufficiently, flaws potentially carry over into the next, often in the form of ‘givens’. For example, an electric car is a classic s-curve jump from internal combustion engines. But for reasons that include design efficiency, compatibility with existing infrastructure, and perhaps most importantly, consumer cognitive comfort, much of the supporting design and technology carries over from previous designs. We have redesigned the engine, but have only evolved wheels, breaks, etc., and have kept legacies such as 4+ seats. But automotives are in many, one of our more stable foundations. We have had a lot of time to stabilize past s-curves before jumping to new ones. But newer technologies such as AI, social media and quantum computing have enjoyed far less time to stabilize foundational s-curves before we dance across to embrace closely spaced new ones. That will likely increase the chances of unintended consequences. And we are already seeing the canary in the coal mine with some, with unexpected mental health and social instability increasingly associated with social media

What’s the Answer? We cannot, or should not stop innovating. We face too many fundamental issues with climate, food security and socio political stability that need solutions, and need them quite quickly.

But the conundrum we face is that many, if not all of these issue are rooted in past, well intentioned innovation, and the unintended consequences that derive from it. So a lot of our innovation efforts are focused on solving issues created by previous rounds of innovation. Nobody expected or intended the industrial revolution to impact our climate, but now much of our current innovation capability is rightly focused on managing the fall out it has created (again, pun intended). Our challenge is that we need to continue to innovate, but also to break the cycle of todays innovation being increasingly focused on fixing yesterdays!

Today new waves of innovation associated with ‘sustainable’ technology, genetic manipulation, AI and quantum computing are already crashing onto our shores. These interdependent innovations will likely dwarf the industrial revolution in scale and complexity, and have the potential for massive impact, both good and bad. And they are occurring at a pace that gives us little time to deal with anticipated consequences, let alone unanticipated ones.

We’ll Find a Way? One answer is to just let it happen, and fix things as we go. Innovation has always been a bumpy road, and humanity has a long history of muddling through. The agricultural revolution ultimately allowed humans to exponentially expand our population, but only after concentrating people into larger social groups that caused disease to ravage many societies. We largely solved that by dying in large numbers and creating herd immunity. It was a solution, but not an optimum one. When London was in danger of being buried in horse poop, the internal combustion engine saved us, but that in turn ultimately resulted in climate change. According to projections from the Club of Rome in the 70’s, economic growth should have ground to a halt long ago, mired in starvation and population contraction. Instead advances in farming technology have allowed us to keep growing. But that increase in population contributes substantially to our issues with climate today. ‘We’ll find a way’ is an approach that works until it doesn’t. and even when it works, it is usually not painless, and often simply defers rather than solves issues.

Anticipation? Another option is that we have to get better at both anticipating issues, and at triaging the unexpected. Maybe AI will give us the processing power to do this, provided of course that it doesn’t become our biggest issue in of itself.

Slow Down and Be More Selective? In a previous article I asked if ‘just because we can do it, does it mean we should?’. That was through a primarily moral lens. But I think unintended consequences make this an even bigger question for broader innovation strategy. The more we innovate, the more consequences we likely create. And the faster we innovate, the more vulnerable we are to fragility. Slowing down creates resilience, speed reduces it. So one option is to be more choiceful about innovations, and look more critically at benefit risk balance. For example, how badly do we need some of the new medications and vaccines being rushed to market? Is all of our gene manipulation research needed? Do we really need a new phone every two years? For sure, in some cases the benefits are clear, but in other cases, is profit driving us more than it should?

In a similar vein, but to be provocative, are we also moving too quickly with renewable energy? It certainly something we need. But are we, for example, pinning too much on a single, almost first generation form of large scale solar technology? We are still at that steep part of the learning curve, so are quite likely missing unintended consequences. Would a more staged transition over a decade or so add more resilience, allow us to optimize the technology based on real world experience, and help us ferret out unanticipated issues? Should we be creating a more balanced portfolio, and leaning more on more established technology such as nuclear? Sometimes moving a bit more slowly ultimately gets you there faster, and a long-term issue like climate is a prime candidate for balancing speed, optimization and resilience to ultimately create a more efficient, robust and better understood network.

The speed of AI development is another obvious question, but I suspect more difficult to evaluate. In this case, Pandora’s box is open, and calls to slow AI research would likely mean responsible players would stop, but research would continue elsewhere, either underground or in less responsible nations. A North Korean AI that is superior to anyone else’s is an example where the risk of not moving likely outweighs the risk of unintended consequences

Regulation? Regulation is a good way of forcing more thoughtful evaluation of benefit versus risk. But it only works if regulators (government) understand technology, or at least its benefits versus risks, better than its developers. This can work reasonably well in pharma, where we have a long track record. But it is much more challenging in newer areas of technology. AI is a prime example where this is almost certainly not the case. And as the complexity of all innovation increases, regulation will become less effective, and increasingly likely to create unintended consequences of its own.

I realize that this may all sound a bit alarmist, and certainly any call to slow down renewable energy conversion or pharma development is going to be unpopular. But history has shown that slowing down creates resilience, while speeding up creates instability and waves of growth and collapse. And an arms race where much of our current innovative capability is focused on fixing issues created by previous innovations is one we always risk losing. So as unanticipated consequences are by definition, really difficult to anticipate, is this a point in time where we in the innovation community need to have a discussion on slowing down and being more selective? Where should we innovate and where not? When should we move fast, and when we might be better served by some productive procrastination. Do we need better risk assessment processes? It’s always easier to do this kind of analysis in hindsight, but do we really have that luxury?

Image credit: Pixabay

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‘Innovation’ is Killing Innovation. How Do We Save It?

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'Innovation' is Killing Innovation. How Do We Save It?

GUEST POST from Robyn Bolton

How do people react when you say “innovation?”

Lean forward, eyes glittering, eager to hear more
Stare blankly and nod slowly
Roll their eyes and sigh
Wave their hands dismissively and tell you to focus on other, more urgent priorities.

If you answered C, you’re in good company.

Innovation is a buzzword. Quick searches of Amazon and Google Scholar result in 100,000+ books and 200,000+ articles on the topic, while a scan of the SEC’s database yields 8,000 K-1 filings with the word “innovation” in 2020 alone.

“Innovation” is meaningless, like all buzzwords. There’s a reason that practitioners and consultants insist on establishing a common definition before starting innovation work. I’ve been in meetings with ten people, asked each person to define “innovation,” and heard 12 different answers.

But all this pales in comparison to the emotional response it elicits. Some people get incredibly excited, bouncing out of their seats, ready to bring their latest idea to life (whether it should be brought to life is a different story.). Some nod solemnly as if confronted by a necessary evil, accepting a fate beyond their control. Most roll their eyes because they’ve been through this before and, like all management “flavors of the month,” this too shall pass.

“Innovation” is killing Innovation

The emotions and opinions we tie to “innovation” overwhelm the dictionary definition, making it difficult to believe that the process and, more importantly, the result will be different this time.

We need a different word.

One that has the same meaning and none of the baggage.

This may feel impossible, but if “literally” can mean “figuratively” (do NOT get me started on this 2013 decision) and the Oxford English Dictionary can add 700 new words in 2022, surely we can figure this out.

10 alternatives to ‘Innovation’

The following options are sourced primarily from conversations with other experts and practitioners.

Invention
Ideation
Incubation
Improvement
Creation
Design
Growth
Transformation
Business R&D*

Yes, #10 is intentionally missing because…

What do you think?

Finding a new word (or maybe changing how “innovation” is perceived, understood, and pursued) is a group effort. One person alone can’t do it, and a few people on a call complaining about the state of things certainly won’t (we’ve tried).

What do you think?

Do we need a different word for “innovation,” or should we keep it and deal with the baggage?

If we need a different word, what could it be? What do YOU use?

If we keep it, how do you combat the misunderstanding, eye rolls, and emotional baggage?

Let us know in the comments.

* This option came directly from a conversation with a client last week, and I kinda love it.

We discussed the challenge of getting engineers to stay in a discovery mindset rather than jumping immediately to solutions. Even though they work in R&D (the function), he observed that 99.9% of their work (and, honestly, their careers) is spent on the D in R&D (development).

That’s when it clicked.

Research begins with investigation and inquiry to understand a broad problem and then uses the resulting insights to solve a specific problem. It is a learning process, just like the early stages of Innovation. And, just like in the early days of Innovation, you can’t predict the result or routinize the work.

Development focuses on bringing the “new or modified product or process to production,” Just like the later phases of Innovation when prototyping and experimentation are required, and risk is driven out of the proposition.

Traditional R&D focuses on technical and scientific exploration and solutioning,

Innovation focuses on market, consumer/customer, and business model exploration and solutioning.

It is R&D for the business.

Business R&D.

Image credits: Pixabay

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Innovation Is Driving Away Your Top Talent

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Innovation Is Driving Away Your Top Talent

GUEST POST from Robyn Bolton

You want and need the best, most brilliant, most awesome-est people at your company. But with unemployment at a record low, the battle for top talent is fierce.

So, you vow not to enter the battle and invest in keeping your best people and building a reputation that attracts other extraordinary talents.

You offer high salaries, great benefits, flexible work arrangements, the prestige of working for your company, and the promise of rapid career progression. All things easily matched or beaten by other companies, so you get creative.

INNOVATION!

Your best people are full of ideas and have the confidence and energy to make things happen. So, you unleash them. You host hackathons and shark tanks. You install idea collection software and run contests. You offer training on how to be more innovative. You encourage employees to spend 20% of their time on passion projects.

And they quit.

They quit participating in all the opportunities you offer.

They quit sharing ideas.

They quit your company,

Not because they are ungrateful.

Or because they don’t want to innovate.

Or because they don’t have ideas.

They quit because they realize one of the following “truths”

They’re not “Innovators”

High performers believe they need to work on an innovation project to progress (because management explicitly or implicitly communicates this). But when they finally get their chance, they struggle. The project falls behind schedule, struggles to meet objectives, and is quietly canceled. They see this as a failure. They believe they failed.

But they didn’t fail. They learned something very uncomfortable – they’re not good at everything.

Innovation is different than Operation. When you’re operating, you’re working in a world full of knowledge, where cause and effect are predictable and “better” is easily defined. When you’re innovating, you’re working in a world full of assumptions, where things are unpredictable, patterns emerge slowly, and few things are defined. Most people are great at operating. Some people are great at innovating. Extraordinarily few are great at both.

Innovation is a hobby, not an imperative

The problem with innovation efforts like hackathons, shark tanks, and “20% Time” is that people pour their hearts and souls into them and get nothing in return. Sure, an award, a photo with the CEO, and bragging rights motivate them for a few weeks. But when their hard work isn’t nurtured, developed, and brought to a conclusion (either launched or shelved), they realize it was all a ruse.

They are disappointed but hope the next time will be different. It isn’t.

They stop participating to spend time on “more important” things (their “real” work). But they still care, so they keep tabs on other people’s efforts, quietly hoping this time will be different. It isn’t.

They grow cynical.

They choose to stay and accept that innovation isn’t valued or resign and go somewhere it is.

Their potential is bigger than your box

“I felt like Dorothy in the Wizard of Oz. Before the training, the world was black and white. After, it was full color. I don’t want to go back to black and white.”

For this person, the training had gone wonderfully awry.

The training built their innovation skills but motivated them to find another job because it opened their eyes. They realized that while they loved the uncertainty and creativity of innovation, their place in the organization wouldn’t allow them to innovate. They were in a box on an org chart. They no longer wanted to be in that box, but the company expected them to stay.

But are these “truths” true?

As Mom always said, actions speak louder than words.

Who does your company value more – innovators or operators? The answer lies in who you promote.
Is innovation a strategic priority? The answer lies in where and how you allocate resources (people, money, and time).
Do you want to retain the person or the resource? The answer lies in your willingness to support the person’s growth.

Speak the truth early and often

If a top performer struggles in an innovation role, don’t wait until the project “fails” to reassure them that operators are as (or more) important and loved as innovators. Connect them with senior execs who faced the same challenges. Make sure their next role is as desirable as their current one.

(Or, if innovators are truly valued more than operators, tell them that, too.)

If innovation is an imperative, commit as much time and effort to planning what happens after the event as you do planning the event itself. Have answers to how people will be freed up to continue to work on their projects, money will be allocated, and decisions will be made.

(Or, if innovation really is a corporate hobby, follow the model of top universities and let people participate f they want and give everyone else time off to pursue their hobbies).

If you want to retain the person more than the resource, work with them to plot a path to the next role. Be honest about the time and challenge of moving between boxes and the effects on their career. And if they still want to break out of the box, help them.

(Or, if you want them to stay in the box, tell them that, too.)

Don’t let Innovation! drive away your top talent. Use honesty to keep them.

Image credits: Pixabay

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

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

Rethinking Customer Journeys — by Geoffrey A. Moore
What Have We Learned About Digital Transformation Thus Far? — by Geoffrey A. Moore
Design Thinking Facilitator Guide — by Douglas Ferguson
Building A Positive Team Culture — by David Burkus
Questions Are More Powerful Than We Think — by Greg Satell
3 Examples of Why Innovation is a Leadership Problem — by Robyn Bolton
How Has Innovation Changed Since the Pandemic? — by Robyn Bolton
5 Questions to Answer Before Spending $1 on Innovation — by Robyn Bolton
Customers Care About the Destination Not the Journey — by Shep Hyken
Get Ready for the Age of Acceleration — by Robert B. Tucker

BONUS – Here are five more strong articles published in March that continue to resonate with people:

The Trust Network — by Mike Shipulski
Our Innovation is All on Tape — by John Bessant
The Loyalty Gap — by Shep Hyken
Just Because We Can, Doesn’t Mean That We Should! — by Pete Foley
A Guide to Effective Brainstorming — by Diana Porumboiu

If you’re not familiar with Human-Centered Change & Innovation, we publish 4-7 new articles every week built around innovation and transformation insights from our roster of contributing authors and ad hoc submissions from community members. Get the articles right in your Facebook, Twitter or Linkedin feeds too!

Have something to contribute?

Human-Centered Change & Innovation is open to contributions from any and all innovation and transformation professionals out there (practitioners, professors, researchers, consultants, authors, etc.) who have valuable human-centered change and innovation insights to share with everyone for the greater good. If you’d like to contribute, please contact me.

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

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The Pitfalls of Crowdsourcing

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How to Overcome Them to Spur Innovation

The Pitfalls of Crowdsourcing: How to Overcome Them to Spur Innovation

GUEST POST from Diana Porumboiu

There is a lot of buzz around open collaboration as a driver for innovation. Studies, academia, research, and the myriad of examples from companies are boasting about the amazing results brought by ideas from external parties. A study shows that 85% of the top global brands have used crowdsourcing during the last decade.

But is crowdsourcing truly effective to spur innovation? Even though its popularity increased so much, there’s also plenty of evidence that dispute its effectiveness.

As tempting as it is to fall into the trap of the latest trends in innovation methods, it’s not wise to jump headfirst. So, we decided to write this article and show you the hard facts of crowdsourcing, which will help you decide if this is something your organization can benefit from.

For this, we’ll explain the pitfalls of crowdsourcing and provide practical tips on how to overcome them. To put things in perspective, let’s start with the broader picture, of what crowdsourcing is, or isn’t.

What is crowdsourcing?

As the word indicates, crowdsourcing is all about leveraging the power of the crowds. If you’ve been reading our blog, or worked with innovation topics before, you might think that we are actually referring to open innovation. Not quite. Indeed, the two terms are oftentimes used interchangeably, and the concepts are similar.

But it’s best to make the difference between the two, because setting on the right terminology will also help you better communicate your innovation initiatives to your organization, and to external stakeholders too.

Basically, both crowdsourcing and open innovation refer to engaging external individuals to participate in the innovation process by suggesting ideas and solutions to a specific topic.

Crowdsourcing is the practice of obtaining ideas, solutions, or services from a large, sometimes undefined group of people through an open call. It is a process that leverages the collective intelligence and creativity of a crowd to solve problems, generate new ideas, or carry out tasks.

On the other hand, open innovation includes many other activities that involve people outside the initial working group (open data, scouting, trend research, idea management, etc.). If you want to learn more about the topic, our blog provides vast resources on open innovation which you can find here.

Now, while open innovation, as the name states, is specifically done to generate more innovation, crowdsourcing is used in other contexts too. Methods like crowd labor, crowdfunding, or crowd curation can be valuable if you need to outsource routine and well-defined tasks, manual work or fund your project. These can, in fact, be part of an innovation strategy, but they are not specifically targeting innovation.

That’s where crowdsourcing for innovation comes into play, and what we’ll focus on next.

The pitfalls of crowdsourcing

While crowdsourcing can be an effective way to generate ideas, solve problems, and engage with a community, unless it is properly planned, executed, and managed, it can come up short.

Let’s take a closer look at each of these pitfalls.

Risk management:

There are many risks that come with open collaboration, and some of the most cited are intellectual property and data privacy. Organizations are apprehensive about exposing themselves to the large public and weary about potential conflicts that could arise from ownership, and copyright as well as exposure to competitors.

So, when considering crowdsourcing as part of your innovation strategy, you should weigh the risks associated with it.

There are four main things to keep in mind when it comes to legal risks associated with crowdsourcing:

Existing patents and patents protection for technical solutions
- Trademarks applicable when sourcing new product names, logos or brands
Design of the visual appearance of new products
Copyrights for any original texts

That’s why it’s best to have clearly pre-defined contractual terms, NDAs and confidentiality agreements that deal with intellectual property ownership and data protection. So, make sure to establish clear ownership and copyright guidelines upfront.

This can include requiring contributors to agree to terms and conditions that grant the sponsoring organization the right to use and modify the contributions. Providing clear attribution and recognition for contributors can also help to avoid disputes over ownership. Rewarding participation doesn’t just help with motivation and engagement, but it can also mitigate the legal risks.

Crowd management:

The success of your crowdsourcing initiative hinges on the participation of individuals who provide ideas. However, many crowdsourcing projects fall short due to low engagement levels, inadequate idea generation, or low quality.

These issues may arise because contributors don’t recognize the significance of their contributions, lack motivation, misunderstand project requirements, or are unaware of the initiative.

Because crowdsourcing initiatives require a lot of time, effort and specific skills it’s best to delegate the project to someone who is not involved in everyday innovation activities (if you have those already in place).

Even so, crowdsourcing should still be aligned with the overall innovation and strategic goals, and therefore managed as part of existing processes.

To ensure crowdsourcing runs smoothly, contributors are engaged, decide on the roles and responsibilities for managing the process and ensure that there is adequate support for contributors.

Also, to reach the right people, and as many as possible, you should design effective campaigns that encourage participation.

To ensure quality control establish clear guidelines and criteria for contributions. This can include specific requirements for content, format, and presentation, as well as screening and review processes to filter out low-quality or irrelevant contributions.

Using a platform that allows for peer-review or voting can also help to separate the wheat from the chaff. This what can also facilitate evaluation, which we’ll explore next in more detail.

Idea evaluation:

Evaluating ideas is one of the most complex and challenging aspects of idea management, particularly when it comes to crowdsourcing initiatives where you have a significant number of ideas to sift through and assess.

First, it can be time-consuming and overwhelming to select the ideas to develop.
Second, ideas and perspectives might differ so there will be inconsistency and biases in the evaluation process.
Third, there is a tendency to pick the familiar over the distant ones.
And last, there is also the issue of the quality and level of detail of ideas varying widely, making it difficult to determine which ideas are truly innovative and valuable.

With all these challenges, you could overlook potentially great ideas. What’s more, in a crowdsourcing environment, there is often limited interaction between the idea generators and the evaluators, which can make it challenging to provide feedback and refine the ideas further.

To mitigate this, you need a methodical framework for evaluating ideas. You can learn everything about idea evaluation from this article.

In short, to create an evaluation process that works for you, it’s best to decide on a set of criteria that can help you sift through the ideas. For example, Viima’s evaluation tool gives you the flexibility to choose your own metrics and then analyze and make decisions based on those criteria, without the hassle of going through each of every idea individually.

To have a clearer understanding of how this works in practice, try out the crowdsourcing board template. We set it up so you can easily and safely start collecting ideas from outside the organization.

But remember that even with the best tool, before opening up the organization to the crowds, you will still have to work out your internal process and how that fits into the bigger picture, which takes us to the next point.

Process integration:

Poorly designed or executed processes can lead to low-quality submissions or misunderstandings about the goals of the initiative. A study suggests that besides the issue of managing crowds, organizations also fail to create a process around it.

This is a trap in which many organizations fall. Unless you build a process and plan that goes beyond the first steps of the crowdsourcing initiative, you might waste a lot of time and distract internal teams from using the time and resources on actually executing the strategy.

So, first thing first is to ask yourself if crowdsourcing will serve a bigger purpose. If so, how will it be part of your internal processes and what resources it will require? Crowdsourcing shouldn’t impede internal practices and processes. It should align with the overall strategy and provide value for the organization.

Crowdsourcing shouldn’t impede internal practices and processes. It should align with the overall strategy and provide value for the organization.

Although we have discussed a number of potential pitfalls of crowdsourcing, it’s important to recognize that these issues are often complex and multifaceted. As such, there is rarely a single reason for failure.

To provide a more comprehensive understanding of crowdsourcing, we will next look at some examples of both failed and successful initiatives.

When crowdsourcing goes wrong

1. Pepsi Refresh

In 2010, Pepsi launched “Pepsi Refresh”, a crowdsourcing initiative that invited people to submit their ideas for projects that could benefit their communities, with the winning ideas receiving funding from Pepsi.

While the initiative generated significant attention, it was ultimately considered a failure. Even though in terms of reach and visibility the campaign was a great success, the goal of increasing sale was missed. In fact, “Pepsi Refresh” did the opposite, losing the parent company some $350m.

One reason was the lack of alignment between the initiative and Pepsi’s core brand message. While Pepsi had traditionally focused on promoting its products, the Refresh Project shifted the company’s focus to community engagement and social responsibility.

Another issue with the Refresh Project was the complexity of the submission and voting processes. There were also concerns about transparency and fairness in the voting process. Some critics suggested that the system was easily manipulated, allowing certain ideas to receive more votes than they deserved, while others were unfairly overlooked.

This outcome highlights the importance of ensuring alignment with business strategy and values, as well as the big role played by transparency.

2. Nokia’s “IdeasProject”

Nokia’s “IdeasProject” was a crowdsourcing initiative launched in 2008 to gather ideas from customers and the public for the company’s product development. While the initiative generated significant interest and engagement from users, it ultimately failed to produce significant results, and was eventually discontinued.

One reason for the failure of the IdeasProject was a lack of follow-through and implementation of the ideas generated. While thousands of ideas were submitted and discussed on the platform, few were actually developed or brought to market by Nokia. This led to disillusionment and disengagement among users, who felt that their contributions were not valued or taken seriously.

Another issue was the lack of clear communication and marketing of the IdeasProject. Many customers and potential contributors were not aware of the initiative or did not understand its purpose, which limited the overall reach and impact of the platform.

3. Yahoo’s “Assignments”

In 2007, Yahoo launched “Assignments,” a platform aimed to leverage the collective intelligence of its users to generate high-quality content. The initiative allowed users to submit original content, including articles, photos, and videos, which other users could rate and review. Yahoo planned to use the best-rated content to enhance its news and information websites.

Yahoo failed to create a strong community around the initiative, which made it difficult to generate high-quality content. Furthermore, there were concerns about copyright violations, as some of the content submitted by users was copyrighted material.

Because the platform was plagued with issues, including a lack of quality control over articles submitted and disputes over payments to writers, the platform was eventually shut down in 2012.

When crowdsourcing goes right

Despite the challenges associated with crowdsourcing, we should acknowledge that there is still potential for success, and not all crowdsourcing efforts are doomed to fail.

1. Linux

Linux is a popular open-source operating system that was developed through a crowdsourcing initiative. The project was started by Linus Torvalds in 1991, who was a computer science student at the University of Helsinki in Finland. Torvalds wanted to create a free and open operating system that could be used by anyone, and he enlisted the help of other developers from around the world to contribute to the project.

The project’s success is attributed to its collaborative and decentralized development model, which fosters innovation and customization, as well as a strong community of passionate and supportive developers. Moreover, Linux’s technical merits, such as stability, security, and flexibility, make it a popular choice for a diverse range of applications, from web servers and supercomputers to smartphones and home appliances.

2. Ford

The “Make it Driveable” crowdsourcing campaign by Ford was launched in 2018 to gather ideas and solutions for making vehicles more accessible to people with disabilities. The campaign invited individuals and organizations to submit their ideas for features or modifications that would make driving and traveling in a car easier for people with disabilities.

The campaign engaged a diverse range of people and organizations, including disability advocates, engineers, and designers, in the co-creation process who generated a broad range of innovative ideas and solutions.

The “Make it Driveable” campaign showcased Ford’s innovation and leadership in the automotive industry, demonstrating the potential for crowdsourcing to drive meaningful change and create value for both the company and its stakeholders.

3. Lego

As mentioned above, Lego’s crowdsourcing platform, Lego Ideas has been running successfully since 2008. The platform allows Lego fans to submit their own designs for new Lego sets, and the community votes on their favorite designs. The Lego Ideas platform has been hugely successful, with several of the winning designs becoming popular and highly sought-after sets.

For Lego, crowdsourcing is a cost-effective approach to supplement its in-house capabilities and expand their line of products. Even more, because of the voting system they can assess whether a product idea has potential and demand among its customers.

For participants, Lego Ideas provides a valuable platform to share and contribute to the company’s mission of inspiring future builders. Users can gain recognition from their peers for their ideas and benefit financially if their product is successfully released to the market.

These are just a few examples which show how crowdsourcing can be applied successfully, as long as it’s in line with the company’s core values and goals, and it’s built on a framework that enables systematic use of the ideas from outside the organization.

But as previous examples have shown, crowdsourcing can also go wrong even for the most successful organizations. These examples can hopefully help you make a more informed decision, and inspire in the way you approach crowdsourcing, or open collaboration in general.

To recap, you need alignment between your crowdsourcing initiatives and the overarching strategy, integration with internal processes, a framework that enables idea management, evaluation and development and last but not least, an effective campaign to gather the crowds around your organization.

How to start crowdsourcing

First thing first. Does crowdsourcing align with your current strategic plans? If it does, the first step is to develop a clear plan for using crowdsourcing effectively.

If you are not sure which way to go, as a first step in choosing your approach, you can find inspiration in this chart from Deloitte, which shows a variety of crowdsourcing activities that cater for different needs.

Viima Crowdsourcing 1

Depending on your strategy, industry, and your company profile, you will probably know what type of crowdsourcing is most appropriate for your organization.

This will help you decide on other factors such as the type of contributions you are after, the resources required, and the audience you will target.

Viima Crowdsourcing 2

1. Define your goals and set boundaries

The first step is to set clear goals for your crowdsourcing campaign. What do you want to achieve: is it brand awareness, ideas for improving products or customer satisfaction?

Decide on a set of metrics that will help you evaluate the success of the campaign and measure its impact. This will help you adjust as needed but also set realistic targets about the outcomes you think are possible. If you’re set to get disruptive or completely novel ideas that require technical knowledge and complex solutions, you have to carefully consider whom you want to target with the campaign.

2. Define the target audience and the engagement mechanisms

This step is essential for the success of your crowdsourcing. Without the right participants, you won’t have enough relevant ideas.

Think about who would have the most knowledge and expertise in this area and who would be most interested in providing their ideas and insights. Consider demographics such as age, occupation, location, and interests.

Depending on the goals you set or the types of ideas you are after, you will need different audiences. Sometimes there might be more generic ones, while in other cases you will want specific people with knowledge of the topic or interest in the field. On the other hand, sometimes it is more beneficial to have a diverse audience that can bring new and fresh ideas.

Once you have identified your target audience, you need to develop engagement mechanisms that will motivate them to participate in your crowdsourcing campaign.

Engagement mechanisms refer to the various ways in which you can interact with your target audience and encourage them to contribute their ideas. These mechanisms may include online platforms, social media channels, email campaigns, targeted advertising, events, and rewards or incentives.

It’s important to remember that engagement mechanisms should be designed specifically for the target audience.

3. Decide on a platform to support your activities

Once you have decided on the goals, determined the target audience, and the engaging mechanisms, you should next look for a platform that can cater to all your needs.

The platform should act as a transparent communication and exchange forum for participants. It should be easily accessible and simple to use, but also flexible enough to allow different use cases.

As mentioned above, many crowdsourcing failures are related to the inability of organizations to manage and integrate the initiative in their existing processes. Providing feedback and encouraging ongoing participation are also other important elements to consider when scouting for a crowdsourcing tool.

To get an idea of what open innovation platforms are out there and how they can be used for crowdsourcing, you can read this Guide to Open Innovation Platforms: How to Unlock the Power of Collaboration.

The selection criteria should consider factors such as accessibility to the target audience, the ability to integrate relevant engagement mechanisms to promote ongoing participation, and the capability to distribute incentives after the completion of activities.

4. Pilot and iterate

Consider starting with a pilot initiative to test the approach before scaling up.

No matter how well you prepare for something new, like crowdsourcing might be for some, you will most likely stumble a couple of times. And that’s completely fine.

No amount of research and shortlisting will give you the full scope of how it works in practice for your organization. That’s why it’s important to pilot on a smaller scale. And once you’re happy with the pilot results you are ready to scale up.

Doing pilots allows you to test the platform, check for compatibility with the platform, and test your plan and ways of working.

If you are not sure about the first step, get started with a platform and see how it would work in practice internally. Some vendors offer free user-based versions, like us here at Viima, and some have demos or other free trials.

Additionally, piloting may also help evaluate if you’re searching via the wrong criteria (of if your goals are misguided), or if your ways of working or processes are wrong for what you want to achieve. Also, consider using feedback from participants to iterate and refine the initiative over time.

Conclusions

As you can see, just as there are good parts about crowdsourcing, there are also bad ones. There is no one size fits all solution when you want to innovate, and just like many other methods and tools, crowdsourcing can be a great enabler for innovation.

Regardless of the pitfalls and numerous failures from other companies, crowdsourcing can still be highly beneficial for your organization.

To summarize, let’s recap the positive aspects of using crowdsourcing for innovation and the main factors to consider to fully leverage its benefits.
First, for crowdsourcing to work well, it should make sense for the organization’s strategy and overall goals. Make a plan, assess the needs and the capabilities to manage a process like this. Because indeed, crowdsourcing should be designed as a process that complements, and doesn’t hinder other activities within the organization.

Second, make sure you choose the right platform from the get-go. For optimal results you should aim for something that is flexible enough that allows multiple uses, from external idea collection to managing the entire innovation process.

Lastly, don’t over-rely on technology either, because that is just a tool that helps you move forward and be more efficient. The true benefits come when you start building connections, nurture talent and find new approaches to solve problems.

Image credits: Viima, Pixabay

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