You may insist, as much as you want, that “the ideas are a dime a dozen,” but if you ever laid a hand on a real corporate innovation project, you would know that every NPD process starts with an idea, a quality novel idea.
That means that your troubles, as a corporate innovator, start almost immediately: after generating a lot of ideas, either through internal brainstorming or crowdsourcing, you now must select and nurture this “quality novel idea,” the one that will drive your NPD process to a successful launch.
So, how do you go about that, while dealing with the sheer volume of “raw” ideas and trying at the same time to avoid biases that are intrinsic to any selection process?
If you want academic science to help you, I have two news: bad and good. The bad news is that researchers still struggle with identifying novelty. The good news, though, is that they’re working on that.
A recent issue of the Organization: Innovation & Management magazine is dedicated to the topic of novelty. I strongly recommend you look it up and read at least an introductory article by Deichmann, Cattani, and Ferriani. Below, I’m summarizing four articles from the issue that I found the most interesting from a practitioner’s point of view.
The perils of biases and feedback
Heiman and Hurmelinna-Laukkanen remind us that your idea selection process can go wrong even before you assemble a stable of potential winners; you can derail it when formulating the problem you want to solve.
Heiman and Hurmelinna-Laukkanen show that different biases can impair the formulation of strategic problems, steering organizations toward suboptimal solutions. Of various biases, two have the most pronounced negative effect on problem formulation: cognitive (e.g., familiarity and confirmation biases) and motivational (the one manifesting as the influence of personal desires and emotions).
Interestingly, the study demonstrates that awareness of cognitive bias can mitigate its intensity; however, motivational bias remains resistant to awareness alone, indicating the need for deeper organizational or cultural interventions.
Beyond detection, the journey of an idea within an organization is heavily influenced by the feedback. Chen, Magnusson, and Björk investigate how feedback affects idea selection in internal crowdsourcing environments. Their research shows that positive feedback boosts idea acceptance, while negative feedback, although potentially detrimental to selection, can drive valuable revisions that improve idea quality.
It’s here that biases may kick in again as feedback delivered by managers often signals to the rest of the evaluators that an idea is ready for selection, whether this is true or not. By encouraging diverse input, including from experts, organizations can therefore enhance the legitimacy of ideas, ultimately leading to more robust innovations.
AI to the rescue
Now, that we know that AI/LLM tools can successfully generate novel ideas, it’s only logical to expect them to become involved in idea evaluation. That’s the topic covered by Just, Ströhle, Füller, and Hutter. The authors explore the use of language models like SBERT, Doc2Vec, and GPT-3, to automate novelty detection among the pool of crowdsourced ideas.
By measuring semantic distance from existing reference sets, they show the effectiveness of AI in flagging novel ideas, with SBERT outperforming other models in aligning with human assessments.
Interestingly, the study highlights that AI is particularly effective in evaluating ideas that are shorter in description and when comparing these ideas to existing product categories rather than to other crowdsourced ideas. A word of caution: AI often overestimates the novelty of ideas that are conceptually less innovative but uniquely structured, reinforcing the need for a hybrid approach that blends AI with human intuition.
Perfecting your pitch: “how” vs. “why”
Even the most innovative ideas require effective communication to secure buy-in. Falchetti, Cattani, and Ferriani analyze the impact of framing strategies on the reception of novel ideas. They show that radical, disruptive ideas are best pitched with concrete “how” framing that clarifies their practical application and mitigates uncertainty. In contrast, incremental ideas that build on existing concepts benefit from abstract “why” framing, aligning with audience expectations.
A mismatch between the novelty of an idea and its framing can hinder its attractiveness, suggesting that innovators, both entrepreneurs and corporate innovators, must carefully tailor their pitches to the nature of the idea when seeking to maximize its appeal to investors and decision-makers.
Lessons learned
As organizations continue to navigate the complexities of the innovation process, they should address the issue of detecting and selecting the most promising novel ideas. Four approaches—dealing with biases in problem formulation, fostering unbiased feedback, leveraging AI for novelty detection, and aligning communication strategies with the nature of ideas—will provide corporate innovators with a good place to start optimizing the process.
Innovation managers hate the line “Don’t bring me problems, bring me solutions.”
They insist that before the problem-solving process starts, a thorough analysis of the underlying problem must take place; collecting solutions can only ensue when a root cause of the problems has been identified and properly defined.
Albert Einstein’s quote is often invoked in this context: “If I had only one hour to save the world, I would spend fifty-five minutes defining the problem, and only five minutes finding the solution.”
I absolutely support this point of view. I like to argue that you can’t successfully cure a disease (solve the problem) unless you diagnose its real cause (define the problem). I call it the 80:20 rule of successful problem-solving: in my experience, 80% of unsuccessful problem-solving campaigns fail because the problem presented to the group of solvers was not properly defined; only 20% do so because of a poor match between the problem and the solvers.
And yet, I’m not ready to replace the hegemony of the “solution-first” orthodoxy with the “problem-first” one. Taking sides in the “problem-solution” dilemma reminds me of the centuries-old philosophical battle over which came first, the chicken or the egg.
I believe that we should adopt a more holistic approach by establishing instead a sustained problem-solving process.
With such a process in place, the question of what is more important, a problem or a solution, will simply lose its relevance. Firms and teams will be constantly looking for problems, both old and emerging, and then define these problems in a specific and actionable way. A solution-generating phase, involving various techniques (brainstorming, co-creation with customers, internal and external crowdsourcing, etc.) will follow, with the best solutions being selected and implemented. A solved problem will be immediately replaced with the one waiting for a solution — or by the one emerging from the implementation of a newly-acquired solution.
The problem-solving process based on a sustainable portfolio of problems-to-be-solved will extract the best from the employees. Some people are better at spotting trends and sensing troubles, whereas others excel at finding fixes; with a constant flow of problems and solutions, everyone will find something to get excited and engaged.
As for managers, they may try this line: “Bring me problems, then solutions, then problems again…” Or can anyone propose a shorter version of the same?
Of the many “rules” attributed to Jeff Bezos, his two-pizza rule is perhaps the most famous: every internal team should be small enough to be fed with two pizzas — ostensibly to make teams more innovative and meetings more productive.
When it comes to scientific research, however, the world ignores Bezos’s wisdom. As large-scale scientific collaboration becomes increasingly popular, research groups grow bigger and bigger.
A world record seems to belong to a physics paper published in 2015. It has 5,154 authors. Only nine pages of the 33-page article describe the research itself; the rest lists the authors and their affiliation. Can you imagine how many pizzas you would need to provide lunch for such a tight-knit group of collaborators?
The proponents of “the bigger, the better” approach could point to at least one argument in support of their position: a positive correlation between a team’s size and a citation impact of a product that this team has produced. This correlation holds not only for STEM research, but also for social sciences, art and humanities, and patents. The bigger the team of collaborators, the greater the buzz their work is generating.
And yet, Bezos might be having the last laugh.
A group of scientists from the University of Chicago designed an advanced, more nuanced citation-based index capable of discriminating between “disruptive” and more conventional (“consolidating”) research contributions.
Their logic was this: when future citations to a given article also reference a substantial proportion of that article’s own references, then the article can be seen as consolidating its scientific domain. However, when future citations do not acknowledge the article’s own references, the article can be seen as disrupting its domain. (For example, the index shows that articles directly contributing to Nobel prizes tend to exhibit high levels of disruptiveness; at the other extreme, review articles tend to be highly “consolidating.”)
Researchers analyzed more than 65 million articles, patents, and software products generated over 1954–2014. They show that smaller teams tend to come up with more new ideas and opportunities (disruptive contribution), whereas larger teams tend to just develop existing (consolidating contribution).
They also show that work from larger teams often builds on more recent and popular developments, so that attention to their work comes immediately. By contrast, contributions by smaller teams go more deeply into the past and, if successful, project further into the future.
I suggest applying these results to the incremental vs. disruptive innovation dichotomy. When pursuing incremental (profit-consolidating, so to speak) innovation, firms would seem to benefit from creating larger teams that could rapidly expand on recent product development gains. On the other hand, achieving disruptive innovation goals would be more plausible by establishing many small groups pursuing diverse projects — essentially mimicking the approach used by VC investors.
By the way, if someone happens to bump into Bezos, ask him on my behalf, what type of pizza, in his opinion, benefits innovation more: Italian-style or Chicago deep-dish?
A few years ago, I came across a report with the inspiring title Unleashing the Power of Innovation. Composed by a reputable management consulting company, the report set out to figure out what was going on with innovation around the world. For this purpose, the authors of the report approached 246 CEOs and served them with a set of softball questions.
The respondents didn’t disappoint: their answers were confident and convincing. Yes, we see innovation as a priority for our companies. Sure, we consider ourselves innovation leaders and visionaries, not simply sponsors of innovation programs. Of course, strong business leadership and the right culture are the key ingredients for innovation success. Absolutely, we take personal responsibility for directing and inspiring innovation.
Nice, isn’t it.
My sense of elation was suddenly shaken, though, when I reached the last question of the survey. It asked the respondents what constraints were stopping them from “being more innovative.”
The three top answers to this question were: “financial resources,” “existing organization culture,” and “lack of talent.”
Wait a minute! Why do the captains of industry consider the above constraints as something that is completely out of their control, like a natural disaster?
Is it not within the CEO’s authority to allocate enough financial resources to pursue innovation activities? Is it not the responsibility of a CEO to implement corporate policies fostering the culture of innovation? Is it not a CEO’s job to create conditions attracting and retaining innovative employees?
Is that how they take personal responsibility for directing and inspiring innovation?
I can’t overstate it: nothing will happen in any company aspiring to innovate without active personal involvement from the C-suite. Nothing.
Unfortunately, over the years, many CEOs have mastered the art of talking about innovation, delivering well-rounded answers to friendly questions in non-confrontational surveys and interviews.
But frighteningly many of them still have what I call a “cloudy vision” of the very fundamentals of the innovation process. More than a few CEOs take a hands-off approach to innovation management, proudly claiming instead that “in our company, innovation is everyone’s job.” And while talking non-stop about the culture of innovation, they neglect to introduce specific corporate policies encouraging and rewarding their employees’ innovation efforts.
Acts of leadership may come in many shapes and shades. On occasion, it can be a sentence said in the right place at the right time. A story that happened some time ago illustrates this point.
A large multi-national company invited me to a ceremony celebrating the launch of a major open innovation initiative in one of its leading R&D divisions. I was representing a firm that provided a platform supporting the initiative.
Highlighting the importance of the occasion, the ceremony was attended by a very big boss from the corporate headquarters. In his pep talk, the boss (I’ll call him John) spoke about the virtues of open innovation, the importance of the new initiative, and the need for everyone in this location to get involved. He concluded his talk with a customary “Any questions?”
A young fellow in the crowd of scientists raised his hand. Apparently sensing an opportunity to impress the high-profile visitor, he said: “John, I’m so busy with my current projects. How can I find time to run an open innovation campaign and then go through a pile of external submissions, while simultaneously running multiple experiments?”
John looked back at the young fellow for a few long seconds (too long seconds, I thought) and then said: “Look, we’ve charged you with solving a problem that is important to our company — and we want you to succeed. I personally don’t care how you do that. If running experiments is enough, fine. However, if you fail, we’ll ask you: what have you done, in addition to running your own experiments, to have this problem solved? And please, don’t tell us then that you were too busy to go through a pile of external submissions.”
By the expression on the young fellow’s face — and by the silence that suddenly filled the room — I realized that John’s message got across. I smiled to myself. By saying just a few words, John had managed to achieve what in many organizations takes years: he helped create the culture of innovation in this particular R&D division.
This image was created with the help of Microsoft Designer
A friend of mine, an innovation consultant, likes to joke: “Innovation is simple…but not easy.”
The same can be said about open innovation too. Prof. Henry Chesbrough, who introduced the concept of open innovation in his now-classic 2003 book, Open Innovation: The New Imperative for Creating and Profiting from Technology, defined it as using external sources of knowledge and expertise to advance internal R&D. What can be simpler than that?
And yet, the adoption of open innovation has been far from seamless. Open innovation might look simple…but it’s not easy. Firms attempting to incorporate the open innovation “component” into their corporate innovation strategy face numerous barriers — and a recent excellent article by Justyna Dabrowska and co-workers describes some of them.
Of course, many of these barriers are endemic to each firm (remember Leo Tolstoy’s “Happy families are all alike; every unhappy family is unhappy in its own way”?), but some barriers are quite ubiquitous. In this piece, I’d like to review — based on my own advisory experience — four common barriers to the adoption of open innovation and possible approaches to overcoming them.
Treating open innovation as a “special” type of innovation
There is a reason why innovation isn’t easy.
Firms are obsessed with execution. Predictability of outcomes and the precise match between planned and achieved results are the metrics against which most firms measure their performance and performance of their employees.
Innovation is different. By its very nature, it’s highly unpredictable and relies on constant experimentation, with many experiments ending up in no more than a useful learning (as a matter of principle, I refuse to call this “failure”). The unpredictability of outcomes makes innovation difficult to manage, especially when firms try to move their innovation targets beyond incremental improvements of existing products.
Open innovation adds a twist to this complexity by increasing the level of uncertainty because now, you need to innovate with “strangers.” This fear of losing control over the innovation process forces firms to slow the adoption of bona fide open innovation tools like crowdsourcing and rely more on interaction with a narrow circle of tested suppliers and business partners.
To overcome this barrier, open innovation should be closely aligned with the overall corporate innovation strategy. As I argued in my previous article, we have to consider open innovation as part of a single “innovation body.” While one side of this body, internal innovation, represents the innovation potential of the firm’s employees, the other side, open innovation, reaches out to the diverse pools of external talent.
In practical terms, in firms that have just started using open innovation tools, the open innovation team should reside within a larger corporate innovation unit. As the open innovation programs mature, this team will grow and, at some point, may become a unit on its own. But starting with a separate open innovation team from the very beginning is likely to set it up for failure. (I know, I used to work for an organization that did just that.)
Overcoming the Not-Invented-Here Syndrome
As it happens with the adoption of any new paradigm, successful adoption of open innovation requires cultural change — and cultural change isn’t something that comes easy (or simple) to any firm.
A cultural problem most often associated with the adoption of open innovation is so-called Not-Invented-Here (NIH) Syndrome, a rejection, by internal teams, of ideas and solutions that did not originate within the firm.
(We have to realize that the NIH Syndrome manifests not only as a rejection of external knowledge and expertise but also as resistance to intra-company collaboration, when individual units are often reluctant to share their findings with others. I’m not even sure that the NIH Syndrome is more acute when “external” knowledge and expertise are involved, as I saw — and more than just on one occasion — corporate teams more willing to accept solutions from “outside” than from the people/teams residing in the next cubicle.)
There are no simple ways to overcome the NIH Syndrome, and it takes time. Firms should start promoting a cultural shift from problem-solving to solution-finding. This approach postulates that employees are ultimately responsible for the project outcome. How this outcome is to be achieved — by solving the problem internally or by finding a suitable external solution — is of secondary consideration. What is important is how fast this outcome has been achieved and at which cost.
To this end, I strongly recommend reading the excellent article by Hila Lifshitz-Assaf, Dismantling Knowledge Boundaries at NASA, describing how the NIH Syndrome was dealt with at the Space Life Science Directorate at NASA.
Mishandling Open Innovation Tools
Adding to the adoption problems is a widespread confusion over available open innovation tools. Sure, some open innovation techniques, such as crowdsourcing, are not intuitive and need training and experience to use. But others, such as working with customers, suppliers, and partners is something that many firms are quite familiar with.
Unfortunately, what is missing is a clear understanding that each specific open innovation tool is only good when applied to a matching innovation task. Some tasks are better performed using tools from a “co-creation” basket, others require crowdsourcing, yet some may be achieved only with engaging startups.
It falls on academics, business writers, and innovation practitioners to educate innovation teams on the classification of open innovation tools and good practices to use them.
Fear of Revealing “Secrets” and IP Concerns
A surprisingly common and persistent fear when adopting open innovation is the possibility of revealing proprietary information to competitors. You can often hear: “What will happen if we include some sensitive data into our open innovation brief? We cannot control who will read it.”
Or: “If we launch this open innovation initiative, our competitors will immediately know our strategy and our direction.” Perhaps, but aren’t your competitors already aware of what your strategy and your direction are?
These concerns, while real, are often overblown. In the era of digital transformation, the pace of innovation is increasing, and going “open” helps firms sustain this pace by shortening time to market and reducing R&D costs. These days, competition is won or lost on being able to over innovate your competitors, not trying to keep them in the dark. A good example of this approach was shown by Tesla in 2014 when it announced it was opening to anyone its portfolio of patents related to electric car technology. Explaining the move, Elon Musk wrote that Tesla would compete and win relying not on secrecy but on the talent of its engineers.
Besides, techniques exist to write an open innovation brief in such a way that the identity of the firm that sponsors it will be hidden. Moreover, very often, it is possible to write a problem statement without even revealing the technical application behind the problem.
Another concern is so-called “IP contamination,” a fear that solutions coming from outside will “contaminate” IP generated within the firm. Sure, this is a real concern, but again, techniques exist (the “need-to-know” distribution of external information, using “IP-buffer” intermediaries, etc.) that can deal with this issue.
Indeed, open innovation is not easy. But it can be learned, and the benefits of mastering this tool will soon pay for the effort.
This image was created with the help of Microsoft Designer
Remember the famed Bell Labs, once a powerful R&D center for the telecommunication equipment company Lucent Technologies? Bell Labs’ researchers are credited with the development of radio astronomy, the transistor, the laser, the Linux operating system, and the programming languages C and C++. Nine Nobel Prizes (eight in Physics and one in Chemistry) have been awarded for the work conducted at Bell Labs.
And yet, all the intellectual might of Bell Labs did not prevent Lucent from consistently losing its market share to Cisco, a company that did almost no internal research.
Remember Palo Alto Research Center (PARC), which for 30 years has been part of Xerox Corporation? Like their Bell Labs’ peers, the PARC researchers had all the reasons to be proud of their accomplishments. Laser printing, Ethernet, GUI, the computer mouse are just a fraction of what has been conceived and developed at PARC.
But despite receiving lavish R&D investments from the parent company, PARC had failed to create significant value for Xerox and its shareholders.
What unites Bell Labs and PARC? In retrospect, we can say that the inability of two innovation powerhouses to provide a competitive advantage to their parent companies signaled the sunset of the era of closed innovation and the dawn of the era of open innovation.
What Went Wrong with Closed Innovation?
For an eternity, internal R&D has been viewed as a strategic asset and indispensable competitive tool for any large firm. Because internal R&D was expensive, the large and powerful used it as a weapon to protect their market position from the less funded competitors. If you were big, you could innovate and win; if you were small…well, bad luck.
And then, new entrants to the market began showing up en masse. What was remarkable about them is that they conducted little or no basic in-house R&D. Instead, they preferred to cooperate with other, often smaller, companies engaged in more basic research.
Three major factors have contributed to this trend. First, the abundance of highly-trained people with college and post-graduate degrees, fueled by the increasing internal mobility of the workforce along with the growing inflow of high-quality professionals from abroad. Knowledge and experience have ceased being the exclusive property of a few; they began belonging to “everyone,” prompting the Sun Microsystems co-founder Bill Joy to remark: “No matter who you are, most of the smartest people work for someone else.”
Second, the web and the host of telecommunication technologies have dramatically reduced the cost of starting and running a business. As a result, small and nimble startups have begun relentlessly challenging large and inflexible incumbents.
Finally, the very nature of innovation has changed. Modern innovation occurs at the cross-borders of different disciplines, and no company, no matter how large, can afford hiring researchers from many different fields. Now, the most disruptive innovation happens when people with different but complementary skills and experiences put their heads together—regardless of where they work.
By publishing his now-classic 2003 book, Open Innovation: The New Imperative for Creating and Profiting from Technology, Prof. Henry Chesbrough was the first who said it loud and clear: the era of closed innovation was over.
Does Internal R&D Have a Future?
It would be a huge mistake, however, to think that the end of “closed innovation” means the end of internal R&D. Quite to the contrary: internal R&D will play a crucial role in any firm’s innovation process. What has changed, though, is that internal R&D has stopped being closed innovation; it is now internal innovation.
I like to argue that open innovation is not a special type of innovation; it is part of a single “innovation body.” Open Innovation serves as a branch extending over the corporate walls to reach out to the diverse pools of external talent. But it can be successful only if it’s organically connected to the other side that is utilizing the innovation potential of the company’s employees.
In many respects, it’s internal innovation, not open, that represents the foundation of the corporate innovation strategy. Only internal innovation teams can identify and properly formulate problems facing the firms. Only internal innovation teams can fully understand the value of incoming external solutions to select those that make corporate sense. Only internal innovation teams can ensure the successful integration of external information with the knowledge available in-house.
It’s only at this special midpoint of the problem-solving process — at the stage of generating potential solutions to the problem — that open innovation is superior to internal.
Firms, therefore, should consider internal and open (“external”) innovation as different, complementary tools in their innovation management toolboxes. There is no sense in discussing which tool is better; each should be used at its proper time and place.
Building Internal Innovation Networks
Some firms organize their internal innovation activities in the form of internal innovation networks (IINs).
In addition to supporting open innovation, there are at least four important benefits IINs can bring to any firm.
First, IINs provide a communication platform between different corporate units that in many firms often have no institutional space to discuss strategic issues. By providing such a platform, IINs increase the efficiency of the decision-making process and reduce the need for face-to-face meetings, something that any large firm with many units spread around the globe can certainly appreciate.
Second, IINs help foster the culture of collaboration, bringing together corporate units traditionally involved in the innovation process, such as R&D and Marketing, with those that not (Business Development, Finance, Legal, etc.).
It’s useful to remember that the notorious “Not-Invented-Here Syndrome” manifests not only as a rejection of external knowledge and expertise but also as resistance to intra-company collaboration, when individual units are often reluctant to share their findings with others. By breaking internal silos and promoting intra-company collaboration, IINs enhance the overall innovation potential of the firm.
Third, IINs can be used to find solutions to problems individual units have failed to solve on their own. Again, such problem-solving could be especially effective in multinational corporations with numerous units spread over geographic and time zones. People in different units, often brought together as a result of M&A, rarely communicate with each other and almost never meet face-to-face. Yet, often one unit may possess specific knowledge that is desperately needed — and can be immediately implemented — in another.
Connecting such “dots” through IINs can result in significant savings of time and money for internal R&D.
Finally, IINs help identify the firm’s emerging thought leaders, who — especially in junior positions and in geographically remote units — often remain unnoticed to the corporate leaders. IINs provide a voice to every employee regardless of their rank and location in the firm. Besides, the very format of online communication is especially attractive to younger workers playing an increasingly important role in the global marketplace.
In summary, when developing a viable corporate innovation strategy, firms must create a balanced portfolio of internal and external/open innovation programs. Yet corporate innovation leaders should always remember that the full potential of any innovation program can only be realized by the concerted effort of properly connected people within firms.
Or, putting this differently, the power of corporate innovation comes from the strength within.
This image was created with the help of Microsoft Designer
As someone who was born and grew up in the Soviet Union, I know a thing or two about the shortage of foods and goods.
Our “out-of-office” life was a perennial chase of the hard-to-get stuff, which was pretty much everything you’d need to live above a mere subsistence level.
This has instilled in my compatriots and me one simple habit: buy something first, while it’s still available, then decide whether you need this something or not. This approach was especially useful with imported items, which were rare, often completely unknown, and whose value was therefore even more difficult to assess in advance.
One such item was “French perfume,” and I’m using the quotation marks here deliberately. When buying a precious bottle — on the black market or through a friendly connection — you didn’t have the luxury of knowing the brand of the future acquisition. It was just it, a bottle of “French perfume.” Pondering if the intended receiver of the item, your wife or girlfriend, would prefer Chanel, Guerlain, or Magie Noire, was completely pointless: you could buy only what you were offered.
On the positive side, your loved one wouldn’t care much, either; she would just be delighted with the gift. She would also appreciate your effort to please her — and be proud of your ability to get things done.
Today’s corporate innovation reminds me of this “French perfume.”
Volumes have been written about the 3-Horizon Model of Innovation that places innovation projects into incremental, “adjacent,” and transformational buckets, each implying a different time horizon and funding level. A complementary, equally useful, classification of corporate innovation projects into market-creating, sustaining, and efficiency innovations, each corresponding to a specific stage of business model development, has also been proposed.
And yet, time and again, our corporate innovation leaders can’t provide a working definition of what innovation means for their organizations. It’s just that, “innovation.”
Innovation charters, a formal document outlining the major aspects of the organization’s innovation strategy, are almost unheard of. Attempts to introduce portfolio management of innovation projects are often met with a deadly fire because “structure” supposedly kills innovation. Worse, many corporate innovators sincerely believe that every innovation must be “disruptive,” while all other types of it are for losers.
The lack of understanding of the various types of innovation inevitably leads to confusion about the available innovation tools. A simpleidea that for each innovation objective, there must be a specific innovation tool most suited for this objective, sounds almost foreign. Instead, one-size-fits-all fads follow each other like ocean waves hitting the corporate shorelin— hackathons, skunkworks, innovation labs, corporate accelerators, corporate venture funds — with inevitable complaints of low innovation returns coming later. “Idea generation” campaigns are omnipresent, confusing minds, draining resources, frustrating participants, and resulting in pretty much nothing.
Steve Blank has a shrewd definition for our corporate innovation process: innovation theater. My friend Andy Binns at Change Logic likes to use an equally colorful term: innovation zoo. I humbly hope that my term, “the French perfume innovation,” will become as popular as Steve’s and Andy’s.
What is to be done?
My solution is simple, if not quite revolutionary: education.
We need to get back to the drawing board and help organizations understand the very basics of innovation: definitions, typology, infrastructure, processes, metrics, and incentives. We need to create a set of short narratives (“Innovation101,” so to speak) giving organizations a place to start, in a practical and intuitive way.
And we need help from academic researchers studying innovation.
Don’t get me wrong. I’m not calling on them to stop deepening our theoretical understanding of the innovation process. Instead, I’m urging them not to forget that by producing knowledge that the innovation practitioners can’t use, they make their future work less meaningful. Nor am I saying that the “new models of innovation” are completely useless. What I’m saying is let’s learn first to use the models we already have.
The image was created with the help of Microsoft Designer
How often, when taking an over-the-counter drug, do you read the following note on the label?
“Adults and children 12 years of age and over: one tablet; children under 12 years of age: ask a doctor.”
Pretty often, I guess.
And I’m sure that the fact that children should take drugs differently from adults doesn’t surprise you.
But what is so magic about the age 12 in this particular case? What if the age of your child is 11 and a half years? 11 years and 11 months? Will you “ask a doctor” or take a risk of giving your little one the much-needed relief?
Welcome to the world of pediatrics, a branch of medicine that deals with medical care for children!
Conventional definitions of a “pediatric patient,” a scientific term for a child, bracket the childhood into the age period ranging from the birth to 18 years (although some professional organizations in the United States extend the age limits of pediatrics from fetal life until age 21).
Yet it’s clear that while such a range may make legal sense, it’s too broad to be useful in medical practice. As one report puts it, it’s ridiculous to compare “a 34-week-old premature infant” with “a 17-year-old high school football player.”
To address this problem, more precise age definitions have been introduced, dividing “pediatric patients” into neonates, infants, toddlers, preschoolers, school-agers, and adolescents. However, with so wide variations in individual rates of child development — not to mention cases of physical or mental retardation — placing specific age numbers doesn’t really help.
There is one parameter, though, that seems reasonable, at least for the purposes of dosing drugs: weight. For example, San Mateo, California, County’s medical guidelines define pediatric patients as someone weighting less than 80 pounds. But again, with the spread of childhood obesity reaching epidemic proportions, a child’s weight can be grossly misleading.
We therefore urgently need better predictors of children’s real, biological, age. Let’s call them biomarkers of childhood.
We need to identify and validate a series of biological markers — naturally-occurring molecules collected from easily available body fluids, such as urine and saliva — to follow stages of a child’s physiological and mental development.
These markers will tell us how our child progresses through his or her childhood; these markers will eventually tell us that our child has reached adulthood.
I see at least two areas where biomarkers of childhood can be useful.
First, and the most obvious, is medical care. Although the need for such biomarkers has been long recognized, this area of biomedical research is still in its infancy (no pun intended). This negatively affects both pediatric care as well as child-specific drug development.
But why restrict biomarkers of childhood to medical use? Why not to ask more general questions about the biological differences between children and adults?
What makes your child a child? And although answering this question will require contribution from many different fields, biomarkers of childhood could provide objective and measurable input. I easily see their application in the juvenile justice system, as the most obvious example.
Given the enormous amount of information needed to create a comprehensive list of biomarkers of childhood, it’s clear that this job is well beyond capacity of one single person or even single organization.
The most reasonable venue is to use crowdsourcing that would allow collecting data points from large groups of people around the world.
I therefore call on any party interested in child healthcare and well-being — whether commercial, government, or non-profit — to sponsor a crowdsourcing campaign aimed at creating a comprehensive list of biomarkers of childhood.
I volunteer to work with any non-profit organization to help define specific parameters of such crowdsourcing campaign and to choose an appropriate crowdsourcing platform.
Why We Should Stop Pretending that We Don’t Want Illegal Immigrants in This Country
This image was created with the help of Microsoft Designer
As an innovation manager, I regularly remind my clients that the most important part of the problem-solving process is to correctly define the very problem that they want to solve.
The sad reality is that many organizations—small and large, corporate and non-profit—fail to identify the root cause of their problems. Instead, they immediately focus on finding something—anything!— that may look like a solution.
To me, this is equivalent to taking Tylenol to relieve a headache even before knowing what caused it: hangover, mild cold, chronic migraine, or advanced glioblastoma.
But if you ask me: “Who are the worst offenders of the ‘problem-first’ rule?”—my answer will be immediate: our politicians. Day in and day out, in multiple venues available to them, our elected representatives demonstrate a remarkable inability (unwillingness?) to define the problems they’re trying to solve in our name and on our money.
One of the glaring examples of this syndrome is the “problem” of illegal immigration.
I don’t even want to repeat the horror stories you can hear these days about the mortal danger faced by our country from illegal immigrants. The topic has become so hot that it has reached the top of the most important issues of this year’s presidential election.
Solutions to the problem proposed so far by our politicians don’t strike me as serious, but regardless of which type of solution you prefer—providing illegal immigrants with the path to naturalization or deporting them all—you’re likely to agree that our immigration system is broken.
Is it?
Legal Jobs for “Illegal People”
The truth is that our immigration system works exactly as it was designed—and it was designed to provide a steady flow of people who enter the country illegally.
Let me explain.
But first, let’s agree that most people entering the United States, whether legally or illegally, come here not to enjoy our freedoms, landscapes, historic sites, or food; they come here to work, to earn a living for themselves and their families.
Illegal immigrants, people who enter the country without proper documents, represent an essential—and irreplaceable—part of the US working force. Why? They take on jobs that few Americans would be willing to take due to low pay and poor working conditions.
Here is a chart that presents the share of undocumented workers by industry:
Illegal immigrants they might be, but they do perfectly legit work here, eh?
But the best part of having these undocumented workers—and the reason why we want them here—is that they cost less than American citizens or legal immigrants. Illegal immigrants work, pay taxes, and spend money here, but they don’t receive benefits, like Social Security or disability pay, to which Americans or legal immigrants are entitled.
Besides, undocumented workers have no legal rights. As a result, they don’t complain, don’t strike, and don’t ask for more lest they be exposed and potentially deported. Ideal, completely hassle-free employees!
No other country in the world—I mean, among those that can be called civilized—has access to such a wonderful pool of cheap labor force. I would argue that illegal immigration represents a competitive advantage that the US economy maintains over the economies in Europe, Canada, Australia, and Japan.
Sounds too far-fetched? Here is what experts say.
In a paper published earlier this year, Wendy Edelberg and Tara Watson of Brookings Institution argue that the entry of new migrants into the U.S. in 2022-2023, a large part of them being undocumented, has ensured a healthy growth of the US economy even as the Fed sharply raised interest rates to bring down inflation.
Indeed, in 2023, the U.S. was the fastest-growing G7 economy, and Edelberg and Watson insist that at least part of this surprising strength can be explained by the steady inflow of new workers from abroad, an economic stimulus largely unavailable to other G7 countries.
Other economists agree. According to the Morgan Stanley Chief US Economist Ellen Zentner, illegal immigration’s impact on the labor market was a “big positive for the economy” and a key factor behind the soft landing narrative. Zentner, too, believes that illegal immigration was the reason why, in 2023, the U.S. had such fast economic growth, while inflation and wage growth decelerated.
So, far from poisoning the blood of our country and sucking up its limited resources—as some US politicians claim—illegal immigrants may well have saved us from recession.
Slaves, Postdocs, and NCAA Athletes
I’d further argue that using illegal immigrants to boost the growth of the American economy has its historic precedent: slavery.
As pointed out by David Reynolds in his book “America, Empire of Liberty,” forced black labor was a powerful engine of the American economy, especially in the South, in the 17th and 18th centuries. Cheap and deprived of elementary rights, slaves helped alleviate a chronic labor shortage perennially plaguing the colonies since the very beginning.
Slavery is long gone, but the reliance on cheap labor lives on, with undocumented immigrants from Central and South America playing essentially the same role as their African predecessors centuries ago.
And if you look around, you can see shadows of slavery, understood as using pools of cheap workers as a means of creating economic value, in some quite unexpected corners of American life.
Take, for example, our science, indisputably the world’s best by any standard. True, we spend a lot of money on research, but what sets American science apart from other countries is our unique institute of postdocs.
Postdocs are scientists with freshly acquired PhDs who come to academic labs for what is euphemistically called “postdoctoral training.” But trust someone (me) who used to be a postdoc in his prior life: training is the last thing you get when “doing a postdoc.”
Postdocs work, and work hard; they’re true workhorses of American academic science, spending endless hours in the lab on weekdays, weekends, and holidays. And they deliver: while comprising less than 70% of non-faculty researchers in US universities, postdocs contribute to almost 90% of scientific publications coming out of these institutions.
And what about postdoctoral pay? The US state that pays postdocs the highest salary, $67,000 per year, is Oregon; the lowest is Florida: $41,000 per year, which is $19.54 per hour (actually less, given the number of hours postdocs spend at work). Not much for someone who often graduated from a top school and then spent 5-7 years as a grad student, to get a PhD, the highest scientific qualification available in the U.S.
And if you wonder…Yes, you’ve guessed it right: international postdocs, who make up 57% of the country’s STEM postdoc population and who are shown to be more productive than US citizens and permanent residents, receive lower pay than the latter.
What motivates postdocs? A hope for a bright future. They believe that by spending another 5-7 years in “postdoctoral training”—working hard and subsisting on a salary way lower than their real earning potential—they will secure a permanent academic position or a highly paid job in industry.
So let me repeat: it’s the institute of postdocs—highly qualified, motivated, and cheap workers—that provides American science with a competitive advantage that helps it maintain its leading status.
Or, take NCAA athletes. Remember that until 2021, the NCAA athletes were not compensated for their participation in national tournaments, and this is even though in 2021 alone, the NCAA generated $1.15 billion in revenue.
Like their postdoctoral brethren, NCAA athletes were motivated by a hope: that by kicking their asses and risking their health—for free!—they’ll get noticed by recruiters and be drafted by professional leagues, which will secure their financial well-being for the rest of their lives. In the meantime, the NCAA bosses were gobbling up billions.
Sure, I understand the difference between slaves, illegal immigrants, postdocs, and NCAA athletes. However, I encourage you to see what they all have in common: they are parts of the unique to the United States system of exploiting pools of cheap (or free) labor to create economic value, and to provide the American economy with a competitive advantage.
A Simple Solution That No One Wants
Did you know that the existing immigration laws were written about 60 years ago and haven’t been seriously updated for the past 34 years? Doesn’t it strike you as odd that our elected representatives have spent decades doing nothing to fix the system they call “broken”?
And this is even though a simple solution to the problem has been lying in plain sight all along: to criminalize hiring illegal immigrants, to make it illegal to employ people without proof of work authorization.
(This approach that can be called “demand-side criminalization” is conceptually similar to the one used by some U.S. states (Nevada, for example) to curb prostitution. This is when, instead of going after sex workers, law enforcement was charging clients seeking their services.)
Do you think many employers would risk criminal prosecution for hiring illegal immigrants? Do you think that, facing a threat of such prosecution, they won’t find ways to check the legal status of their workers? You bet.
I also bet that such a simple solution will never be implemented, for our lawmakers perfectly understand that this would kill the golden goose of cheap labor our businesses enjoy.
For the same reason, I’m pretty sure that the idea of massive deportation of illegal immigrants won’t take off, either.
The latest stats show that there are about 11 million undocumented immigrants in the country. The logistics of such a monumental law enforcement operation are completely obscure, and the proponents of the idea don’t rush to explain to us how the illegals would be found, where they’d be sent, and which specific law enforcement agency (or agencies) will be in charge.
Then, there is the stubborn topic of money. The best estimates suggest that each deportation would cost about $13,000 in current dollars. The total cost of the proposed deportation would thus amount to $143 billion. Who’s going to pay for that, using the favorite rhetorical question of this election season?
But most importantly, the experts are clear about the economic consequences of such a move. According to Adam Posen, the president of the Peterson Institute for International Economics (PIIE), the proposed mass deportation will result in a major depletion of the labor supply. Two things will happen then. First, shrunken labor typically means slower economic growth. Second, it would increase the price companies will have to pay to attract workers, which will cause inflation. (The PIIE experts project that the deportation of around 1.3 million undocumented workers could lead to a cumulative three-year increase of inflation by 1.3%.) A recession is likely to follow.
Are We Ready for a Solution?
Sometimes, the inability—or, to be more precise, the unwillingness—to properly define a problem is rooted in a painful realization that solving it would lead to consequences much worse than the problem itself.
This is how I see the problem of illegal immigration. We want to solve it, but we also want to keep illegal immigrants coming, no matter what we may say to each other in the heat of a political discourse.
That’s why I consider the problem of illegal immigration unsolvable, at least in the short term.
Sure, some palliatives could be implemented. For example, we must realize at last that the majority of people coming to this country are looking for a job rather than for a political asylum, as we tend to believe. We therefore should adjust the quota of immigration visas accordingly, giving more of them to the former and fewer to the latter (essentially following the pattern of Canada and Australia).
We can also lower the educational requirements for the people coming to the country on temporary working visas; not everyone needs a college degree to work in construction or hospitality.
But we should also remember that by giving legal rights, however limited, to people who used to be “illegal,” we’ll inevitably increase the cost of their labor. This will diminish their economic attractiveness to businesses and therefore deprive the American economy of a benefit it has become so addicted to.
This image was created with the help of Microsoft Designer
A solid consensus would seem to exist that customer feedback gathered through market research is a key to successful product innovation.
And yet, I’m surprised how often one can hear dissenting voices. Some people — usually those without hands-on experience in the innovation process — claim that paying too much attention to customers stifles innovation and reduces it to a mere incremental improvement of existing products (which these people consider anathema to the “true” innovation).
In support of their point of view, they love to quote Steve Jobs: “It’s really hard to design products by focus groups. A lot of times, people don’t know what they want until you show it to them.”
A plausible interpretation of this quote might be that had Jobs listened to his customers, Apple would still have been making incremental improvements to Apple-1.
Another supportive quote is attributed to Henry Ford: “If I had asked my customers what they wanted they would have said a faster horse.”
I remember reading a brilliant piece by Tristan Kromer, who provided an interesting twist to the Ford quote:
“If the customer asks for a faster horse, do not build a faster horse. Ask, ‘Why do you want a faster horse? What would you use it for?’ If the customer wants a faster horse to move cargo across town, a car might be a great invention. When the customer wants a faster horse to win a horse race, a car is a terrible invention.”
It was then when it hit me. I re-read Ford’s quote. Re-read it again, too: “If I had asked my customers…they would have said.”
Ford didn’t ask his customers! He simply assumed that all they wanted was a faster horse. And yet, he invented a car. Good for him!
It’s still unfortunate that Ford didn’t develop a habit of asking his customers what they wanted. I doubt that many of them would have said they wanted a faster horse. Most of them would have replied, as Kromer suggested, that they needed to move cargo across town.
And if Ford kept asking follow-up questions — which cargo, for which distances, at which speed — useful feedback would have inevitably emerged.
Who knows, had Ford talked to his customers, the first Ford truck — arguably the best Ford Motor Company’s invention ever — would have appeared earlier than 20 years after the first Ford car.
Curiously, in the 1930s, the Toyota Motor Corporation invented the 5 Whys technique, a simple but powerful approach to getting employee and customer feedback. I don’t want to sound obnoxious but had Ford, not Toyota, invented the 5Y, it might have been Ford, not Toyota, being today the second largest car manufacturer in the world.
Of course, one should understand the difference between two related, overlapping, yet distinct forms of customer feedback: customer wants and customer needs.
What focus groups produce is what the customer wants: a demand for a faster horse or a faster computer. It takes more time and effort — and more sophisticated market research tools — to identify customer needs behind customer wants.
True, people often don’t know that they want a product until you show it to them. But it is only after they realize that they need this product that they are ready to pay for it.
There is no innovation without customer feedback — either in the form of wants or needs.
And what about Steve Jobs? Read his quote again, too. Jobs didn’t like focus groups, arguably a messy tool for collecting consumer feedback. But he says nothing about other forms of market research. Did he loathe them all? I don’t know. Do you?
Regardless, a genius like Steve Jobs can afford to eschew proper market research and trust his guts. Feel yourself on par with Jobs? Go ahead and try to develop new products without doing market research first.
Just don’t be surprised if your innovation outcomes will be less impressive than Jobs’s.
innovationobserver.com 