As Artificial Intelligence (AI) keeps conquering creative fields, from visual arts to music, the debates on whether AI can be truly “creative” show no signs of abating. But a much more practical question is, do listeners perceive AI-composed music differently from human-composed?
Two recent studies, one by Shank et al. (2022) and another by Zlatkov et al. (2023), explore these questions and reveal interesting insights into how people judge AI-generated music.
Do We Like AI-Composed Music Less?
The study by Shank et al. (2022) investigated whether people like music less when they believe it was composed by AI. In a first set of experiments, participants listened to excerpts of classical and electronic music and rated how much they liked them while also guessing whether they were composed by a human or AI. The results showed that listeners were more likely to assume that electronic music was AI-composed and tended to like it less if they believed this was the case.
In the next set, the researchers directly manipulated the information given to listeners about composer identity. They found that for classical music, participants liked the excerpts less when they were told it was AI-generated. This suggests a clear bias against AI composers, particularly in genres, like classical music, that are traditionally associated with human emotional expression and creativity.
…Or We Don’t Care?
The study by Zlatkov et al. (2023) explored a similar question from a different angle. Their experiment involved 163 participants who listened to five human-composed and five AI-composed musical pieces. The participants were divided into two groups: one was told the correct composer identity, while the other was deceived. The researchers hypothesized that those who knew that a piece was AI-generated would rate it lower.
Surprisingly, the evidence didn’t support this hypothesis. Unlike previous findings, Zlatkov et al. found that listeners did not necessarily dislike music just because it was AI-generated. However, researchers acknowledged limitations in their study design as they didn’t explore the role of musical style, listener background, and other contextual factors in shaping perceptions of AI-composed music.
…And Should We Care?
Both studies provide yet another example of the complexity of human perception of AI creativity. While one suggests that people have an inherent bias against AI-generated music, particularly when it challenges traditional notions of musical craftsmanship, the other indicates that this bias may not be as universal as previously thought; instead, it depends on context and how AI music is introduced to listeners.
The larger point, however, is that AI-composed music is good enough to fool people into believing that it was human-generated.
So, as AI-generated music becomes increasingly sophisticated, it’s not its quality but rather human perception that will represent a major hurdle to its adoption. Whether AI compositions will ever be embraced on equal footing with human-created music will therefore depend not just on technical advancements but also on changing cultural attitudes toward creativity itself.
And let’s be honest. You’re listening to a piece of music that gets you. Does it matter who—or what—composed it?
In my previous article, “In Silico Ideation,” I reviewed academic literature describing the application of LLM algorithms to generating new product ideas. Now, I want to review what is known about LLMs’ ability to generate other creative content. This article is about poetry (and short stories).
Can You Tell Who Wrote That Poem?
If you think you can easily spot the difference between AI-generated and human-written poetry, think again. In a 2024 study by Porter and Machery, 1,634 participants were randomly assigned to evaluate poetry from 10 well-known poets and poems generated by ChatGPT-3.5 written in the style of each poet.
Guess what? The participants failed to tell the difference between the two sets. Even more surprising, they were more likely to mistake AI-generated poems for human work than the other way around. Moreover, ChatGPT-3.5-generated poetry not only passed as human-written but was rated higher for overall quality, rhythm, and beauty compared to works by famous poets.
The researchers call this the “more-human-than-human” effect. When people like a poem, they tend to assume it must have been written by a human. This bias plays out consistently across experiments, regardless of participants’ experience with poetry.
However, there was a twist: when explicitly told that a poem was AI-generated, participants rated it lower than when told it was human-written, revealing persistent biases against machine creativity.
Enhancing Human Creativity
AI isn’t just creating content on its own—it’s also changing how humans create it. A 2024 study by Doshi and Hauser found that prior access to a pool of AI-generated “seed” ideas improved the novelty and usefulness of human-written short stories by 6.7% and 6.4% respectively. Stories inspired by AI prompts were also rated as more enjoyable and better written.
The most intriguing finding? AI appears to be a great equalizer. Writers with lower measured creative abilities saw improvements of up to 11% when using AI “seed” ideas, effectively closing the gap between them and their more naturally creative peers.
The Collaboration Sweet Spot
It also appears that generating creative content is more effective when humans collaborate with LLMs rather than when either party works alone. A 2023 study by Hitsuwari and colleagues found that while AI-generated haiku and human-made haiku were rated equally beautiful, AI-generated haiku with human intervention received the highest beauty ratings.
Again, participants couldn’t reliably distinguish between human and AI authors. Moreover, the higher the AI-generated haiku was rated, the more likely people were to believe it was human-made.
The Diversity Angle
There’s a potential downside to AI-induced creative enhancement. The Doshi and Hauser study found that AI-assisted stories showed higher similarity to one another and the AI-generated prompts. This suggests a reduction in the diversity of creative output, raising questions about AI’s role in fostering true originality over time.
Implications for the Future
These studies collectively point to several important implications:
1. Indistinguishable creation: The line between human and AI creativity is rapidly blurring, at least for shorter creative formats like poetry.
2. Democratization of creativity: AI tools can help level the playing field, potentially allowing those with less natural creative talent to produce work of similar quality to highly creative individuals.
3. The collaboration advantage: The highest quality creative output may come from human-AI partnerships rather than either working independently.
As AI continues to evolve, so will our understanding of creativity itself. Rather than seeing AI as a replacement for human creativity, these studies suggest we might be moving toward a future where AI becomes an extension of human creative capabilities—enhancing, equalizing, and potentially transforming how we create art.
The question isn’t whether AI can be creative, but how our collaboration with LLM systems will reshape the very notion of creativity itself. I’ll come back to this topic in my future articles.
As with every emerging general-purpose technology, Generative AI (GenAI) is searching for problems to solve. Finding the most fitting will take time. I consider it pointless to look for the things that GenAI can’t do; instead, I prefer focusing on what it already can.
One of the few areas where GenAI has already demonstrated its usefulness is innovation. In a recent PPT presentation, “Powering Front-End Innovation with AI/LLM Tools,” I explored how AI can enrich the front-end of the innovation process. In this article, I’ll review academic literature describing the application of LLM algorithms to one specific stage of this process: generating new ideas.
Faster, Cheaper, Better
Meincke et al. (2023) appear to be the first to use an LLM algorithm to generate new product ideas. The authors took advantage of a pool of ideas created by MBA students enrolled in a course on product design in 2021 (that is, before the wide availability of LLMs). The students were given the following prompt:
“You are a creative entrepreneur looking to generate new product ideas. The
product will target college students in the United States. It should be a physical good, not a service or software. I’d like a product that could be sold at a retail price of less than about USD 50…The product need not yet exist, nor may it necessarily be clearly feasible.”
200 ideas generated by the students were used as a benchmark to compare with two pools of ideas generated by OpenAI’s ChatGPT-4 with the same prompt. One set comprised 100 ideas generated by ChatGPT with minimal guidance (zero-shot prompting); the other 100 ideas generated by the model after providing it with a few examples of high-quality ideas (few-shot prompting).
The first important discovery made by Meincke et. al. was that ChatGPT was generating new product ideas with remarkable efficiency. It took one human interacting with the model only 15 minutes to come up with 200 ideas; a human working alone generated just five.
This dramatically reduces the cost of new ideas generated by ChatGPT. Under specific conditions described in the article, generating one ChatGPT idea costs $0.65 compared to $25 for an idea generated by a human working alone. That means a human using ChatGPT generates new product ideas about 40 times more efficiently than a human working alone.
Faster and cheaper. But what about the quality of the ideas?
To assess the quality of all 400 ideas, the purchase intent measurement through a consumer survey was applied. Measured this way, the average quality of ideas generated by ChatGPT is statistically higher than the ones generated by humans: 47% for ChatGPT with zero-shot prompting and 49% with few-shot prompting vs. 40% for human-generated ideas.
Moreover, among the 40 top-quality ideas (top decile of all 400), 35(!) were generated by ChatGPT.
The only consolation for us humans was that the mean novelty of human-generated ideas was higher than the ones generated by the model: 41% vs. 36%. Besides, ChatGPT-generated ideas, especially with few-shot prompting, exhibited higher overlap, limiting their diversity compared to human ideas. Unfortunately, the novelty itself didn’t affect purchase intent.
Prompting Diversity
In a follow-up study, Meincke et al. set out to improve the diversity of ChatGPT-generated ideas by using 35 different prompting techniques. The authors used the same framework as in the previous study: seeking ideas for new consumer products targeted to college students that can be sold for $50 or less.
Meincke et al. show that of all 35 prompting approaches, Chain of Thought (CoT) prompting, which asks the LLM to work in multiple, distinct steps, resulted in the most diverse pool of ideas; its diversity approached the level of the ideas generated by the students.
The authors also showed a relatively low overlap between ideas generated using different prompt techniques. That means that a “hybrid” approach—using different prompting techniques and then pooling the ideas together—might be a promising strategy for generating large sets of high-quality and diverse ideas.
From Students to Professionals
One of the limitations of the above two studies was that human-generated ideas were created by students. One might argue that students, being less experienced, couldn’t come up with higher-quality ideas that would beat the algorithm.
This limitation was addressed by the study of Joosten et al (2024). In this study, professional designers and ChatGPT-3.5 were assigned identical tasks of generating novel ideas for a European supplier of highly specialized packaging solutions. A total of 95 ideas were generated, 43 by humans and 52 by ChatGPT. All the solutions were evaluated, in a blind fashion, by the company’s managing director, a seasoned innovation expert.
The results show that when assessed by the overall quality score, ChatGPT generated better ideas than professionals. More specifically, ChatGPT-generated ideas scored significantly higher than humans’ in perceived customer benefit, while both sets scored almost identically in feasibility.
Interestingly enough—and in contrast to the results of Meincke et al.—ChatGPT-generated ideas scored significantly higher in novelty. As a result, ChatGPT produced more top-performing ideas in terms of novelty and customer benefit.
Similar results were obtained by Castelo et. al (2024). These authors compared ideas for a new smartphone application that were generated by GPT4 and professional app designers. The authors showed that GPT4-generated ideas were ranked as more original, innovative, and useful.
Furthermore, Castelo et al. used a text analysis approach to determine what specifically made GPT4-generated ideas superior. To do so, they compared two types of creativity—creativity in form (when the language used to describe an idea is more unusual or unique) and creativity in substance (when the idea itself is more novel)—and found that GPT4 outperformed humans in both types of creativity.
Complementing the above two studies is the work by Si et al. (2024) who analyzed the ability of Claude 3.5 Sonnet to generate research ideas (in the field of Natural Language Processing), rather than new product ideas. Comparing ideas generated by the LLM model with those generated by professional NLP researchers, the authors showed that the LLM-generated output was ranked as more novel, although slightly less feasible, than the one generated by human experts.
LLMs vs. Crowds
Of all known idea-generation techniques, crowdsourcing is considered one of the most effective, a consistent source of ideas whose novelty, quality, and diversity exceed those created by individuals and small groups (of experts and laypeople alike). One, therefore, could hope that at least a crowd of people would beat an LLM algorithm in an idea-generating competition.
Alas.
Boussioux et al. (2024) designed crowdsourcing content to generate circular economy business ideas. In total, 234 ideas were generated (and evaluated by 300 independent human judges): 54 by a human crowd of creative problem solvers and 180 by GPT-4.
Indeed, solutions proposed by the human crowd exhibited a higher level of novelty, both on average and at the upper end of the rating distribution. Yet, GPT-4 scored higher in the ideas’ strategic viability for successful implementation, as well as environmental and financial value. Overall, the solutions generated by the algorithm were rated higher in quality than the crowd-generated solutions.
Elaborating on findings by Meincke et al. (2024), Boussioux et al. found that a special prompting technique, prompt-chaining, resulted in the enhanced novelty of GPT-4-generated solutions without compromising their overall quality.
Once again, the authors demonstrated the high cost-efficiency of the LLM-assisted idea-generation process: under specific conditions used by the authors, it took 2,520 hours and $2,555 to generate 54 “human” solutions; the same numbers for LLM-generated solutions were 5.5 hours and $27.
Some Final Thoughts
As recently as a few years ago, the conventional wisdom was that AI tools would only be used to automate routine knowledge work but that the creative part of this work would remain in the human domain. Recent developments forcefully disprove this discourse.
One can split proverbial hairs while assessing the novelty or feasibility of ideas generated by LLMs. But one thing is clear: the overall quality ofLLM-generated ideas is at least as high as the one generated by us humans. And all this is only at a fraction of the time and cost of human ideation.
That means that in silico ideation is here to stay, which allows firms to shift their attention from the ideation stage of the innovation process to later stages, such as idea incubation and prototyping.
At least until LLMs show us they are better at these stages too.
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.
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