The Death of Brainstorming
Innovation is the Holy Grail for organizational growth. But new research shows that traditional brainstorming may not be the best tool to tap in to creative thinking. Here are practical techniques from behavioral science and GenAI to help employees innovate better.
Traditionally, brainstorming has served as the fount of innovation for organizations of all sizes across all industries. A group of employees gets together in a room to innovate out-of-the-box ideas around developing a new product, optimizing an existing process, or solving a challenging problem.
This technique has served as a brilliant diamond of creativity, working wonders to facilitate the innovative ideas that helped give birth to our modern world … or so goes the traditional narrative on brainstorming.
Recent research, however, has increasingly shown that this kind of brainstorming may be more like a cubic zirconia than a brilliant diamond. In fact, those studies showed that the supposed usefulness of brainstorming is misleading, and it may be less effective than the simpler technique of asking employees to come up with ideas on their own.
Moreover, our modern, post-pandemic workplace has led to a situation in which it’s increasingly difficult for business leaders to get employees into the same physical room to pursue brainstorming. More teams work in a distributed manner, either from workplaces across the globe or from home on their hybrid/remote days. Indeed, in the second quarter of 2024, less than one-third of companies (31%) required their corporate employees to work five days a week in the office, according to the Scoop Flex Index. And traditional brainstorming using remote tools is much more difficult than in-person brainstorming.
Given both the research on the surprisingly low effectiveness of brainstorming and the shrinking number of companies requiring full-time, in-office work, it’s time to announce the death of brainstorming as the primary tool for innovation. Instead, HR leaders need to rely on a combination of behavioral science and digital technology to help their companies pursue innovation for competitive advantage in the modern world.
Innovation in Distributed Teams
Many leaders are skeptical that true innovation is possible in distributed teams. Sam Altman, CEO of OpenAI, said the rise of remote work has undermined innovation, claiming that “one of the tech industry’s worst mistakes” was believing that you “didn’t need to be together in person and … there was going to be no loss of creativity.”
The narrative that traditional, in-person work environments offer the sole breeding grounds for innovation has dominated the discourse. In reality, this narrative is not only outdated but fundamentally flawed in today’s technologically driven world.
Consider a new study in the prominent scientific journal Nature. This comprehensive analysis spearheaded by a team of researchers from Oxford University and the University of Pittsburgh delved into a staggering expanse of data—over 20 million scientific studies and 4 million patent applications spanning a half-century.
Analyzing trends from the 1980s to the present, the data revealed a fascinating narrative: The once-wide chasm between the innovative outputs of in-person and distributed teams has been steadily narrowing.
The 1980s marked the debut of the first scientific distributed collaboration platform. Back then, the data hinted at a somewhat bleak picture for distributed teams. They faced a 5% innovation deficit compared to their in-person counterparts. It was as if distributed collaboration carried an invisible 5% tax on creativity and breakthroughs.
As we fast-forwarded into the new millennium, the landscape began to shift. Between 2000 and 2010, this innovation gap dwindled down to a mere 1%, indicating that the barriers once posed by physical distance are gradually losing their grip. That period witnessed the birth and rapid adoption of technologies tailor-made for distributed collaboration, such as Trello, Zoom, Google Drive, and Slack.
But the real plot twist emerged post-2015. In this period, the narrative flipped completely. The once-negative coefficient, a marker of the distributed work disadvantage, not only zeroed out but took a surprising leap into positive territory. It’s a remarkable turnaround, a testament to the evolving efficacy of distributed collaboration.
The studies underscore the crucial role played by robust internet connectivity in enabling and enhancing distributed collaboration. Specifically, teams whose members had better broadband connectivity experienced improved outcomes on innovation. That evidence further supports the idea that refinements in distributed work tech tools—which are enabled by fast broadband—offer the key to improved innovation.
Traditional Brainstorming: Benefits and Challenges
Traditional face-to-face whiteboard brainstorming sessions may feel super productive, but numerous studies have shown that this format is substantially worse for producing innovative ideas than even individuals coming up with ideas separately, not to mention alternative and more-effective best practices.
Behavioral science research has revealed that participants in traditional brainstorming sessions enjoy them and believe them to be effective for generating ideas. That comes from two areas identified by scientists:
- Idea synergy, meaning that ideas shared by one participant help trigger ideas in other participants. Experiments show that synergy benefits are especially high if participants are instructed to pay attention to the ideas of others and focus on being inspired by these ideas.
- Social facilitation, meaning the benefit of social support from working on a shared task. Participants feel motivated when they know they’re collaborating with their peers on the same goal.
Sadly, these benefits of brainstorming come with costs attached. One of the biggest problems is called production blocking.
Did you ever participate in a brainstorming session during which you had what you felt to be a brilliant idea, but someone else was talking? Then, the next person responded and took the conversation in a different direction. By the time you had a chance to speak, the idea seemed not relevant or too redundant, or maybe you even forgot it.
If that has never happened to you, you’re likely an extrovert. Introverts have a lot of difficulty with production blocking. It’s harder for them to formulate ideas in an environment of team brainstorming. They generally think better in a quiet environment, by themselves or with one other person at most. And they have difficulty interrupting a stream of conversation, making it more likely for their idea to remain unstated.
Those with a more pessimistic than optimistic personality also struggle with brainstorming. Optimists tend to process verbally, spit-balling half-baked ideas on the fly. That’s perfect for traditional brainstorming. By contrast, pessimists generally process internally. They feel the need to think through their ideas to make sure they don’t have flaws. Although brainstorming explicitly permits flawed ideas, it’s hard for pessimists to overcome their personality, just like it’s hard for introverts to generate ideas in a noisy team setting.
Pessimists are also powerfully impacted by a second major problem for traditional brainstorming: evaluation apprehension. Many pessimists feel worried about sharing their ideas openly due to social anxiety about what their peers may think.
Finally, more-junior or lower-status team members often feel reluctant to share controversial ideas that challenge existing practices or the territory associated with high-status team members. Those ideas are often the most innovative, but they remain unsaid, whether due to production blocking or evaluation apprehension.
Using Behavioral Science to Improve Traditional Brainstorming
In the early 1990s—long before the rise of distributed work—researchers concerned with the faults of traditional brainstorming worked on developing new techniques. In working with clients to help them adapt to distributed and flexible work models, I used recent advances in behavioral science to adapt these techniques to the modern workplace through what I call “asynchronous brainstorming.”
This process starts with selecting digital collaboration tools. Google Forms or Microsoft Forms, ideal for anonymous text-based idea submissions, and MURAL, a virtual whiteboard suitable for visual brainstorming, stand out as prime examples. Here are the three steps: (See box for a case study of asynchronous brainstorming below):
STEP 1: Use digital collaboration tools to input ideas. Teams may opt for real-time virtual collaboration, in which participants first have a brief discussion via a video conferencing tool and then input their ideas into the chosen digital collaboration platform, tapping social facilitation. More frequently, they use an asynchronous approach, which allows team members to add ideas independently by a set deadline, catering to different time zones and thinking styles.
In either case, the use of the digital collaboration tool addresses the potential problem of production blocking. Moreover, you can allow participants to submit ideas anonymously, which addresses the problem of evaluation apprehension.
STEP 2: Evaluate and provide feedback. In the second step, the brainstorming meeting facilitator accesses the back end of the collaboration platform, removes duplicates, combines similar ideas, breaks ideas up into categories, and sends them out to all team members. Following this, the team engages in evaluating and providing feedback on the ideas.
Anonymous methods for commenting, rating, or voting foster an unbiased assessment based on criteria such as novelty, practicality, and usefulness, again preventing the problem of evaluation apprehension.
STEP 3: Discuss and finalize ideas. The process culminates in this third step. Widely distributed teams might convene in a follow-up video call for this stage, while teams that are in close enough physical proximity would benefit from an in-person meeting to finalize discussions. Implementing the selected ideas and assigning follow-up tasks ensures that the brainstorming session translates into actionable projects.
The key strengths of asynchronous brainstorming include its inclusivity, its capacity to elicit a wide range of ideas, and its flexibility. By removing evaluation apprehension and production blocking, the technique accommodates introverts, pessimists, and junior/lower-status team members. This approach is an effective method for fostering innovation in all workplaces, whether for distributed teams or even fully in-person teams.
A Case Study in Asynchronous Brainstorming
By merging digital collaboration tools and more traditional techniques, an “asynchronous brainstorming” process is an effective method for fostering innovation, whether for distributed or in-person teams. (See description of the three-step process at left.)
Here’s an example of how asynchronous brainstorming works in practice:
Applied Materials—a Fortune 200 semiconductor manufacturing firm with offices around the globe and a hybrid schedule of three days in the office for its non-frontline staff—sought to boost its innovation to meet growing industry challenges. I trained their 400-plus global leadership team in asynchronous brainstorming during their annual CEO kickoff session for their strategy work.
During this videoconference meeting, I first explained the technique and then led the entire group in a real-time asynchronous brainstorming activity. I asked all the global leaders to spend a few minutes inputting their ideas on how they could use asynchronous brainstorming in their departments into a Microsoft Form, which included spaces for the person’s idea and name.
Anonymity wasn’t necessary for this exercise. In fact, providing names was motivating. I then shared my screen to display an Excel spreadsheet where each leader's idea appeared in real time. This proved highly engaging—as it consistently does during such training sessions—and it motivated more people to participate.
Particularly motivating was my promise to share the results with everyone after the training, providing all participants with the benefits of the crowdsourced idea bank. The attachment of names to ideas also helped, as leaders could receive credit for their contributions.
GenAI Offers an Innovation Powerhouse
The 2010s featured collaboration technology that improved innovation in distributed work, but the 2020s will feature a whole new era of technology boosting innovation. A particular benefit is integrating generative artificial intelligence (GenAI) into the creative process. For example, ChatGPT-4 got a score of over 99% in the Torrance Tests of Creative Thinking.
My clients find that an AI-driven strategy doesn’t just match but often exceeds traditional levels of innovation, catalyzing fresh, groundbreaking ideas and fostering an environment where creativity thrives, unbound by the constraints of physical collaboration of being in the same room and relying on traditional brainstorming.
Specifically, a technique I developed for my clients leverages GenAI for individual idea generation, enhancing creativity and reducing reliance on traditional in-person collaboration. Moreover, it can be used by individual employees to create fleshed-out ideas before bringing them to the wider team, offering even more options than asynchronous brainstorming.
Here are five steps for using GenAI to spark innovation: (See box for a case study in how this process worked at a major retailer in the box below.)
STEP 1: Generate wide range of ideas. The process begins with the initial idea generation, in which individuals input a basic concept or problem statement into a GenAI tool. This tool then produces a wide range of ideas, perspectives, and solutions, allowing for the exploration of various angles. By leveraging the AI’s capacity to think outside conventional human patterns, users can uncover innovative approaches and unique insights that might be overlooked in traditional brainstorming sessions. By leveraging the AI’s capacity to think outside conventional human patterns, users can uncover innovative approaches and unique insights.
STEP 2: Refine and evaluate. In the second step, the AI assesses the generated ideas for potential impact, feasibility, and market readiness. This evaluation helps to identify the most promising ideas, which are then shortlisted for further discussion within the team. By sifting through and analyzing these ideas, the AI ensures that only those with the highest potential are considered, streamlining the decision-making process and saving valuable time and resources.
STEP 3: Enhance creativity with AI-assisted tools. These tools, which include AI-assisted design software, predictive analytics, and simulation programs, further develop and visualize the shortlisted ideas. By adding depth and clarity to each concept, these advanced tools help to refine the ideas into more concrete and actionable plans. This step is crucial for transforming initial, raw ideas into well-developed proposals that can be effectively communicated and implemented.
STEP 4: Collaborate and integrate. Next, individuals bring their AI-enhanced ideas to the team for discussion. These discussions are enriched by the data-backed, innovative concepts generated and refined by the AI. Whether conducted remotely or in person, these meetings benefit from the well-thought-out ideas, leading to more productive discussions. I encourage teams located close enough to meet in person, if possible, at this stage because face-to-face interactions can enhance collaboration.
STEP 5: Establish a continuous feedback loop. Feedback and insights gained from team discussions are fed back into the AI system, creating a cycle of ongoing improvement and innovation. This iterative approach ensures that the AI continues to learn and adapt from human input, constantly refining and enhancing the quality of the ideas generated. By maintaining this continuous feedback loop, the process fosters a dynamic environment of perpetual growth and creativity.
A Paradigm Shift for Innovation
The implications of these findings on innovation and brainstorming are profound for HR leaders, especially in fast-paced industries for which staying ahead of the curve is crucial. The belief that innovation is geographically bound to being in one room and relying on the traditional technique of brainstorming is being challenged by empirical evidence from behavioral science as well as the rapid development of modern technology.
Whether teams use asynchronous brainstorming to get input from widely distributed teams or solo staff members use GenAI to develop well-fleshed-out ideas, we are seeing the death of face-to-face brainstorming as the default for innovation for any HR leader who wants to help their organization maintain a competitive advantage in our quickly changing world.
A Case Study in GenAI Brainstorming
The explosion of generative artificial intelligence (GenAI) has revolutionized the innovation process, suggesting ideas, offering data-driven insights, and playing devil’s advocate. This integration leads to more diverse and comprehensive ideation, pushing beyond conventional boundaries.
So how does this work in practice? I’ve suggested a five-step process to help teams maximize their use of GenAI to spark innovation and creativity (see “Generative AI Offers Innovation Powerhouse” in main article). Here’s a case study of how developers at a major retailer’s e-commerce platform used this GenAI brainstorming process to improve the site’s functionality:
Each team member inputted their version of a problem statement into a GenAI tool that was pre-trained on the company’s data. The AI suggested a range of improvements across various areas of the platform. This step helped the team uncover innovative ideas that might not have emerged through conventional brainstorming methods.
In Step 2 (the refining and evaluating ideas phase), team members used AI to assess the diverse suggestions based on feasibility, user satisfaction potential, and implementation difficulty. They leveraged the AI to simulate reactions from various customer demographics to the proposed innovations. This allowed the team to understand how different user segments would respond to each idea. Ultimately, they identified personalized search results as the most promising idea due to its high potential to improve user experience.
To further develop the personalized search results concept, the team used AI-driven analytics and simulation software. These tools helped the team develop and test new search algorithms. By modeling user interactions and predicting the impact of personalized search results, the team was able to refine the concept. This step was crucial in transforming the initial idea into a clear and actionable implementation plan, ensuring that the new feature would be both effective and user-friendly.
During Step 4 (the collaborative integration phase), the development team held meetings to discuss and finalize the AI-enhanced search feature. The data-driven insights and detailed models produced by the AI-assisted tools facilitated effective collaboration among team members. These discussions led to the successful integration of personalized search results into the e-commerce platform. The team’s ability to combine AI-generated insights with human expertise proved to be a significant factor in the feature’s development.
Finally, the team established a continuous feedback loop to ensure the ongoing enhancement of the personalized search feature. They collected user feedback and performance metrics, which were then fed back into the AI system. This iterative process allowed the team to continuously improve the search functionality, ensuring it remained efficient and user-friendly over time.
End result: Implementation of the personalized search results feature significantly improved the user experience on the retailer’s e-commerce platform. User engagement metrics saw a marked increase, with a 20% spike in the average session duration. Customers found products they were interested in more quickly, resulting in a 15% boost in the conversion rate. Overall customer satisfaction scores increased from 78% to 90%, with specific praise for the search function. Most importantly, the retailer saw an 8% sales increase attributed to improved search functionality, highlighting the impact of the new feature on the company’s bottom line.
Gleb Tsipursky serves as the CEO of the future-of-work consultancy Disaster Avoidance Experts. He authored the best-selling books ChatGPT for Thought Leaders and Content Creators (Intentional Insights, 2023) and Returning to the Office and Leading Hybrid and Remote Teams (Intentional Insights, 2021).