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Creativity Hack: Combining Unrelated Ideas

One of the most potent ways to find creative ideas is to take two completely unrelated concepts and try to combine them. This ability to associate unrelated ideas is a natural process for our brains but we often underuse this capability in finding novel ideas.  

About The Hack

Associative thinking, the ability to combine unrelated ideas, underlies a lot of innovation we see in the real world. Google search, one of the most well known inventions, is the product of associative thinking. When Sergei Brin and Larry Page were students working on improving search, they hit upon an interesting insight. The problem that they were trying to solve is to point users to high quality web pages that contain information users would find useful But how do you determine which websites are good and which ones are not? Their “aha” moment came when they realized that academic journals have a mechanism to identify high quality papers — the number of times a paper is cited by others. Applying the same concept to web pages, they realized that the more a web page is linked to by others the more authoritative it must be. They used that idea to create their first algorithm to rank web pages and Google was born! 

To use associative thinking in product design, find random objects or concepts and try to connect it to your central problem. For example, suppose you are tasked with making a new kind of mug. You then think of different objects or attributes, not typically associated with a mug, and see if there are ways to combine it. Suppose you picked a ball to combine with a mug. The simplest way to combine would be a ball-shaped mug. But, you could go further and use an attribute of the ball in a more meaningful way. Let’s say you pick “inflatable” as an aspect to incorporate. That leads you to creating an inflatable mug that is easy to pack on trips and provides good thermal insulation thanks to the layer of air in between. 

Summary

Finally, here is a quick summary of the creativity hack and how to use it in product design or with students.

DescriptionTo find a creative idea for product improvement, try to combine a random object or attribute with the product. 
ExampleIn designing a new kind of mug, you combine it with a ball. One attribute of the ball is “inflatable” which leads to the idea of an inflatable mug. The mug is useful because it packs more easily for hiking trips and also provides better thermal insulation due to the layer of air in between.  
Tips Instead of combining objects directly, use an attribute of the random object to combine. That often leads to more interesting and novel ideas  
ExtensionsTo build associative thinking in students, ask them to incorporate other famous characters (fictional or otherwise) into their stories, or do a project that combines their hobbies with a subject they are learning (e.g. music and math)
Creativity Hack: Combining Unrelated Ideas
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Creativity Hack: Designing Through Metaphors

Metaphors by themselves are creative acts — they bring together two unrelated concepts and provide a fresh way of looking at something familiar. Consider the overused and cliched metaphor about creativity, “thinking outside the box”, which implies stepping back and approaching the problem from a different direction. The origin of the metaphor is believed to come from the nine-dots puzzle, where you have to connect a 3×3 grid of dots using four lines or less without lifting the pen. The only way to solve the puzzle, that most people miss, is to connect the lines outside of the imaginary “box” created by the dots. Once you understand the principle behind the puzzle, the meaning of “thinking outside the box” becomes much more clear. 

About The Hack 

Metaphorical thinking can be extended to help trigger creative ideas in product design. The type of ideas that this process generates might come across as more surprising and fun, compared to the typical incremental innovation ideas, and therefore this hack makes for a useful addition to the initial ideation phase. 

As a real world example, suppose you are trying to come up with new feature ideas for your document collaboration tool (e.g. Google Docs). To trigger creative ideas your goal is to try and combine “collaboration” with different natural or artificially created phenomena. Let’s say you pick your phenomenon to be “shadows.” You then explore characteristics of the phenomena that might apply to collaboration. One aspect of shadows is that they hide or make something less visible than the parts that are well lit. Applying this to your product, an idea could be to use AI to selectively add shadows to parts of the document that are more solidly fleshed out. This simple mechanism can nudge collaborators or reviewers to focus on parts of the document that need more work or clarity, thereby improving overall group productivity. 

You can iterate through the process to generate more ideas. For example, another phenomenon could be “name carved on a tree”. What does this phenomenon imply? Why do people carve their names (e.g. “Josh was here”) on trees? It could be that people want to memorialize their presence or perhaps a way to mark their achievement after a long hike. Applying that to our example of collaboration, one idea could be to use AI to determine the order of authorship on a document based on how much different collaborators have contributed to the document. Many times the order of authors is predetermined before the work is actually done and doesn’t get updated based on actual outcome. This feature could make the process more fair for everyone.  

The idea behind this hack is to explore different metaphors because not all of them will yield immediate insights. However, once you get an opening it might help trigger more ideas in that direction.  

Summary

Finally, here is a quick summary of the creativity hack and how to use it.

DescriptionApply metaphorical thinking to come up with new product design ideas. Come up with a few natural or artificial phenomena (like shadows, fresh tracks on snow etc.), identify characteristics of the phenomena and apply that to the product under consideration.
ExampleApplying “shadows” to a document collaboration tool could suggest an idea where parts of the document are shadowed to indicate that those sections are complete and nudge collaborators to focus on other sections. 
Tips Not all phenomena will lead to fresh insights, so if no ideas get triggered in a few minutes then pick a different one.  
ExtensionsOnce you get a new direction through metaphors, you can reframe the problem and come up with more ideas. Using the previous example, you could reframe the problem as “how can we guide collaborators to the section that will improve efficiency?” Reframing then leads to a different set of ideas, for example guiding someone to a particular section because someone else is already working on another. 
Creativity Hack: Designing Through Metaphors
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What Neuroscience Tells Us About Learning

Students today spend more time on academic learning than generations before. They cover more ground – learning things like programming or environmental science that their parents didn’t have to fret about – and spend more hours doing homework after school. One study found that in the sixteen-year period from 1981 to 1997, there was a 25% decrease in time spent playing outside and a 145% increase in time spent doing homework. 

As our society advances even more, students will have to cover more and more content, not just during their K1-2 school years but throughout their careers. By some estimates, students growing up today will have to learn entirely new domains and reinvent their careers every few years. Learning is no longer limited to younger ages but is becoming a lifelong journey. 

What does this really imply?

Students have to learn to learn –  acquire knowledge and master concepts faster – without which they will find it harder to stay abreast of new developments coming their way. But it’s not just about superficially memorizing things. Students will have to understand how to apply their newfound knowledge to problem-solving. In other words, learning has to become a more efficient process in terms of speed, depth, and understanding.

Thankfully, advances in neuroscience are giving us clues on how to make learning more efficient. Understanding how the brain processes information can help students take charge of their own learning, not just in their student years but throughout their life.

Neuroscience Of How Our Brain Learns

At a high level, we can view learning as a three-step process. When we encounter any new information, our brain first encodes this information and places it in short-term memory. For example, if you come across a new fact, say learning about a new breed of dog, the information first goes into your short-term memory. The next day, you might recall that your childhood friend had a similar-looking dog, and now you start to remember other details about the dog – how friendly it was, how it played, and so on.

At this stage, your memory is in long-term storage; it continuously consolidates other pieces of information that you already had. Over time you might add more connections to this piece of information, maybe a joke you heard about it, and it starts to get more and more enmeshed with other pieces of memory. 

After a few days, you might forget the name of the breed and try to recall it. You struggle a bit and then remember your friend’s dog, the joke, and other bits of memory that were tied to it. And then the name suddenly comes back to you, and you get a sudden burst of relief! 

A few days later, as you share a story about your childhood friend, her dog and the name of the breed come to your mind effortlessly, and you marvel at how well you remember this now. 

The picture above encapsulates how our memory works. Once we consolidate information into our long-term memory, subsequent retrieval and reconsolidation help to strengthen the memory traces and make it easier to recall information in the future. 

Forgetting Is The Path To Learning

Over the last couple of decades, neuroscientists have discovered interesting things about how our memory works, and counterintuitive as it sounds, forgetting information is an important aspect of remembering! Our brain is constantly pruning information that it thinks it doesn’t need so that it can serve the really important bits of information faster. 

Imagine if your brain stored every little nugget of information that it receives – the color of the shirt a passenger wore in the subway, or the name of the street your friend in another state lives on – it would make it much harder to find the useful information that you really need. So if you don’t need any piece of information, its retrieval strength starts to get weaker. However, when you try to recall something that you have forgotten, i.e., when you have to struggle a bit to remember it, that’s when the brain gets a cue that this particular memory is important and might be needed again.

So, with the process of retrieval, it starts to reconsolidate the information – find newer connections to other traces of memory so the memory is stored more strongly. As a result, this process of forgetting and remembering actually helps you learn better. 

Neuroscience-based memory models give us clues on how to structure our learning for maximum effectiveness. Here are three ways to boost your learning.

Repurpose Failure

When students don’t remember or don’t apply concepts correctly, it’s a sign that the information has been stored weakly in the brain. However, instead of feeling that they are ‘not cut out’ for this kind of work, students need to understand that their failure is simply a sign for their brain to reconfigure and become more efficient. Human brains are designed to learn through mistakes, so it makes sense to reframe forgetting as what it really is – a trigger that tells us that we need to take additional steps to ensure learning is complete. Students should use the opportunity to review concepts again and try to reconcile the mistakes so their understanding of the subject increases.  

Adopt Active Learning Strategies & Neuroscience

Adding some challenge to the learning process that taps our brain’s natural mechanisms to process, store and understand information can significantly boost learning. Such challenges are ‘desirable difficulties’ because they make learning more efficient. Here are a few strategies that students and teachers can adopt: 

  • Retrieval Practice: When learning new information, periodically quiz yourself about the central ideas and new terms encountered without looking at the text. This forces your brain to fetch the answers from long-term memory, and repeated retrieval is going to strengthen your memory.
  • Spaced Learning: To add more desirable difficulty to learning, practice retrievals after a period of time. When you start forgetting, you exert more effort in trying to remember, which then cues the brain to store the information more deeply. The gap between learning and retrieving can be anything from a day to a week – the key is that the gap should allow for some forgetting to happen.
  • Interleaving: Instead of waiting to thoroughly master one concept before moving on to the next, try mixing up different kinds of problems or concepts once you feel you have gained sufficient understanding in one. Not only does this make good use of spacing, but it also allows you to spot connections or differences between different kinds of problems. 

Research studies show that such strategies can be very effective in the classroom. In one study, students who practiced math problems in three sessions spaced apart by a week performed twice as well on the final test compared to students who did all the practice problems in one session.  In another study, students performed significantly better on their science exam when a practice quiz one month before the exam interleaved concepts on the quiz. 

Associative Learning & Neuroscience

Another useful strategy in learning is to connect the information you are learning to other pieces of knowledge you already possess. If retrieval practice creates deep roots, then associative learning creates more branches that help anchor the information better. To build associative learning

  • Find an analogy: Ask yourself if the new concept is similar to any other piece of information that you already possess. As an example, you might make a connection between gravity and magnetism as both involve a force that they can’t see and attract objects. 
  • Find a personal connection: In some cases, your personal experience can be helpful in finding connections about what you are learning. For example, while learning about the ice age, you might remember an earlier trip to Grand Coulee, where they saw how the Missoula Floods carved out a massive canyon in a very short time. The scale and impact of the event will give you an enhanced perspective of the topic and deepen your understanding. 

Conclusion

By understanding the neuroscience behind learning, students can take charge of their own learning. The key to efficient learning is to add and embrace the right kind of challenges that push our brains to reconfigure themselves. Unless students lack relevant background or specific skills to make sense of the concept in front of them, such challenges should be welcomed instead of dread. 

With a deeper understanding of the learning process, students can try different approaches and customize them to their needs. As an example, for some students, one day of spacing might be enough, whereas, for others, it might be a week. For the latter set, practicing a skill every day might not be as effective because they haven’t forgotten enough for reconsolidation to take place. With some trial and error, students can identify strategies that work best for them and become smart learners. 

This article first appeared on edCircuit

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Assessing The Creative Health Of An Organization

With the proliferation of AI tools and their tremendous potential to improve productivity, leaders are reevaluating business priorities, and more specifically changes they need to make to work and culture. Given that AI can now handle many tasks better than humans, it stands to reason that creativity will take on an increasingly important role. It not only provides a sustainable competitive advantage but also improves employee engagement and organizational resilience.

Our understanding of creativity has evolved considerably over the last couple of decades. Despite the common perception that creativity is a fuzzy skill that you are either born with or not, we now know that creativity is a highly cognitive skill that can be tracked and improved. If companies want to get a better understanding of employee creativity and how it can be converted to meaningful innovation, they first need to assess their existing levels of creativity and how their culture might be inadvertently stifling innovation. Research has shown several different dimensions at varying organizational levels impact creativity and innovation. 

The Innovation Pipeline

At the heart of any disruptive innovation is a creative idea. The creative idea often starts small and over time, with iterations and feedback, becomes a breakthrough one. The 4-C model captures the level of creativity found in the real world starting with mini-c all the way to the boundary pushing Big-C. In an organizational setting, it makes more sense to view it in three phases (little-c, Pro-c and Big-c) as mini-c creativity is associated with personally meaningful ideas whereas employees typically offer ideas that are creative in broader contexts. 

The picture above shows a simplified view of the organizational innovation pipeline. (As a side note, we refer to innovation as gathering broad support among the management/company to take an idea to market, as opposed to market success which is the more common definition. We believe that within an organizational setting our definition is more appropriate). 

The creative journey starts with one person who conceives the idea, does some simple checks and determines that the idea has potential (in other words, it is both novel and useful). She then shares the idea with her team who debate the idea in more depth and through constructive discussions improve the idea even more – finding ways to make the idea more appealing to a broader set of people, or finding solutions to remove some of the challenges in the original idea. The team then decides to build a prototype to test out the idea further with real people. So from little-c, the idea moves on to Pro-c. Finally, the idea gets buyoff from management who allocate additional resources to make the idea launch ready. There might be more in-depth user level testing and additional iterations involved at this stage. Eventually the creative idea transitions into an innovative one that has a high chance of success in the marketplace if the right processes and checks were in place. At each stage of progression, the creative idea becomes more sophisticated and more potent, finally culminating in a (hopefully) breakthrough innovation. 

The fundamental challenge organizations have is to ensure that the pipeline allows ideas to flow freely and mature, so enough of them make it to the innovation stage. This is where deliberately building an innovation-friendly culture becomes essential. 

How Culture Impacts Innovation

An organization’s culture can either nurture or stifle innovation. To understand different ways that innovation gets affected in an organization, let’s look at it from the perspective of an idea as it makes its way through the innovation pipeline. 

Individual Level

A creative idea starts with a person who perhaps notices a problem or finds an interesting connection. If a sufficient number of ideas are not being generated at the beginning of the funnel, then the likelihood of reaching a breakthrough idea becomes low. Here are a few ways that ideas don’t go past the first stage:

  • Creative Capacity: If someone lacks creative confidence or specific creative thinking skills they might be coming up with few or minimally creative ones. Or, they might not be getting any time in their schedule to reflect and think creatively. Either way, their capacity to produce creative ideas is diminished. 
  • Motivation to share ideas: Assuming that people are capable of coming up with potentially creative ideas, the next barrier we hit is sharing ideas. People are less inclined to share an original idea if they feel the idea might be ignored or judged poorly, thereby affecting their social standing. Or people might simply not want to share their ideas, if they feel that they don’t get due credit for their work. In general, organizations that are hierarchical, risk averse or biased, disincentivize people from sharing their ideas. 

Team Level 

Most people assume that psychological safety is the main thing you need at a team level to allow good ideas to emerge. While this is a necessary first step, it’s not sufficient. For an idea to grow from little-c to a more improved Pro-c version, it needs to go through some extensive discussion. The main benefit of taking an idea to a group is that different perspectives and different ideas clash in a meaningful way to create something much more powerful. This crucial step separates mediocre teams from stellar ones as it requires both high cognitive and high emotional skills from the whole team. When done poorly, ideas can zoom past straight to innovation where they then face a higher chance of failure. Below are two broad ways teams fail at this stage:

  • Critiquing Instead Of Creating: The most common mistake that people make is to focus on fault-finding, with the intent of choosing the “best” idea instead of trying to create the best possible version of each incoming idea. People might also lack skills to engage in constructive debates and end up creating either a conflict-averse culture or a highly competitive one where ideas don’t get a chance to grow. 
  • Not Experimenting: Simply talking is usually not enough for an idea to be evaluated thoroughly. Data collected through prototypes or mini-experiments can lead to more healthy debates. Cultures that incentivize bold, visionary thinking without the rigor of research or experimentation create conditions (“pipe dream” culture) where people chase shiny ideas that often turn out to be riddled with insurmountable problems.  

Organizational Level

At the highest level leaders need to create structures and behavioral norms to support innovation throughout the organization. Without adequate support, it’s nearly impossible to convert employee creativity into organizational innovation. 

  • Formal Structures: To take incoming Pro-c ideas to market-ready innovations, organizations need to have formal programs that systematically and equitably review all incoming ideas. Many companies create hackathon-like programs as avenues for employees to exercise their creativity but such programs fail to produce any meaningful innovation as they are not integrated into the regular work process. Companies also fail to create formal incentive programs specifically for creativity that tap into people’s intrinsic motivation. 
  • Behavioral Norms: Company leaders play a crucial role in setting norms that promote an innovation-friendly culture. Do they explicitly solicit ideas from employees? Do they encourage their employees to challenge the status quo? Do they involve their employees in setting vision and values? Such behaviors create a more egalitarian culture that motivates employees to go above and beyond. 

Innovation Readiness Assessment

With the increasing importance of creativity and innovation in the business world, leaders need to understand in what ways their current culture supports or stifles innovation. Our Innovation Readiness Assessment is a research-based tool that helps identify bottlenecks in the innovation pipeline. It incorporates multiple dimensions that are known to impact creativity including work characteristics and biases, and covers all stages of the innovation pipeline.

Edgar Schein, the renowned organizational psychologist and author of Organizational Culture and Leadership, noted “the only thing of real importance that leaders do is to create and manage culture.” By staying vigilant about how their culture influences innovation, leaders can ensure their company’s long-term success in a hyper competitive world.  

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Capitalizing On The Promise of AI

The rapid proliferation of Artificial Intelligence (AI) tools like ChatGPT have created both awe and anxiety among people, and led to intense debates about the future of humanity. How should people prepare for a world where AI has a significant presence? What does the future of work look like?

Business leaders have an equally enormous task ahead of them. What are the threats and opportunities posed by AI? What changes do they need to make to their strategy, operations or culture in order to stay relevant? And how does one handle this radical transformation of work while keeping employees engaged?

One estimate says that AI will unlock $15.7 trillion in productivity gains by 2030. However, companies have to take deliberate steps to adapt their organizations in order to capitalize on the potential that AI offers.   

With Productivity Commodified, Creativity Becomes More Salient

AI is enabling productivity gains that are orders of magnitude higher than what humans can accomplish. AI can spit out code snippets or new graphics in a matter of seconds, compared to hours it takes a person. 

Competing with AI on skills where it can outperform people is pointless. Any business that doesn’t adopt AI productivity and employs people to do the same tasks, will soon fail. Instead, the most promising approach is to harmoniously coexist with AI where people focus on tasks that are uniquely human, like creativity, while AI handles more routine tasks. 

Focusing on innovation is important from a differentiation perspective too. When everyone uses AI to help improve their productivity, productivity becomes commodified. The only way then, to stand out among your peers is by using creativity – finding new ways to use the latest technology or creating new capabilities that delight customers. As an example, the low-code/no-code platforms are allowing people from different industries, including health and finance, to create their own applications and improve their workflows. 

Creativity is where humans have a distinct advantage. AI hasn’t performed well when it comes to creative tasks because creativity, by definition, requires production of ideas that don’t exist before. Even though AI is transforming the creative industry, the nature of its output is only superficially creative. Current set of AI tools are only good at rearranging existing elements in different ways to give an illusion of creativity. When prompted to create something that is truly original, tools like ChatGPT tend to fail. 

Leaders need to recognize that human creativity is now the differentiator for their business and they need to invest heavily in fostering internal innovation. 

Laying Off People Is The Wrong Strategy

Staying ahead of the AI acceleration requires proactively redesigning the boundary between human and AI work. This is a continually shifting line and businesses have to be agile when it comes to rearranging work for maximum productivity and innovation. 

As a business leader, it might be tempting to think that with AI taking over more and more tasks, your company needs fewer people to do the work that was being done before. This mindset can be dangerous for a company’s competitive advantage because it does not tap into the productivity gains that any new disruptive technology like AI unleashes. Barring a few domains (like some service industries), for any industry that has scalability potential and demands continuous innovation, reducing overall headcount will slow progress and almost certainly backfire in the long run. 

To see why this approach is damaging in the long run, let’s assume that your work requires some level of innovation to stay competitive in your market and therefore needs humans-in-the-loop. As more work starts shifting towards AI, it seems reasonable to reduce headcount in order to retain the same level of productivity for a much smaller cost. 

However, after a certain level of reduction, people become the critical resource in an organization. Even assuming that people are only doing tasks related to creativity that AI doesn’t handle well,  the organization becomes limited by the level of innovation it can harness from its employee base. As the AI capability expands even more (at a much faster rate than people upskilling), the extra capacity remains untapped due to limited people resources. After a short-term boost in productivity and lower costs, organizations get hamstrung in their ability to produce outsized innovations that can put them ahead of their competitors. 

Instead of simply reducing headcount to maintain the current levels of productivity, leaders need to take a long-term approach to talent management. Retaining and hiring people with the right skill set for innovation will help organizations take advantage of growing AI capabilities and provide higher levels of productivity and innovation compared to their peers. 

Engineering A High Performance Culture

A natural consequence of the increased productivity offered by AI, is that it allows more complex work to be possible. Complex work is high on both innovation and productivity, and the more complex the work, the more you need to tap into the intelligence and expertise of others. However, handling complexity, when humans are a critical part of the loop, is tricky. Groups can behave as intelligent swarms but they can also arrive at incredibly poor outcomes. How smartly a group behaves depends on the overall ability of individuals in the group as well as how independently they are allowed to think. Relatively smart people when thinking without the influence of others, cancel out each other’s errors and biases leading to much better decision making and problem solving that wouldn’t be possible at an individual level.

The single most important thing that organizations can do, in addition to hiring good talent, is to set up processes, tools and norms that allow people to contribute ideas and participate in decision making in independent ways. Without the right performance-focused culture, organizations will find it hard to capitalize on the higher complexity demands. 

Leadership Takeaways

Any transformative technology, by its very definition, radically changes the way people do things and opens up new markets and opportunities. How companies respond to the new environment determines how well they can capitalize new opportunities. 

Compared to disruptive technologies of the past, AI places new challenges due to the rapid pace of development. Companies need an agile approach to managing people and work. 

  • Innovation Management is key: With high productivity gains that benefit everyone, It’s inevitable that people-work will shift towards creativity. How well companies harness employee innovation will determine how they differentiate in the market and tap into the value that AI provides.
  • Hire for the long-term: Automation eliminates some jobs but typically creates many more new ones. A Deloitte study found that automation in the UK created over 4x more jobs than it eliminated that on average paid more. Instead of laying off people in order to boost short-term efficiency metrics, companies should focus on retaining and retraining employees to handle the higher workloads that are bound to come. 
  • Build a culture of performance: With AI taking over time-consuming tasks, more complex work that involves higher innovation and productivity will now be possible. Complex problem solving relies on individuals with different expertise to work together towards a common goal. Leaders will need to create a high-performance culture that incentivizes both individual expertise and swarm intelligence.