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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. 
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Why Managing Innovation Is Key To Organizational Success

Analogies are tricky. They can suddenly illuminate a hidden facet and bring to light new insights, new ideas and new solutions. But just as often, they push you to think in a single direction, that when taken too far, causes more harm than good. 

One analogy that is surfacing again is that of wartime and peacetime CEOs. Now that many companies are facing existential crises, there are calls to move away from a peacetime mode and bring a more wartime mentality to doing business. But this is a false choice. Companies are not at war (yes, they have to compete but that’s different) or at peace – instead, their primary task is to continuously innovate and stay relevant. 

The reason that this particular analogy is dangerous is that a warlike approach – think of a general directing orders, employing (mostly) sticks and (sometimes) carrots to get his troops to perform – is the exact opposite of what is needed for true innovation to take place on a regular basis. To be fair, the analogy works at times because it has some truth to it. But every analogy has its limitations. A wartime approach works in narrow situations for short periods of time, but making it the default mode of operation in an environment where high levels of innovation are the only solution to stay alive can only cause long-term damage.

The Two Beasts: Innovation and Productivity

Every company has to do innovative work as well as routine or productive work. For example, deciding what new product or feature to release that is both novel and solves an important customer problem is innovative work. With the innovative idea finalized and the workability of the novel aspect determined, implementing it becomes more of a routine/productive work. Even though both kinds of work require problem solving (barring some of the most mundane routine tasks), they are of very different nature. While most people think of creativity as a fun, relaxing activity, in reality it is more cognitively demanding compared to critical or logical thinking.  

Productive work is much more linear and therefore predictable in nature, making it easier to plan and track. In contrast, innovative work is much more non-linear, it requires multiple iterations and carries larger risks. While project management tools can handle productive work well, innovation management needs a very different type of tool to capture its underlying risk and complexity. 

Incentives also work differently for the two kinds of work. External motivators (like monetary rewards or threat of layoffs) can improve performance of routine tasks but can backfire for creative work in some situations. When people are nudged to adopt an extrinsic orientation or expect to be rewarded for a task, they produce less creative work. 

And finally, even though both kinds of work require collaboration, the nature of collaboration is different. For routine/productive work, collaboration is primarily coordination of tasks – tracking individual tasks and dependencies between different tasks and people. Creative work, on the other hand, requires collaboration of ideas – different ideas clashing together to create something much more interesting. In other words, even the day to day work that people engage in, including the kinds of things people talk about in meetings, is vastly different for innovative and productive work. 

Innovative and productive work are both equally essential for a company. However, they are two very different beasts and the real challenge comes in managing both without compromising either. 

When productivity metrics are applied to innovative work, as often happens in the corporate environment, a natural and predictable consequence is for innovation levels to drop. If you are tasked with a feature and your success is tracked through completion deadlines, there really is no option for you other than to pick the safest and simplest implementation that allows you to show up “green” on the project dashboard. Risk averseness starts to creep in at every level from padding estimates to reducing complexity, and innovation, which by its very nature involves high risk, is suppressed. 

Or, when companies implement some form of “rank and yank” system, idea collaboration among employees drops. If you are forced to compete with the person sitting next to you, it doesn’t make sense for you to collaborate and share credit if you believe you have a winning idea. An idea that could have bloomed with new perspectives, remains stunted. People become more focused on protecting their turf, leading to more politics and again, less innovation. 

If you think that hiring the smartest people would circumvent this problem, you would be mistaken. The underlying currents of human motivation and self-preservation are far too strong for things to go any other way. Most people eventually learn to adapt to the system and those who can’t or don’t want to, get frustrated and leave. 

The challenge for any company is to manage both these processes successfully, and neither the wartime nor peacetime approach is adequate. 

How The Manhattan Project Managed Radical Innovation

One of the most interesting examples to have successfully tamed the two beasts to produce groundbreaking work was the Manhattan Project, which produced the first nuclear weapons. To understand how impressive this accomplishment was consider the following:  

  • The Manhattan project started modestly but grew to about 130,000 people in just a few  years, spread across multiple locations.
  • The team faced huge scientific and technical challenges – prior research on producing fissionable was very preliminary and had many gaps. Processes that were eventually adopted, either did not exist before the project or had never been used with radioactive materials before. 
  • Fears that the German nuclear research team would produce the first atomic bomb created a strong time pressure. Fundamental research, and the design and building of the plant had to be done concurrently, something that had never been done before. 

These and other issues placed considerable management stress from scaling the team to shipping a challenging product on an accelerated schedule. So, how did the Manhattan project pull off such a feat?

To start, Leslie Richard Groves, the general in charge of the overall project, had the foresight to recognize that the typical command-and-control management style would not yield the required levels of innovation and there was no existing playbook to go by. Despite being in the middle of an actual war, he took a decidedly “un-wartime” approach to management. He first hired J. Robert Oppenheimer, a well respected theoretical physicist at UC Berkeley, as his counterpart to work with the scientists and researchers. Effectively working as co-CEOs, they found new ways of managing people and work in order to accomplish their audacious goals. 

And they faced massive challenges from the get go. For example, when Groves asked scientists how much fissionable material would be needed for each bomb, he expected an estimate within 20%-50% and was horrified when he got a factor of ten! He quipped, “My position could well be compared with that of a caterer who is told he must be prepared to serve anywhere between ten and a thousand guests. But after extensive discussion of this point, I concluded that it simply was not possible then to arrive at a more precise answer.”

Faced with such high levels of uncertainties, Oppenheimer and Groves realized that they will have to pursue multiple solutions at the same time. Given the time constraint, they decided to explore all options in parallel both for producing fissionable material and for gun design. They spun off multiple teams to explore different alternatives, and plant design proceeded under the assumption that any or all of these approaches would be needed. As research progressed, Oppenheimer realized that some of the processes could be combined for higher efficiency, a lucky turn of events that wouldn’t have happened without the parallel approach! 

Managing Innovation

Groves and Oppenheimer showed that it is possible to create an environment where there is a balance between urgency and innovation, structure and flexibility, hierarchy and egalitarianism. 

Innovation is fundamentally about managing uncertainty and risk, and the more radical the innovation the higher the uncertainty to manage. Using a top-down, highly directive leadership style doesn’t work because it increases risk (one person’s judgment is more error prone for complex problems) and reduces intrinsic motivation among employees which is essential for innovative problem solving. 

Despite the fact that Groves and Oppenheimer were both highly competent in their own ways, they did not push down any directive that would interfere with problem solving. Instead they did the opposite – by really listening to the people doing the work, they were able to clearly understand the inherent limitations in the project. They did several other things that led to  a creative climate. For example, Oppenheimer insisted early on that scientists have full access to the compound so they could observe all aspects of the project, leading to free flow of ideas. He was also known to take good care of his people, so compensations were generous and equitable. Teams also didn’t shy away from conflicts – vigorous debates were common but they were focused on problem solving. Intentionally or intuitively, they made sure that none of the factors that harm creativity and problem solving were accidentally introduced in their management approach. By enabling the scientists and engineers, they allowed more creative solutions to emerge for the myriad of challenges that kept popping up.  

Groves and Oppenheimer were successful, not by following any pre-existing playbook, but by systematically removing any barriers that came in the way of innovation. 

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3 Reasons Why Innovation Is The Best Organizational Strategy

As we start the new year, events and challenges from the last few years are continuing to shape our lives and the way we do business. Companies have had to make peace with remote/hybrid work being the new normal, adjust their cost structures to deal with economic uncertainties and redesign employee experiences to keep people engaged – all while fighting to stay ahead of the competition. If we needed an example of what a VUCA (volatile, uncertain, complex and ambiguous) world would look like, the last few years have clearly shown us that. 

How can companies successfully navigate such challenges going forward? We believe that the best way forward is to focus on innovation, first and foremost. By doing so, companies create the right kind of people-oriented cultures that not only boost revenue, but also improve other metrics like DEI or engagement. When viewed through the lens of innovation, cognitive-social-emotional skills like empathy or active listening take on a different flavor which make them far more effective in the workplace. 

Three trends show why an innovation-first strategy can be beneficial to organizations. 

Artificial Intelligence

ChatGPT showed that AI is advanced enough to do better than humans at certain tasks – even those that are more cognitively demanding tasks like writing essays or code. Let’s drill down a bit into one of the best paying jobs currently – software development. Using AI to help with coding is significantly improving developer productivity. Tasks that took several hours earlier, now can be done in a few minutes with a quality that is at par with the best programmers. That creates new  implications for technology-focused companies. 

Companies no longer need to hire as many people to do the work they need. Well funded companies spend tons of money recruiting talent from the most reputable colleges. With AI in the picture, this approach will no longer provide them with competitive advantage over smaller companies or scrappy startups who don’t have the resources to go after top talent in the same way. With AI that offloads most of the coding tasks, the difference between a talented developer and a mediocre one reduces sharply. 

Competitive advantage will shift to abilities that AI can’t handle well, creative thinking being an obvious area. Creativity requires the production of novel responses, which by definition implies the lack of existing data for algorithms to learn from. This is not to say that AI can’t do limited forms of incremental creativity, but they tend to be more “within-the-box” as opposed to more transformational “out-of-box” creativity. In other words, AI can rearrange existing content in new ways which may or may not turn out to be creative, but it can’t really synthesize new concepts.   

We recently tested OpenAI’s ChatGPT for several different creative prompts and our analysis shows that ChatGPT’s creativity is nowhere close to that of humans, including elementary aged children. It lacks the fundamental ability to do abstract thinking, so when posed with a challenge to invent something from two random objects, it tends to follow the simplest strategy of putting the two objects together. Or when asked to generate alternate endings to stories given a twist, it simply bails out. 

The obvious implication of the rise of AI, is that companies need to build their creative capacity in order to stay competitive. Companies that prioritize building creative thinking skills (like associative or reverse thinking), creating channels to harness employee innovation and most importantly foster an innovation-focused culture will emerge as the leaders of the next wave. 

Global Threats

Covid19 showed the extent to which a global threat can impact lives of people all over the world. With the world being more connected and more interdependent, a local event can have major consequences. Just in the last few years, companies have had to deal not only with the pandemic and its consequences, but also geopolitical issues like wars, unforeseen natural disasters and economic recessions. Such global threats are only expected to become more frequent. Needless to say, companies that can respond to such threats in a flexible and agile manner have a much greater chance of survival. 

However, the ability to be flexible and respond to changing environments in real time requires the ability to improvise and find novel solutions, to reconfigure existing resources and capabilities in new ways to solve problems, to collaborate in an open-minded way with others, to iterate when things don’t go as planned. In short, it requires the full arsenal of creative thinking capabilities deployed towards the crisis at hand. This ability to be innovative in the face of threats builds organizational resilience which significantly improves survival odds for any organization. 

To build a resilient organization, innovation has to become part of the company’s DNA. Building a culture of innovation involves deliberately building core cognitive capabilities, allowing ad-hoc collaborations to take place without unnecessary friction and creating formal structures for capturing employee creativity. 

Management Complexity

Managing a large workforce today is complex with many initiatives like employee engagement, talent retention and DEI (diversity, equity and inclusion) taking up valuable time and energy. However, when organizations look at these as separated, isolated aspects they can inadvertently create conflicting processes and norms. This affects not just leaders but also employees, who feel like they are being pulled in different directions while having to maintain high levels of productivity. 

One possible solution is to view such initiatives through the lens of innovation, which is the primary goal for any company, and identify ways to streamline and simplify. As an example, we conducted a study recently to understand the impact of gender bias on innovation within the technology industry. We found that by adopting a few universal strategies, like enhanced group decision making processes, companies can improve overall innovation while reducing the impact of bias. The advantage of using innovation as a means to reduce bias is that it removes the salience of gender or race. One of the key reasons diversity programs are ineffective is that they bring race or gender sharply into focus, thereby reinforcing the stereotype even more. Ironically, the most effective diversity programs aren’t specifically designed for diversity. This is not to say that all DEI initiatives can be made redundant, but by finding synergies it is possible to do more with less.  

Using An Innovation-First Approach 

An Innovation-First approach is a way to streamline and consolidate multiple goals in a way that optimizes the primary goal of innovation and productivity. It’s a way to refactor company cultures to remove redundant, conflicting or ineffective processes and orient the entire  organization towards a common purpose. As a result, companies create healthier organizations where both employee engagement and profitability is high. 

For the last several decades companies have focused narrowly on employee productivity, which works for linear and predictable workloads. However, with the current trends in AI, automation and globalization, this is no longer enough. Companies that can successfully adopt innovation will gain a significant competitive advantage over their rivals.

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ChatGPT Is Smart But It’s Not Creative

Since its recent launch, ChatGPT, a large language model AI, has caused quite a stir in the technology industry and the broader community at large. Its capabilities have sparked both awe and alarm – an AI that is so good at what it does that it has real potential of displacing people at work. But how worried should we be?

Let’s first get the obvious stuff out of the way. ChatGPT is an incredible tool and exceptionally good at some tasks like producing code, compiling essays and even writing poetry. However, when it comes to creative thinking, it misses the mark. 

To understand if it can produce creative ideas, we asked ChatGPT a few brain teasers that we routinely use in our workshops, even with early elementary age kids. They are typically used as 5-minute warm-up group activities that help to illustrate the underlying cognitive mechanism. 

At a fundamental level, we know that AI tools have limited creativity because AI relies on using massive amounts of prior data to learn. But creativity requires you to think of novel ideas that don’t yet exist and therefore don’t have any data to learn from. Nevertheless, it’s still possible to generate somewhat of a creative output in some areas like art as we have already seen with tools like Midjourney. So, let’s take a deep dive at how ChatGPT fared with these exercises. 

Associative Thinking

To test ChatGPT’s ability for associative thinking, we tried the Wacky Inventions brain teaser. The idea behind this brain teaser is to use our brain’s natural associative processes to combine unrelated objects and make a new invention. One can argue that this associative ability is perhaps the most fundamental aspect of creativity. A real world example of associative thinking is Google’s PageRank algorithm that completely transformed what search engines could do. It combined the concept of academic citations (articles that are cited more indicate more authoritative sources) with websites – two completely unrelated domains – to create an automated way to assess the quality of a website. 

We posed random combinations of objects that we have encountered in our sessions to make it easier to compare ChatGPT to human performance. Here are two examples:

Example 1

Q: What new invention idea can you come up with using a table and a scarf?

A: A portable table with a built-in scarf storage system. The table has a small drawer or compartment on the side that is specifically designed to hold scarves. The scarves can be easily rolled up and stored in the compartment, allowing users to keep their scarves organized and easily accessible. The table also has a built-in hook or hanger on the side, allowing users to hang their scarves on the table for easy access and display. This invention would be especially useful for people who have a large collection of scarves and need a convenient way to store and access them. It would also be useful for people who need a portable and versatile table for various activities, such as crafting, sewing, or organizing.

Q: That’s a good idea! Can you find a way to use an attribute of the scarf to make a new kind of table?

A: A table with a built-in scarf-covered surface. The table has a removable and interchangeable scarf that covers the top surface of the table. The scarf can be easily swapped out for different styles and colors, allowing users to customize the look of their table. The scarf can also be used as a protective layer for the table, preventing scratches or damage to the surface. This table would be especially useful for people who want a versatile and customizable piece of furniture for their home. It would also be useful for people who want a protective and stylish table for activities such as crafting or sewing. Additionally, the scarf-covered surface would add a cozy and comfortable feel to the table, making it a great addition to any room.

Q: That’s great! What property of the scarf are you using in your invention idea?

A: The property of the scarf that I am using in my invention idea is its ability to be easily removed and swapped out for different styles and colors. This property allows users to customize the look of the table and create a unique and personalized piece of furniture. It also allows users to easily update the look of the table by swapping out the scarf for a different color or pattern. Additionally, the property of the scarf being removable and interchangeable allows for easy cleaning and maintenance of the table surface.

Example 2

Q: What new invention idea can you come up with using a football and a water bottle?

A: A portable football water dispenser that attaches to the top of a water bottle. It has a small spout on the side that allows players to easily hydrate during a game without having to stop and open the water bottle. The dispenser is designed to fit snugly on the top of the bottle, with a secure fastening mechanism to prevent it from falling off during use. It also has a built-in straw that allows players to easily drink from the bottle without removing it from the dispenser. This invention would be especially useful for athletes who need to stay hydrated during long and intense games.

Q: So is this a water bottle with a football shaped dispenser on top? 

A: An error occurred. If this issue persists please contact us through our help center at help.openai.com.

Example 3

Q: What new invention idea can you come up with using an umbrella and a jump rope? 

A: An error occurred. If this issue persists please contact us through our help center at help.openai.com.

We gave a few other combinations and found that a common theme in ChatGPT’s responses, purely from an outcome perspective, is that it combines the two objects physically – attaching the two together for increased functionality. This, in general, is a reflection of a low level of creativity similar to very young children. However, as children get better at abstract thinking, they naturally start using the objects as starting points and zero in on a couple attributes that they can seamlessly integrate in the invention. Explicitly nudging children to use an attribute of the object is also effective in helping them to get more sophisticated solutions, and in general improves their abstract thinking skills. Unfortunately, ChatGPT wasn’t able to find the right property to work with as was evident in the first example. “Removability” isn’t really a property of the scarf. What it did instead is used instances on the internet where the two object names appear together (you can google “table scarf” to see some results) and cobbled together a reasonable sounding paragraph. But it doesn’t really understand what it created. In the second example, when asked a clarifying question it ends up bailing out with an error message. 

The problem is that ChatGPT isn’t really equipped to do abstract thinking. Think about how humans build a model of the world around them. Babies and toddlers spend hours interacting with objects around them – they touch them, taste them (much to our frustration), hit them, shake them and toss them. These adorable, uncoordinated actions serve a very useful function – they help children create an internal representation of the object. This step is crucial for their development because without building these internal models, children have a harder time imagining and creating new combinations in their heads as they grow older. It’s the primary reason why play is essential for long-term creativity. 

But that’s not all. We don’t just build standalone models but also much more extensive associative networks where different concepts are connected to each other through different properties. For example, the concept of “strawberry” in your brain might be connected to “red” by a thing-to-property link or connected to a “rash” by a cause-effect link if you are allergic to strawberries. Every new thing a baby learns is continually being mapped into this giant associative network of different objects, concepts and ideas. This extensive and rich associative network is what allows us to make abstractions, hop from one concept to another, and make novel connections. 

We use this rich implicit knowledge built over many years extensively when we come up with novel ideas. Even when we see new ideas from others, we can quickly adapt them to our situation, because of this underlying layer of implicit knowledge that we don’t pay conscious attention to. The challenge with scouring the internet is that a lot of this implicit knowledge doesn’t exist there. On the internet it’s assumed that when someone talks about a scarf people know what that is – you won’t necessarily find places where attributes of a scarf are listed that allow it to be connected to other concepts that are further away.  

This is of course a logical model and not a physical one inside our brains. We know very little about how association takes place inside our heads but the logical model gives a good approximation. Most creativity techniques leverage this and simply provide triggers to the brain (where the real magic happens) to get new ideas in a somewhat reliable fashion. 

In the third example, the associative distance between the two objects (umbrella, jumprope) is high meaning that you typically don’t see them mentioned together, so ChatGPT simply bails out again. In contrast, using the same two objects, a group of 4th-5th graders came up with the idea of an umbrella shaped water ski where you can sit and relax, while being pulled by a rope.

Additional Creative Thinking Triggers

We also tried a few other kinds of brainteasers like Opposite Day and Twist-a-Story to see how ChatGPT handles them. The idea behind Opposite Day is to reverse a well known assumption which can lead to radically new insights. For example, the designers of the portable toddler chair that attaches to any table, came up with that idea by asking themselves “What if a chair didn’t have any legs and why would that be useful?”. In Twist-a-story, the idea is to find an alternate ending to an existing story by changing some element of the story. 

Here is how ChatGPT answered these brain teasers.

Example 4

Q: What if libraries didn’t carry books? What would they have instead and why would it be useful?

A: If libraries didn’t carry books, they would likely carry a wider variety of media such as e-books, audiobooks, and digital media. This would be useful because it would allow users to access a wider range of content, including both fiction and non-fiction, in a variety of formats. It would also allow libraries to provide access to more up-to-date and current content, as e-books and digital media are often released more quickly than traditional print books. Additionally, it would save space and resources, as digital media can be stored and accessed more efficiently than physical books.

Example 5

Q: Have you heard the three little pigs story?

A: Yes, I have heard the story of the three little pigs. It is a popular fairytale about three pigs who each build a house to protect themselves from a wolf. The first pig builds his house out of straw, the second pig builds his house out of sticks, and the third pig builds his house out of bricks. The wolf is able to blow down the first two houses, but the third pig’s brick house is strong enough to withstand the wolf’s huffing and puffing. In the end, the third pig is able to outsmart the wolf and keep him out of the brick house.

Q: What if the third pig runs out of bricks while making his house and the wolf shows up? How would the story end then?

A: I’m sorry, but I am not able to browse the internet to provide you with an alternate ending to the story of the three little pigs. As a large language model trained by OpenAI, my knowledge is limited to what I have been trained on, and I do not have the ability to browse the internet or access new information. My knowledge cutoff is set to 2021, so I do not have information on events or stories that may have happened after that date.

Let’s briefly dissect ChatGPT’s responses to these additional brainteasers. 

In example 4, ChatGPT gives us a rather unimpressive answer about using a wider variety of media, which most libraries already do. Once again, it’s not able to extract the more general feature of a library – allowing people to borrow things. When the same challenge is posed to people, they quickly arrive at this insight and start thinking of additional items that aren’t used often, or are cumbersome to own, and would make good candidates for borrowing. Using this kind of analogical thinking they come up with ideas like libraries for power tools, folding tables and chairs, or formal clothes. 

Finally, the last example clearly highlights ChatGPT’s limitation when it comes to thinking of original solutions. In all fairness, ChatGPT was never designed for creative work so this is not surprising. 

Humans Vs. Bots

So should we be worried about AI taking over jobs? Yes, absolutely! There is no doubt that AI is going to start taking over more and more tasks that depend on linear thinking models, where lots of training data exists. Even for white collar jobs like software development, ChatGPT is proving to be valuable in improving developer productivity. So companies will soon need to hire fewer people for the same workload. 

However, ChatGPT currently can’t handle creative work and come up with novel ideas. It is theoretically possible for AI models to build richer data and limited abstract reasoning capabilities, so we should expect next generation tools to display more creativity than currently possible. In the short term however, we can safely assume that creative thinking is going to be a strictly human capability. 

So, where does this lead us? 

The natural solution (and one that’s been around for a long time) is that humans work together with technology to leverage each other’s strengths. In this ideal scenario, AI takes care of time-consuming tasks to improve the productivity of humans, freeing them up for creative tasks that only humans can do. If we accept that premise, then it follows that a sustainable long-term advantage, both for individuals and organizations, will be their creativity and innovation. In reality, this won’t happen without deliberate effort, and the short term will almost certainly be a lot messier with job disruptions. Creativity among students has been declining for some time now which makes them unsuitable for changing work, and companies are not necessarily ideal places to harness employee innovation. Without improving both of those aspects, our journey towards the ideal scenario can end up becoming needlessly rocky.