The Death of “Average” Creativity: Lessons from 100,000 Data Points

Being “Creatively Good Enough” is No Longer a Strategy when it comes to competing against AI. The baseline for professional creativity has just been reset.

A massive study involving over 100,000 participants has recently delivered a sobering reality check for every leader. The findings are clear: Generative AI can now outperform the average human in divergent thinking.

In tasks like the Divergent Association Task (DAT), which measures the ability to connect unrelated concepts, AI isn’t just competing; it is exceeding the human mean.

For years, we have been researching creativity for innovation leadership. This new data confirms a shift we’ve noticed ourselves over recent years: we are entering an era where being “average” at creative problem-solving is no longer a viable career or business strategy.

Navigating the Innovation Gap

As a senior leader, this shouldn’t be a cause for alarm, but a call for strategic realignment.

While AI excels at the volume and speed of creative output, as measured by recognized metrics like the Torrance Test of Creative Thinking (TTCT), our research shows that humans still hold the creative ceiling. By continually overseeing and monitoring the creative process and including AI as a tool rather than deferring to it completely, we can maintain creative control.

Here are three key areas of concern and the strategic takeaways that can ensure your leadership remains in that unbeatable top tier:

1. The Fixation Effect: Original vs. Derivative

The most crucial finding of the 100,000-person study is that while AI beats the average, it still cannot touch the top 10% of human creators. This was a gap even GPT-4 couldn’t cross.

AI’s creativity is fundamentally derivative – it synthesizes existing data rather than producing ideas from lived experience. There is always a risk that AI is simply repeating non-validated or hallucinated patterns from its training, which is known as the fixation effect.

A Carnegie Mellon study found that using search engines during brainstorming leads to fewer original ideas as teams converge on predictable answers. The more we over-rely on AI, the more we face cognitive debt and a decline in our own executive function.

The takeaway is that leadership now is about using AI to clear the mundane work so your people can engage in brain-only ideation, activating deeper memory and the genuine originality required for high-level breakthroughs.

2. Resisting Premature Closure: Ambiguity as a Strength

AI is trained to provide definitive, high-confidence solutions. This often leads to premature closure or closure bias, where the machine favors structured answers early in the process.

Humans possess the emotional tolerance and intuitive patience to embrace ambiguity. While AI ranks and selects divergent pathways prematurely, humans can hold multiple conflicting ideas without forcing a resolution, allowing deeper insights to emerge.

The takeaway is that it is important to use AI for exploration rather than finding definitive answers. For example: try utilizing “etymological prompting”, or asking the AI to explore word origins and historical shifts, to push the machine out of its predictive average and into more unexpected territory.

3. The Experience Gap: From Brainstorming to “Brain-Steering”

AI can mimic metaphorical reasoning, but it doesn’t experience abstract thought or understand the human stakes of a wicked problem (a problem with multiple facets that is difficult to solve). This experience gap is still a challenge for AI models tasked with generating highly original ideas.

AI generates a high volume of ideas, especially at higher temperature settings, but it lacks the human intuition to know which risks are viable. Humans remain significantly more effective at evaluating the feasibility of ideas and executing them in complex, real-world practical settings.

The takeaway is that your value has shifted from Idea Generator to Idea Architect. You must therefore learn to steer the machine toward strategic relevance, providing the metaphorical and abstract thinking that AI currently only simulates.

Every investigation needs a baseline. Be honest: which of these four profiles best describes how your team is currently interacting with generative AI?

Option 1: The Idea Architect (CQ Lead)

Option 2: The Efficiency Trap (Average)

Option 3: The Fixation Effect (Stuck)

Option 4: Still Investigating (CSI)

The Forensic Analysis: A New Suspect in the Lineup

For years, we’ve investigated the “Killers of Creativity”- the internal psychological and external environmental factors that can stifle innovation. With this latest data, we have a new person of interest to investigate: “AI”.

If we allow generative tools to become our primary source of ideation, we aren’t just gaining efficiency, we are potentially witnessing the “premeditated” death of original thought. The fixation effect, closure bias, and experience gap are just three forensic markers of a creative process under threat.

AI hasn’t killed creativity yet, but it has certainly raised the stakes. It has made average the new baseline, which means the only way to survive is to elevate your own Creative Intelligence (CQ).

The case is clear: If you aren’t actively steering the machine to the “top 10%” ceiling, you are simply assisting in the disappearance of your own competitive advantage.

The scene is set. The evidence is in. It’s time to stop the investigation and start the intervention!

Ready to elevate your team’s Creative Intelligence (CQ)?

The debate continues on whether AI’s high scores reflect genuine creativity or advanced pattern recognition. We argue that AI should augment human creativity, acting as a powerful tool for ideation rather than a replacement for it.

AI has raised the floor. Your job as a leader is to raise the ceiling. We help senior executives move beyond “average” AI usage and into the top tier of innovative leadership.