Why AI boosts creativity for some employees but not others

Generative AI is increasingly embedded into day-to-day workflows across organizations globally. Employees are using AI tools like ChatGPT to brainstorm solutions, explore alternatives, summarize information, and accelerate projects. As these tools become more capable, many organizations hope they will spark higher levels of creativity, enabling employees to generate more novel and impactful ideas.

Yet, despite this promise, the creative payoff has been surprisingly inconsistent. The authors find that generative AI can indeed boost employee creativity, but the gains are not universal. Specifically, employees with stronger metacognition — the ability to plan, evaluate, monitor, and refine their thinking — are more likely to experience creative gains from using generative AI, because they can use it more effectively to acquire the cognitive job resources that fuel creativity.

For leaders and organizations, this finding reframes the challenge of AI-enabled creativity: to unlock AI’s potential for boosting workplace creativity, organizations must go beyond simply rolling out new tools; they also need to invest in developing employees’ metacognition and promote the thoughtful, strategic use of AI so employees can translate AI outputs into more effective creative performance.

The research

To understand how and for whom generative AI enhances creativity, the authors focused on an important insight from creativity research: employees produce more creative ideas when they have sufficient cognitive job resources. These resources include two key elements: a) information and knowledge, and b) the opportunity to adjust work methods and tasks, such as switching between complex and simple tasks and taking mental breaks.

The authors’ research proposes that using generative AI can increase employees’ cognitive job resources in two key ways. First, by expanding knowledge: although employees’ own knowledge is limited, generative AI can provide large amounts of information within seconds. This expands employees’ knowledge base and enables them to integrate insights across domains. Second, by freeing mental capacity: when generative AI handles tasks such as summarizing texts, managing data, and drafting content, it reduces employees’ cognitive overload, allowing them to redirect resources to complex problem-solving. Employees can also use AI to support complex, cognitively demanding tasks while periodically shifting to simpler ones, allowing them to restore mental capacity and break fixed mindsets.

However, access to AI tools alone does not guarantee that employees can acquire the cognitive resources needed for creativity. Employees differ substantively in their ability to leverage AI to obtain these cognitive job resources. The authors found that a key differentiator is employees’ metacognition: their ability to actively monitor their thinking while completing tasks. For example, employees with strong metacognition usually think through the steps to perform a task, keep track of how effective their approach is, and adjust when they notice a lack of progress. This ongoing reflection makes them more aware of their knowledge gaps, the demands of the task, and their own mental states. Therefore, they can better understand what information they need and when to shift gears or take breaks to disrupt fixed thinking patterns and restore cognitive capacity. By contrast, employees low in metacognition are more likely to accept AI’s first answer, rely on default outputs, and fail to check whether AI’s suggestions are accurate or relevant. As a result, employees with stronger metacognition are far better positioned to use AI tools to acquire the cognitive job resources that fuel creativity, whereas those with weaker metacognitive skills see few creative gains from AI.

To examine these ideas in real work settings, the authors conducted a field experiment with 250 employees at a technology consulting firm in China. The results were clear. Employees with stronger metacognition became more creative when they used AI — they generated ideas that were judged as more novel and more useful. But for employees with weaker metacognition, AI made little difference. In other words, only employees who knew how to engage thoughtfully with the tool were able to use AI to expand the cognitive resources that fuel creativity.

In short, the authors’ research reveals a pivotal insight for leaders: generative AI does not automatically make employees more creative. What matters is whether employees have the metacognition to use AI in a reflective way. The central question for leaders, therefore, is not whether employees use AI, but whether they have the metacognitive skills to engage with it thoughtfully and strategically — turning AI’s suggestions into creative insights.

How leaders can help boost employee creativity

The following steps can help organizations and leaders maximize the creative impact of generative AI.

1. Help employees use AI to expand the cognitive job resources that fuel creativity

Generative AI can enhance employees’ creativity by expanding their access to information and knowledge and freeing up their mental capacity for creative problem-solving. Leaders should encourage employees to use AI to gather diverse information, explore multiple angles, and offload routine tasks to restore cognitive capacity. By using AI to enlarge their knowledge base, break fixed mindsets, and reduce cognitive overload, employees create the conditions that make creative insights more likely. However, the findings suggest that these benefits depend largely on how employees engage with AI — pointing to the importance of metacognition, which is highlighted in the next takeaway.

2. Raise awareness that metacognition is the engine of AI-supported creativity

Leaders might assume that integrating generative AI into their workflows will automatically make all employees more creative. Yet the research shows that creative gains tend to occur among employees who can actively monitor their own thinking and then evaluate, question, and refine AI outputs. In practice, this means employees must treat AI suggestions as starting points rather than final answers — iterating on them, probing gaps, and challenging assumptions. For example, two employees using the same AI tool may end up with very different results: one may accept AI’s first suggestion without checking, while the other may examine its accuracy, push for alternatives, and integrate new insights. The latter approach is far more conducive to creativity. Leaders should help employees understand this distinction to facilitate more productive AI engagement.

3. Build metacognitive skills through targeted and scalable training

Leaders should consider employees’ metacognitive abilities when implementing AI and invest in developing these abilities through training. Companies can offer short training sessions that introduce metacognition and walk employees through real examples of AI errors, asking them to anticipate, detect, and correct those mistakes. Longer programs can focus on helping employees build deeper habits of planning, monitoring, and evaluating their thinking. Even simple checklists — clarifying the problem, determining how to evaluate AI’s suggestion, and exploring alternatives — can shift employees from passive reliance on AI to more active, strategic engagement.

4. Design workflows that promote active, iterative engagement with AI

Leaders should design workflows that position AI as a thinking partner rather than a shortcut. Instead of encouraging employees to use AI for quick answers, leaders should establish processes that involve generating multiple perspectives, comparing and critiquing AI outputs, and refining ideas across several rounds. Over time, organizations may even consider employees’ metacognitive capabilities when hiring for AI-intensive or creativity-intensive roles. But for most companies, building these skills through training and day-to-day practice will be more scalable than relying on selection alone.

Caveats and limitations

When applying these insights, leaders should consider several limitations. First, the findings are based on a single organization in China. Although the underlying mechanisms are likely to generalize, people’s attitudes toward AI may vary across countries and industries. Second, other personal traits — for example, motivational traits such as a strong desire to learn or to pursue ambitious goals — may also influence how effectively employees engage with AI to enhance creativity. Third, the study examined short-term effects within a single week. The long-term consequences of sustained AI use remain open questions. Organizations should regularly assess how AI use influences employee learning and skill development over time.

In sum, the research shows that generative AI can meaningfully enhance creativity — but only for employees with strong metacognition. By pairing AI deployment with deliberate support for metacognitive thinking, organizations can unlock deeper insights, accelerate innovation, and ensure that employees drive the tool rather than letting the tool drive them.

Adapted from: “Why AI Boosts Creativity for Some Employees but Not Others”, by Jackson G. Lu, the General Motors Associate Professor of Management at the MIT Sloan School of Management, Shuhua Sun, an Associate Professor of Management and the Callais Professor of Entrepreneurship at Tulane University’s A. B. Freeman School of Business, Zhuyi Angelina Li, an Assistant Professor in the Department of Organization and Human Resources at the School of Business, Renmin University of China, Maw-Der Foo, the President’s Chair and Professor of Entrepreneurship at Nanyang Business School, Nanyang Technological University, and Jing Zhou, the Deputy Dean and Mary Gibbs Jones Professor of Management at the Virani Undergraduate School and Jones Graduate School of Business at Rice University, published on Harvard Business Review on 06 January 2026.