AI Fluency as an essential element towards a smarter workforce 

As artificial intelligence (AI) becomes increasingly embedded in our daily workflows, the ability for everyone across an organization to effectively use these tools is critical. While much of the conversation has centered on assessing AI-related skills and usage in a workforce, the real challenge—and opportunity—lies in cultivating fluency around AI from within. In other words, how can leaders guide their teams to not just use AI, but to think with AI? AI fluency may effectively unlock the full potential of AI, as well as their teams, making it essential for organizational success. 

AI fluency is more than just a set of technical skills; it’s the ability to effectively partner with AI. Yet, before a workforce can become truly AI-fluent, it’s important to realize that people are all at different points of progression to fluency, and this progression can actually be described as a continuum, often with four zones. On the far left are the AI abstainers—those who have a deep-seated distrust of AI and vow never to use it. Next are the AI-ambivalent users, who comprise a broad middle group, consisting of people who are aware of AI but feel uncertain or intimidated by it, and are not actively using it. Third, the AI-engaged, have overcome their ambivalence and are actively engaging with AI tools. However, their usage is often basic and not fully optimized. Finally, there is a small group of AI-fluent. These individuals don’t just use AI; they partner with it.  

Moving employees along this continuum is a core challenge for leaders today. Importantly, the journey from abstainer to fluent is not about technical training alone. It’s about building the cognitive foundation that makes true partnership with AI possible. An understanding of neuroscience – how our brains learn, remember, and perform – offers this cognitive foundation. To build AI fluency, we must foster a set of cognitive skills that enable rapid learning – each of which is founded in key neuroscience principles that underpin human learning and cognition.  

Embrace a Growth Mindset to enable experimentation 

Adopting any new technology, especially one as rapidly evolving as AI, can be a challenge and trigger uncertainty, which often registers as threatening in the brain. This is where a growth mindset—the belief that one’s abilities can be improved over time—can buffer this uncertainty. From a neuroscience perspective, a growth mindset frames challenges and mistakes not as threats, but as opportunities for improvement and learning, and can trigger a sense of reward in the brain. With a growth mindset, employees can experiment with AI and engage in the trial-and-error process, both of which are fundamental to learning. 

Imagine a marketing team using a new AI tool. A team with a fixed mindset might stick to basic prompts, fearing that complex queries will yield poor results and reflect badly on their abilities. A team with a growth mindset, however, sees this as a learning opportunity. They will experiment with different prompts, analyze the outputs, and refine their approach. This iterative process accelerates their path to proficiency. 

Build Cognitive Flexibility by shifting between ‘why’ & ‘how’ thinking 

The ability to adapt and shift our thinking or behavior to achieve our goals as we work with humans or with AI requires cognitive flexibility. As we learn to “think” with AI, we can build our flexibility by regularly shifting our focus between our objective – the “why” – and the ways we’ll get there, the “how.” Regularly reminding ourselves of the “why” provides direction, and boosts flexibility in the “how,” allowing for creative and iterative processing with AI. This could mean crafting varying or different types of prompts, breaking down tasks in new ways, or integrating AI into workflows at different steps.  

People often get stuck at an impasse when thinking at the level of the “how,” stalling progress. Leaders can support their teams when they reach such an impasse with AI by reminding them to consider the “why”, which further fosters their ability to be flexible. This shift in thinking could trigger the team to brainstorm different ways to work with AI to reach their goals, such as trying different prompts, or asking the AI to adopt a specific persona.  

Mitigate potential cognitive biases 

The final step towards AI fluency is developing an awareness of our cognitive biases, or mental shortcuts, and strategies for mitigating them. These biases pose risks when working with AI, as they can unintentionally lead us to accept flawed outputs or miss crucial details. 

Researchers have uncovered many different types of cognitive biases that impact us at work, and in an AI-driven world, one of these types – the expedience biases – are of critical importance. This type of bias is our tendency to act too quickly, and might manifest as accepting the first AI output without verifying its accuracy or considering alternatives. One form of expedience bias is the automation bias, or our tendency to over-rely on automated systems and trust their outputs without question. This can lead to a failure to catch errors in the AI output, transforming a helpful tool into a source of misinformation. Another example, confirmation bias, is our tendency to favor information that confirms our existing beliefs. When using AI, this can lead us to craft prompts that provide the answers we want to hear and uncritically accept content that aligns with our views. 

Leaders can mitigate these biases by fostering a culture of critical thinking. Encourage team members to play devil’s advocate, seek out alternative AI-generated perspectives, use the same prompts in different AI tools, and begin to use AI to challenge the output of AI itself. Always treat AI output as a starting point for analysis, not a final answer.  

From instruction to intuition  

Building an AI-fluent workforce doesn’t happen by distributing a manual. It happens by creating a culture that embraces learning through a growth mindset, fosters adaptability with cognitive flexibility, and respects the cognitive limitations of the human brain by mitigating common biases. Through this process, organizations can begin to shift their people along the AI-fluency spectrum towards a trustworthy and effective partnership with AI.