In a recent announcement (July 2025 in Techerati), entry-level jobs are expected to shrink by 32% due to ChatGPT. While only one data point, it is an indicator that being able to use AI, while critical for functioning in today’s world, is not enough. If a job’s tasks can be done via ChatGPT, that job is on its way to being automated. Is AI Literacy enough to be competitive in the workplace? We unpack what comes after Literacy, AI Fluency, and describe a way to think about the progression of AI skills.
A Quick AI Literacy Recap
Many definitions of AI Literacy exist, and all are quite reasonable. Many capture the core idea that an awareness of AI, how it works, and how to work with it in daily life, is now a requirement for all people on the planet as we move towards increasing AI ubiquity in all industries. However, this is where the definitions typically end. What is unclear is what exists beyond literacy, who needs more than AI Literacy skills, and if so, what they need.
Moving From AI Literacy To AI Fluency
If we take inspiration from language, how would we define literary and fluency?
Merriam-Webster defines literate as “being able to read and write”, and more generally as “having knowledge or competence”. In contrast, fluent is defined as “capable of using a language easily and accurately” and “having or showing mastery of a subject or skill”. Literacy is a basic ability, and fluency is the next level of skill in an area or domain.
In a past article, I articulated the 4 Cs as applied to AI Literacy (Concepts, Context, Capability, and Creativity). These segments are not statements of depth; they are areas of knowledge or skill. AI Literacy can be thought of as the basic level of knowledge in each of these areas, while AI Fluency is the next level of depth, understanding, and sophistication.
Can You Give Me An Example?
Let us take an example of a dental practitioner:
AI Literacy for such a professional can include the ability to use ChatGPT or other AIs to solve daily problems (both within their professional or personal lives). It can involve an understanding of how online retailers use personal information to recommend products, or an awareness of what AI-related initiatives are coming up for vote in their next electoral ballot.
AI Fluency, on the other hand, is deeper and likely more domain-specific. AI is a vast field, and every subset of AI has extreme depth. As such, it is not realistic to expect to develop in-depth AI skills in every domain. A dental professional may want to focus on their domain, becoming knowledgeable in such areas as: how dental screening tools are using AI, what algorithms are being used and why, and where the latest research on the intersection of AI and their field is being conducted.
While the above example focuses on dentistry, similar conclusions can be drawn for other fields. For example, in education, for teachers to be able to educate students to be AI literate, they will themselves need to know more than the basics. In other words, teachers will likely need to be AI fluent to guide students to be AI literate. In software, the CEO of Github recently observed that he expects AI to increase the number of developer jobs, but with these developers having skills in leveraging advanced AI to rapidly create software products. These new skills will require developers to understand the landscape of available tools, and the tradeoffs between state-of-the-art AIs such as DeepSeek vs. Llama, and select the best for their task.
Is There Anything Beyond AI Fluency?
I believe so. There is a level of mastery that even practicing professionals in a domain may not need, which is a requirement for researchers exploring new techniques. Returning to the previous example, a recent research advancement of AI in dentistry is Deep Learning for 3D Bone Segmentation, published in Nature Scientific Reports in 2024. Does every dentist need to understand this and similar papers in depth to be AI Fluent? I don’t believe so. Should AI Fluency require that they be aware that such research directions exist in their field, and know where to access the research if they wish to explore in depth? Yes, I believe so.
Revisiting The 4 Cs With AI Fluency
Given this, how do the 4 Cs map to AI Fluency relative to AI Literacy? AI Literacy is general to life. AI Fluency is increasingly domain-specific. The domain may be your profession or some other area of interest. AI Fluency emphasizes depth which comes with a narrowing of breadth.
AI Fluency builds upon AI Literacy. Each of the 4 Cs has meaning in both AI Literacy and a deeper, more focused meaning in AI Fluency. For example:
– A concept in AI Literacy may be that AIs need to learn. The same idea in AI Fluency can include more details on how AI learning occurs in your domain. Returning to the dental example, it can include an understanding of how dental images are used in AI programs to identify medical conditions.
– Context in AI Literacy can include how AIs use your personal history to recommend products, while context in AI Fluency can include an understanding of AI-powered products in your field, such as this article in the dental profession. Fluency can also include an in-depth understanding of any laws or best practices emerging in your career area as they relate to AI.
– Capability in AI Literacy can include the ability to use AIs like ChatGPT in daily life, including how to use AI to analyze reports and write professional emails, with an understanding of what AI use is appropriate and allowed in your company. AI Fluency, on the other hand, can include skills like the ability to build an AI Agent to automate tasks for you in your job.
– Creativity in AI Literacy generally refers to finding new ways to solve problems, or new problems to solve, using AI. An example can be to develop a workflow for writing documents where you write the first draft and iterate on subsequent revisions using ChatGPT’s input. Creativity in AI Fluency also means new solutions and tackling harder problems, but in this case with more depth and likely a domain-specific focus. An example in our dentistry context could be searching for and adopting products in the domain that leverage AI or building AI agents to solve problems.
Creator or Consumer?
I have in the past written about the importance of being a creator and not just a consumer in the world of AI. The shift from AI Literacy to AI Fluency is not a shift from consumer to creator. It is possible to create and exercise creativity with both levels of AI knowledge. The difference is that the solutions you create with AI Fluency are likely to reflect the depth of your understanding of your domain and how it can be effectively combined with specific AI algorithms, software, and tools, to solve problems.
Does AI Fluency Require Coding?
In technical fields, likely yes, but in general, I don’t think so. There are so many no-code and low-code tools available today, with trends like Vibe Coding (providing iterative directions to an AI to generate and refine code) rendering it ever easier to build new solutions. The key is understanding and creativity, not necessarily in-depth coding skills.
Takeaway Message
The AI world is evolving so fast that it is not clear what will be enough to maintain competitiveness in the workplace. However, it is becoming clearer that AI Literacy is a necessary criterion but likely not a sufficient one. Understanding how AI is deeply embedded in your chosen profession is important to staying competitive through ever-changing AI technologies, adapting and evolving as the technology does.
