Making GenAI Adoption Sticky: Practice #3

Moving Beyond AI Literacy

This post was originally published on Substack and is being cross posted here.

I was asked recently about the concept of AI literacy and fluency for an upcoming podcast episode.

My interest in this topic dates back long before generative AI was part of my consciousness, to my time as a Spanish teacher and a new mom watching my own kids acquire language.

Watching my children, I was fascinated that they understood my words long before they could produce language on their own. Literacy, then, I concluded, must start before language production—it must include absorbing meaning even before we can articulate it.

As a language teacher, my goal was always for my students to be able to navigate life in a Spanish-speaking country regardless of whether they were a beginner or advanced language student.

One of the early hacks I shared with my students was called “circumlocution.”

The literary definition of circumlocution is “a way of writing or speaking using more words than necessary.” (Yamasaki, 2023)

Though perhaps frowned upon in writing, it was a gift to students learning the language. The key to fluency, I taught them, wasn’t that they knew every vocabulary word; rather, that they knew how to get around words they didn’t know with the vocabulary they did.

As a consultant in the early days of generative AI I remember feeling frustrated at one point that I was reading a couple of hours per day about generative AI but still couldn’t talk about it. I started talking about generative AI to myself in the car, in front of a mirror, and in the shower. Doing so increased my confidence and the automaticity with which I spoke. I started to notice a difference in my fluency.

Many years ago, also unrelated to AI, my colleagues and I were using a new project management tool. We knew how it worked and how to maneuver within the platform. I remember my boss asking, “What do you wish it could do?”

Total silence.

None of us could answer that question because though we had the literacy to use the tool, we didn’t have the fluency to transfer what we knew about that tool to a new context.

If you look at every learning rubric out there—Web’s DOK, Bloom’s—those rubrics put learning transfer, or the ability to take learning and apply it to novel contexts, in the third level of proficiency. If you look at teacher evaluation rubrics, they move from teacher-led to student-directed in the third level of proficiency, because you need to move beyond literacy to some level of fluency in order to empower others to do for themselves.

It’s here that the analogy to AI becomes super clear. With generative AI, having the literacy to understand basic concepts and functionalities is only the beginning. True fluency means being able to not just translate that understanding into real-world applications, but use it to fundamentally rethink our approach to solving problems. Just as my students learned to maneuver around missing vocabulary by employing circumlocution to operate beyond their capacity, we must move beyond AI literacy if AI is going to serve as a genuine catalyst for transforming how we approach our work.

This journey from literacy to fluency, therefore, is less a series of proficiency labels and more, a continuum, along which we travel as we build confidence and develop the ability to adapt. Whether experimenting with language, project management tools, or generative AI, the continuum is similar: learn the fundamentals, apply your learning in context, transfer the learning to “What if” and “Can it do x” scenarios. and finally, re-design how we approach the work in front of us.

Developing fluency may require us to practice in unconventional settings—explaining concepts to a colleague over coffee or continuing to have the space to play with new AI tools at work. These informal moments of practice build the neural pathways that transform conscious knowledge into intuitive understanding. Just as language fluency emerges from countless small interactions rather than formal study alone, AI fluency develops through regular engagement and experimentation.

Created with Napkin.ai

The journey from literacy to fluency follows a recognizable path. First, learn the fundamentals of AI, then, apply your knowledge to solve specific problems. Evidence of building confidence will begin to emerge in the form of the questions you and your colleagues ask: "What if we used a large language model to summarize customer feedback instead of manually coding responses?" or "Can this image generation tool create custom illustrations for our reports?" These types of inquiries represent learning transfer, where you are no longer just operating within known application, but exploring the edges of possibility.

A team with true AI fluency, for instance, might move from simply using Generative AI to create a newsletter to redesigning their entire communication strategy around human + AI collaboration—shifting from viewing the tool as a faster way to do traditional work to reimagining what the work itself could be.

❓How are you helping stakeholders move beyond AI literacy?

References

1. Yamasaki, P. (2023, January 13). What is circumlocution? Definition and Examples | Grammarly. What Is Circumlocution? Definition and Examples | Grammarly. https://www.grammarly.com/blog/literary-devices/circumlocution/?msockid=3ce750b7950c65913550445394f964e1

2. The graphic within this post was created by Napkin.ai.