The Calculator Before the Math

In education there is something referred to as a “teachable moment.” These moments can happen for any number of reasons, but the most powerful ones are usually tied to a natural question forming in someone’s mind.

When you make a mistake and wonder why it happened or how to avoid making it again. When an important real-world event occurs and you begin asking how it happened, why it happened, or how it affects you. Those moments matter because when the mind begins asking questions, it is signaling readiness for the next layer of understanding.

As an educator, trainer, or coach, teachable moments are important because once the mind indicates it is ready, you can suddenly approach concepts, theories, and ideas that may not have connected beforehand. In schools, these moments are so valuable that teachers will often intentionally introduce activities designed to create curiosity and prime students for deeper learning.

Doing so can be tricky. If the activity does not naturally facilitate an organic question, you are often left with a room full of people who are not yet ready for what comes next. When the brain has not had time to build the proper scaffold needed to make connections, even good information struggles to stick because the learner has nothing meaningful to attach it to yet.

I reflect on this often in the work I do now. Building a culture of operational literacy with AI inside organizations functions very similarly to what I used to do as an educator. More importantly, it is something many organizations are only beginning to realize they actually need.

The reason it has taken so many organizations this long to arrive at that realization is two-fold.

The first is tied directly to what I just described: questions drive learning.

When tools like Google or Alexa were introduced, people immediately understood how to interact with them because they fit naturally into the way humans traditionally acquire information. You asked a question and received an answer. I still remember when Alexa was new and people would spend entire evenings trying to discover funny or unexpected responses.

AI is different. AI can function like a question-and-answer tool, but that is not really what it was designed for. Using AI exclusively that way is similar to giving students calculators before they understand what the math underneath is actually doing. They may still arrive at an answer, but the moment the problem changes or the answer looks unusual, many no longer know how to evaluate whether the result even makes sense. That distinction matters more than many organizations initially realized.

There are absolutely teachable moments with AI. People receive poor outputs and wonder why. They attempt to follow a workflow and cannot get it to function consistently. They become frustrated, lose confidence in the system, and return to the “old way” of doing things. The problem is that many organizations accelerated past those learning moments too quickly.

AI arrived with promises to make work faster, easier, more accurate, and more efficient — and it absolutely can do those things. The issue is that many organizations rushed directly into operationalizing AI by implementing workflows, systems, and prebuilt prompts before their teams had developed the foundational habits and repetition needed to think effectively within those environments.

In education, learning progression matters. Students do not develop mastery simply because they were shown the final system or given the answer key. Competence develops through repetition, adjustment, feedback, recognition of patterns, and gradually learning how to evaluate their own thinking along the way. Many organizations unintentionally skipped that stage.

Because organizations moved so quickly from experimentation to implementation, the natural repetition and questioning that would have helped employees learn how to evaluate outputs, recognize patterns, identify drift, and adjust systems never fully developed. In many cases, teams were handed workflows before they had enough interaction with AI to understand why those workflows functioned in the first place.

Instead, what often exists is a workforce that knows how to “copy” and “paste,” alongside another group that avoids the systems entirely and continues relying on the “old way” whenever possible.

As organizations become increasingly dependent on AI-assisted workflows and systems, the need for professionals who can operate effectively within those environments becomes essential.

This is operational literacy with AI.

Operational literacy with AI is the ability to operate successfully within AI workflows and systems. It is the ability to move AI toward a desired outcome, adjust it along the way, and critically evaluate the output when it arrives. It is also the ability to recognize the patterns that should naturally precede workflows, understand what does not need to become a workflow, identify drift inside systems, and know how to correct it.

If learning and development teams are not intentionally teaching these skills inside their organizations, then they are not fully preparing their teams for what is already beginning to happen next.

If we introduce tools that fundamentally change how work happens without intentionally helping teams learn how to think and operate within those environments, we should not be surprised when confusion, inconsistency, and dependency begin to appear.

Operational literacy with AI is not simply about learning how to use a tool. It is about learning how to think, evaluate, adapt, and operate responsibly inside a new kind of working environment.

For organizations unsure where to begin, we have created several free resources designed to help teams start building those habits intentionally.