It is okay to be unaware, aware, informed, knowledgeable, or a practitioner. What matters is being honest about where you stand and letting that awareness guide what you do next.
This matters especially when using AI. An unfamiliar subject can become a clear explanation, a recommendation, or a plan within a single conversation. You now have something useful to examine. How much you understand still needs to be established.
That distinction belongs in every business conversation where someone may act on what we say.
Five states of understanding
I find these five descriptions useful for thinking about a particular subject or task. They give us a way to say what we know, what we can judge, and where we need help.
Unaware
Something relevant has not entered your field of view. You might be considering an AI support tool without realizing that part of your business depends on unusual customer requests it cannot handle.
Self-awareness here means accepting that you have blind spots. You cannot name every gap you have not discovered. You can make room for other people to point one out.
Aware
You recognize that the subject exists and may matter. You know AI could help with customer support, but you have not explored how it would work in your company.
That is enough to begin asking questions and decide whether to learn more. It is a useful, honest starting point.
Informed
You have gathered information and can describe the main ideas. You may have read about the support tool, compared options, and watched a demonstration.
You can explain the proposal. You may still need help judging whether it covers the situations that matter. Being informed gives you material to reason with; the conclusions still need examination.
Knowledgeable
You understand enough to explain why an approach works and under what conditions it might fail. You can assess evidence, question assumptions, and recognize meaningful limits.
For the support system, you can examine whether its answers fit company policy and which requests need a person. You have grounds for judgment within a defined scope.
Practitioner
You apply understanding in real situations and learn from the results. In the support example, you have operated the service, dealt with exceptions, and revised the process when it failed.
Practice should include feedback. Repeating a procedure for years is limited evidence of competence if you never examine its outcomes. Useful experience includes knowing what it has taught you and where it leaves you uncertain.
Your state depends on the task
The same person can be a practitioner in sales, knowledgeable about a market, and only aware of the technology supporting a new proposal. These descriptions change with the subject and the situation. They are not permanent labels for people. Knowledge and practice can develop unevenly.
And we do not need to become practitioners in everything. An executive can make a sound decision with help from people who know more about the details. What matters is knowing which judgments they can assess personally and which require someone else’s expertise.
AI can make understanding harder to judge
Imagine a manager asks AI for a report on entering an unfamiliar market. The result includes competitors, customer segments, estimated costs, and a recommendation. The manager reads it and takes it to a leadership meeting.
What does the report demonstrate about the manager’s understanding? That depends on what they can explain and verify. Why were those competitors included? Where did the cost estimates come from? Which assumption would reverse the recommendation?
AI may have helped the manager learn. It may also have supplied language they can repeat without being able to assess. The finished report alone cannot tell us which happened.
A study first released in 2023 involving 758 Boston Consulting Group consultants offers a useful illustration. On selected tasks suited to GPT-4, consultants with AI completed more work, more quickly, and at higher quality. On a task the researchers selected because the model struggled with it, AI users were 19 percentage points less likely to reach the correct answer than those working without AI. (hbs.edu, hbs.edu)
That experiment tested a particular model and set of tasks. I take the practical lesson to be that we should check AI’s contribution to the actual work in front of us. An impressive result elsewhere supplies only part of the evidence.
Self awareness should change what we do
It is okay to be new to a subject. It is okay to remain informed without choosing to become a practitioner. Our responsibility is to make those limits visible when other people may rely on our judgment.
That requires more than saying, “I am no expert.” If a decision exceeds your understanding, involve someone who can evaluate it. If evidence is incomplete, identify what still needs checking. The greater the consequences, the more carefully you should establish that the necessary knowledge is present.
Business leaders can make this easier by asking, “What have we verified?” and “Who has experience with this?” They can also make it easier for someone to say, “I understand the outline, but I need help evaluating the proposal.” That admission gives a team something concrete to work with.
Experienced practitioners need the same honesty. Conditions change, and a familiar problem may contain an unfamiliar assumption. Expertise remains useful when it stays open to correction.
Before you act on an AI answer
What can I explain in my own words?
Which important claims have I checked?
Where am I relying on judgment I cannot assess?
What evidence would change the recommendation?
Who should help evaluate this before we act?
AI can help us explore those questions. It can offer explanations, challenge assumptions, and provide opportunities to practice. For important decisions, check the evidence outside the conversation and bring in relevant experience.
There is no shame in saying, “I am only aware of this so far.” That sentence tells us where the next useful conversation should begin.
It is okay to be in any of these states, as long as you are self-aware. Especially when using AI. Give yourself permission to learn, and let your actions reflect what you actually understand.
Nicely said