AI Field Notes: Clarity, Context, and Calibration

By the time leaders reach this point in the conversation about AI, most have accepted a fundamental truth: the tool itself is not the problem. The results they are getting, however, are still inconsistent. Some outputs are helpful. Others miss the mark entirely. This is where leadership behavior becomes decisive.

AI improves when leaders do three things well: define problems clearly, provide the right amount of context, and exercise disciplined judgment over what to trust, verify, or discard. These are not technical skills. They are leadership behaviors.

Clarity: Define the Problem Before Asking for the Answer

One of the most common causes of poor AI output is vague problem definition. Leaders often ask broad questions and expect precise results.

Prompts such as “provide a risk assessment for equipment maintenance” sound reasonable, but they lack essential information. What type of organization is this for? What equipment is being referenced? Is this mechanical equipment, medical equipment, or HVAC systems? What is the operational or regulatory purpose of the assessment?

Without clarity, AI fills in the gaps on its own. The result may sound polished, but it is rarely usable.

Clarity also applies to the desired output. Leaders often know what they want in their heads but do not state it explicitly. Asking for a table, a bullet list, or alignment to an existing template dramatically improves usefulness.

A helpful way to think about this is to treat AI like a new employee or an intern. When someone is new, leaders naturally explain the task in more detail, provide background, and clarify what a good deliverable looks like. Those same instincts apply here. Defining tone, format, and expectations is not micromanagement. It is direction.

Context: Enough to Reason Well, Not Enough to Bias the Answer

Context is where AI shifts from generic to relevant. Providing the right details allows the model to narrow its reasoning and focus on what actually matters to the leader.

However, context can also become noise. Oversharing opinions or conclusions too early can create an echo chamber that reinforces existing beliefs rather than offering objective analysis.

One effective technique is assigning intentional roles. Asking AI to act as a skeptical reviewer, compliance advisor, or neutral analyst strengthens the output by introducing balance. Context should help the model reason, not tell it what to conclude.

Good context answers three questions: what environment this applies to, what constraints exist, and what decision the leader is trying to make.

Calibration: The Leader Stays in the Loop

Calibration is where judgment matters most. AI can produce answers that sound confident, credible, and complete, even when they are wrong.

Experienced leaders recognize this risk. Polished language lowers skepticism. It becomes easy to forward an answer without checking it, especially when time is limited. That is how leaders get burned.

Calibration means treating AI output like a junior staff draft. Some parts may be useful. Some require verification. Others should be discarded entirely.

The same intern mindset applies here. Leaders would never take a new employee’s first draft and send it out without review. They read it, question it, validate key facts, and refine it before it represents their judgment. AI output deserves the same level of scrutiny.

Practical calibration includes checking sources, verifying links, and validating information against trusted references. When sources do not exist or do not hold up, leaders must challenge the output and re-ground the task. This is not a failure of the tool. It is a reminder that accountability still rests with the leader.

Over-Trust Is the Greater Risk

In practice, over-trusting AI is far more common than dismissing it too quickly. Fear of hallucinations may slow adoption, but misplaced confidence creates real consequences.

Leaders who use AI responsibly develop a healthy skepticism. They appreciate speed and insight, but they never abdicate judgment. AI accelerates thinking. It does not replace it.

A Repeatable Leadership Habit

When the output truly matters, leaders should slow down at the beginning rather than fix problems at the end.

Two habits consistently improve results:

First, ask AI to help improve the prompt. Inviting the model to ask clarifying questions before generating content improves structure and relevance.

Second, you’ll need sources and verify them. Click the links. Assess whether the sources are credible and applicable. If the information cannot be validated, it should not be used.

Better Results Come From Better Leadership

Clarity, context, and calibration are not new expectations. Leaders practice them every day with people. Applying those same behaviors to AI closes the gap between experimentation and reliable results.

The leaders who benefit most from AI are not the most technical. They are the most disciplined in how they think, communicate, and decide.