The Near-Miss Log

Cover for The Near-Miss Log, a weekly habit for logging caught AI mistakes, AI leadership thinking by AImpactNI

Every AI mistake your team catches is free tuition. Write it down in sixty seconds, review it weekly, and fix the system before the miss becomes a hit.

Most teams using AI already have a quiet hero. Someone spots the wrong figure in a draft report, the client name pasted into the wrong proposal, the confident summary that quotes a policy that doesn't exist. They fix it, sigh, and move on. Nobody records it. Next month, someone else catches the same kind of mistake, or doesn't.

That is where AI leadership thinking has to grow up. A caught error is not a non-event. It is evidence: about where the tool is weak, where your checks work, and who is quietly carrying the risk for everyone.

The Near-Miss Log turns those catches into learning. Aviation and healthcare have done this for decades. Leaders using AI can do it in one shared sheet.

Why Caught Mistakes Disappear

Relief erases memory

When an error is caught in time, the feeling is relief, not curiosity. The fix takes two minutes, the moment passes, and the pattern behind it stays invisible.

Nobody wants to look careless

If raising a near-miss feels like admitting you almost shipped a mistake, people stay silent. Blame drives the evidence underground, exactly where you can't learn from it.

The catcher carries hidden load

The same two or three careful people end up checking everyone's AI output. Without a record, their work is unseen, unthanked, and impossible to plan around.

How to Run the Near-Miss Log

1. Agree what counts as a near-miss

A near-miss is any AI output that would have caused real harm if a human had not caught it: a wrong fact, a wrong recipient, exposed data, a bad number, an invented source, or a tone that would have hurt a relationship.

2. Log it in sixty seconds

One row in a shared sheet, five fields, no essay:

  • What the AI produced: the wrong output, in one line.
  • Who caught it: name the catcher, not the person who almost shipped it.
  • How it was caught: a checklist, a second reader, gut feel, or luck.
  • Likely harm: what would have happened if it had gone out.
  • Severity: 1 annoying, 2 costly, 3 trust-breaking.

3. Review weekly for fifteen minutes, without blame

Once a week, the team lead reads the log aloud and asks one question: what pattern do we see? Group similar rows, then pick one fix: a better prompt, a new check, a narrower scope, or a change to the tool's Mandate Card. If "luck" shows up often in the "how it was caught" column, that's your loudest signal.

4. Apply the rule of three

Three near-misses of the same kind means the system changes, not the people. Pause the use case, tighten the check, or retrain the habit. One miss is an incident. Three is a design flaw.

What AI Leadership Thinking Gains

Risk you can see

Instead of a vague sense that "AI sometimes gets things wrong," you know which tasks, which tools, and which kinds of error. That turns anxiety into a short, specific fix list.

Credit where care lives

Thank the catcher by name in the weekly review. Care becomes visible, and the culture shifts from hiding mistakes to hunting them. It pairs well with The Dissent Slot: both make it safe to say "wait, this is wrong."

Start This Week

Create a five-column sheet called Near-Miss Log and share it with your team today. Ask everyone to log one catch, however small, before Friday. Hold the first fifteen-minute review next Monday.

The mistakes you catch are the cheapest lessons you will ever get. Stewardship means not wasting them.

AImpactNI | Next Gen Leadership Thinking using AI

Comments

Post a Comment

Popular posts from this blog

AI Leadership: Redefining Decision-Making in the Digital Age

Redefining Decision-Making in the Digital Age In the age of algorithms and ambient intelligence, leadership is undergoing a seismic shift. No longer confined to intuition, hierarchy, or legacy experience, decision-making is being reimagined through the lens of artificial intelligence. This transformation isn’t just technological—it’s philosophical, ethical, and deeply human. From Gut Instinct to Data-Driven Insight Traditional leadership often valorized the "gut feeling"—a mix of experience, bias, and charisma. But AI disrupts this paradigm by offering: Real-time analytics that outpace human cognition Predictive modeling that anticipates outcomes with uncanny accuracy Scenario simulations that test decisions before they're made Leaders now face a choice: cling to intuition or embrace augmentation. The most forward-thinking are doing both—using AI to challenge assumptions, validate instincts, and expand strategic horizons. The Rise of Algorithmic Empathy Con...
Read more

The Calibration Log

Trust in AI should be earned by track record, not by how confident the answer sounds. Most teams decide how far to trust AI the way they would judge a stranger: by how polished it sounds. But AI writes a shaky guess and a solid forecast in the same assured tone. Without a record, trust drifts on mood: one bad miss and the team stops listening, one lucky hit and it stops checking. The Calibration Log fixes that with a simple running record: what AI suggested, what you decided, and what actually happened. Review it once a month, and you will know, from your own evidence, where AI has earned a longer leash and where it still needs a human gate. Why confidence is a poor guide Fluency hides uncertainty A model rarely sounds unsure. Leaders tend to over-trust it in unfamiliar areas, exactly where they are least able to check it. Quiet wins go unnoticed Teams also under-trust AI where it has been reliably right, because nobody kept score. Good stewardship means trust rests on res...
Read more

The Skill Reserve

Keep the judgment your team needs when AI takes the easy reps. AI is very good at the first pass: the draft, the summary, the first cut of the forecast, the shortlist. That is a real gift of time. It is also a quiet risk, because the first pass is where people used to learn. When the reps disappear, judgment thins out, and nobody notices until the day the tool is wrong, down, or out of its depth. Stewardship means handing the next generation a team that can still think without the tool. The Skill Reserve is a simple practice for that: name the few skills your team must never lose, and protect a small amount of unaided practice for each. Why the reps matter The first pass is the classroom Most of us learned to spot a weak argument by writing weak arguments and having them corrected. If AI writes every first draft, people end up reviewing polished text instead of building their own reasoning. Skill loss is silent No dashboard shows a skill fading. You see it later, when a rev...
Read more