Design the training programme that accompanies an internal AI rollout.

Interviewer's question: "Design the training programme that accompanies an internal AI rollout." Yolanda Prescott runs ramp operations training at Falkirk Ground Services. Her ramp agents are about to start using an AI tool that drafts the delay-cause code after every aircraft turnaround.

How to answer this, stage by stage

Nobody is grading whether you can list training modules. They're grading whether your curriculum survives the first time the tool is confidently wrong under real time pressure.

Let's learn

Before any of this, a ramp agent picked a delay code from memory in about ninety seconds, right after sprinting back from an aircraft, almost always the same generic code, because the finer categories were hard to recall under real time pressure.

Four steps, and the third one is where the guessing has always lived.

The new AI tool reads turnaround data and drafts a delay-cause code in seconds, for the agent to confirm or correct. Falkirk's first training rollout was a straightforward feature tour: here's the screen, here's the confirm button, here's how to override it.

The feature tour taught agents where every button lived. It didn't teach them what a wrong draft actually looks like, so the first time the tool confidently produced one, most agents confirmed it without a second look, exactly the way they'd been trained to.

At its worst: a feature-tour-only rollout ships across the whole ramp, agents rubber-stamp wrong drafts during every rushed turnaround, and Falkirk spends months untangling billing disputes with airlines who were charged for the wrong delay.

What I would leave alone: Falkirk's actual turnaround procedures on the tarmac don't need to change at all. The fix lives entirely in how agents are trained to read the tool's output, not in how the ground work itself gets done.

The lesson: a feature tour teaches where the buttons are. Only real failure cases teach someone what wrong actually looks like under pressure.

Now here is the same thing as a story

The short version above is what you'd pitch to Falkirk's operations director. Read this one for how the near miss actually forced the redesign.

The ramp gets its worst rush around 4pm, when three aircraft turn around back to back and every agent is sprinting between gates.

Yolanda Prescott's first training rollout, the feature tour, went fine on paper. Agents could navigate the tool's screen confidently within a single afternoon session. Adoption looked strong in week one.

Same tool, same rushed afternoon. Only one of these two agents had actually seen this shape of mistake before.

In week two, during the 4pm rush, the tool drafted a delay code blaming a late inbound crew, when the real cause was a mechanical hold, an easy mistake to make when two causes happen close together. An agent trained only on the feature tour confirmed it in the same three seconds he confirmed everything else, and it went out to be billed to the wrong airline before anyone caught it.

The billing dispute took six weeks to untangle and cost Falkirk a real client relationship's worth of goodwill. Yolanda pulled every wrong draft the tool had produced during its quiet internal testing phase, thirty of them, and rebuilt the entire curriculum around spotting and fixing exactly those thirty patterns.

Four steps per case, repeated thirty times, and that repetition is the whole curriculum.

Run the near miss forward under the new curriculum. The exact combination, a crew-swap and a mechanical hold landing close together, is one of the thirty cases every agent has already practiced spotting. The draft still comes out wrong. This time, it doesn't leave the gate uncorrected.

SPARK, in one screenNot a training checklist. SPARK is what forces the curriculum to survive the exact mistake that made this training necessary.

Three real ideas, and every one of them would have cost training time the near miss didn't allow for.

The recap, one line per letter: situation is a rushed guess with no real cause data, payoff is catching a wrong draft before it bills the wrong airline, anchor is thirty real failure cases practiced hands-on, risk is the training surviving a rushed 4pm shift, and keep out is the heavier content deliberately left for later.

And if you want to be sure it really works, try it somewhere elseSame five letters, a home-improvement retail chain instead of an airport ramp. Nothing else about the two jobs is alike.

Pikeworth Home & Hardware trains its returns-desk staff on an internal AI tool that drafts return-fraud-flag documentation for suspicious returns.

Six weeks from a built curriculum to full coverage, and the middle milestone is the one that proved it actually worked.

Mapped onto SPARK: situation is a returns clerk guessing at a fraud flag from a rushed customer interaction, often defaulting to "approve" under pressure to keep the line moving. Payoff is catching a wrongly cleared fraud flag before a repeat offender walks out with a refund, not memorizing the tool's dropdown menus. Anchor is a curriculum built from real past cases where the tool wrongly cleared or wrongly flagged a return, practiced hands-on. Risk is a busy Saturday afternoon return line, the exact moment a feature-tour-trained clerk waves through a draft they never learned to question. Keep out is full fraud-pattern statistics training and every rare return-policy edge case, both saved for a later refresher.

The generic feature tour sits alone in the least useful corner, exactly where it belongs.

Swap the trigger and it still runs.
Speed: an interviewer caps you at thirty seconds. Say "build training from real failure cases, not features, and check it against an actual mistake before calling it done," and stop.
Cost: if there's no time to gather thirty real cases before launch, start with the five worst ones from testing and add more each week, since five real cases still beat a feature tour of zero.
The model gets better, for real: if the tool's draft accuracy improves over time, the training still matters, because rarer mistakes are exactly the ones agents are least prepared to notice, having seen fewer of that shape by then.

Where people run it wrong.
They build training around the interface instead of around what a wrong answer actually looks like.
They treat a calm classroom session as proof the training works, without ever testing it against a real rushed shift.
They try to cover every rare case on day one and run out of time for the common ones that actually matter most.

How to use it live. When someone asks you to design training for an AI rollout, ask yourself first: has this curriculum ever been checked against something the tool actually got wrong. If the answer is no, it's still a feature tour wearing a training programme's name.

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