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For Operations & Delivery

Know where every job stands, the moment it changes.

Schedules move, files sit on different laptops, and the people who need to know hear about it late. AI can keep the status current from the files you already have, and draft the update for everyone affected.

Sound familiar?

  • "When the plan changes, I change the date, but the dashboard doesn't update."
  • Everyone keeps their own folder, so nobody is sure which drawing is the latest.
  • Contractor quotes are checked line by line, and items still slip through.

What it looks like in practice

A maintenance coordinator at a call-centre operator kept a schedule of 30, 60 and 90-day maintenance items. In class, the coordinator described the problem to AI in plain words, and the status column became a formula. Each date change now updates the status by itself.

Another example

At a waterworks engineering firm, the owner asked AI to read the project schedule and say what was done. The answer came back as a new status column: drawing approval in progress, material procurement not started. The team checked each line against the file before keeping it.

When something doesn't work yet, you'll hear it

In one coaching session we tried having AI save email attachments into the right project folder. It returned the text, not the files, and Alan said so in the room and took it away to solve.

How the training fits your role

  • AI Foundation, open class: one day, one method, practised on a task from your own operations work.
  • In-house and AI Coaching: built around your operations and project processes, role by role.

Notes for operations teams

Three ways to get AI work done, and how to choose

Your team can learn to do the AI work, build it with a guide alongside, or have it built and then own it. Three questions tell you which way fits each piece of work, and most businesses use all three.

Fix it before you automate it

Automation runs whatever version of the task it is given. A quick test tells you whether a task is ready — and getting it ready is usually smaller work than people expect.

Keep your AI instructions in a file

The instructions you give AI for a repeated task are worth keeping in a plain document. Use the file every time, update it whenever you correct a result, and the task gets better with every run, whoever on the team runs it.

Split AI work into asking, doing and checking

AI work goes better with three roles kept apart: you decide and write down what good looks like, a fresh AI session does the work from that instruction alone, and someone with no stake in the result checks it against your description.

AI can be confidently wrong

AI writes by predicting what fits, so it can give an answer that is specific, fluent and wrong. Treat every AI answer as a draft, and check the parts that carry weight, such as numbers, references and names, before you rely on it.

Two kinds of automation, and how to choose

AI automation handles work that varies. Coded automation handles work that must run the same way every time. Most good workflows use both, each where it fits.

All notes

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