Scope an AI automation pilot in Hong Kong

An AI automation pilot should answer one business question: does this workflow become faster or more reliable when AI handles a defined part of it? In Hong Kong, that question also needs an approved data path and a person accountable for the result. A working demo alone does not answer it.

I would scope the pilot on one page before buying licences for a whole team. Take a weekly management update as a worked example. Staff collect figures from approved spreadsheets, chase comments by email and assemble a short pack. The example is illustrative, not a reported client engagement.

Set one outcome for the AI automation pilot

Write down the current process before changing it. How many packs are produced in a month? How long does assembly take? How often does a number or source need correction? Who approves the pack now? If those answers are unknown, measure two ordinary cycles first.

Then pick one outcome. For example: reduce preparation time while keeping the existing sign-off and source checks. Do not combine drafting, sending, forecasting and approval into one pilot. Each adds a different failure mode.

A pilot earns a rollout when people can explain its failures as clearly as its wins.

Draw the data and access boundary

List the files, systems and people the workflow may use. Identify personal, client or commercially sensitive information. Decide whether the pilot runs inside an organisation-approved AI environment or on cleared sample data. Give the pilot the narrowest access needed, and keep a record of who approved it.

The Office of the Privacy Commissioner for Personal Data's AI model framework covers governance, risk assessment, human oversight and training for organisations using AI with personal data. It does not grant permission to upload work material to any tool. Your own policies and contracts still apply. My guide to work data and ChatGPT sets out the practical questions for a first test.

For the management pack, the first pilot might read approved spreadsheet exports and draft a summary into a review folder. It should not send the pack or update source figures. A human can compare the draft against the underlying table before any wider access is considered.

Test the output on ordinary and awkward cases

Collect a small set of representative past tasks, including an incomplete file, a late correction and a number that changed sign. Run the workflow repeatedly. Mark errors by type: wrong figure, unsupported explanation, missing source, inappropriate tone or accidental disclosure. Record how long human checking takes, because an automation that saves ten minutes in drafting but adds twenty minutes of review has failed its purpose.

Name the reviewer before the test. For the management pack, an analyst checks each claim against the source and a manager approves release. The model does neither job by default. This is the same discipline I recommend when choosing an AI workshop for a finance team: practise the review, not only the prompt.

Agree the decision before anyone calls it a success

Set a review date and three possible decisions: expand, revise or stop. Expand only if the measured result is useful, the errors are understood, the team can operate the workflow and the data owner accepts the controls. Revise if a narrow change can address the failures. Stop if the process is too inconsistent, the data boundary cannot be met or checking costs more than the work saved.

Write down who owns the workflow after handover. Include the input instructions, permissions, a sample of a good output, a checking checklist and a contact for failures. If the pilot depends on one consultant's personal account, it is not ready for a wider rollout.

A pilot will not repair bad source data or unclear authority. It may reveal those problems early, which is useful in itself. If your team is still learning the basics, corporate AI training in Hong Kong may be the right starting point; the free prompt library offers small exercises. If you already have a recurring bottleneck, I can help you scope one test and decide what should happen after it.