The playbook
Two editions.
One for people running a business, one for people running a practice. Read it once end to end, then use the scans and checklists as working tools.
The AI Operating Playbook
For owner-managers, founders and finance leads at Singapore SMEs. 27 pages.
- Where AI actually pays in a back office — and where it doesn't
- Worked examples: receivables chasing, the month-end pack
- PDPA, record-keeping and the InvoiceNow timetable
- What a machine should never be the last step before
- A 30-day pilot roadmap with a decision at the end
The AI Operating Playbook for practitioners
For practitioners, partners and practice owners in Singapore. 28 pages.
- Where the hours in a practice actually are
- Confidentiality, quality management and documentation, applied to AI-prepared work
- What the file has to show
- The judgements that can never be delegated
- A pilot roadmap with a decision at the end
What's inside
Not a technology catalogue.
It names capabilities, not products — because the tool you pick this year won't be the tool you run in three.
Choose the right work
An opportunity scan and a scoring method that stops a long list of interesting ideas becoming a scattered programme.
Design it so it can be trusted
The control points, the exception paths, and the difference between a workflow and a demonstration.
Stay inside the rules
What Singapore's data protection and record-keeping obligations mean in practice, for work nobody wrote them for.
Know where the line is
The decisions that cannot be delegated to a machine, regardless of how capable it becomes.
Prove it in 30 days
A week-by-week roadmap where each week ends in evidence and a decision — including permission to stop.
Start tomorrow
Checklists, a success contract, and eight starter task specifications you can put to work immediately.
Most guidance about AI is written by people who advise on AI. This is written from the other side of the problem.
Under a statutory framework that punishes carelessness, in a jurisdiction that expects records to be produced on demand years after the fact. That forces questions a pilot project never has to answer. What happens when the output is wrong and someone has already relied on it? Who is accountable when the machine did the work? Which decisions can never be delegated, no matter how good the technology gets?
Want help putting it to work?
Talk to us about the first workflow in your business or your practice.