Generative AI becomes more useful in executive support when it is treated as one stage in a controlled process, not as the owner of the work.
A practical sequence is:
INPUT → AI TASK → CHECK → HUMAN DECISION → RELEASE
1. Name the authoritative input
Start by stating which material the model may use as factual authority. A meeting workflow might use an approved agenda, current notes, and the live action register. A briefing might use the current calendar plus verified project updates.
This matters because a model can otherwise blend supplied facts with plausible background knowledge. If two sources disagree, define which one wins or mark the result as a conflict that needs a person to resolve.
2. Define the AI task
Describe the transformation, not merely the topic.
Instead of “help with this meeting,” ask for a specific output such as candidate decisions, actions, owners, due dates, and open questions from the supplied notes. State what must not be inferred. Missing owners or dates can remain UNKNOWN or TO CONFIRM.
3. Design the check before generation
“Review carefully” is not a useful control. Match the check to the likely failure.
Dates can be checked against the live calendar. Decisions can be traced back to notes. Numerical work can be recalculated in the spreadsheet. Research claims can be reopened at their sources. External communication can be scanned specifically for commitments and disclosure.
The more consequential the output, the stronger the independent check should be.
4. Name the human decision owner
A draft can look finished while still lacking authority.
State who can approve a deadline, accept a meeting, authorize expenditure, decide a project position, or release an external message. AI can prepare options and drafts; it should not acquire authority simply because the wording is confident.
5. Define release
Release is the condition that turns prepared work into usable work. It may mean sending an email, updating a tracker, placing an approved brief in a shared folder, or presenting a recommendation.
Write the condition explicitly: for example, release only after names, dates, commitments, and attachments have been checked against source.
Improve the workflow when corrections repeat
If the same mistake keeps appearing, change the input package, instruction, verification step, or exception path. Do not rely on remembering the same correction every time.
A workflow earns a place in a reusable library only when the whole process, including checking and correction, is easier and reliable enough for its purpose.
Next, apply the same model to meeting preparation and follow-up or review information boundaries before prompting.
The complete book is The AI Workflow Playbook for Executive Assistants.