Before you start: three things to settle
Decisions that decide whether the rest of this guide applies to you at all.
US data transfer must be acceptable
Both Claude and ChatGPT process your material on US infrastructure. If your board has ruled that out, the consumer apps in this guide aren't your answer — but there are two other routes. See section 11 →
A Pro or Max plan is enough
You don't need Enterprise for this. What you do need is to change the default settings yourself — steps 01 to 03 below. The paid consumer plan plus correct settings is the working setup.
Enterprise buys less than you'd think
Zero-data-retention terms are negotiated for API and developer use. They don't automatically extend to the friendly app and web interface you'll actually be working in. Read the specific terms before paying for the upgrade.
Everything below is about your own account hygiene. It doesn't replace the confidentiality obligations you signed as a director. If a board pack is marked as not leaving the boardroom, no settings page changes that.
Turn off model training
This is the one that matters most, and it is on by default on consumer plans. Until you switch it off, your chats and the files in them can be used to train and improve the models.
Turning training off is not the same as data never being stored. Conversations still sit in your account history until you delete them, and are retained for a period for safety and abuse review. If a document is too sensitive to sit in a cloud account for months, it's too sensitive to paste in.
Never rate a reply
This is the trap nobody warns you about. Turning training off governs the automatic use of your data. Clicking thumbs-up or thumbs-down is you voluntarily submitting that conversation for human review — and it goes through regardless of what your privacy settings say.
If a reply is wrong, tell it so in the chat and ask again. That corrects the answer without sending anything anywhere. Save the feedback buttons for a throwaway chat about the weather.
Turn memory off
Memory can be a useful feature, but it opens two risks. Irrelevant information from old conversations gets pulled into new ones and confuses the model. And when you work across several companies, what you did for one leaks into the work you do for another. Turn it off.
You lose a little convenience: each conversation starts cold. Step 05 gives that context back in a controlled way, one company at a time.
Cloud tasks — optional
Cowork tasks can run either on your own computer or in Anthropic's cloud. With this switched off, new tasks run locally, and the material largely stays on your own disk — your files stay in your folders, and only what the model actually needs to see leaves the machine.
If you have already accepted US processing back in decision 1, running tasks in the cloud is not a problem in itself. The training and memory settings from steps 01 to 03 apply either way, and the cloud environment is private to your session.
Turning it off is about keeping the material physically on your own computer. Some boards care about that a great deal; for others it makes no difference. Decide once, then be consistent.
One project per company
A project is a walled-off workspace: its own instructions, its own files, its own chats. One project per company you sit on, each pointed at a folder that belongs to that company alone. This is what replaces memory — context that is deliberate and scoped instead of ambient and leaky.
Point it at that company's folder — and only that folder
The folder you connect is the boundary. If a project can see a parent folder holding three companies' material, you have rebuilt the problem you just switched memory off to avoid.
Acme A/S / Board 2026-09 / — a subfolder per meeting inside the company folder.Write the standing instructions
The instructions are read at the start of every chat in that project. This is where the company's strategy, your focus as a director, and the documents that matter most belong. Keep it short enough that you'll actually maintain it.
I sit on the board of Acme A/S, a B2B logistics software company, roughly 40 employees, majority owned by a family foundation. My focus as a director is operational excellence and digitalisation. I am not the finance specialist on this board. The current strategy is in strategy-2026.pdf. Refer to it when you assess whether something is on or off plan. When you analyse board material: be direct about what looks wrong, flag what is missing, and don't soften bad numbers.
Analyse the pack
The routine, once the setup above is done: board material for the September meeting goes into a subfolder of that company's folder. Open the project. Ask it to read that folder. Everything else is a variation on one prompt.
The starting prompt
Adapt it as you learn what you actually want from it — this is a starting point, not a formula.
Read all materials for the upcoming board meeting in folder <name>.
Identify:
- Concerning changes in key metrics, spending, or other
strategically important areas
- Inconsistencies or conflicting priorities across the materials
- Five core questions to ask about the strategy and ongoing projects
Cite the document and page for every point you make.
That last line matters more than it looks. It forces every claim back to a source you can check in thirty seconds, and it makes a fabricated number obvious rather than plausible.
Follow-ups worth having ready
- "What is management not telling us here?" — surfaces gaps between the narrative and the numbers.
- "Compare this pack with the previous quarter's. What changed in the story?" — requires both packs in the folder; catches quietly revised forecasts.
- "Argue the opposite case for the main proposal." — a fast way to test whether a recommendation survives contact with a sceptic.
Keep the instructions current
The project instructions are only as good as the last time you touched them. The cheapest moment to update them is right after a meeting, when the pack and the minutes are both sitting in the folder and the discussion is still fresh. Ask for a summary written specifically to be pasted into the instructions — not a meeting summary, which is a different thing and far too long.
Read the board pack and the minutes from the meeting in folder <name>.
Write a condensed status note that I can paste directly into my project
instructions. Cover:
- Key projects: current status, owner, and what was decided
- Decisions taken, with date and any conditions attached
- Open questions carried into the next meeting
- Numbers or targets I should hold the next pack against
Rules:
- Maximum 250 words, plain statements, no preamble
- Standing context for future meetings, not a record of this one —
write in present tense
- Leave out anything that will be stale in three months
- Output only the text to paste, nothing else
Then paste it in — over the old note, not under it
Open the project, click the pencil next to Instructions, and replace last meeting's note with the new one. Keep the permanent part — who you are, what the company does, where the strategy lives — and swap only the status section. Instructions that only ever grow eventually stop being read, by you and by the model.
Do this for three meetings and the assistant stops reading each pack cold. It knows which projects were already late, which forecast was already revised once, and which question you asked last time and never got answered. That is where it starts being worth more than a fast first read.
The same thing in ChatGPT
If you'd rather work in ChatGPT Plus, the shape of the setup is the same. The menu names differ and OpenAI moves them around more often, so treat this as a map rather than a click-path — and verify each one on your own account.
| What you're doing | In Claude | In ChatGPT |
|---|---|---|
| Stop training on your data | Settings → Privacy → Help improve our AI models, off | Settings → Data controls → Improve the model for everyone, off |
| Avoid volunteering a chat | Don't click thumbs up/down under a reply | Same — the feedback icons submit the conversation for review |
| Stop cross-chat bleed | Settings → Memory → both toggles off | Settings → Personalization → Memory off, including reference chat history |
| Scope material per company | Projects, each with its own connected folder | Projects, each with its own uploaded files and instructions |
| Standing context | Project instructions | Project instructions (keep global custom instructions generic) |
Global custom instructions apply across every chat and every project. Anything company-specific you put there follows you into every other company's workspace. Keep global instructions about you; keep company detail in the project.
Pre-meeting checklist
Where judgment stays yours
Used this way, an AI assistant is a fast, tireless first reader. It will catch the line in the appendix that contradicts the summary, the metric that quietly changed definition, and the project that has slipped twice without anyone naming it. That is real value on a 90-page pack you got four days before the meeting.
What it doesn't have is everything that isn't in the documents: the CEO's track record on optimistic forecasts, the tension between two shareholders, why the CFO went quiet last time this came up. It also does not know what it doesn't know — an assistant will produce five confident questions whether or not the pack gave it enough to be confident about.
So treat the output as a well-briefed junior's memo: useful, quick, worth reading, and never something you'd repeat in a board meeting without checking. The questions it hands you are candidates. Which ones you actually ask, and what you do with the answers, is the part of the job that doesn't delegate.
The settings in steps 01 to 03 take fifteen minutes and are the difference between a private tool and a public one. The workflow in steps 05 to 07 is what makes it worth the fifteen minutes.
Need the data to stay in the EU?
Everything above assumes it is acceptable for your material to be processed in the US. For some boards it isn't: a customer contract forbids it, the company handles personal or health data, or the pack simply contains something you won't send across the Atlantic on a consumer subscription.
That doesn't mean giving up on the workflow. It means changing where the model runs. Two routes are realistic today.
EU-hosted models
The same frontier models, served from European data centres through a cloud provider rather than the consumer app — with a simple interface on top. You keep near-current model quality and processing stays in the EU. The cost is setup: an account, a data processing agreement, and someone technical for an afternoon.
A model on your own laptop
For the most sensitive material, an open-weights model running locally. Nothing leaves the machine — no account, no network, no retention question to answer. You give up some capability and you need a reasonably powerful laptop, but for reading a board pack and questioning it, that trade can be worth making.