What can AI do for a business or financial consultant?
The analysis and writing layer: organizing findings into a deck, drafting an executive summary, summarizing meetings, scaffolding a model, and explaining concepts to a client. None of these capabilities depends on the client's name — they work just as well on COMPANY_001.
- Executive summaries — turning a long analysis into one sharp page of findings.
- Deck structure — the right narrative order for findings and recommendations.
- Table work — spotting trends and phrasing conclusions from tokenized data.
- Meeting summaries — raw bullets into a clean recap with action items.
Which materials at a consultancy are the riskiest to paste?
The materials the client would show no one: internal financial statements, models with real assumptions, strategic plans and payroll data. For financial advisors serving individuals — also portfolios and personal data of clients like John Miller.
- Internal financials — true margins, cost structure, debt.
- Financial models — projections and assumptions that are the heart of the trade secret.
- Strategic plans — market entry, a merger, a restructuring — before they're public.
- Employee and payroll data — which is also personal data under privacy law.
Why is 'just a small summary' still a problem?
Because a confidentiality clause doesn't distinguish a big paste from a small one — any transfer to a third party counts. And even a short excerpt gives things away: the client's name in a header, a competitor in a paragraph, a margin figure that appears only in the internal report.
Vendor privacy policies don't resolve this either: terms vary between tiers, and control of the server side is never yours. What is yours is the content at the moment of pasting — which is why the rule is to clean it first.
What does a safe workflow look like for a consultant?
- Replace the client's name with COMPANY_001, competitors with COMPANY_002 onward, and people with PERSON_001 — consistent tokens keep the document coherent.
- Replace telling numbers — margins, salaries, prices — with tokens or consistently shifted values.
- Clean metadata: the Author field, comments and tracked changes expose names even in a 'clean' file.
- Do the AI work — analysis, wording, structure — on the clean copy.
- Restore real names and numbers only in the final file, on your own machine.
The principle serves any organization adopting AI — see the safe AI adoption guide — but for a consultant it's doubly critical: confidentiality is the product. The same approach carries over to startups handling NDAs, and the general never-paste list makes a good appendix to any engagement policy.