Recommended practice for using AI in valuation
AI can improve workflow, but it should assist—not replace—professional judgement, evidence verification and accountability.
Useful applications
- Drafting information-request lists and interview questions
- Summarising industry material and lengthy agreements
- Checking model logic, formulas and internal consistency
- Generating scenario ideas and sensitivity tables
- Improving plain-language explanations and report structure
High-risk applications
- Accepting unverified comparable companies or transactions
- Using invented citations, market data or discount-rate inputs
- Uploading confidential client data to uncontrolled systems
- Producing a value range without purpose and case details
- Letting AI make the final professional judgement or sign-off
A practical control framework
Define permitted uses. Separate administrative assistance from analytical judgement.
Protect confidentiality. Review data-processing terms and avoid exposing identifiable client information.
Verify every input. Trace market data, legal terms and technical claims to reliable sources.
Document human review. A qualified professional should challenge outputs and remain accountable.
Disclose material use where appropriate. Consider client, employer, regulator and professional requirements.
Preparing documents?
Review the practical checklist of financial, business, asset, contract, intangible-asset and startup information commonly requested.
Why “AI says the fee should be…” is unreliable
AI may combine unrelated assignments, outdated market commentary and different jurisdictions. Unless it receives the purpose, complexity, company scale, data condition, reporting requirements and review expectations, its estimate is little more than a guess.