How to Use AI Responsibly as an Executive: A Personal Governance Framework

The first responsibility of an executive using AI is to know when AI has contributed to a piece of work and to ensure that contribution is appropriate.

This matters because: the AI may have introduced errors you have not caught; others relying on your work deserve to know its provenance if it affects its reliability; and your own professional judgement is what you are accountable for, not the AI's.

In practice this means: review all AI-assisted outputs before using them. Never sign off a board paper, a regulatory submission, an investor communication, or a personnel decision without personally reading and verifying the final content. AI-assisted does not mean AI-reviewed: you are the reviewer.

For board-level communications and regulatory submissions, the accountability for accuracy rests with you regardless of whether AI helped draft the content.

The distinction between enterprise AI tools (Microsoft 365 Copilot, Claude enterprise API, Azure OpenAI) and consumer AI tools (chat.openai.com, claude.ai, gemini.google.com) matters enormously for data governance.

A practical rule: treat consumer AI tools the way you would treat a public internet forum. You would not post a client contract, a personnel record, an M&A document, or commercially sensitive business information to a public forum. Apply the same standard to consumer AI tools.

Enterprise AI tools deployed with appropriate data protection commitments are the right environment for business data. If your organisation has not yet deployed such tools, advocate for doing so rather than using consumer tools as a workaround.

You set the tone for your team. If you use consumer AI with sensitive data, your team will assume this is acceptable.

AI systems hallucinate. They produce confident-sounding incorrect statements with no warning sign. Before making a decision, signing a document, or communicating information to others based on AI analysis, verify the critical facts independently.

This rule is proportionate to consequences: routine tasks with low consequences can accept more AI risk. Decisions with significant financial, legal, or reputational consequences require rigorous verification.

A useful threshold: would you have a junior analyst's work reviewed before acting on it? Apply the same standard to AI-generated analysis. An AI is not more reliable than a competent analyst; it is faster.

Ask for sources. Ask the AI to locate specific evidence. Check calculations. The few minutes spent on verification is insurance against the consequences of acting on a confident-sounding AI error.

The question of when to disclose AI use is not yet settled in most organisational contexts, but the principle of transparency when it matters is clear.

Areas where transparency is important: regulatory submissions (AI assistance should be disclosed where required by the relevant regulator); professional publications (journals, industry bodies, and many publications now have AI disclosure requirements); investor communications where material judgements are presented (investors are entitled to know if the analysis was AI-generated without editorial input); and internal documents where the quality of the analysis affects significant decisions.

For routine communications, drafts, and internal work, the disclosure standard is lower. Your team does not need to know that you used Copilot to tighten an email. Your board does need to know if the financial model in the business case was produced by AI without expert review.

The principle: disclose where others are relying on your work as reflecting your analysis and judgement, and where AI has substituted for rather than supported that judgement.

You set the AI culture for your organisation. The practices you visibly adopt, the standards you hold your team to, and the questions you ask about AI use in decisions and communications all signal what is acceptable.

Good executive AI practice to model: using enterprise AI tools rather than consumer tools with business data; verifying AI outputs before acting on them; being curious about and engaged with AI tools rather than dismissive or uncritical; asking questions about AI use in work presented to you ('what AI tools were used in preparing this analysis?').

Poor executive AI practice to avoid modelling: using consumer AI with sensitive data; accepting AI-generated analysis without review; dismissing AI use as inappropriate rather than engaging with when it is and is not appropriate; or treating AI as a way to reduce thinking rather than accelerate it.