AI adoption and governance
A responsible AI framework for a growing business
Use consequence, ownership and evidence to make safe progress practical.
What principles should the framework contain?
Principles should be specific enough to guide choices: lawful and fair use, purposeful data handling, appropriate transparency, human accountability, security, reliability and routes for challenge. Translate each principle into observable requirements rather than leaving teams to interpret broad values alone.
How should AI risks be tiered?
Assess affected people, data sensitivity, autonomy, reversibility, financial or legal consequence and the availability of human review. Low-risk internal assistance may use streamlined controls; consequential customer or workforce decisions require deeper evidence and approval.
Who owns responsible AI?
A cross-functional forum can maintain the framework, but every use case still needs a business owner. Technology, legal, risk and data specialists advise within their disciplines; executive accountability cannot be outsourced to a committee or supplier.
How does the framework stay alive?
Maintain a portfolio of uses, owners, suppliers, incidents, evidence and review dates. Update controls as workflows, models, regulation and business consequences change.
Put the answer to work
Use this guidance against one live decision rather than treating it as a general checklist. Name the outcome, owner, evidence and next review point, then record what the business will do differently. Where the choice carries material legal, technical, financial, security or people consequences, bring the relevant specialist into the decision while keeping business ownership explicit.
What to carry into the work
- Turn principles into usable rules
- Increase control with consequence
- Keep a named business owner
- Review the live portfolio regularly