Responsible AI begins with the use case
The appropriate controls depend on what the system does, what data it uses, who may be affected and what happens when it is wrong. Governance should follow risk rather than a generic checklist.
Human oversight must be real
A human reviewer needs enough context, authority and time to challenge an output. Simply placing a person at the end of an automated workflow does not guarantee meaningful oversight.
Privacy and security belong in design
Organisations should understand what information enters a system, where it is processed, who can access it and how long it is retained before moving from experiment to adoption.
Accountability cannot be delegated to a model
Name the business owner, define appropriate use, establish escalation and retain evidence for important decisions. Organisations remain responsible for their legal, privacy and regulatory obligations.
Keep governance proportionate
Small and mid-market organisations can begin with an AI register, clear acceptable-use rules, risk-based approval, supplier review, testing and periodic monitoring without creating unnecessary bureaucracy.
Discuss Responsible AI
Move from experimentation to proportionate, practical AI governance.