AI Governance & Human Review Systems

AI adoption fails when teams either block everything or let unreviewed automation run. We write operating rules people can actually follow.

AI adoption fails when teams either block everything or let unreviewed automation run wild. We create sensible operating rules: what AI may draft, what requires approval, how data is handled, how outputs are checked, and how teams document risk without slowing every project to a crawl.

The Two Failure Modes

One organisation bans AI outright, so it happens anyway on personal accounts with no oversight. Another allows everything, and discovers months later that customer data went somewhere it should not have, or that an unreviewed draft went out under the company's name.

Both come from the same absence: nobody wrote down what is allowed.

What Governance Looks Like In Practice

  • AI usage policies — what may be drafted, what must be reviewed, what is off limits.
  • Approval workflow design — routing that fits existing process instead of adding a committee.
  • Prompt and output QA — a repeatable check rather than individual judgement.
  • Data handling rules — what may be pasted into which system, stated plainly.
  • Audit logs and reporting — the record you need if a decision is ever questioned.
  • Team training — so the rules survive contact with deadlines.

Proportionality

Governance that slows every task to a crawl gets routed around, which leaves you less safe than before. We aim for rules strict where the risk is real and light everywhere else.

What's included

  • AI usage policies
  • Approval workflow design
  • Prompt and output QA
  • Data handling rules
  • Audit logs and reporting
  • Team training

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