02 Govern · Governance Implementation
Turn written expectations into operating controls.
A governance framework is only useful if it changes what happens on a Tuesday afternoon. We build the policy, the accountability structure, the review points and the training that make responsible AI use the default path rather than the careful exception.
Typical scope
- AI policy and supporting standards
- Accountability model and RACI
- Permitted and prohibited use schedule
- Human review and escalation procedures
- Role-based staff training
- Decision logging and monitoring cadence
Who this is for
Firms with AI in use and a policy that has not caught up.
- Firms with a generic AI policy that staff do not consult in practice
- Risk and compliance leaders asked to evidence oversight to insurers or clients
- Practices standardising governance across merged or acquired offices
- Leadership teams that want to widen AI use without widening exposure
- Firms preparing for client due-diligence questionnaires on AI use
Outcomes
What changes for the firm
Controls people follow
Governance expressed as short, specific rules attached to the workflows where the work happens.
Clear accountability
Named owners for approval, oversight and escalation, so no use case sits with nobody.
A defensible record
Logging and monitoring that produce evidence continuously rather than during a scramble.
What Alacrix does
We design governance around the firm you actually run.
The framework is built from your use cases, your obligations and your operating model. It is deliberately minimal: enough control to make each use case defensible, and no more, because unnecessary process is the fastest way to lose adoption.
- Draft the AI policy and supporting standards in plain British English
- Define the accountability model and approval route for new use cases
- Set permitted and prohibited use by data classification and work type
- Specify human review thresholds and what reviewers are accountable for
- Design the escalation path, incident review and change process
- Deliver role-based training for fee earners, support teams and leadership
Control set
What gets built
Six components that together turn stated expectations into an operating control environment.
Policy that reflects practice
A short, readable AI policy tied to the firm's real use cases, professional obligations and client commitments.
Accountability and RACI
Named ownership for approval, review, monitoring and incident handling - at partner, function and workflow level.
Permitted and prohibited use
Clear boundaries by tool, data type and work type, written so staff can apply them without asking every time.
Human review points
Defined thresholds for where review is mandatory, what a reviewer is checking and what constitutes acceptance.
Escalation and incidents
Routes for concerns, near misses and failures, with a review process that produces a change rather than a note.
Decision logging
A consistent record of AI-assisted decisions: inputs, tool and version, reviewer, outcome and rationale.
Deliverables
What you leave with
Governed, not generic
Governance that is embedded, not filed.
The difference between a governance document and a governance framework is whether the control is attached to the workflow. We build the attachment.
- Each control is mapped to a specific tool, workflow or decision point
- Review steps are written into the process, with an owner and a threshold
- Training is tied to the actual tools staff use, with worked examples
- Monitoring produces a periodic evidence pack, not an annual assertion
Engagement process
How the work runs
- 01
Frame
Confirm use cases, obligations and appetite with leadership, risk and the teams doing the work.
- 02
Build
Draft the policy, controls, review points and logging design, then test them against live workflows.
- 03
Embed
Train the firm, activate the monitoring cadence and hand over an owned, maintainable framework.
Next step
Make the policy something the firm can rely on.
Most engagements begin with an AI Risk & Readiness Audit so the framework is built on observed practice rather than assumption.