Adopt & Improve · Governance & Optimization
Keep access, quality, cost, and change visible after launch.
The operating structure for AI in production: ownership, access, evaluation, monitoring, incident handling, and a measured improvement cycle.
Systems of record
- CRM
- ERP / finance
- Service desk
- Document store
- HR / ATS
Integration layer
- Authentication
- Permissions
- Data contracts
- Retries and errors
- Audit log
Workflow and AI-assisted steps
- Intake
- Retrieval
- Drafting
- Routing
- Human decision gates
Measurement layer
- Cycle time
- Quality and rework
- Exception rate
- Adoption
- Cost per run
Implementation blueprint
How this is actually built and run
01 · Trigger
A scheduled review, a change request, an incident, or a metric moving outside its agreed range.
02 · Context and data
Usage logs, override records, quality samples, access lists, and the control definitions agreed at design time.
03 · AI task
Summarise sampled outputs and surface patterns in overrides, exceptions, and drift for human review.
04 · Human control
People decide what is acceptable, what changes, and what is switched off. The review record is written and kept.
05 · System action
Produces the review pack, updated control documentation, and a tracked action list.
06 · Evaluation
Sampled quality review against the original acceptance criteria, repeated on a fixed cadence.
07 · Monitoring
Continuous metric tracking with thresholds and named alert recipients.
08 · Ownership
A governance owner runs the cadence; each capability keeps its own business owner.
The problem
What this solves
Systems drift. Models change, data changes, teams change, and cost creeps. Without an owner, an evaluation set, and a review rhythm, quality degrades silently and nobody notices until a customer or an auditor does.
When this solution fits
- Several AI tools are in use with no shared oversight
- Nobody owns quality after go-live
- Reviewers, clients, or auditors are asking questions you cannot answer
- Costs are rising without a clear view of what is driving them
- Access to AI tools and data has never been formally reviewed
Example workflow
A quarterly operating cycle for live AI systems
01
Inventory
Every AI-assisted workflow recorded with its owner, data scope, and controls.
02
Evaluation run
Each system re-tested against its evaluation set to detect drift.
03
Monitoring review
Quality, escalation, cost, and incident data reviewed against thresholds.
04
Access review
Permissions, integrations, and vendor changes checked and re-approved.
05
Improvement backlog
Findings prioritised into a backlog with owners and target dates.
What is implemented
- AI system inventory with named owners
- Policy, approval, and change-management structure
- Access, retention, and vendor review practices
- Evaluation sets and scheduled re-testing
- Monitoring, alerting, and incident escalation paths
- Scheduled performance review and a prioritised improvement backlog
Systems and data inputs
- Inventory of AI tools, integrations, and vendors in use
- Existing security, data, and procurement policies
- Usage, quality, and cost telemetry from the live systems
- Named owners from the business and IT
Human control
How oversight is designed in
- Every live system has a named accountable owner
- Approval thresholds define what an AI-assisted workflow may do unattended
- Sampling and exception review are scheduled, not ad hoc
- A documented shutdown path exists for every system
- Incidents route to people with the authority to stop the workflow
Tangible outputs
- AI system inventory and control register
- Evaluation and monitoring reports per period
- Access and vendor review records
- Incident log and response documentation
- Prioritised improvement backlog
Measurement model
How we know it is working
| Measure | How it is tracked |
|---|---|
| Quality against evaluation sets | Scored re-runs per period, tracked for drift |
| Escalation and exception rates | Volume and trend by workflow |
| Cost per workflow | Usage-based cost tracked against expected volume |
| Incident count and time to resolve | Logged incidents with resolution durations |
| Control coverage | Share of live systems with an owner, evaluation set, and review date |
Boundaries
What this solution does not promise
- We do not certify your organisation against SOC 2, ISO 27001, HIPAA, GDPR, or any other standard
- Governance reduces and surfaces risk; it does not eliminate it
- We cannot guarantee model behaviour, vendor uptime, or vendor policy changes
- Regulatory interpretation and legal advice remain with your own advisers
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Find the AI opportunities worth implementing in your business.
Start with a structured assessment of your workflows, systems, and data, and leave with a prioritized view of where AI can create real value.