Set the enterprise AI strategy
Map priorities, use cases and readiness. Define investment criteria, sponsorship, decision rights and a roadmap for adoption.
OutputEnterprise AI strategy and use-case portfolio
Home
AI Strategy & GovernanceAI ambition.
AI ambition.
Accountable decisions.
Define enterprise AI strategy, policies and governance controls. Connect the use-case portfolio to approvals, evaluation and continuous log and transaction monitoring through Trinetra.
The business priority
Who owns AI risk from strategy through operation?
AI initiatives can grow faster than ownership, policy and operational oversight. Governance needs to follow the use case into the workflow: what it can access, what it can do, who reviews it and what evidence remains.
A use-case inventory, business owners, data-access requirements and the available logs and transaction records.
Key capability highlightsSpecific work.
Specific work.
Useful outputs.
Agree the scope, dependencies and responsibilities before work begins. Each workstream produces a defined output.
Define policy and lifecycle controls
Establish acceptable use, data and access rules, human review, third-party assessment and risk-based approval requirements.
OutputAI policy and control framework
Evaluate before wider use
Design representative test cases and acceptance criteria. Examine output quality, relevant safety and fairness risks, privacy, security, review effort and exception handling before release.
OutputAI evaluation and release-gate pack
Operationalise monitoring with Trinetra
Define relevant log and transaction sources, monitoring requirements, retention and access. Design review queues, escalation and evidence linked to configured controls.
OutputTrinetra monitoring design and operating playbook
Implementation strategySequence the work.
Sequence the work.
Make the gates clear.
An indicative delivery route. Sequencing and timing follow the scope, dependencies and business readiness. The stages below define the evidence needed to proceed.
| Stage | Practical work | Decision gate | Accountable roles |
|---|---|---|---|
| 01Discover | Inventory use cases, owners and affected workflows. | Portfolio scoped | AI sponsor |
| 02Govern | Agree policy, risk tiers and approvals. | Controls approved | AI governance forum |
| 03Validate | Evaluate tasks, permissions and exceptions. | Release decision | Use-case + risk owners |
| 04Monitor | Configure agreed logs and transaction oversight. | Coverage validated | Trinetra + IT owners |
| 05Review | Use monitoring evidence to reassess the use case. | Risk review recorded | Business owner |
Governance built into the workClear decisions.
Clear decisions.
Traceable evidence.
Confirm the roles and authority at the start. Record important decisions, control evidence and the route for unresolved risks.
Explore the core governance pillarUse-case approval
- Accountable role
- Business + risk owner
- Evidence
- Purpose, risk assessment and decision
Release readiness
- Accountable role
- Use-case owner
- Evidence
- Evaluation results and control checks
Monitoring coverage
- Accountable role
- Monitoring owner
- Evidence
- Source mapping and review evidence
Exception handling
- Accountable role
- Accountable reviewer
- Evidence
- Triage, decision and closure trail
Illustrative deliverable examplesSee what the work
See what the work
can put in your hands.
Original mockups, built to show the structure and level of detail. All records, roles and statuses are illustrative; these are not client results or live product screens.
01 / Assessment & design
Trinetra / AI governance workspace
Concept mockup / synthetic log and transaction examples
02 / Delivery & governance
Implementation & governance blueprint
The stages, decisions, owners and evidence connecting the recommendation to delivery.
What you take forwardDefined deliverables.
Defined deliverables.
A usable handover.
The final pack reflects the agreed scope, with decisions, limitations, owners and next actions documented.
- Enterprise AI strategy and use-case portfolio
- AI policy and control framework
- AI evaluation and release-gate pack
- Trinetra monitoring design and operating playbook
Make progress observableMeasure the change
Measure the change
that matters.
Establish the baseline and agree the measures before assessing improvement. These examples are discussion points, not promised results.
Portfolio accountability
Use cases with an owner, assessed purpose and approval status.
Evaluation quality
Evidence against task-specific criteria, including difficult cases.
Monitoring coverage
Agreed sources captured and transactions reviewed under the defined controls.
A useful starting point
A use-case inventory, business owners, data-access requirements and the available logs and transaction records.
Discuss this priorityMonitoring covers the agreed log and transaction sources. Define collection, access, retention, review rules and escalation before activation.
Methodology reference: NIST AI Risk Management Framework. Relevant primary guidance for the agreed assessment scope.
Your next decisionMake your next
Make your next
decision clearer.
Start with the business priority, the constraints you face and the decisions worth addressing together.
Discuss your priorities