Enterprise AI Strategy and Governance Framework
Align AI initiatives to business outcomes while establishing governance controls for risk, quality, and accountability.
Strategic Foundation
- Prioritize a clear set of business use cases by value and feasibility.
- Define success metrics before selecting platforms or models.
- Assign executive ownership and cross-functional decision rights.
Governance Operating Model
Define who approves, who builds, who monitors, and who intervenes when risk thresholds are crossed.
- Policy layer: Acceptable use, data handling, and model review standards.
- Control layer: Access controls, testing gates, and release checks.
- Monitoring layer: Quality drift, incident tracking, and remediation workflow.
- Audit layer: Decision logging, evidence records, and periodic review cycles.
Risk Domains to Cover
Security Risk
Unauthorized access, leakage, or unsafe integrations.
Data Risk
Poor data quality, policy violations, or residency conflicts.
Model Risk
Inconsistent output quality, bias, or brittle behavior.
Operational Risk
Insufficient ownership, tooling, or incident response readiness.
Compliance Risk
Regulatory obligations not reflected in deployment controls.
Commercial Risk
Unpredictable usage costs and restrictive contract terms.
Implementation Sequence
- Set policy baseline and governance ownership.
- Run pilot use cases with explicit controls and measurement.
- Standardize evaluation and deployment workflows.
- Scale to additional teams with shared templates and guardrails.