AI+ Organization · Reference
Foundation Readiness Check
Confirm your organization is ready to scale AI safely. Score each item Yes or No, then total your results.
Assessment Metadata
Scoring Method
- Mark each item as Yes or No.
- Count total Yes results.
- Use the interpretation table below.
| Score | Interpretation | Recommended action |
|---|---|---|
| 0–17 | High readiness risk | Fix core gaps before production AI deployment |
| 18–27 | Partial readiness | Start only with low-risk, supervised use cases |
| 28–34 | Strong readiness baseline | Proceed with controlled scale-up |
1. Process Maturity (4)
- Key processes are documented with steps and decision points.
- Process owners are assigned and accountable.
- Performance baselines exist (time, error, cost).
- Exception paths are mapped beyond the happy path.
2. Data Foundation (5)
- Data inventory is complete for major datasets.
- Classification scheme is defined and applied.
- Data quality standards are defined per dataset.
- Lineage is documented for critical datasets.
- Dataset ownership is assigned.
3. Information Security (5)
- Security policy is documented and approved.
- IAM and RBAC follow least privilege.
- Secrets management is in use for credentials and keys.
- Secure engineering practices apply to AI components.
- AI-related incident response plan is tested.
4. Privacy and Compliance (5)
- PII is mapped with legal basis and purpose.
- Applicable regulations are identified and tracked.
- Privacy-by-design controls are applied by default.
- Legal review is required before deployment.
- Retention and deletion policies cover AI training data.
5. Technology Baseline (5)
- Cloud and AI platform standards are defined.
- API standards and service catalog exist.
- Identity and SSO integration is available.
- Observability standards are enforced.
- CI/CD includes test, security, and approval gates.
6. People and Change (5)
- AI literacy baseline exists for employees.
- Executive sponsors are named and active.
- Change management capability is in place.
- Reskilling paths exist for impacted roles.
- Feedback channels exist for AI adoption issues.
7. Ethics and Principles (5)
- AI principles are formally adopted.
- Ethics owner or committee is assigned.
- Bias assessment process is defined.
- Explainability standards exist for high-impact decisions.
- Override and appeal paths are documented.
Summary
| Domain | Items | Yes | No | Priority gaps |
|---|---|---|---|---|
| Process Maturity | 4 | |||
| Data Foundation | 5 | |||
| Information Security | 5 | |||
| Privacy and Compliance | 5 | |||
| Technology Baseline | 5 | |||
| People and Change | 5 | |||
| Ethics and Principles | 5 | |||
| Total | 34 |