Section 12
Continuous Evaluation & Improvement
Measure → Evaluate → Govern → Improve → Re-architect, as a permanent capability.
AI architecture is not static. Models, data, prompts, tools, regulations, threats, costs, business requirements and user behavior change. AI4EA therefore establishes continuous evaluation as a cross-cutting discipline.
Measure→
Evaluate→
Govern→
Improve→
Re-architect→back to Measure
12.1 Evaluation Domains
- Business outcomes and user adoption
- Model quality, errors and hallucination
- Safety, security and policy compliance
- Latency, reliability and operational resilience
- Consumption, cost and unit economics
- Emerging risks, regulations and architecture alternatives
Significant changes should trigger architecture reassessment rather than being treated solely as operational adjustments. Continuous evaluation connects operations back to Enterprise Architecture and investment governance.
12.2 Governance Lifecycle
Governance should be treated as an ongoing capability across planning, building, deployment, operation and evolution.
| Governance view | Elements |
|---|---|
| Guiding Principles | Fairness · Safety · Transparency · Privacy · Accountability · Human-Centric · Sustainability |
| AI Lifecycle | 1 Plan (assess and prepare) · 2 Build (develop responsibly) · 3 Deploy (release with controls) · 4 Operate (monitor and manage) · 5 Evolve (improve and adapt) |
| Enablers | Policies & Standards · Data Governance · Tools & Platform · Roles & Organization · Metrics & Reporting · Training & Awareness |
| Expected Outcomes | Trusted AI Solutions · Reduced Risk · Regulatory Compliance · Business Value · Customer Confidence · Sustainable Innovation |