Executive summary
Organisations are scaling generative AI faster than evidence, ownership, and oversight models can absorb. This outlook summarises public-interest priorities for boards and policymakers in 2026.
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Problem statement
Residual risk acceptance is often informal. Without inventories, evaluation sets, and incident playbooks, trust erodes under audit pressure. Literacy decays faster than model versions — continuous training is a control, not a perk.
Priority domains for 2026
- Use-case inventory and residual-risk tiering
- Evidence packs and retention (decision logs, citations, refusal records)
- Human oversight gates for high-impact decisions
- Operator literacy via Academy pathways
- Inclusive chapter capacity building across regions
What “good” looks like
| Signal | Weak | Strong |
|---|---|---|
| Inventory | Shadow AI experiments | Named use cases + data classes |
| Oversight | Ad-hoc review | Documented HITL gates |
| Evidence | Slide decks | Exportable assurance packs |
| Training | One-off webinars | Pathway credentials on /learn |
Recommendations
- Publish a board-readable AI risk register
- Fund continuous training as a governance line item
- Treat third-party standards as educational references; licensed frameworks (including UAAF) require written approval
- Pilot citation + refusal guards on one high-impact RAG path before enterprise rollout
Related WAIG resources
- Sovereign AI Architecture Patterns
- Board checklist for AI governance
- AI Governance Maturity Assessment
- Governance Readiness Office Hours
Content Attribution
References public standards literacy (ISO/IEC 42001, NIST AI RMF). WAIG Foundation does not claim ownership of third-party standards text. Not legal or certification advice.
© WAIG Foundation Research — 2026