Most central banks are using AI, but only 4% reported high compliance-monitoring capacity
An early-2026 BIS/IFC survey of 65 central-bank jurisdictions and institutions found AI projects moving into production while governance, data readiness, specialist staffing and monitoring still lag. The results are self-reported and do not measure productivity or safety outcomes.
The short version
- What happened
- The early-2026 survey covered 65 responding jurisdictions and institutions. More than two-thirds reported AI projects in production, including small-scale use, compared with about half in 2024.
- Who it affects
- Central-bank AI can affect the staff producing statistics, supervising firms and supporting policy analysis. Clear inventories, accountable human review and tested controls matter before model outputs influence decisions that reach households or businesses.
- What remains unproven
- Self-reported institutional survey rather than an independent audit
Keep in mind
Self-reported institutional survey rather than an independent audit
65 responding jurisdictions and institutions; question denominators vary
Respondent composition differs somewhat from the 2024 survey
No system-level accuracy, bias, security or incident measurements
No productivity, policy-quality or public-service outcome evaluation
Authors' views do not necessarily represent the IFC, its members or the BIS
Source analysis · 1 linked source. Primary analysed source date: 28 October 2026.
Read the full report · 3 min The evidence, methods, implications and limitations
AI use is spreading faster than some readiness controls
The Irving Fisher Committee surveyed 65 central-bank jurisdictions and institutions in early 2026. More than two-thirds said they had AI projects in production, including small-scale deployments, up from about half in 2024. Almost two-thirds now treat AI as a priority. Organisational strategies were already in place at 38% of respondents and being drafted at another 31%.
The control picture was less mature. Only 4% reported high capacity to monitor compliance with AI guidance; 54% described moderate capacity, 27% said monitoring was under development and 15% called it limited. Data readiness was also uneven: 21% had a strategy in place, 41% planned one and 29% did not plan one. These gaps matter when central-bank tools touch statistics, supervision, market analysis or internal decisions.[1]
The survey maps readiness, not whether AI improved decisions
Infrastructure and people remain concentrated constraints. Sixty-five per cent reported dedicated AI infrastructure, and 73% employed AI professionals; among those, 81% said specialists represented less than 2.5% of staff. Most respondents allocated less than 5% of their budget to AI, while almost 80% cited IT costs as a concern. External models and providers therefore remain part of the operating picture.
These are self-reported institutional answers, not audited inventories. Response bases differ by question, and the 2024 and 2026 respondent groups are not identical, although the report says composition changes did not materially alter aggregate comparisons. The study does not show that AI made policy, supervision or services better—or that guidance was followed in practice. Independent system inventories, incident reporting and outcome evaluations would change the assessment.[1]
The distinction that matters
Readiness signals from the 65-response survey
Production use, strategy and monitoring are related but not interchangeable measures.
Use
AI projects in production, including small-scale deployment
More than two-thirds of respondents
Strategy
Organisation-wide AI strategy in place
38% of respondents
Monitoring
High capacity to monitor compliance
4% of respondents