{"resourceId":"ohio-governance-blueprint","versions":[{"version":"legacy/2026-08-27/ohio-governance-blueprint","resource":{"id":"ohio-governance-blueprint","title":"Federated governance moves approved state use cases into production","organization":"State of Ohio","sector":"State government","geography":"Ohio, United States","publishedAt":"2025","sourceName":"Ohio’s Blueprint for Empowering Statewide AI Innovation","sourceLabel":"NASCIO state innovation case study","sourceUrl":"https://www.nascio.org/awards-library/awards/ohios-blueprint-for-empowering-statewide-ai-innovation/","evidenceClass":"public-sector-association","outcomeClass":"emerging","topics":["infrastructure","data-security","governance-procurement","accessibility-workforce","operating-model"],"finding":"Ohio combined a central AI Council with agency participation, mandatory training, risk review, procurement checklists, and data classification for a growing statewide portfolio.","sledRelevance":"A federated model can create a repeatable path from agency idea to approved deployment without forcing all domain decisions into one central team.","evidence":"The association case study reports more than 100 approved use cases, 33 in use, and seven accessibility use cases in production.","architectureImplications":"Create common intake, reference architectures, data classification, reusable platform services, and higher-risk architecture review across agencies.","governanceImplications":"Define decision rights across the AI Council, agency owners, security, legal, accessibility, data governance, and procurement, with one risk-tiered production gate.","securityPrivacyImplications":"Use data classification and security review to determine approved hosting, model access, logging, and human oversight for each risk tier.","caveats":"Deployment counts are association-supplied and do not independently establish benefit, equity, reliability, or cost-effectiveness."}},{"version":"enrichment/2026-09-05T02:33:27.019Z/ohio-governance-blueprint","resource":{"id":"ohio-governance-blueprint","title":"Federated governance moves approved state use cases into production","organization":"State of Ohio","sector":"State government","geography":"Ohio, United States","publishedAt":"2025","publicationDate":null,"eventDate":null,"sourceName":"Ohio’s Blueprint for Empowering Statewide AI Innovation","sourceLabel":"NASCIO state innovation case study","sourceUrl":"https://www.nascio.org/awards-library/awards/ohios-blueprint-for-empowering-statewide-ai-innovation/","evidenceClass":"public-sector-association","outcomeClass":"emerging","topics":["infrastructure","data-security","governance-procurement","accessibility-workforce","operating-model"],"finding":"Ohio combined a central AI Council with agency participation, mandatory training, risk review, procurement checklists, and data classification for a growing statewide portfolio.","sledRelevance":"A federated model can create a repeatable path from agency idea to approved deployment without forcing all domain decisions into one central team.","evidence":"The association case study reports more than 100 approved use cases, 33 in use, and seven accessibility use cases in production.","architectureImplications":"Create common intake, reference architectures, data classification, reusable platform services, and higher-risk architecture review across agencies.","governanceImplications":"Define decision rights across the AI Council, agency owners, security, legal, accessibility, data governance, and procurement, with one risk-tiered production gate.","securityPrivacyImplications":"Use data classification and security review to determine approved hosting, model access, logging, and human oversight for each risk tier.","caveats":"Deployment counts are association-supplied and do not independently establish benefit, equity, reliability, or cost-effectiveness.","streamIds":["state-government"],"roles":{"sales":"Interpretation — Customer problem: agencies need a repeatable route from an AI idea to an accountable deployment while retaining domain expertise. Stakeholders: statewide CIO and AI council, agency program owners, procurement, legal, security, accessibility, and data leaders. Discovery: where does intake stall; which decisions are central or local; and are risk tiers and production approval criteria consistent? Value hypothesis: shared intake, training, and assurance may reduce ambiguity and incomplete approvals. Potential engagement: map the present approval process and pilot a federated governance workflow. Ohio's reported portfolio and accessibility deployments illustrate implementation activity. Unsupported claims: these association-supplied counts do not establish faster approval, improved equity, reliable services, financial return, or applicability of Ohio's rules in another state.","engineering":"Interpretation — Fit: apply the blueprint to a multi-agency portfolio needing common controls across different use cases. Architecture and integration: connect intake records, data classification, reference architectures, shared services, and higher-risk review to the existing approval process. Prerequisites: agreed decision rights, risk definitions, agency owners, and an inventory of available platforms. Constraints: hosting and oversight must vary with actual data and risk; a single approval template cannot resolve every agency dependency. Security: map each tier to access, logging, approved model/hosting boundaries, and human oversight. Proposed validation: walk representative lower- and higher-risk use cases through intake to production review and demonstrate correct routing, required evidence, documented exceptions, and retained local accountability.","delivery":"Interpretation — Work: define council and agency responsibilities, assemble intake and procurement checklists, deliver mandatory role-relevant training, and trial the production gate. Dependencies: executive support and available security, legal, data, and accessibility reviewers. Ownership: the central council maintains shared policy and assurance; agency service owners operate and evaluate approved use cases. Skills and adoption: coach teams in classification, evidence preparation, and escalation so the process is usable. Governance checkpoints: intake completeness, risk review, release authorization, and periodic continuation or retirement review. Proposed acceptance: every sampled use case has an owner, classification, required approvals, operating measures, and a tested pause/retire path. Risks include central review becoming a bottleneck and treating deployment counts as evidence of public value."},"retrievedAt":null,"enrichedAt":"2026-09-05T02:33:27.019Z","enrichmentBasis":"archived evidence"}}]}