{"resourceId":"public-sector-digital-transformation-review","versions":[{"version":"legacy/2026-08-29/public-sector-digital-transformation-review","resource":{"id":"public-sector-digital-transformation-review","title":"New systematic review finds GenAI value depends on institutional transformation","organization":"Stockholm University","sector":"Government operations and digital transformation","geography":"International","publishedAt":"August 29, 2026","sourceName":"Public-sector digital transformation in the age of generative AI","sourceLabel":"Discover Artificial Intelligence systematic review","sourceUrl":"https://link.springer.com/article/10.1007/s44163-026-02077-3","evidenceClass":"academic-research","outcomeClass":"emerging","topics":["knowledge-work","developers-agents","infrastructure","data-security","governance-procurement","accessibility-workforce","operating-model"],"finding":"A newly published qualitative systematic review synthesizes 125 peer-reviewed articles from 2021 through 2026 on public-sector digital transformation and AI, framing GenAI as an amplifier of broader institutional change rather than a stand-alone technology deployment.","sledRelevance":"The review consolidates a wide public-administration evidence base around the recurring conditions SLED leaders confront: legacy systems, data fragmentation, skills, leadership, organizational inertia, inclusion, public value, and cross-boundary governance.","evidence":"The review used PRISMA-aligned search and thematic synthesis across major information-systems, computing, and public-administration databases. It finds that GenAI intensifies established transformation challenges and that durable value depends on alignment among technology, organization, workforce, and public-service goals.","architectureImplications":"Treat GenAI as part of an enterprise transformation architecture spanning legacy modernization, interoperability, data governance, shared platforms, service design, and evaluation rather than as a collection of disconnected assistants.","governanceImplications":"Use portfolio governance that connects each AI use case to a service owner, public-value objective, workforce change, inclusion assessment, data dependency, and modernization roadmap.","securityPrivacyImplications":"The cross-system nature of transformation means identity, data protection, records, interoperability, and third-party dependencies must be assessed across the service lifecycle, not only at the model boundary.","caveats":"This is a qualitative literature synthesis, not a new causal evaluation of a specific deployment. The underlying studies vary in methods and geography, and much of the literature predates the most capable current agentic systems."}},{"version":"enrichment/2026-09-05T02:33:27.019Z/public-sector-digital-transformation-review","resource":{"id":"public-sector-digital-transformation-review","title":"New systematic review finds GenAI value depends on institutional transformation","organization":"Stockholm University","sector":"Government operations and digital transformation","geography":"International","publishedAt":"August 29, 2026","publicationDate":"2026-08-29","eventDate":null,"sourceName":"Public-sector digital transformation in the age of generative AI","sourceLabel":"Discover Artificial Intelligence systematic review","sourceUrl":"https://link.springer.com/article/10.1007/s44163-026-02077-3","evidenceClass":"academic-research","outcomeClass":"emerging","topics":["knowledge-work","developers-agents","infrastructure","data-security","governance-procurement","accessibility-workforce","operating-model"],"finding":"A newly published qualitative systematic review synthesizes 125 peer-reviewed articles from 2021 through 2026 on public-sector digital transformation and AI, framing GenAI as an amplifier of broader institutional change rather than a stand-alone technology deployment.","sledRelevance":"The review consolidates a wide public-administration evidence base around the recurring conditions SLED leaders confront: legacy systems, data fragmentation, skills, leadership, organizational inertia, inclusion, public value, and cross-boundary governance.","evidence":"The review used PRISMA-aligned search and thematic synthesis across major information-systems, computing, and public-administration databases. It finds that GenAI intensifies established transformation challenges and that durable value depends on alignment among technology, organization, workforce, and public-service goals.","architectureImplications":"Treat GenAI as part of an enterprise transformation architecture spanning legacy modernization, interoperability, data governance, shared platforms, service design, and evaluation rather than as a collection of disconnected assistants.","governanceImplications":"Use portfolio governance that connects each AI use case to a service owner, public-value objective, workforce change, inclusion assessment, data dependency, and modernization roadmap.","securityPrivacyImplications":"The cross-system nature of transformation means identity, data protection, records, interoperability, and third-party dependencies must be assessed across the service lifecycle, not only at the model boundary.","caveats":"This is a qualitative literature synthesis, not a new causal evaluation of a specific deployment. The underlying studies vary in methods and geography, and much of the literature predates the most capable current agentic systems.","streamIds":["state-government","local-government"],"roles":{"sales":"Interpretation — Customer problem: isolated AI pilots may leave the legacy systems, fragmented data, skills, and service design barriers that constrain public value untouched. Stakeholders: executive service sponsors, enterprise architecture, data and workforce leaders, digital delivery, and affected communities. Discovery: which service outcome needs improvement; what cross-system dependencies block it; and how does AI fit the existing modernization roadmap? Value hypothesis: aligning a bounded AI use with institutional change may improve its chance of useful adoption. Potential engagement: a service-readiness and dependency assessment tied to a concrete use case. Unsupported claims: the 125-article qualitative synthesis does not quantify return, prove a causal benefit from modernization, or establish performance of current agentic systems.","engineering":"Interpretation — Fit: use the review to assess an AI proposal within the enterprise service architecture. Architecture and integration: map legacy interfaces, interoperability, data governance, shared platforms, and evaluation alongside the proposed model capability. Prerequisites: an explicit service goal, data owners, current dependency maps, and evidence about the existing workflow. Constraints: organizational inertia and fragmented data cannot be resolved by adding a model endpoint; the underlying literature spans varied contexts and technologies. Security: trace identity, records, data protection, and third-party dependencies across the full service lifecycle. Proposed validation: walk a representative transaction or knowledge task end to end, identify unresolved handoffs, and compare the AI-assisted option with the current process and feasible simpler improvements.","delivery":"Interpretation — Work: connect the use case to a modernization roadmap, resolve prioritized data and process dependencies, plan workforce change, and evaluate service results. Dependencies: cross-boundary cooperation, legacy-system owners, leadership support, and staff capacity. Ownership: the service sponsor owns the public-value objective; architecture/data owners maintain dependencies; workforce and delivery leads manage changed responsibilities and adoption. Skills and adoption: develop process and data literacy alongside tool use, and include affected users in service design. Governance checkpoints: readiness, dependency completion, pilot outcomes, and portfolio continuation. Proposed acceptance: the service has an owner, measurable objective, documented inclusion assessment, and demonstrated end-to-end operation against its agreed baseline. Risks include disconnected assistants, unowned handoffs, and mistaking a broad literature synthesis for deployment-specific proof."},"retrievedAt":null,"enrichedAt":"2026-09-05T02:33:27.019Z","enrichmentBasis":"archived evidence"}}]}