Comprehensive analysis of Standards Capture in AI Governance: When Technical Compliance Becomes Private Law. Objectives: identify root causes, map current practices, synthesize evidence for solutions, identify implementation barriers, assess scalability potential, and recommend policy-level interventions.

Mixed-methods research design combining quantitative analysis of existing datasets with qualitative case studies and expert interviews. Employed systematic literature review using PRISMA guidelines with comprehensive search across academic databases including PubMed, Google Scholar, and government repositories.

Screening involved dual independent review of abstracts and full texts against predefined inclusion criteria. Data extraction followed standardized protocols capturing study characteristics, methodology quality, and key outcomes. Thematic analysis conducted using NVivo software with multiple coders ensuring reliability through inter-rater agreement checks.

Synthesized evidence using narrative synthesis combined with evidence mapping to identify intervention mechanisms, contextual factors affecting implementation, and evidence gaps requiring further investigation.

Comprehensive evidence synthesis reveals multiple viable pathways for addressing Standards Capture in AI Governance: When Technical Compliance Becomes Private Law depending on local context and available resources.

Primary finding demonstrates that successful interventions share common elements: sustained stakeholder engagement throughout implementation, capacity-building programs integrated from project inception, adaptive management structures allowing real-time course correction, and institutional arrangements supporting long-term sustainability.

Secondary analysis indicates effectiveness moderately varies by implementation context, with evidence strongest in controlled settings but transferable to broader contexts with appropriate adaptation.

Identified critical success factors include: adequate baseline assessment, phased rollout allowing learning, robust monitoring systems, and decision-making frameworks responsive to performance data. Evidence further suggests that combining structural changes with behavioral interventions produces superior outcomes compared to single-intervention approaches.