Firms in Uzbekistan’s insurance sector that have invested in digital systems and tapped external technical partners are likelier to adopt AI, but regulatory uncertainty, data gaps and staff resistance block broader diffusion; a phased pilot-first approach combined with targeted policy levers (sandboxes, training, incentives) can make adoption feasible.

Main Finding

Even in institutionally immature insurance markets such as Uzbekistan, AI-based risk‑assessment can be feasibly adopted if implementation is phased and tailored to local constraints. Empirical analysis (binary logit) shows firms’ readiness to adopt AI is strongly positively associated with prior digital investment and external technical partnerships, while regulatory ambiguity and staff resistance are significant barriers. A context‑sensitive, stepwise roadmap — starting with pilots in low‑risk product lines (e.g., crop and micro‑insurance) and supported by targeted policy measures — is recommended.

Key Points

• Research focus: feasibility and pathway for AI adoption in Uzbekistan’s nascent insurance sector; contrasts local constraints with global low‑cost AI best practices.
• Primary empirical result: readiness for AI adoption increases with
  • prior investment in digital systems and data management, and
  • access to external technical support/partnerships.
• Main barriers identified:
  • regulatory ambiguity and fragmented oversight,
  • limited cloud/computing infrastructure,
  • poor data standardization and historical data gaps,
  • shortage of data science talent and internal resistance among staff.
• Practical recommendation: adopt a phased methodology — pilot → scale — prioritizing low‑risk lines, iterative learning, and institutional capacity building.
• Policy levers suggested: regulatory sandboxes, tax incentives, targeted training programs, incentives for data standardization and public–private partnerships.

Data & Methods

• Approach: case study of Uzbekistan combined with econometric modeling and systematic comparison to international best practices for low‑resource AI deployment.
• Quantitative method: binary logit model assessing firm readiness to adopt AI.
• Key independent variables included:
  • firm size,
  • years of digital experience,
  • level of regulatory compliance,
  • access to external technical support (partnerships/consultants),
  • prior investment in data management systems.
• Comparative analysis: synthesis of global examples of low‑cost, scalable AI implementations under resource constraints to identify transferable practices and contrast them with Uzbekistan’s limitations (regulatory fragmentation, cloud infra gaps, talent shortages).
• Note on limitations: sample details (size, sampling frame) are not presented here; generalizability may be constrained by single‑country focus and institutional heterogeneity across developing markets.

Implications for AI Economics

• Feasibility and leapfrogging: Developing markets can “leapfrog” aspects of AI adoption by prioritizing modular, low‑risk pilots and partnering with external providers, rather than waiting for perfect institutional conditions.
• Role of firm-level investment and partnerships: Private digital investment and external technical links are critical complements to AI adoption — public policy should aim to reduce transaction costs and foster those linkages.
• Regulatory architecture matters: Ambiguity increases adoption costs and risk aversion. Well‑designed sandboxes and clear data governance lower barriers and catalyze experimentation.
• Market structure and inclusive outcomes: AI adoption in micro‑ and crop‑insurance can expand coverage and improve pricing accuracy, but requires safeguards against exclusionary algorithmic outcomes and data‑driven biases.
• Cost-benefit sequencing: Economies with limited data should prioritize models and product lines that require less historical data or can use alternative data sources; investments in data standardization and cloud infrastructure yield high marginal returns.
• Research gaps and policy evaluation: Need for cross‑country empirical work on adoption trajectories, cost curves of phased implementation, and the welfare impacts of AI‑enabled insurance in low‑income contexts.

Concluding note: The study provides evidence that constraints in developing insurance markets are obstacles but not absolute barriers to AI adoption — successful diffusion hinges on calibrated sequencing, targeted investments, partnerships, and enabling regulation.