AI can recast how policymakers and firms interpret next-generation free-trade deals by linking regulatory alignment, digital trade rules and supply-chain dynamics to firm capabilities. The manuscript provides a reusable assessment architecture and governance protocols for Indian SMEs, but remains conceptual until implemented and empirically validated.

Main Finding

AI methods provide a rigorous, multi-level framework for evaluating how new-generation free trade agreements (FTAs) affect small and medium-sized enterprises (SMEs) in India. Rather than treating FTAs as static tariff schedules, the manuscript argues they must be analysed as evolving socio-technical environments—operating via regulatory alignment, standards, digital trade rules, supply-chain coordination, sustainability norms and institutional adaptation—and that AI-enabled assessment can link policy design to firm capabilities, labour dynamics and export competitiveness. The work delivers a publisher-ready conceptual and methodological foundation plus practical analytic outputs (assessment architectures, model families, validation protocols, reproducible workflows and governance tools) that embed uncertainty, heterogeneity and trustworthiness as core concerns.

Key Points

• Contemporary FTAs transmit effects through regulatory and institutional channels (standards, digital trade rules, supply-chain governance), not only tariffs.
• AI-enabled analytics can integrate multiple levels of decision-making: policy, firm strategy, labour markets and export outcomes.
• The manuscript emphasizes combining causal reasoning and statistical explanation with predictive modelling to support both inference and forecasting.
• Practical contributions include: assessment architectures, model families, validation protocols, reproducible analytics workflows, governance mechanisms and policy translation tools.
• Trustworthiness, heterogeneity and uncertainty are treated as constitutive design constraints (not afterthoughts) for models and analyses.
• The framework targets researchers, SME leaders, analysts and policymakers—aiming to make FTA evaluation actionable for strategy and policy.
• Focus context: SMEs in India and how trade liberalization shapes export competitiveness and employment dynamics.
• The approach is explicitly oriented toward reproducibility, MLOps and responsible AI governance for policy-relevant analytics.

Data & Methods

• Conceptual and methodological integration: research design logic, statistical explanation, causal reasoning, and predictive modelling.
• Scalable data engineering and MLOps to operationalize large, heterogeneous data sources and maintain reproducibility and model governance.
• Validation protocols and model families designed to support both causal identification and predictive performance while accounting for uncertainty and heterogeneity.
• Reproducible analytics workflows to enable transparent inference, replication and policy translation.
• Implied data types and linkages (consistent with the framework): regulatory texts and timelines, firm-level export and employment records, supply-chain and transaction logs, standards/compliance datasets, and institutional capacity metrics.
• Emphasis on protocols for trustworthy AI: uncertainty quantification, robustness checks, provenance, and governance mechanisms for model deployment in policy contexts.

Implications for AI Economics

• Measurement: Expands what counts as “trade policy effects” beyond tariffs to include regulatory alignment, digital rules and supply-chain coordination—requiring new indicators and data integration strategies.
• Causal + Predictive Synthesis: Demonstrates the value of combining causal inference with machine learning to both explain mechanisms and predict firm-level responses under alternative FTA scenarios.
• Heterogeneity & Distributional Analysis: Calls for models that explicitly model firm- and sector-level heterogeneity (SME capabilities, digital readiness) and labour-market impacts.
• Policy Design & Evaluation: Offers an operational analytics stack that can support ex ante FTA appraisal, real-time monitoring, and ex post evaluation with reproducible evidence.
• Governance & Trustworthy AI: Highlights the need for governance, validation and translation layers so AI models inform policy responsibly and transparently.
• Institutional Capacity: Suggests research and policy investments in data infrastructure, MLOps, and institutional learning to convert analytic outputs into actionable SME support and regulatory design.
• Research Agenda: Encourages empirical validation in context (e.g., India), development of multi-level models linking institutional change to microeconomic outcomes, and methodological work on integrating causal identification with scalable ML workflows.

Overall, the manuscript reframes FTA evaluation as a socio-technical modelling problem where responsible AI, reproducible analytics and institutional capacity jointly determine whether trade liberalization translates into improved SME competitiveness and employment outcomes.