Mission AI ROI strategy framework for space using Amazon Bedrock

This article introduces the MARS (mission AI ROI strategy) framework, designed for aerospace programs to measure generative AI return on investment while maintaining strict governance and regulatory compliance.

• MAIR metric weights mission velocity, cost efficiency, and risk exposure to calculate aerospace-specific AI ROI beyond standard financial models
• GA-TCO denominator includes governance, compliance, and mission assurance costs unique to regulated space programs
• Risk-tiered governance model restricts optimization levers (caching, routing, batching) based on data sensitivity and consequence severity
• Token optimization directly reduces GA-TCO costs while maintaining acceptance rates and error escape thresholds
• Three pillars—ROI measurement, governance controls, and token optimization—form a reinforcing cycle that prevents export control violations
• Satellite program example demonstrates 6.4 MAIR ratio with prompt caching improving output efficiency 2.7x without quality loss
• Implementation sequence: classify workloads, establish cost attribution, apply optimization by tier, build ROI baselines, report quarterly with audience-weighted metrics

The framework enables aerospace organizations to justify AI investments defensibly across engineering, finance, and executive stakeholders while respecting ITAR/EAR constraints and mission-critical safety requirements.