Calculating the Return on Investment (ROI) of AI

This article outlines a practical methodology for calculating AI return on investment by aligning investments with business impact through cost allocation and business value measurement.

• Classify AI use cases as external (revenue-generating) or internal (productivity-focused) and distinguish between structured and unstructured interactions
• Calculate total cost of ownership including direct costs from managed AI services and indirect costs like storage, data transfer, monitoring, and agentic AI operations
• Use granular cost allocation mechanisms in Amazon Bedrock such as IAM Principal-Based allocation, Request-Level Metadata, and Application Inference Profiles
• Define measurable business value metrics in three categories: Business and Product Revenue, Development Metrics, and Revenue Support Metrics
• Calculate Cost per Outcome by dividing AI costs by business value metrics to establish the unit-level building block for ROI
• Track Cost per Outcome regularly to evaluate ROI impact, scale successful initiatives, and make informed keep-or-stop decisions

By shifting from reactive cost tracking to proactive value management, organizations can transform AI from a line item into a strategic engine that drives measurable business value.