Three Approaches to Measuring and Managing AI ROI

2026-06-23 · Source: MIT Sloan Management Review · Field: Business & Management — Corporate Strategy & Leadership, Consulting & Professional Services · Depth: Intermediate, quick

Summary

Many organizations struggle to effectively track and grow returns from their artificial intelligence investments, with AI ROI often perceived as elusive and imprecise. Recent research, based on interviews with over 30 CEOs and senior leaders, identifies three practical approaches to measure and manage AI ROI, reflecting varying AI maturity levels and strategic intents. The article emphasizes that ROI measurement varies significantly based on the type of AI technology, such as Analytical AI (e.g., prediction, optimization) which yields directly attributable financial returns for targeted use cases, versus Generative AI, which offers broad applicability for knowledge work tasks, improving speed, quality, or volume, requiring deliberate translation into financial impact. Industry context also heavily influences ROI definition, with examples ranging from supply chain streamlining in consumer goods to creative throughput and lead conversions in B2B marketing.

Key takeaway

For executives struggling to quantify AI investments, recognize that effective ROI measurement is not one-size-fits-all. You must adopt an explicit approach tailored to your organization's AI maturity, the specific AI technology deployed (analytical vs. generative), and your industry context. Failing to define clear ROI metrics risks generic tool rollouts that rarely yield credible, lasting returns. Prioritize assessing your current AI maturity to identify necessary steps for translating AI activity into tangible business value.

Key insights

Effective AI ROI measurement requires explicit approaches tailored to AI technology, industry, and organizational maturity.

Principles

• AI ROI measurement is not standard practice.
• ROI varies by AI technology type.
• Industry context shapes ROI definition.

In practice

• Streamline supply chains with analytical AI.
• Boost creative throughput using generative AI.
• Improve lead conversions via generative AI.

Topics

• AI ROI Measurement
• Generative AI Applications
• Analytical AI
• Business Value Translation
• Organizational AI Maturity
• Industry-Specific AI

Best for: CTO, Director of AI/ML, VP of Engineering/Data, Executive

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Editorial summary, takeaway, and curation by AIssential. Original article published by MIT Sloan Management Review.