How to Measure GenAI ROI? The 4 Metrics I Use

The ROI Dilemma

The hardest question an AI Product Manager faces from executives isn’t “How does this work?”, but rather, “Is this actually saving us money?”

Generative AI is inherently expensive to run. If you cannot quantify its value, your project will be killed when the hype cycle ends.

Time-to-Task Completion (TTC) Reduction: Does the AI assistant actually make the human faster? We measure the baseline time it takes an employee to complete a task (e.g., generating a client report) versus the time it takes with AI assistance.
Deflection Rate: In customer service or internal IT desk scenarios, how many tickets were completely resolved by the AI without human escalation?
Quality Assurance Score (QA): Speed isn’t everything. We use LLM-as-a-judge (a secondary, stronger model) to score the output quality of the initial model against human-written gold standards.
Token Efficiency ROI: The cost of generating the output versus the estimated cost of human labor for the same output. If 1,000 queries cost $10 in API fees but save 50 hours of human labor, the ROI is mathematically undeniable.

Stop selling “magic.” Start selling verified operational metrics.