Early AI adoption metrics focused on usage (e.g., tokens consumed), leading to wasteful “token maxing.” Successful teams quickly pivoted to measuring real business impact, such as the overall speed of the software delivery lifecycle, to gauge AI effectiveness and drive true ROI.

Technical metrics like "accuracy" are often the wrong measure for ML projects and can mismanage expectations. To achieve success, projects must be evaluated using business KPIs like profit, savings, or ROI. This aligns data science with business goals and reveals the true value of imperfect predictions.

True ROI of AI isn't found in usage metrics like token counts. It's measured by identifying entire, expensive projects (e.g., a $4M manual document conversion) and using AI to make the problem 'vanish,' completing the work in hours instead of months.

To achieve clear ROI on AI initiatives, enterprises should focus on business problems that are already being measured. Without baseline performance metrics (like call volume or software delivery speed), it's impossible to quantify the improvement an AI system provides, often leading to "POC purgatory."

Demanding a direct, line-item ROI for foundational AI initiatives is like asking for the ROI on Wi-Fi—it's the wrong question. Instead of getting bogged down in impossible calculations, leaders should focus on measuring the business outcomes enabled by the technology, such as innovation speed or new product creation. Obsess on outcomes, not direct financial return.

Proving the ROI of clinical AI can take years if based solely on patient outcomes. Instead, focus on early, measurable operational wins that are known proxies for better care. Track metrics like increased clinician capacity and higher patient engagement rates to prove the system's value and build momentum.

Don't rely on traditional project milestones to gauge AI progress. Instead, measure success through granular unit economics and operational metrics. Metrics like 'cost per release' or 'cycle time per feature' provide immediate feedback on whether your strategic hypothesis is valid, enabling rapid iteration.

Leaders often expect AI to produce a shiny, marketable feature. When AI’s value is 'invisible'—baked into workflows to improve efficiency—translate those gains into concrete financial outcomes like cost savings or accelerated revenue, rather than focusing on the process improvements themselves.

A common failure is defining an AI pilot's success with engineering metrics like accuracy or latency. True success is a business outcome, such as the finance team trusting the AI's output enough to stop manually double-checking it. Success metrics must be framed in terms a CFO would accept.