Measuring ROI for AI Projects

Sally-Anne Baxter
Oct 22
2 min read

Not sure how to measure ROI for AI Projects?

Traditional ROI models don't work for AI projects.

You can't always point to direct cost savings or revenue increases in year one. AI often delivers value in ways that don't fit into a standard business case template. The benefits are real but they are different.

The 3 Metrics that actually matter for AI ROI:

✔️ 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐕𝐞𝐥𝐨𝐜𝐢𝐭𝐲

𝐖𝐡𝐚𝐭 𝐢𝐭 𝐦𝐞𝐚𝐬𝐮𝐫𝐞𝐬: How much faster your teams make informed decisions

▶️ Before AI, your demand planning team took 5 days to analyse market data and make forecasts.

▶️ With AI-powered insights, they do it in 2 hours. That's not just efficiency, it's competitive advantage.

▶️ Faster decisions mean you respond to market changes before your competitors do.

𝐇𝐨𝐰 𝐭𝐨 𝐪𝐮𝐚𝐧𝐭𝐢𝐟𝐲 𝐢𝐭: Track the time from "question asked" to "decision made" for key business processes. Calculate the value of making those decisions 10x, 50x, or 100x faster.

✔️ 𝐄𝐫𝐫𝐨𝐫 𝐑𝐚𝐭𝐞 𝐑𝐞𝐝𝐮𝐜𝐭𝐢𝐨𝐧

𝐖𝐡𝐚𝐭 𝐢𝐭 𝐦𝐞𝐚𝐬𝐮𝐫𝐞𝐬: The improvement in accuracy across critical processes.

▶️ Your AI model reduced forecasting errors from 15% to 3%.

▶️ Your quality control AI catches defects that humans miss 40% of the time.

▶️ These aren't abstract improvements, they translate directly to reduced waste, fewer customer complaints, and lower operational costs.

𝐇𝐨𝐰 𝐭𝐨 𝐪𝐮𝐚𝐧𝐭𝐢𝐟𝐲 𝐢𝐭: Measure the baseline error rate, track the improvement, and calculate the cost of each error prevented (returns, rework, customer churn, compliance fines).

✔️ 𝐎𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐲 𝐂𝐨𝐬𝐭 𝐀𝐯𝐨𝐢𝐝𝐚𝐧𝐜𝐞

𝐖𝐡𝐚𝐭 𝐢𝐭 𝐦𝐞𝐚𝐬𝐮𝐫𝐞𝐬: The value of risks you didn't take and problems you prevented.

▶️ This is the hardest to quantify, but often the most valuable.

▶️ Your AI flagged a supply chain risk 3 weeks before it would have caused a production shutdown. It identified a customer churn pattern that let you intervene before losing a £2M account.

▶️ Traditional ROI models struggle with "what didn't happen," but your board needs to understand this value.

𝐇𝐨𝐰 𝐭𝐨 𝐪𝐮𝐚𝐧𝐭𝐢𝐟𝐲 𝐢𝐭: Document near-misses and prevented issues. Estimate the cost if the AI hadn't flagged them. Build a portfolio of "disasters averted."