AI ROI: How enterprises can measure the return on AI investments

Enterprises investing in AI need to know whether the expected business value justifies the cost, and whether that value is actually being realized once AI is deployed.

But measuring enterprise AI ROI presents some distinct challenges. AI costs can vary with how systems are designed, deployed, and used, while operational improvements in speed and productivity don’t always translate cleanly into financial value.

In this post, we’ll unpack what makes AI ROI difficult to calculate, and explain how enterprises can account for those difficulties when forecasting and measuring returns.

Model and inference costs are among the most visible costs of running AI, but they represent only part of the total cost. A credible ROI estimate also includes upfront implementation costs and ongoing technical and operational costs, such as infrastructure, integrations, monitoring, human oversight, and engineering support.

AI ROI is the financial return generated by an AI investment relative to the total cost of deploying and operating it. That return can be realized through cost savings, additional profit from sales, or both.

ROI can be calculated as: ROI = (financial benefit - total cost) ÷ total cost x 100

Importantly, the operational improvements brought about by AI don’t necessarily translate directly into financial returns. Faster task completion, greater output, or higher-quality work may well produce financial value, but a positive ROI ultimately depends on whether that value exceeds the cost of achieving it with AI.