How it works
AI ROI is a way to assess whether the value obtained from an artificial intelligence initiative justifies the resources invested. ROI stands for return on investment, or return on investment. A common formula is ROI = (benefício líquido - investimento) / investimento, expressed as a ratio or percentage.
Before calculating, the organization needs to define what counts as a benefit and how it will be measured. It can be additional revenue, reduction in rework, time freed for higher value tasks or fewer errors. These measures should be tied to a baseline and a comparison period, not just impressions that “the team got faster”.
Investment includes more than access to the model. Consider integration, engineering, infrastructure, data, security, human review, team training, monitoring and maintenance. If these items are left out, the result can look more favorable than the real cost of operation.
Practical example
A company tests AI to prepare draft support responses. Before the pilot, it records how long the team takes to resolve common types of requests and how many responses need correction. During the test, it compares results with the previous process and accounts for review time and deployment costs.
If the tool speeds up writing but increases rework or generates responses that require extensive checking, the net benefit can be small. If it frees up time without reducing quality, the company can choose to redirect that capacity to more complex cases. That value needs to be estimated with clear criteria, not automatically counted as headcount reduction or cash savings.
A reliable comparison also considers what would have happened without the initiative. A seasonal change in ticket volume, for example, can explain part of the result. When possible, use a bounded pilot and compare similar teams, periods or tasks.
What it is for
AI ROI helps prioritize projects, compare alternatives and decide whether a pilot should be scaled, adjusted or ended. It also creates a common language between technical teams and leadership, as long as hypotheses, costs and results are documented.
Do not use the metric in isolation. Benefits such as quality, accessibility, learning and risk reduction can be important even when they are hard to convert immediately into money. Likewise, an estimated saving does not offset privacy violations, security failures or customer harm.
Define the metric before deployment, record the source of each data point and separate observed results from projections. Run sensitivity analysis to check which assumptions change the conclusion. That way, ROI becomes a transparent decision tool, not just a presentation number that confirms a choice already made.