The KPIs to measure when implementing AI in business process automation

AI is increasingly used in business process automation to improve productivity and reduce manual effort. To understand whether an AI implementation is truly successful, it is important to measure the right KPIs. In practice, there are two indicators that matter most: hit rate and time saved.

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Hit rate: measuring AI accuracy in real processes

AI is not always correct, which makes hit rate a crucial KPI. Hit rate shows how often the AI produces the correct result without any manual editing or intervention.

If a process with an AI step is run 100 times, the hit rate indicates how many of those outcomes are immediately usable. This KPI gives insight into how well prompts are designed and how effectively the AI is embedded into the process. A higher hit rate means the process runs more smoothly with less human involvement.

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What a good hit rate looks like depends on the type of process and the amount of time saved when the AI is correct. In some cases, even a lower hit rate can still deliver value. The important part is that hit rate can be improved over time by refining prompts, finetuning and adjusting the process design.

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Time saved: the main driver of business value

The second key indicator is time saved. Like all forms of business process automation, AI is mainly about increasing productivity by reducing the time it takes to complete work.

Time saved is measured per item that goes through the process. This could be the time saved per applicant in a recruitment process or per purchase order in procurement. By looking at time saved per item, organizations get a clear picture of how much effort AI removes from daily operations.

Time saved directly relates to costs saved. As AI becomes more accurate and the hit rate increases, more items pass through the process without manual handling. This means the total time saved grows, and the financial impact becomes more visible.

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How hit rate and time saved work together

Hit rate and time saved are closely connected and should always be measured together. The higher the hit rate, the more items move through the process without manual correction, and the more often the time saving is realized in full.

When the hit rate is low, time savings are reduced because employees still need to review or adjust AI output. As hit rate improves, time saved increases, and the overall impact on costs and effort becomes clear. Together, these two KPIs show whether AI is delivering real value to the organization and turning automation into measurable business results.

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Connecting KPIs with sound workflow design

Hit rate and time saved show whether AI is delivering value in a business process, but they depend heavily on how AI is applied in your workflows. These KPIs improve when AI is used to convert unstructured data into structured inputs, when prompts limit variability, and when AI tasks are small and clearly defined.

Using AI as an input processor rather than a decision-maker, combined with deterministic process logic and human oversight, helps keep automation reliable. These principles are explained in more detail in this article and provide a practical foundation for improving AI-driven process automation over time.