Ultimately, both translate into the same outcome: reduced effort and money saved across your processes. Measure these two, and you can measure AI’s business value.

These two metrics are closely linked. A higher hit rate means more cases where you achieve full time savings. Together, they show the real impact of AI on productivity.

2️⃣ Time saved.
How much time is saved per application — per recruitment case, purchase order, or any other process step where AI is applied.

1️⃣ Hit rate.
Out of 100 AI-driven actions, how many are correct without human edits? Hit rate shows how well your prompts, models, and process design work. It’s also the KPI you can systematically improve over time.

Now: Dealers create quotes directly in-store. Prices are calculated automatically based on all (sub)parts, and quotations are emailed instantly. The application saves significant time in the quoting process.

A clear win for efficiency and dealer empowerment 👏

They built a dealer quotation tool with Triggre to eliminate pricing errors and delays.

Before: Incorrect price lists, manual checks, and frequent calls led to mistakes and wasted time.

❗ If the outcome of the previous step has a significant impact on the business, humans should often remain the final step — reviewing and approving results. This limits risk while still saving significant time.

🔗 For more on this topic, head to the latest article on the Triggre Blog

➡️ If that doesn’t work because the input is unstructured, LLMs can help. They can turn unstructured data into structured data, enabling automation where it wasn’t possible before.

➡️ If something can be done deterministically, traditional automation and system integrations are still the best option. They’re proven and predictable.

AI is just another tool in the toolbox. Before LLMs, processes were mostly deterministic. If a step followed clear rules, you automated it. If it involved unstructured data or judgment — like reviewing motivation letters — humans handled it. That logic still applies.

In practice, two KPIs matter most: hit rate and time saved. Together, they show whether AI is truly reducing workload, costs, and friction in real processes, not just demos.

Swipe to see how to measure AI impact properly ⤵️

For operational AI, don’t ask models to create more than necessary. Feed them context, and ask for precise answers.

✅ If you give AI rich context (documents, policies, historical data) and ask for a very specific result, you push the probabilistic nature of the model into the input, while keeping the output controlled and predictable. And predictability is exactly what business processes need.

❌ If you give AI very little context and ask it to generate a lot of text, it has to “fill in the gaps”. The more it fills in, the more variability you introduce. That variability may be fine for brainstorming or personal use, but it’s risky in structured business processes.

On the Triggre Blog, we unpacked why these two KPIs matter, how they relate to each other, and how to use them in practice. Read the full article via the link in the comments. 💬

Together, these indicators show whether AI is actually improving productivity and delivering business value. A strong hit rate increases trust in automation, while time saved translates directly into efficiency gains.