How to Measure ROI from Enterprise Generative AI Initiatives

Frequently Asked Questions (FAQs)

1. How long does it take to see ROI from enterprise generative AI?
Most well-scoped deployments show measurable operational gains within three to six months, but financial ROI that satisfies finance teams typically takes nine to eighteen months, especially for custom-built systems with a longer implementation runway.

2. What's a realistic ROI benchmark for generative AI projects?
Reported outcomes vary widely by function; knowledge-work tasks often see 20–35% time reductions, while customer operations see 30–45% handle-time reductions. Treat any single "average ROI" figure with caution; function-level data is far more reliable than headline numbers.

3. Should ROI be measured differently for custom AI solutions versus off-the-shelf tools?
Yes. Custom solutions carry higher upfront development costs, so their ROI timeline is longer, but they typically show stronger long-term returns because of tighter workflow integration and lower ongoing correction costs.

4. What's the biggest reason AI ROI calculations turn out wrong?
Missing baseline data. Without a documented pre-AI cost or time figure, every ROI claim afterward is an estimate rather than a measurement.

5. Which teams should own AI ROI tracking?
A cross-functional group with finance, the business unit sponsoring the use case, and IT/data science. Finance ownership specifically prevents soft, unverifiable benefits from being counted as hard savings.