IgnyteAI ROI measurement: the €10B problem nobody has solved yet
Walk into any Fortune 500 today and you'll find dozens of AI pilots. Walk into the CFO's office and you'll find a question nobody can answer: did any of them work?
The measurement vacuum
Traditional software ROI is straightforward — fewer FTEs, faster cycle time, hard cost saved. AI ROI is fuzzy. A copilot that "saves 30 minutes per day per knowledge worker" is impossible to verify. Did the worker actually save the time? Did the time become more output, or just more Slack?
Why current tools fail
Product analytics measure usage, not outcome. LLM evals measure quality, not business impact. Finance tools measure spend, not attribution. There's no system that ties "this prompt, this user, this workflow" to "this revenue or this cost saved."
The shape of the solution
Whoever wins this category will combine:
- Workflow instrumentation (what task was the human doing before the AI helped?)
- Outcome attribution (did the downstream metric actually move?)
- CFO-grade reporting (numbers a non-technical exec can defend in a budget review)
Why now
Three forces collide in 2026: AI budgets shifting from "experiment" to "operate", boards demanding accountability, and the first wave of AI deployments aging into a renewal cycle. The buyer is finally motivated.
This is a generational opportunity. Use Pain Radar to find the specific complaints driving it.