AI ROI
The return on investment from AI initiatives — what you actually get back, measured against what you spent. Harder to calculate than traditional IT ROI because AI produces probabilistic, variable outputs rather than deterministic ones, and the value is often in time saved or quality improved rather than direct revenue. Most companies undercount the real cost (engineering time, change management, ongoing maintenance) and overcount the benefit (what would people have done without the tool?). Realistic ROI measurement starts before the pilot, not after.
In practice
Your team spent 40 hours a month on manual report writing. After deploying Claude, that drops to 8 hours. The time saved — multiplied by hourly cost — minus the API and implementation costs — is your AI ROI. The hard part is measuring the less tangible benefits: faster decisions, fewer errors, higher employee satisfaction.
Related concepts
Where AI ROI shows up
3 articlesYou're not implementing it yourself — but you're approving the budget, setting expectations, and accountable for the results. Here's the executive-level view: what to ask, what to expect, and where things go wrong.
Pricing AI products is harder than pricing regular software because your costs are variable and your value is hard to measure. Here is the framework that actually holds up.
An honest assessment of where the value actually is — and how to avoid the flashy-but-useless use case that burns trust and budget.