ROI of an AI deployment (return on investment)
What an AI deployment gives back compared with what it cost: the gain minus the full cost, divided by that cost, over a stated period.
The gain is usually hours saved, errors avoided or extra revenue, counted in money. Estimates tend to go wrong on the cost side: besides the build it includes model usage (see cost per task), integrations, the time people spend reviewing the output, and maintenance when models or source systems change. ROI needs a baseline measured before the start and a period, for example twelve months. The payback period answers the related question of when the spend is recovered. A demo has no ROI yet, because nothing was measured on real work.
Related terms
- Payback period: How long it takes for savings or extra revenue from an investment to cover what it cost.
- Time to value (TTV) and business case: Time to value is how long from signing until the buyer sees the first measurable benefit; the business case is the one page that justifies the spend.
- Cost per task (agent unit economics): What one finished agent task really costs: tokens, retries, tool calls and the human time to review it, compared with doing it another way.
- Pilot success criteria (exit criteria): Measurable conditions agreed before a pilot starts that decide whether it passed and the buyer moves to a full contract.
- Pilot purgatory (PoC purgatory): When AI proofs of concept and pilots keep running or get dropped without reaching production, or go live but cost more in human oversight than they save.
- Forward Deployed Engineer (FDE): An engineer who works with a customer or business team to turn AI models into systems that run in their environment.
