AI ROI benchmark · Customer support
Customer support AI ROI benchmark
A conservative model for agent-assist, response drafting, and knowledge retrieval, grounded in the strongest available field evidence.
Modeled result
This is a planning scenario, not a market average. Replace every input before making a purchase decision.
| Current labor on the workflow | 800 hours/month |
|---|---|
| Loaded labor cost | $32/hour |
| Share suitable for AI assistance | 65% |
| Productivity lift on that share | 14% |
| Savings realized in practice | 70% |
| Software and usage cost | $1,200/month |
Monthly gross value = current hours × loaded cost × addressable share × productivity lift × realization rate. Net value subtracts software cost. Capacity has value only if the business can redeploy it, avoid new cost, or produce more useful work.
Run your own numbersWhere to use it
Good pilot candidates
- Retrieve approved answers during live conversations
- Draft responses for agent review
- Summarize contacts and tag common issues
Keep a human decision
- Refunds, account closures, or policy exceptions without approval
- Answers that rely on missing customer context
Evidence and limits
The 14% field-study result is unusually relevant, but it came from one software company and an agent-assist deployment. This model discounts it further through a 70% realization rate.
- Generative AI at WorkNBER working paper 31161; later published in the Quarterly Journal of Economics · Published 2023-04-24
A field study of 5,179 customer-support agents found a 14% average productivity increase, with larger gains for less-experienced workers.
Read the full methodology, compare the other function benchmarks, or test a 30-day pilot against your own baseline.