AI ROI benchmark · Software development
Software development AI ROI benchmark
Translate bounded coding speed evidence into a conservative planning model for implementation, tests, and documentation.
Modeled result
This is a planning scenario, not a market average. Replace every input before making a purchase decision.
| Current labor on the workflow | 640 hours/month |
|---|---|
| Loaded labor cost | $85/hour |
| Share suitable for AI assistance | 35% |
| Productivity lift on that share | 25% |
| Savings realized in practice | 55% |
| Software and usage cost | $800/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
- Draft repetitive implementation code
- Create test cases for reviewed behavior
- Explain unfamiliar code before human verification
Keep a human decision
- Merge without tests and review
- Assume generated dependencies or security patterns are current
Evidence and limits
The controlled GitHub study tested one bounded HTTP-server task. This model applies a smaller 25% lift to 35% of engineering time and discounts realized savings.
- The Impact of AI on Developer Productivity: Evidence from GitHub CopilotarXiv preprint · Published 2023-02-13
In a controlled experiment, developers with Copilot completed a bounded coding task 55.8% faster. The result should not be generalized to whole-team delivery.
- Navigating the Jagged Technological FrontierHarvard Business School working paper; later published in Organization Science 37(2) · Published 2023-09-15
A field experiment with 758 consultants found faster, higher-quality work inside the model capability frontier and worse accuracy on a task outside it.
Read the full methodology, compare the other function benchmarks, or test a 30-day pilot against your own baseline.