Estimating ROI of AI Developer Tooling
As engineering leaders see their AI tooling bills rise, many are asking how to estimate the ROI of that spend.
Their engineers offer qualitative feedback that AI agents are significantly speeding up certain tasks, but the finance department sees quantitative dollar figures. This contrast creates a pressure to quantify the AI payoff.
Measuring engineering output effectively is infamously difficult to achieve. Fortunately, there is still a way to estimate AI effectiveness – by comparing outcomes between when AI assistance is present and absent. The following is an experimental technique for doing so.
A comparative experiment
Select or create two teams to participate in the experiment. Ideally they should be similarly matched in seniority and domain expertise.
Have the teams select a set of tasks. The two teams then work through each task independently.
For each task, one team uses AI, and the other does not, in alternation. This reduces the impact of differences in skill levels between the teams.
On each task, developers track the engineering time taken to complete it (including any rework).
Alternatively, if duplicating efforts is unpalatable and additional measurement noise is acceptable, the teams can select a series of task pairs, where they have estimated each task in the pair to require approximately the same effort.
The following simplified example illustrates the calculation mechanics:
| Time taken per task | Task 1 | Task 2 |
|---|---|---|
| Team A | (AI) 10 hrs | (No AI) 25hrs |
| Team B | (No AI) 20hrs | (AI) 15 hrs |
Total observed efforts:
- Hours with AI: 25
- Hours without AI: 45
- Difference: 20
That difference represents an approximation of the additional developer hours that would have been needed if the AI had not been available.
It allows us to estimate the ROI, as shown in this example:
- Employee fully-loaded cost per hour: $50
- Total additional developer costs to complete the tasks without AI: 20 * $50 = $1000
- Estimated AI tooling costs to complete the tasks: $200
- Additional developer costs minus AI costs: $800
- ROI: $800 / $200 = 400%
These estimates are local to the teams, tools, and tasks involved, and would need to be revalidated over time as AI models advance and team experience levels increase. But since they are quantifiable and based on observed outcomes, they allow a defensible calculation of ROI on AI spend.