AI ROI Dashboard
Connect AI investment with effective PR delivery, cost efficiency, and estimated value.
The AI ROI dashboard helps engineering leaders connect AI investment with delivery outcomes. It combines organization-level cost metrics with pull request activity to show productivity, cost efficiency, and estimated value over the selected period.
Use this dashboard to understand whether AI-assisted development is contributing to more effective pull requests, how efficiently teams are delivering, and how the results compare with the previous period. The dashboard is available to every customer with Metrics Builder enabled.
Open the dashboard
- In LinearB, select Metrics.
- Open Metrics Builder.
- Under LinearB Catalog, select AI ROI.
- Set the Date Range you want to analyze.
The dashboard applies the selected date range to its calculated metrics and charts. Metric cards may also show the percentage change from the preceding comparison period.
Configure cost metrics
Before interpreting ROI, review the dashboard's cost inputs:
- AI Monthly Spend: Your organization's monthly spend on AI development tools.
- Dev Annual Cost: The average annual cost of one developer.
Use the sliders to enter values that reflect your organization. Changing either input recalculates every dependent cost and value metric.
Important: AI Monthly Spend and Dev Annual Cost are organization-level cost metrics. Keep them current so the dashboard produces meaningful estimates.
Dashboard metrics
|
Metric |
What it shows |
How to use it |
|
Active Team Members |
The number of distinct contributors with qualifying development activity during the selected period. |
Use it as the active developer population behind the per-developer metrics. |
|
Total PRs Merged |
The number of pull requests merged during the selected period. |
Use it to understand total delivery volume. |
|
PRs Merged Per Active Developer |
The average number of merged pull requests for each active developer. |
Use it to compare normalized delivery volume across periods. |
|
Rework Rate |
The proportion of code identified as rework during the selected period. The dashboard uses this rate to discount merged PR volume when calculating effective output. |
Use it as a quality and efficiency signal. Review it with delivery volume rather than in isolation. |
|
Total Effective PRs |
Merged pull request volume adjusted downward by the rework rate, representing the share of delivered output that did not require rework. |
Use it to focus on completed work that meets the dashboard's effectiveness criteria. |
|
Effective Weekly PRs Per Developer |
Total effective PRs normalized by active developers and the number of weeks in the selected period. |
Use it to compare effective throughput across teams or periods of different lengths |
|
Cost Per Effective PR |
Total engineering cost—developer cost plus AI spend—divided by total effective PRs for the selected period. |
Use it to monitor delivery cost efficiency. A lower value indicates that effective pull requests are being delivered at a lower estimated cost. |
|
Total Value Gain |
The reduction in cost per effective PR between the comparison periods, multiplied by the current period's total effective PRs. |
Use it as a directional estimate. The calculation attributes improvement in unit cost to AI investment, although other changes—such as review-process or platform improvements—may also contribute. |
Interpret the trend chart
The AI ROI: Cost Per Effective PR chart plots two measures over time:
- Effective PRs per developer shows normalized effective delivery volume.
- Cost per effective PR shows the estimated cost efficiency of that delivery.
Hover over a point to see the values for that interval. Look for sustained movement across several intervals instead of treating one point as a trend. Increasing effective PRs per developer together with decreasing cost per effective PR can indicate improving efficiency.
View supporting data
Select a metric card to open its supporting data, when drilldown is available. A drilldown can include:
- The query used to retrieve the result.
- A plain-language explanation of the result.
- The contributing developers or pull requests.
- Pagination controls for reviewing additional rows.
For example, the Active Team Members drilldown identifies the contributors included in the count and summarizes their qualifying activity. A pull request metric drilldown can list the pull requests included in the result and provide links to the source repository.
Drilldown results reflect the current date range and dashboard filters. Some result sets may be limited to a maximum number of rows.
Save a widget to another dashboard
To reuse a widget:
- Select the widget's More menu (…).
- Select Save to....
- Choose the destination dashboard.
You can also select Copy widget ID when you need the widget identifier.
Best practices
- Confirm AI spend and developer cost metrics before comparing results.
- Use a date range long enough to reduce the effect of short-term fluctuations.
- Compare cost, throughput, and rework together; no single metric provides a complete ROI assessment.
- Use drilldowns to validate which developers and pull requests contributed to a result.
- Treat cost and value metrics as estimates for directional analysis, not accounting totals.
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