---
title: AI Impact
description: >-
  Measure the impact of AI coding assistants on your software delivery
  performance.
breadcrumbs: Docs > Delivery Performance > AI Impact
---

> For the complete documentation index, see [llms.txt](https://docs.datadoghq.com/llms.txt).

# AI Impact

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This product is not supported for your selected [Datadog site](https://docs.datadoghq.com/getting_started/site.md). ({% placeholder "user-datadog-site-name" /%}).
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##### Join the Preview!

AI Impact is available to all Datadog customers in Preview.
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## Overview{% #overview %}

AI Impact measures how AI coding tools—including coding assistants, review agents, and autonomous agents—affect your software delivery performance. Datadog detects AI contribution from several types of sources. Sources include co-author patterns in commit metadata, provider integrations, pull request labels, and data you send yourself.

## Getting started{% #getting-started %}

Datadog detects some AI activity automatically after [DORA Metrics](https://docs.datadoghq.com/delivery_performance/dora_metrics/setup.md) is set up with deployment, commit, and pull request data. AI tools may be identified through co-author signatures, review comments, and other author information in commits that commonly indicate AI involvement. To more completely capture AI activity in your organization, see [Set Up AI Impact](https://docs.datadoghq.com/delivery_performance/ai_impact/setup.md) to configure sources and mapping rules that identify your specific AI tools and their contributions.

## Impact metrics{% #impact-metrics %}

| Metric              | Definition                                                                                                                                                                                                                                                           |
| ------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| AI-assisted PRs     | PRs containing at least one AI-assisted commit, divided by total PRs.                                                                                                                                                                                                |
| PR Throughput       | Number of PRs deployed per user per day for AI-assisted authors compared to non-assisted authors.                                                                                                                                                                    |
| PR Cycle Time       | Median time from a PR's first commit to merge for AI-assisted PRs compared to non-assisted PRs.                                                                                                                                                                      |
| Change Failure Rate | Failure rate weighted by the proportion of AI-assisted commits in each deployment, compared to the weighted rate for non-assisted commits. For example, if a failed deployment has 3 out of 10 commits assisted by AI, only 30% of that failure is attributed to AI. |
| Recovery Time       | Median recovery time of failed deployments containing AI-assisted commits compared to deployments without.                                                                                                                                                           |

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Change Failure Rate only includes deployments linked to code changes. Configuration-only or infrastructure deployments are excluded to help the comparison reflect the impact of AI on code-related failures. This differs from standard DORA Change Failure Rate, which counts all deployment types.
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For GitHub only, PR-level metrics exclude PRs whose commits are entirely bot-authored. This keeps automated activity out of the non-AI baseline.
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## Further Reading{% #further-reading %}

Additional helpful documentation, links, and articles:

- [Set up AI Impact](https://docs.datadoghq.com/delivery_performance/ai_impact/setup.md)
- [Learn about DORA Metrics](https://docs.datadoghq.com/delivery_performance/dora_metrics.md)
- [Set up DORA Metrics](https://docs.datadoghq.com/delivery_performance/dora_metrics/setup.md)
- [Learn how DORA metrics are calculated](https://docs.datadoghq.com/delivery_performance/dora_metrics/calculation.md)
- [5 pitfalls to avoid when measuring developer experience in the AI era](https://www.datadoghq.com/blog/devex-measurement-pitfalls-ai-era/)
- [Measure the real impact of AI coding tools on software delivery with Datadog AI Impact](https://www.datadoghq.com/blog/ai-impact/)
- [How to measure developer experience in the AI era](https://www.datadoghq.com/blog/how-to-measure-developer-experience-in-the-ai-era/)