View Insights
Learn how to navigate the Org Tree on the Insights dashboard to analyze engineering metrics at the organization, team, and developer levels.
Once you’ve set up integrations, created profiles, uploaded your developer records, and created an Org Tree, AI DLC Insights enables you to explore high-level engineering insights across your organization, including Efficiency, Productivity, and Business Alignment for the entire organization.
Insights are always scoped to the selected Org Tree, allowing you to analyze engineering performance across different organizational structures.
Use the Insights dashboard
To access the Insights dashboard:
From the Harness AIDI navigation pane, click Insights.
Select an Org Tree from the dropdown menu at the top of the dashboard.
Explore organization-wide engineering metrics, including DORA metrics such as Lead Time for Changes, Deployment Frequency, Change Failure Rate, and Mean Time to Restore.
If your organization uses multiple Org Trees, you can switch between them to view insights by selecting a different Org Tree above the Efficiency, Productivity, and Business Alignment tabs. All metrics update automatically based on the selected Org Tree.
For more information on exporting insights, see Exporting AI DLC Insights Insights.
View team-level insights
Beyond organization-wide views, AI DLC Insights allows you to drill into specific teams to analyze localized engineering performance on the Insights page.
To view team-level insights:
Select a specific team on the Org Tree in the left panel and click View Breakdown.
Configure Team Settings (if not already done) by navigating to the Teams page in the left-hand navigation or clicking on the Team Settings icon next in the Org Tree.
Once team settings are applied, all Insights dashboards refresh to reflect metrics scoped specifically to that team, including repositories, contributors, and deployment signals.
This enables consistent comparison between organization-wide performance, team-level execution, and individual contributor activity.
Explore drilldown-level insights
Beyond team-level breakdowns, AI DLC Insights provides drilldown views that allow you to inspect metrics at the developer and record level. Drilldowns help you understand why a metric behaves a certain way by exposing the underlying pull requests, work items, or code changes that contribute to the aggregated view.
Drilldowns are available at the leaf team level after selecting View Breakdown, and they provide the most granular level of insight in AI DLC Insights.
Drilldowns provide contextual detail behind aggregated metrics on the Insights dashboard by exposing the underlying engineering events that contribute to each data point. Not all insights support the same level of granularity; the following metrics support drilldowns:
Efficiency Insights
Team → PR lifecycle → commit-to-deploy flow
Time from code commit to production deployment.
Team → Deployment events → release history
How often code is deployed to production.
Team → Deployment events → incident tracking
Percentage of deployments causing failures or rollbacks.
Team → Incident lifecycle → recovery timeline
Time required to restore service after a failure.
Productivity Insights
Developer → PR list → SCM integration (GitHub, Bitbucket, GitLab, etc.)
Pull request throughput and contribution volume per developer.
Developer → PR timeline view → stage-level timing (First Review, Approval, Merge)
Time taken for pull requests to move from first commit to merge, including review stages.
Developer → Work items → Issue management system (Jira, Azure DevOps, etc.)
Completed work items and delivery throughput per developer.
Developer → PR / commit changes → line-level rework attribution
Portion of code rewritten or replaced based on additions vs deletions. Includes breakdown into: Recent Rework, Legacy Rework, and Lines Added / Deleted / Modified.
Business Alignment Insights
Developer → PR / Work Item → classification + linked changes
Work contributing to security fixes, compliance requirements, or regulatory updates.
Developer → Feature work items → PR / implementation mapping
Work that delivers new product capabilities or customer-facing functionality.
Developer → Operational work items → maintenance activity
Maintenance, operational support, and routine system upkeep work.
Developer → PR / refactor activity → code change attribution
Work focused on improving system quality, reliability, and maintainability.
Developer → Work items without mapping → fallback classification
Work that cannot be mapped to a defined business alignment category.
Each drilldown is scoped to the selected time range and inherits all filters applied at the Org Tree and team level.
Engineering performance benchmarks and goals
AI DLC Insights provides engineering metrics across Org Trees, teams, and developers, but interpretation of these metrics depends on team maturity, workflow, and organizational context. This section provides recommended benchmarks and goals to help teams evaluate and improve their software delivery performance over time.
These benchmarks are not strict requirements. High-performing teams focus on sustained improvement rather than meeting fixed thresholds. Use these benchmarks to interpret insights surfaced in AI DLC Insights.
Start with Phase 1 if your team is new to AI DLC Insights and progress through phases as maturity increases. Focus on trends, not single values and adapt benchmarks to your team structure and workflow.
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