Understand what changed
Read concise summaries of delivery, quality, and team performance. Use the summary to decide which metrics need a closer look.
AI Insights
Find the signal. Focus the next engineering review.
Turn delivery, quality, and AI usage data into summaries of strengths, risks, and next actions. Investigate the evidence behind each finding.
AI Insights
AI cost and usage risksAI Insights
Credit and consumption patterns that need attention
Credit pool exhaustion projectedCriticalAt the current burn rate, the included AI credit pool is projected to be fully consumed by month end.
Compare the usage forecast with your included pool to identify potential overage before the end of the month.
Usage fully covered so farInfoNet billable cost is $0. Gross consumption still represents used AI credit value.
Included credits currently cover the usage cost. Monitor both credit consumption and net billing as the month progresses.
Users with critical usage patternsHigh18 users show consumption significantly above the organization average.
Review consumption patterns and model choices to understand where usage is concentrated.
Cost per active user above targetHighConsumption averages $19.03 per active user against a sample budget target of $15.
Compare per-user consumption and model choices with your AI budget to identify where spending could be better aligned with usage.
Premium-model spend concentrationMediumPremium models account for 72% of AI consumption value in this sample period.
Review which workflows use premium models and whether the consumption pattern matches your budget priorities.
- Reporting period
- July 2026
- Users with critical usage
- 18
- Cost per active user
- $19.03
- Premium-model spend
- 72%
Expand a finding to review the pattern and the follow-up it suggests.
From signals to decisions
Move from a dashboard to a focused discussion
Understand the movement, inspect the finding, and decide what to investigate next.
Focus on risks and strengths
Identify emerging issues and positive trends. Review the supporting scope and period before prioritizing follow-up.
Turn the finding into a next step
Use recommendations to guide the conversation. Assign a follow-up with the team and revisit the relevant trend.
What to look at
Keep the explanation connected to the evidence
Use each insight as the start of an investigation. Follow it into the related metrics and the team’s workflow.
- Dashboard scope and selected period
- Strengths, risks, and supporting metrics
- Related delivery, quality, and usage patterns
- A next action and a follow-up review
- Start with the dashboard context
- Review the selected team, period, and metric scope. The same signal can mean different things in different workflows.
- Inspect the finding and its evidence
- Expand a risk or insight and review the data behind it. Check related patterns in delivery, quality, or consumption.
- Agree on the next review
- Choose an action with the responsible team and follow the trend after the change. Keep the finding connected to its source data.
From signals to decisions
Review signals across the engineering system
Bring the right context to the finding before changing a priority, process, or budget.
Delivery and quality
Follow review delays and delivery movement alongside code quality and recurring symptoms.
AI adoption and contribution
Review adoption patterns with attribution, coding efficiency, and the delivery outcomes that follow.
AI usage and budget risk
Inspect credit pool forecasts, concentrated consumption, and model cost patterns before changing a budget.
What are Oobeya AI Insights?
AI Insights summarize engineering dashboards into explanations of trends, strengths, risks, and possible next actions. Use the findings alongside the source metrics and your team’s context.
What does the preview show?
The preview illustrates AI cost and usage risks, including credit pool exhaustion, concentrated consumption, and model cost patterns. Expand a finding to read its detail.
Can insights help interpret AI coding assistant impact?
Yes. Review findings alongside AI adoption, contribution, efficiency, quality, and delivery trends in AI Impact.
Do insights replace dashboard analysis?
Use the summary to focus the review, then inspect the underlying signals when a finding needs investigation. Check scope, period, and source coverage before acting.
Do recommendations automatically change budgets or workflows?
The findings and recommendations support your review. Decisions about budgets, priorities, and workflow changes remain with the teams responsible for them.
Who can use AI Insights?
Engineering leaders, team managers, DevOps and platform teams, and executives can use the summaries to focus reviews on changes that need attention.
See Oobeya in practice
Bring your engineering questions into focus
Walk through AI Insights with the delivery, quality, and adoption signals your teams already review.