Healthy leverage
Output up, experience stable or improving. AI is accelerating the work without eroding the people doing it. The goal state.
Dashboard · AI Sustainability Reports
See whether AI-accelerated work is sustainable, team by team.
The Ascenda dashboard includes a five-report AI sustainability suite. It tracks the human side of AI adoption: workload shape, developer experience, team-level risk, and the interventions that improve them, all from de-identified, cohort-level signals.
Five reports, one question
Is AI adoption sustainable for the people expected to make it work? Each report answers a different part of that question. The charts below use illustrative data.
Overview
One composite score for the human side of AI adoption, tracked weekly against benchmark.
Team Risk
Which teams are at risk despite strong output, segmented rather than surveilled.
AI Workload
Whether teams are creating with AI, or managing it.
DevEx Health
Whether developer experience is improving or eroding, dimension by dimension.
Interventions
What you did about it, and whether it worked.
Report · Overview
One score for the human side of AI adoption
The overview report condenses work clarity, flow state, cognitive balance, autonomy, and recovery into a composite score tracked weekly against benchmark. Output can rise while this score falls. That divergence is the productivity-experience gap, and it is the earliest signal that adoption is running ahead of human capacity.
Sustainability Dimensions
Organisation vs. benchmark across five dimensions · illustrative data
- Work clarity
- How clear task scope and acceptance criteria stay as AI accelerates delivery.
- Flow state
- Availability of uninterrupted work and completion of complex tasks.
- Cognitive balance
- Mental effort and context-switching burden across the week.
- Autonomy
- Ownership of decisions without excessive oversight of, or by, the tools.
- Recovery
- Off-hours disengagement and the margin left to recharge.
The weekly composite trend built from these dimensions is shown on the AI adoption page.
Report · Team Risk
Find hidden strain before it becomes attrition
Teams are plotted by output signal against experience quality and segmented by risk profile. The most dangerous quadrant is the one delivery dashboards celebrate: high output, declining experience. Analysis stays at team level: no individual rankings, no personal metrics.
Output vs. Experience Quality
Teams plotted by output signal and experience quality; dot size reflects headcount · illustrative data
Healthy leverageHidden strainSupervisory overloadQuality anxiety
Hidden strain
Strong output signals, declining experience quality. These teams look healthy on delivery dashboards while burnout risk builds underneath.
Supervisory overload
Direction, review, and correction burden concentrating on a few people, usually senior engineers. Creation time shifts into managing machine output.
Quality anxiety
Elevated reopen rates and low trust in AI output. Teams iterate rapidly but accept slowly, and the second-guessing carries a cognitive cost.
Report · AI Workload
Are teams creating with AI, or managing it?
Every hour of AI-assisted work has a shape: creating new work, verifying output, or supervising the tools by directing, correcting, and checking. When supervision creeps past a quarter of the week, engineers become managers of machine output, and verification burden crowds out the work AI was meant to accelerate.
Work Mode Distribution
How each team's AI-assisted time splits across creation, verification, and supervision · illustrative data
The report also tracks the oversight load trend: directing, correcting, and verifying share over twelve weeks. That view is shown on the AI engineering teams page.
Report · DevEx Health
Developer experience, measured, not assumed
The report scores every team on three developer experience dimensions, weekly and against baseline, then classifies each team's trajectory as recovering, stable, or declining so early-warning clusters stand out.
Cognitive balance
Context-switching frequency, task fragmentation, and perceived mental demand. Low scores precede error-rate increases and fatigue.
Flow state
Uninterrupted work sessions and completion of complex tasks. Below the mid-fifties signals chronic interruption.
Feedback loops
Speed and quality of the signals developers receive on their output. Degradation precedes autonomy erosion and disengagement.
The 12-week trend view across these dimensions is shown on the AI engineering teams page.
Report · Interventions
Close the loop: act, then measure
Insight without action is dashboard theatre. The interventions report tracks planned, in-progress, and completed actions, from protected deep-work blocks to review guardrails and after-hours policies, and measures each one against the metric it was designed to move.
Intervention Impact
Before and after a four-week flow recovery intervention on one team · illustrative data
How the reports read the signals
Thresholds turn charts into decisions. These are the lines the reporting suite watches on your behalf.
Creation above 55%
The leverage zone. Most AI-assisted time goes into building, with verification and supervision in support.
Supervision above 25%
The warning zone. Supervisory engineering work is creeping, and it rarely retreats on its own.
Flow state below 55
A chronic interruption signal. Teams here become early-intervention candidates before the trend hardens.
After-hours sessions rising
AI use outside working hours reads as a recovery deficit, not a productivity win.
Cohort-level insight. No individual surveillance.
The reports are built on the same trust boundary as the rest of Ascenda. Monitoring people erodes the psychological safety AI adoption depends on, so the suite never does it.
- De-identified by design: no individual metrics, rankings, or timelines
- Team-level cohorts, not personal dashboards
- Individuals get private, role-aware support in the Ascenda app
- Leaders get trend visibility and early warning signals