The entire report, before you pay for one.
Below is a complete Pro Diagnostic, start to finish, nothing held back. The department is composed: an eight-team engineering org built to show the report at full depth. The scoring engine, the six dimensions, the team-level distribution and the bottleneck logic are exactly what runs against your data. Further down, a section built from a real export.
Most organizations rely on dashboard metrics to gauge delivery health. Cycle time charts, velocity trends, and burndown reports give the appearance of visibility, but they rarely answer the question that actually matters: why is delivery breaking down, and where should we intervene first? TAD was built to answer that question with structural precision, not vanity metrics.
Cycle time: 14 days
Flow obstruction: hidden wait states between code review and deployment adding 8+ days of non-work time.
Velocity: declining quarter-over-quarter
WIP overload: 3x recommended items in progress across 4 teams, no limits enforced. Throughput degradation is structural, not capacity.
SLE: 85% within 10 days
SLE integrity failure: target was arbitrary (not statistical). Actual p85 cycle time is 18 days. The SLE creates false confidence.
The department reports an 85% service level within 10 days, but the target was set by convention rather than derived from actual cycle-time data. Measured p85 is 18 days against a stated 10 — the SLE is manufacturing false confidence and driving commitments the teams have no statistical basis to keep.
| Team | Flow | WIP | Throughput | Intake | Structure | Visibility | Total | Band |
|---|---|---|---|---|---|---|---|---|
| Fraud & Riskoutlier | 4 | 4 | 4 | 4 | 4 | 4 | 24 | High Performing |
| Mobile & API Platform | 3 | 3 | 3 | 3 | 4 | 3 | 19 | Functional |
| Card Issuing | 3 | 2 | 3 | 3 | 3 | 3 | 17 | Delivery Risk |
| Core Ledger | 3 | 3 | 3 | 3 | 3 | 2 | 17 | Delivery Risk |
| Customer Onboarding (KYC) | 3 | 2 | 3 | 3 | 2 | 3 | 16 | Delivery Risk |
| Payments Core | 2 | 2 | 2 | 3 | 3 | 2 | 14 | Delivery Risk |
| Regulatory Reporting | 2 | 2 | 2 | 2 | 3 | 2 | 13 | Delivery Risk |
| Lending Originationoutlier | 2 | 1 | 2 | 2 | 2 | 2 | 11 | Systemic Failure |
Scores 7.6 points above department average. This team's practices should be replicated.
Scores 5.4 points below department average. This team needs immediate intervention and targeted support.
Dimension: Structure · Throughput
Dimension: WIP
Dimension: Delivery Visibility & Metrics · SLE Integrity
Dimension: Intake
Every Pro Diagnostic reads your Jira export alongside your answers. Where the two disagree, the report says so, in the buyer’s own numbers. This is a reference run: a real two-person platform team’s export, read against the answers a typical buyer gives, with the organization removed.
You said work typically finishes in 4 to 7 days. In your export, half of all items took more than 10 working days and 85 out of every 100 took up to 36. Eight items open at once, divided by roughly one finished a week, is about seven weeks of queue, which is exactly where that 36 lands. This is a queuing problem, not an effort problem.
- Time to finish: reported as 4 to 7 days. Export shows 85 out of every 100 items took up to 36 working days.
- Predictability: reported as very consistent. Export shows finish times vary by about 100 percent from one item to the next.
- Weekly output: reported as very stable. Export shows it swings by about 80 percent week to week.
- Unplanned work: reported as under 10 percent. Export shows 29 percent of all items were bugs or incidents.
- Limits on open work: reported as held. Export shows 4 items open per person, most untouched for two weeks or more.
The recommendation that followed named the constraint, capped work in progress at two items per person, set three weekly numbers to watch, and said what not to do: no new headcount, no new tool. That is what a finding looks like when it comes from your data and not from a questionnaire.
TAD evaluates delivery systems across six structural dimensions: Flow, WIP, Throughput, Intake, Structure, and Visibility. Each dimension is scored 1-5 based on structured intake responses and, where available, validated against actual tool data from Jira, Linear, or comparable work management platforms. Scores are not subjective assessments. They map to specific operational conditions with defined thresholds.
A team scoring 2 on Flow, for example, has measurable wait-state accumulation between workflow stages, a pattern well-documented in flow-based delivery research. A team scoring 4 on Intake has a defined entry process with capacity-aware gating. Each score has a structural meaning, not a relative ranking. This is what separates a diagnostic from a survey: the scores correspond to specific system behaviors, not opinions.
At the Pro tier, TAD also evaluates SLE integrity (whether the team’s service level expectation is statistically grounded), data confidence (how much of the diagnostic was validated with real tool data versus self-report only), and cross-team dependency patterns. The result is a layered intelligence report, not a single score but a structural map of where the system is breaking down and what to fix first.
Dashboards show what happened. They surface averages, trends, and thresholds. But averages flatten the signal. A department with four teams can show a healthy 12-day average cycle time while one team runs at 6 days and another at 22. The dashboard reports a single number. TAD reports the variance, identifies the structural cause, and tells you which team needs intervention and which team’s practices should be replicated.
This matters because most delivery improvement initiatives start with the wrong team, the wrong dimension, or the wrong sequence. Research from organizations like the Kanban University consistently shows that systemic constraints are invisible to surface-level metrics. A TAD diagnostic eliminates that guesswork by mapping the full system and surfacing root causes, not symptoms. The difference between a symptom and a root cause is the difference between “velocity is declining” and “WIP overload caused by intake gate failure is degrading throughput structurally across three of four teams.”
Hidden wait states, approval queues, and handoff delays that extend items far beyond active work time.
Too many items in progress without limits. The #1 cause of throughput degradation across teams.
Work enters the system outside the normal intake process. Teams are reactive by default.
Shared testing function creating handoff delays for multiple teams. Structure issue, not people issue.
No statistically grounded delivery commitment. Teams make promises they have no basis to keep.
Cross-team blockers that don't surface until delivery is late. Structural, not accidental.
This is what TAD Delivery Intelligence does.
Start with a free directional read on one team. Then get the Delivery Diagnostic: the real root cause proven in your own data, defensible to your board.