Real diagnostic. Real findings.
Below is an anonymized Pro diagnostic from a real multi-team engineering department. The findings, scores, and patterns are from an actual TAD engagement. This is what a full department intelligence report looks like.
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.
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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. Go deeper with Diagnostic Plus for data confidence and SLE analysis across up to 3 teams. Or go straight to Pro for department-level root cause intelligence.