Client Situation
A separate assessment identified a delivery model with significant single-point dependency risk. Workload concentration was high, with a Gini coefficient of 0.55, a Top-1 workload share of 48.9%, and a Top-3 share of 72.4%. Managers mediated 67.8% of throughput events (118 events), compared with 20.7% (36 events) handled by developers. The same body of evidence linked approval dependency to about 30% of blocked tasks. Baseline operational metrics also showed velocity of 8 completed tasks, average cycle time of 18.6 minutes, and work in progress of 10 tasks, creating a measurable starting point for redesign. The finding was straightforward: too much delivery flow was passing through too few individuals.
What the Assessment Found
The assessment concluded that the organization was person-dependent rather than system-driven. Decision rights, approvals, prioritization, and even quality validation were concentrated in a small number of people. That created a structural bottleneck regardless of individual effort or capability. The documents were explicit on what the data did notshow: there was no evidence that the issue was caused by lack of effort, and no evidence that simply replacing individuals would solve the problem. Leadership intervention was therefore needed at the system level through clearer governance, routing rules, and operating controls rather than through blame or additional monitoring.
Consulting Response
The recommended model redistributed ownership by role. Routine intake and triage were shifted to a role-based system rather than informal routing through one person. Approval thresholds were redesigned so that low-risk items could be self- or peer-approved, medium-risk items required peer review with a same-day SLA, and high-risk items required manager approval. The operating model also separated accountability for delivery readiness, engineering feasibility, system hygiene, and QA control. The purpose was not to weaken leadership control, but to move control into rules, gates, and cadences that could scale. This directly reduced dependence on individual availability and made the organization more resilient, more measurable, and easier to manage over time.
Why This Matters
Many organizations mistake heroic individuals for a reliable operating model. This case shows why that is risky. When one person carries nearly half the load, approvals sit in a single queue, and managers mediate most throughput, execution becomes fragile by design. The consulting response reframed the issue from “who is overloaded?” to “how should decisions move?” That is the core of management consulting at the operating-model level: clarifying roles, redesigning decision flow, and building a system that works even when key people are unavailable.
If your business depends on a few overloaded decision-makers, the right fix is usually governance redesign, not more effort.
