Case Study № 3 — AI for Project Management

The problem

Dozens of AI workers without direction produce chaos: duplicated work, unverified claims, and output nobody can trust. The scarce skill is not using AI — it is directing it.

The implementation

A studio model: named desks with captains who decide inside their lanes; commissions confirmed started with proof; multi-lane builds run in sequence, not simultaneity; morning scoreboards; and an accuracy doctrine — report what the customer sees, not what a dashboard claims — enforced as QA.

Scope of work

Workforce design — 65 AI employees across marketing, content, research, training, and media desks
Task direction & sequencing — outcome-level commissions, sequenced for review windows
QA gates — accuracy-is-the-trust standard; repeating errors treated as failure signals and investigated
Reporting — daily scoreboards against a single weekly mission

Proof. 40,000+ supervised work exchanges logged inside one directed operation; every other case study in this series is an output of this system.

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