What's inside
Most AI projects fail before the first line of code — scoped by enthusiasm, bought on demos, abandoned when nobody owns the system after launch. This playbook documents the complete method we use to avoid that arc: the same Scope → Build → Operate process behind every engagement we run, written down so you can apply it, audit it, or hold us to it.
Contents
- Workflow mapping — how to sit inside an operation and measure where the hours actually go, including the interview guides and timing templates we use in week one.
- Scope boundaries — drawing the line around the smallest system that removes the most work, and the boundary anti-patterns that predict failure.
- Acceptance criteria — turning a measured baseline into numeric, signable criteria: metrics families, threshold setting, and measurement methodology.
- Build patterns — the recurring architectures behind agents, knowledge systems, and analytics pipelines, and how we choose among them.
- Managed operations — what running an AI system actually requires after launch: monitoring, model-change management, and the monthly reporting cadence.
- Three worked examples — a quoting system, an intake automation, and a knowledge system, each traced from workflow map to operated system with the scoping artifacts included.
Who it's for
COOs, transformation leads, and IT directors who own an AI initiative and want a delivery method with checkpoints they can inspect — whether they run it with us, another partner, or an internal team.