AI Deployment Workflow
From business-model stress to production deployment
1
Identify the business-model stress
Cost · cycle time · quality · value shortfall · customer experience — map the symptom to the business model
2
Map to the value-chain activity responsible
AI applied to the wrong activity solves a problem nobody is measuring
3
Decompose to process and task
Inputs · outputs · completion criteria · exception paths · current worker assignment
4
Identify the AI-eligible capability at the task
Perception · Reasoning · Generation · Coordination · Anomaly detection
5
Choose the worker pattern for each task
Agent-as-worker
AI-Inside
Hybrid
← most strategic roles need all three
6
Verify the three governance extensions are in place
Engine-enforced confidence gating
Semantic decision lineage
Engine-aware tool protocol (MCP)
7
Confirm goal clarity at the task contract
An agent optimizes for the goal you specified — which may not be the goal you intended
8
Deploy, instrument, measure, refine
KPIs connect task output to business-model outcomes — close the loop