AI Workflow Onboarding
From use case definition to live AI workflows: the implementation journey.
Overview
AI workflow implementation starts small and grows with confidence. We don't deploy a fleet of AI agents on day one. We start with one well-defined use case, prove it works, and expand from there.
Standard timeline: 3–6 weeks for the first live workflow, with additional workflows added incrementally.
Implementation Phases
Use Case Definition Week 1
Identify the highest-value AI use case for your environment. Map the current manual process, define success criteria, and agree on governance boundaries. One use case, well-scoped.
Knowledge Base Setup Week 2
Curate the knowledge base for your use case. Import relevant documentation, runbooks, service descriptions, and historical data. Define retrieval boundaries and access controls.
Workflow Build & Test Weeks 3–4
Build the AI workflow with confidence scoring, guardrails, and audit logging. Test against historical cases. Calibrate confidence thresholds against real-world accuracy.
Supervised Launch Weeks 4–6
Go live with 100% human review. Every AI recommendation is verified by a human before action. This builds confidence in the system and identifies edge cases the test data missed.
After Launch
Once the first workflow is live and calibrated:
- Weekly accuracy reviews: track how often the AI's recommendations match human decisions
- Confidence threshold tuning: adjust auto-execute and review thresholds based on real performance
- Gradual autonomy increase: as accuracy proves out, reduce human review for high-confidence outputs
- Additional workflow rollout: apply the proven pattern to the next use case