Cost and scope guide
AI Automation Scope and Cost Drivers
AI Automation Scope and Cost Drivers organizes the decisions that matter for teams processing documents, email, content, support requests, leads, files, and messy operational data: the current workflow, ownership, implementation choices, rollout risk, and acceptance evidence.
Working artifact
AI Automation integration-boundary map
Use the boundary map to show what crosses systems, where it can fail, and how the result will be reconciled.
| Boundary | Information moving | Failure to test |
|---|---|---|
| LLM and classifier integration | Document classification and extraction | Staff manually interpret repetitive documents |
| OCR and document pipelines | Email routing and support triage | Rules fail on natural-language variation |
| Rules engines and workflow orchestration | Content transformation and report generation | AI output enters systems without review |
The five largest scope drivers
Staff manually interpret repetitive documents. Confirm who encounters it, where it occurs, and what changed before it appeared. Then distinguish the visible symptom from dependencies such as LLM and classifier integration.
- Document classification and extraction
- Email routing and support triage
- OCR and document pipelines
- Rules engines and workflow orchestration
- Confidence thresholds and review queues
What makes an estimate more reliable
For AI Workflow Automation, confirm account ownership, current exports or backups, recovery options, and recent changes before touching production. Preserve exact errors and timestamps that may disappear after a restart or update.
- Current-system inventory
- Representative user journeys
- Known constraints and deadlines
- Named decision owner
When phasing helps
Frame the first scope around document classification and extraction and one observable acceptance journey. Treat email routing and support triage as a later phase unless the evidence shows it is a true dependency.
- Phase 1: evidence and risk control
- Phase 2: smallest useful outcome
- Phase 3: measured expansion
Estimate preparation checklist
Repair fits when the core remains sound. Extension fits when the boundary around LLM and classifier integration is understood. Replacement fits when ownership, architecture, or operating risk prevents a responsible change.
- Desired result
- Systems and vendors involved
- Access owner
- Examples and errors
- Definition of done