Normal workflow
Messages are classified, fields are extracted, tasks are routed, and owners are notified.
Use case
Email and Document Classification
Incoming requests are manually read, categorized, assigned, and tracked.
Workflow model
Normal workflow first. AI assistance only when the path breaks.
Where the workflow breaks
A message is low-confidence, missing fields, or contains mixed requests.
What AI prepares
Separate the request, explain uncertainty, and place it in the right review queue.
What humans approve
Low-confidence classification and customer-sensitive routing need human review.
What becomes a reusable rule
Reviewed examples improve categories, extraction prompts, and routing rules.
Why status quo fails
Manual coordination cannot create reliable control.
Incoming requests are manually read and sorted.
Important attachments are missed.
Task creation depends on inbox monitoring.
Low-confidence classifications are not separated for review.
Workflow Layer
The workflow becomes a controlled sequence.
Classify messages, extract fields, route tasks, and flag exceptions for human review.
Message received
Content classified
Fields extracted
Task routed
Exception flagged
Owner notified
Core features
What the system needs to do.
Email classification
Document extraction
Task routing
Confidence thresholds
Review queue
Connected tools
Tools that can participate in the flow.
GmailOutlookGoogle DriveMicrosoft 365Workflow appAI model
Expected outcome
Faster triage without hiding uncertainty.
Implementation
Sample incoming messages
Implementation
Define categories and extraction fields
Implementation
Build review queue
Implementation
Tune with real examples
Next action
Automate Intake Triage
Tell us where this workflow currently breaks. We will map the AI assistance, approval gates, and first build path.