Case Study · 8 min read
Enterprise back-office automation: from 12-day cycles to 4
How a mid-market B2B services company deployed governed AI agents across order processing, vendor onboarding, and exception handling — without replacing their ERP.
The situation
A 400-person B2B services organization processed 2,800+ vendor and client transactions monthly across NetSuite, Salesforce, and email-driven exception queues. Operations teams spent an estimated 45% of capacity on document review, status updates, and manual handoffs between systems.
The constraint
Leadership had run three AI pilots — chatbots and RPA scripts — that never reached production. Compliance required full audit trails, role-based approvals, and no autonomous changes to financial records without human sign-off.
- •ERP replacement was off the table for 18+ months
- •Data lived in PDFs, email threads, and three systems of record
- •Internal engineering was fully allocated to product roadmap
The approach
AgentBiz scoped a single production workflow: vendor onboarding document intake → validation → ERP staging → finance approval. The architecture included:
- Document intelligence agents extracting fields from W-9s, contracts, and banking forms
- Rules engine for validation with automatic escalation on low-confidence extractions
- Integration layer writing staged records to NetSuite with full request/response logging
- Operations dashboard for queue management and SLA tracking
Results (12 weeks post-launch)
67%
Cycle time reduction
41%
Manual review hours saved
99.2%
Extraction accuracy (approved)
0
Compliance incidents
Client name withheld under NDA. Metrics from production workflow telemetry and operations team time studies.
What made it stick
The program succeeded because it targeted one measurable workflow, embedded governance from architecture day one, and kept humans in control of exceptions. Phase two expanded to order exception management using the same agent patterns and integration standards.
Map your first production workflow
Start with the process that costs the most manual hours — not the flashiest AI demo.