We Rebuilt a Legacy ERP System Using AI Automation — Saving the Client $80,000 per Year

From Fragile Legacy to Flexible Ops

The client ran a decade-old on-prem ERP with custom scripts, manual Excel handoffs, and overnight batch jobs that often failed before payroll and invoicing deadlines. Feature requests took weeks because any change risked breaking brittle integrations. Rather than attempting a multi-year rip-and-replace, we proposed a safer route: stabilize the core, then layer AI automation around it to remove manual work, harden data quality, and shorten business cycles.

Discovery: Where the Time and Money Leaked

In a two-week assessment, we mapped 27 processes across Finance, Supply Chain, and HR. Three hotspots drove most of the cost: (1) invoice intake and 3-way match, (2) inventory adjustments from paper GRNs, and (3) monthly close data wrangling between the ERP and spreadsheets. We measured 2,100+ human hours/year in repetitive tasks, 2.6% re-work due to data entry errors, and vendor late-payment penalties that were entirely avoidable with earlier validation.

Architecture: Augment, Don’t Overthrow

We kept the ERP of record intact and introduced a light “automation edge”: an event bus (for safe decoupling), AI services for document understanding, and RPA where APIs didn’t exist. A small operational data store synced key tables for analytics and anomaly detection. Result: the ERP continued to do what it does best (transactions, audit), while AI services handled the messy, human-heavy edges.

AI in Action: Documents, Matching, and Clean Data

Incoming PDFs and scans (POs, GRNs, vendor invoices) now flow through an AI extraction pipeline that normalizes vendors, line items, taxes, and payment terms. A rules+ML matcher performs 2- and 3-way matching, flags exceptions with ranked reasons, and auto-creates drafts in ERP only when confidence is above threshold. For low-confidence cases, reviewers get side-by-side context rather than hunting across systems. The same pipeline powers GRN digitization, eliminating hand-typed inventory adjustments.

Robotic Flows Where APIs Don’t Exist

Some ERP modules had no safe API surface. For those, we deployed resilient RPA routines with guardrails: role-based credentials, idempotent replays, and change detection when UI layouts shift. Each bot writes structured logs to the event bus so Ops can trace every action. When an underlying screen changes, the bot fails fast, notifying a human rather than silently corrupting data.

Controls, Compliance, and Rollout

We embedded approval steps directly in Slack/Teams, tied to business rules and monetary thresholds. Every automated decision is explainable and traceable to inputs, keeping auditors happy. Rollout followed a “dual-run then flip” pattern: 3 weeks of shadow mode, compare outputs, calibrate thresholds, then switch specific flows to auto-post. No big-bang weekends. No surprises.

Impact: $80,000 Annualized Savings

Within 60 days, invoice cycle time dropped from 5.2 days to 1.7 days, on-time payments rose by 18%, and data-entry error rates fell below 0.6%. The financial impact came from three levers: (1) ~1,400 hours/year of manual entry eliminated, (2) reduced penalties and early-payment discounts captured, and (3) retiring legacy OCR licenses and ad-hoc scripting maintenance. Together, these delivered an annualized saving of roughly $80,000 while improving control and visibility.

What This Means for Your ERP

You don’t need to replace your ERP to get modern performance. Start by identifying the high-friction edges, apply AI for understanding and matching, use RPA sparingly with strong guardrails, and centralize events for observability. You’ll shorten cycles, raise data quality, and create space to plan a future migration on your terms — not under fire drills.