Case Study: Automating Accounting Entries for an SME

Background & Problem

A small business approached us with a clear challenge: their POS / billing software did not integrate with their ERP. Every week, the accounting team exported a file with hundreds of rows and manually created accounting entries (debits and credits), mapping accounts based on rules, validating IDs, and finally uploading to the ERP.

This manual workflow was slow, error-prone, and labor intensive.

Pain Points & Constraints

• They had a fixed monthly budget and limited infrastructure tolerance.
• They required a validation step: accountants needed a simple interface to review, correct, and approve before upload.
• They needed reconciliation: comparing existing ERP entries vs. the automation’s output.
• The manual process took ~3 minutes per accounting entry. With ~500 entries per week, that is 500 × 3 = 1,500 minutes or 25 hours per week.
• A 2 % error rate implied costly rework and financial risk.

Proposed Solution & Execution

Given the constraints (budget, simplicity, need for human oversight), we proposed a lightweight solution leveraging Google Apps Script — macros / scripts inside Google Sheets / Drive — which incurs virtually zero additional cost.

We implemented:

• Scripts to automatically download and parse POS export files.
• Mapping logic to transform rows into accounting entries.
• A user interface (menu, dialog, or sidebar) for accountants to review, correct, and approve entries.
• Reconciliation logic comparing existing ERP records and flagged discrepancies.
• Final upload via API or ERP-compatible format.

The full workflow, when triggered, executed in ~15 minutes, with essentially zero infrastructure cost. Human validation remained a final safeguard.

Results & Metrics

• Reduced the manual effort from ~25 hours per week down to ~15 minutes (plus review).
• Eliminated manual transcription errors (which in similar contexts range from 1 % to 5 %). Source
• High user satisfaction: accountants appreciated not having to type hundreds of lines manually.
• Zero infrastructure cost beyond existing Google Workspace license.

Lessons & Risks

This case reinforces that you don’t always need heavy tech. Simplicity with control often wins. But key risks we managed:

• Input file consistency is critical: bad source data leads to cascading errors.
• Matching logic must handle exceptions and edge cases.
• The human validation step is essential for safety and trust.
• Continuous monitoring of discrepancies, failures, and rework time is necessary.

The broader lesson: many automation and AI initiatives fail more from weak processes than from technology. 70–85 % of AI or GenAI deployments don’t meet expectations.

Our Perspective: What Works & What This Means for Tirion

1. Deliver right-sized solutions — avoid overengineering or forcing AI.

2. Empower humans — manual validation remains a pillar of trust.

3. Leverage existing tools (e.g. Google Workspace) to minimize cost.

4. Design modularly — if you ever upgrade to enterprise RPA, the structure supports it.

This project turned ~25 manual hours into a 15-minute automated process, with higher accuracy and no extra infrastructure. If done wrong, the effort could introduce errors and fatigue; done right, it’s transformative.

Links & References

• Google Apps Script — official documentation.
• Apps Script Case Studies.
• Staple AI: AP automation case studies.
• Citigroup: automation risk in finance.