This repository documents the business problem, solution approaches, and ROI analysis for automating invoice PDF processing in a global FMCG company operating across 20 countries using Microsoft Power Platform + AI Builder.
- Business Problem: Manual invoice processing across multiple countries leads to rising costs, high error rates, SLA breaches, and weak audit trails.
- Solution: Three architectural patterns leveraging Power Platform + AI Builder to automate invoice capture, validation, exception handling, approvals, and posting.
- Outcome: Reduced manual effort, improved accuracy and SLA compliance, enhanced auditability, and strong ROI through phased rollout.
- Context
- Business Problem
- Solution Approach
- High-Level Workflow
- ROI Analysis
- Data
- Assets
- Contributors
- Special Thanks
- Contribution Guidelines
- License
A global FMCG enterprise operating across 20 country with decentralized finance opertaions. Accounts Payable team (AP) process millions of vendor invoices manually.
This repository focuses on designing scalable, automated solutions for Accounts Payable (AP) invoice processing using Microsoft Power Platform and AI Builder.
Manual invoice processing drives rising costs, frequent SLA breaches, high error rates, and fragmented audit trails. With invoice volumes growing rapidly, these challenges scale linearly, creating significant compliance and operational risks.
Detailed pain points, key assumptions, and impact analysis are documented in 01-business-problem.
Invoice automation is not one-size-fits-all. Different organizations and business units have unique requirements for cost, scalability, and performance.
To support this diversity, this repository documents three alternative architectural patterns for processing invoice PDFs using Microsoft Power Platform and AI Builder. Each approach solves the same business problem but optimizes for different operational constraints.
Details of these approaches are documented in 02-solution-approach
- Pattern 1: High Volume / Scalable & Resilient
Additional Patterns
- Pattern 2: Mid Volume / Balanced
- Pattern 3: Low Volume / Cost-Optimized
This diagram represents the high-level workflow for invoice automation. Detailed architectural patterns and technical diagrams are discussed indetail in the Architectural Patterns.
Compare manual vs automated costs and show payback period.
Details are documented in ROI Analysis
- Datasets required for solution design and architecture are stoted in Data
- Static resources used for this documentation are stored in Assets
This solution architecture and documentation were developed collaboratively by:
- Sriram Sivakumar — Solution Archeitecture & Design and documentation.
- Copilot (AI Assistant) — Idea expansion, document writing and content structuring.
- Vinai Sankunni — For guidance on business case presentation and financial approach.
- Fork the repo and create a branch (
feature/<name>). - Add or update Markdown files in the relevant folder.
- Include diagrams- Include diagrams in
/05-assets/diagrams. - Submit a pull request with:
- Summary of changes
- Rationale (business/technical)
This project uses the MIT License, which allows anyone to use, copy, and share this work freely.
Disclaimer: The company name, business scenario, process descriptions, invoice volumes, FTE estimates, cost metrics, and all operational details used in this repository are entirely fictional. They are intended solely to illustrate a realistic enterprise automation use case. Any resemblance to real organizations is purely coincidental. These examples reflect common patterns observed across global finance operations but do not represent any specific company.