Scott Park Group: Using AI to Decode Thousands of AP Emails – and Build the Case for Automation

Brief

We ran a structured AI analysis of Scott Park Group’s entire Accounts Payable inbox using LLM-driven categorisation and clustering. The result was a data-backed breakdown of their communication patterns, a validated list of automation opportunities ranked by impact, and a practical roadmap for building automated inbox agents in Microsoft Copilot Studio. Scott Park Group went from inbox ambiguity to a clear, confident path forward, without exposing any sensitive internal content in the process.

Problem

A high-volume AP inbox creates a specific kind of operational drag. Staff spend time triaging emails that arrive in bulk, many of which are repetitive, supplier confirmations, remittance advices, invoice queries, standard requests, and that triage work is low-value but unavoidable when you can’t see the full picture clearly enough to automate it. The challenge for Scott Park Group wasn’t just the volume, it was the lack of visibility. Without a reliable breakdown of what was actually landing in the inbox, it was hard to know where automation would have the most impact, which categories appeared frequently enough to justify building a solution, and how workload was distributed across the year. Gut feel wasn’t enough to make confident investment decisions about automation tooling, so they needed a data-backed foundation before building anything.

Solution

We conducted a structured AI analysis of Scott Park Group’s entire AP inbox using LLM-driven categorisation and clustering at scale. The system processed thousands of emails, grouped them into meaningful categories, identified volume patterns across the year, and surfaced the most repetitive correspondence types, the ones with the strongest case for automation. Importantly, the analysis was designed to extract structural and categorical insights without exposing sensitive content or internal processes, so Scott Park Group got the clarity they needed without any privacy risk. From the analysis, we delivered a high-level overview of communication patterns across the full year, identification of recurring email types with the strongest automation potential, insights into volume, timing, and workload distribution across the AP team, a prioritised list of email categories to automate first, and a practical roadmap for building automated inbox agents using Microsoft Copilot Studio.

• LLM-driven at scale: manual categorisation of thousands of emails isn’t feasible, but AI clustering made it possible to analyse the full inbox reliably and quickly
• Discovery before build: instead of jumping straight to automation and guessing at priorities, the analysis gave Scott Park Group a validated foundation for every subsequent decision
• Privacy-conscious by design: the analysis extracted patterns and categories without surfacing sensitive supplier or financial content
• Actionable output: the roadmap didn’t just describe the landscape, it told them exactly what to automate first and how to build it in Copilot Studio

Result

Scott Park Group now has a clear, validated picture of their AP inbox: what’s in it, how often different email types arrive, and which categories will deliver the most value when automated. The guesswork is gone. They walked away with a ranked list of automation opportunities, a practical roadmap for building inbox agents in Copilot Studio, and a much stronger foundation for reducing manual processing pressure on the AP team. The discovery analysis didn’t just answer the question of what to automate, it gave leadership the confidence to move forward knowing those decisions were backed by real data.

Under the Hood