The company implemented an intelligent automation architecture designed to operate across its existing agency systems. Rather than replacing every ERP or forcing agencies onto a single platform, the solution created an automation layer that connected applications and standardized the movement of information between them.
The architecture combined unattended RPA bots, AI-powered document intelligence, workflow orchestration, and anomaly-detection agents.
- Automating Financial Data Consolidation
Unattended bots extracted financial and billing information from different ERP systems on a scheduled basis. The data was normalized into a consistent structure before being reconciled against the central financial environment.
Instead of waiting for finance teams to manually identify differences, the automation layer flagged exceptions for human review. Standard transactions could continue through the workflow automatically, while unusual or incomplete records were routed to the appropriate team.
This created a practical model for cross-entity finance automation: different entities could continue using their existing systems while the shared services environment received standardized financial information.
- Automating Vendor Invoice Processing
AI document intelligence was introduced to process invoices arriving in different formats, including unstructured PDF documents.
The AI layer extracted relevant fields such as vendor information, invoice numbers, amounts, dates, and purchase-order references. Once validated, approved transactions could be routed to the appropriate agency ERP.
This reduced repetitive data entry and helped finance teams focus their time on exceptions rather than manually entering every invoice.
Unattended bots collected employee time logs from multiple project management platforms. The information was validated, standardized, and routed to the central payroll and client-billing processes.
This was particularly important in an agency environment where employee time can affect both internal workforce reporting and client billing.
- Automating Creative Briefs and Exceptions
The automation model also extended beyond conventional finance transactions.
AI-driven workflow orchestration processed creative briefs, assets, project milestones, and approvals. When predefined conditions were met, the system automatically triggered the next stage of the workflow and delivered the required information to downstream teams.
Anomaly-detection agents provided another layer of intelligence by identifying unusual records and exceptions instead of treating every transaction the same way.
Together, RPA and AI created a more flexible form of RPA finance automation. Bots handled predictable, repetitive activities, while AI helped interpret documents, identify anomalies, and route exceptions.
- Designed to Scale Across Agencies
The architecture was deliberately designed for scale. New agencies could be connected to the automation layer without requiring the entire network to standardize on one ERP or operational platform.
This approach allowed the company to preserve agency-level autonomy while creating standardized processes at the shared-services level.
The result was an automation model that could expand as the organization acquired additional businesses—turning automation into part of the post-merger integration framework rather than a one-time technology project.
