AP Automation Case Study: How a Marketing Network Saved $200K and 9,700 Hours

Executive Summary: Key Results

A large marketing network operating across 14 entities automated its manual AP process using AI-driven invoice automation.

Metric

Result

The solution combined LangGraph agents, Prisma, Maconomy, and Gemini 2.5 Flash to automate invoice processing, validation, reconciliation, and ERP workflows while maintaining human oversight for exceptions.

The Challenge: Manual AP Across 14 Entities

Managing accounts payable across 14 entities created significant operational complexity for the marketing network. This AP automation case study highlights how manual processes became increasingly difficult to manage as transaction volumes and business operations expanded.

Invoices had to move through multiple manual steps, including invoice receipt, data extraction, validation, reconciliation, approval, and posting. Finance employees spent substantial time moving information between documents, spreadsheets, and finance systems rather than focusing on higher-value activities.

The organization was spending approximately 810 staff hours every month on manual AP activities. At that scale, even small inefficiencies were multiplied across thousands of transactions and multiple entities.

Invoice reconciliation was particularly time-consuming. The process took approximately 4.2 days, creating delays for finance teams and increasing the amount of time required to resolve discrepancies before transactions could be finalized.

Manual processing also contributed to a 12% error rate. Errors in invoice data could result in additional investigation, corrections, communication with vendors, and reprocessing. In other words, every error created another layer of work for the AP team.

The organization also needed to support business growth. Acquisitions meant bringing new entities, vendors, invoices, and processes into the existing AP operation. A model that required additional AP staff every time transaction volume increased would not scale efficiently.

The Solution: AI-Driven Invoice Automation

The organization implemented an AI-driven invoice automation solution designed to connect invoice processing with its existing finance environment.

The solution brought together LangGraph agents, Prisma, Maconomy, and Gemini 2.5 Flash, with each component contributing to a different part of the workflow.

The easiest way to understand the architecture is to think of it as a digital workflow that receives an invoice, understands what is on it, checks whether the information makes sense, determines what should happen next, and sends the transaction to the appropriate finance system.

Understanding the invoice

Gemini 2.5 Flash provides the AI capability used to understand invoice information. Instead of relying entirely on fixed invoice templates, the system can interpret information from invoices presented in different formats. It can identify relevant information such as vendor details, invoice numbers, dates, amounts, and other fields required for processing. This reduces the amount of manual data entry required from AP employees.

Coordinating the workflow

Once invoice information has been extracted, LangGraph agents help coordinate the processing workflow. Rather than treating invoice processing as one simple automated task, the workflow can move through multiple steps. It can determine what information needs to be validated, what action should happen next, and when an invoice requires additional attention.

This is particularly useful for exceptions. If an invoice does not meet the expected conditions, the process does not simply stop. The workflow can identify the issue and route it for appropriate review.

This type of architecture is part of the broader shift toward AI agents in finance and agentic process automation, where AI is used to coordinate multi-step business processes rather than perform only one isolated task.

Connecting the automation with finance systems

Prisma supports the application’s data and processing layer, helping the automation work with structured information throughout the workflow.

Maconomy remains part of the organization’s finance environment and provides the system where relevant financial transactions are processed and maintained.

This means the organization did not need to replace its existing finance system simply to introduce automation. Instead, the AI-driven workflow works alongside the existing environment.

In practical terms, the process works like this:

An invoice enters the AP workflow.
AI interprets and extracts the relevant invoice information.
The workflow validates the information against available data and business rules.
Straightforward invoices continue through the automated process.
Exceptions are identified and routed for human review.
Validated information is passed into the finance environment.
The transaction is processed through Maconomy.

The important distinction is that AI does not replace financial controls or human judgment. Instead, it handles high-volume processing while people remain available for cases that genuinely require intervention.

This combination of AI, workflow orchestration, business rules, and existing ERP infrastructure enabled the organization to move beyond basic RPA and build a more flexible AP automation model.

For organizations evaluating technology options, understanding the difference between traditional automation and AI-enabled workflows is also important when comparing the best AP automation software for their requirements.

Results: Before and After

The implementation produced measurable improvements in productivity, processing speed, accuracy, scalability, and cost.

Metric

Before automation

After automation

Result

810 staff hours reclaimed every month

The organization reclaimed approximately 810 staff hours every month by reducing manual AP activities. Over a full year, that means approximately 9,700 hours of capacity that we no longer spend on repetitive invoice-processing tasks. For a finance organization, reclaimed capacity does not necessarily mean reducing headcount. It can allow AP professionals to spend more time on exception management, vendor relationships, controls, analysis, and other activities that require human judgment.

Reconciliation reduced from 4.2 days to under 18 hours

Invoice reconciliation was reduced from approximately 4.2 days to under 18 hours. This shortened processing cycle helps finance teams move through invoice-related work more quickly and reduces the amount of time transactions remain in manual processing and reconciliation queues.

Error rate reduced from 12% to under 0.6%

The error rate dropped from 12% to below 0.6%. Reducing errors also reduces the downstream work associated with correcting them. Fewer incorrect or incomplete transactions can mean less investigation, re-entry, communication, and reprocessing for AP teams.

Three acquisitions onboarded without additional AP staff

The organization was able to onboard three acquisitions without adding AP staff. This is an important scalability result. As the business expanded, the AP function was able to absorb additional entities and transaction volumes without requiring a proportional increase in administrative resources.

More than $200K in annual savings

The combined impact of automation generated more than $200,000 in annual savings.

The financial benefit came alongside the productivity and process improvements rather than existing separately from them. By reducing manual effort, shortening processing times, lowering errors, and supporting growth without additional AP staff, the organization created measurable value from its automation investment.

For finance leaders building their own business case, these are the types of metrics that should be considered when evaluating the potential value of AP automation.

What This Means for Finance & Shared Services Teams

This case study has broader implications for finance and Global Business Services teams.

Shared services organizations often need to support increasing transaction volumes, multiple business units, and organizational growth while maintaining or improving service levels. AP is a particularly relevant process because it combines high transaction volumes with repetitive manual work.

The results from this marketing network demonstrate how AI-driven automation can change the way shared services capacity is managed.

First, automation can create capacity without requiring proportional headcount growth. Reclaiming 810 hours every month means the organization gained additional processing capacity without simply adding more people.

Second, standardization becomes easier across multiple entities. A common automation layer can help create consistent processing workflows across entities while still allowing appropriate controls and exception handling.

Third, automation can support M&A activity. The ability to onboard three acquisitions without adding AP staff demonstrates how an automated process can absorb additional organizational complexity.

Fourth, GBS leaders should measure more than labor reduction. Useful metrics include hours reclaimed, processing cycle time, error rates, exception rates, cost per invoice, and the amount of additional transaction volume that can be handled without additional staffing.

Finally, AI should not be viewed as a replacement for finance expertise. The more practical model is to automate repetitive work and allow finance professionals to focus on exceptions, controls, analysis, and decisions.

For organizations exploring broader finance transformation, AP can therefore become a starting point for a larger automation strategy across finance and shared services.

How to Get Started with AP Automation

The first step toward AP automation should be understanding your current process rather than immediately selecting a technology. Start by documenting how invoices move through your organization, including where vendor invoice automation could eliminate repetitive manual tasks. Measure invoice volumes, manual processing hours, reconciliation time, error rates, exception volumes, approval delays, and the systems involved. These metrics provide a baseline for evaluating invoice processing automation opportunities and calculating potential AP automation ROI. Understanding these gaps also helps determine which processes can eventually move toward autonomous accounts payable, where AI and automation handle routine invoice processing with minimal human intervention.

A quick way to establish this baseline is through an AP efficiency assessment. The free, 3-minute assessment helps identify potential inefficiencies in your AP process and gives you an initial view of where automation could create measurable value.

Next, identify which activities are suitable for automation and which require human judgment. High-volume, repetitive activities are often strong candidates, while complex exceptions may require a human-in-the-loop approach.

You can then evaluate the technology and integration requirements, including your ERP, invoice formats, business rules, approval workflows, and security requirements. Our AP automation services can help organizations assess and automate invoice-processing workflows.

You can also explore the AP automation business case to understand the financial factors that should be included when evaluating an automation investment.

For a broader overview of invoice-processing technologies and workflows, see our invoice automation guide.

The goal is simple: establish the baseline, identify the opportunity, calculate the potential value, and then determine the right automation approach.

FAQ

How much can AP automation save?

AP automation savings depend on invoice volume, processing complexity, labor costs, error rates, exception rates, and how much of the process can be automated. Savings can come from reduced manual processing, fewer errors, lower reconciliation effort, faster processing, and the ability to handle increased transaction volumes without proportional increases in staff.

How long does AP automation implementation take?

Implementation timelines vary based on the number of entities, invoice volume, ERP integrations, workflow complexity, and the scope of automation. A smaller proof of concept can generally be implemented faster than a multi-entity enterprise deployment. A typical implementation involves process assessment, solution design, integration, testing, validation, user acceptance, and deployment.

What ERP systems does AP automation work with?

AP automation can be integrated with a range of ERP and finance systems. The exact approach depends on the organization’s technology environment and the integration capabilities of the ERP. In this case study, the solution worked with Maconomy as part of the finance environment. When evaluating an AP automation solution, finance teams should consider how the platform connects with their existing ERP, how invoice data is transferred, how validations are performed, and how exceptions are handled.

Can AP automation handle exceptions and edge cases?

Yes. Modern AI-driven AP automation can be designed to handle exceptions and edge cases as part of the workflow. This is one of the key differences between AI-enabled automation and basic rules-based RPA. Instead of requiring every possible invoice scenario to be predefined, AI can interpret information and help determine the appropriate next step. When an invoice cannot be processed confidently, it can be routed to a human for review.

How does AI improve invoice processing accuracy?

AI can improve invoice processing accuracy by interpreting invoice information, extracting relevant fields, and supporting validation against available business and financial data. Unlike rigid template-based approaches, AI can work with invoices that vary in format and structure. However, AI should be combined with business rules, validation checks, system data, and human review where required. This creates a controlled workflow rather than relying on AI alone.

What is the ROI of AP automation?

The ROI of AP automation depends on the organization’s baseline costs and the scope of automation.

A complete ROI calculation should consider:

Manual AP hours
Cost per invoice
Invoice volumes
Error and rework costs
Reconciliation effort
Approval delays
Staffing requirements
Expected automation rate
Software and implementation costs

In this case, the organization achieved $200K+ in annual savings and reclaimed approximately 9,700 staff hours per year. Finance leaders can use these metrics as examples when building their own business case, but the expected ROI should be calculated using the organization’s actual AP data.

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Business

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Core Automation Services

Consulting & Engineering

Free AI Consultation

Services to Departments

Assess Your Enterprise Readiness for Agentic Automation

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Learning

Tools

About

Business

Engagement models

Core Automation Services

Consulting & Engineering

Free AI Consultation

Services to Departments

Assess Your Enterprise Readiness for Agentic Automation

Services to Industries

Assess Your Enterprise Readiness for Agentic Automation

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Learning

Tools

AP Automation Case Study: How a Marketing Network Saved $200K and 9,700 Hours

Executive Summary: Key Results

The Challenge: Manual AP Across 14 Entities

The Solution: AI-Driven Invoice Automation

Results: Before and After

810 staff hours reclaimed every month

Reconciliation reduced from 4.2 days to under 18 hours

Error rate reduced from 12% to under 0.6%

Three acquisitions onboarded without additional AP staff

More than $200K in annual savings

What This Means for Finance & Shared Services Teams

How to Get Started with AP Automation

FAQ

Ready To Transform Your Finance Operations?

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