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The question is no longer whether AI belongs in accounts payable. It’s how finance teams can scale it without sacrificing the trust, visibility, and control they need.
In our global survey of 500 finance professionals across the US, Canada, and the UK, 98% of professionals view AI implementation as important, but 48% are concerned about the risks of using AI in finance.
This tension is the focus of our recent webinar: Applying AI in AP: Balancing Automation and Oversight, where Brendan Madigan, Chief Financial Officer at Limited Run Games, and Alexandra Cancio, Principal at RSM, talk with me about how AI has quickly become a foundational component of accounts payable processes.
The message is clear: the goal isn’t touchless AP at any cost. Controlled automation, in which AI supervised by humans speeds up repetitive tasks, is key to integrating AI into accounts payable operations. This is how AI delivers real, measurable value, and why finance teams can trust the system.
AI in Finance: Balancing AI Power and Human Judgment
Here’s what finance leaders need to do well to scale AI in AP:
AI in AP: AI Efficiency and Financial Oversight
As transaction volumes rise, modern finance leaders know that AI and automation can be effective ways to scale operations without proportionally increasing headcount and to allocate work effectively, so teams can reduce time spent on mundane tasks. That’s key to them delivering higher-value outcomes, but also affects retention.
Skilled accounting professionals want to do meaningful work and rely on AI to handle repetitive tasks or speed up workflows like invoice processing: 85% of UK finance teams and 71% of North American teams currently use AI-based applications.
Successful finance transformation is not about removing finance teams from the process. It is about applying AI where it performs best: in structured, high-volume, low-risk tasks such as invoice capture, data entry, GL account coding, and PO matching, while keeping finance teams in control of review, exceptions, and final decisions.
That’s why effective organizations treat AI-powered AP automation as a force multiplier, not a replacement for finance expertise. When routine accounts payable work is automated, finance teams can spend more time on exception management, risk mitigation, and strategic growth support for the business.
Why AI Is Gaining Momentum in Accounts Payable
Several structural pressures are accelerating the shift toward intelligent automation:
- Bandwidth and resource constraints: Accounting talent shortages and rising labor costs force flat finance teams to manage expanding operational workloads.
- Escalated transaction volumes: Scaling a business inevitably multiplies vendor invoices, supplier queries, and the complexities of multi-entity reconciliation.
- Finance transformation initiatives: Modern CFOs are shifting back-office staff from repetitive data entry to strategic analysis.
AI does not have to replace finance expertise. When implemented thoughtfully, AI amplifies it.
Understand Where AI Delivers the Most Value in AP
AI-powered AP tools can identify patterns, recommend next steps, and help move routine work forward within defined workflows, controls, and approval rules.
The value is not that AI operates independently, but that it supports finance teams with more speed, consistency, and visibility. For example, workflow-specific, human-supervised AI agents can help teams apply AI to the right tasks.
This only works when AI is embedded into the platform and core systems your team already relies on, not bolted on as a standalone tool. Without native integration, AI lacks the context to deliver real impact.
The right accounts payable software creates measurable improvements across every stage of the payables lifecycle, from supplier onboarding through payment reconciliation:
| AP Workflow Stage | AI Capability and Function | Practical Business Outcome |
| Supplier Onboarding and Validation | The self-service portal validates supplier tax IDs across 60+ countries against 3,000+ rules at the point of entry, and AI automatically extracts W-9 data from uploaded tax forms. | Prevents payment failures downstream by catching invalid data before the first invoice arrives, not after a payment bounces. |
| Invoice Processing and Capture | Unlike static OCR tools that require templates and manual cleanup, agentic AI reads, interprets, and fills invoice fields with increasing accuracy. | Eliminates manual rekeying, cutting processing times to an average of two minutes per invoice while significantly reducing data entry errors. |
| Coding Recommendations | Predictive algorithms analyze historical invoice patterns to suggest appropriate General Ledger (GL) account codes, departments, and cost centers. | Standardizes accounting treatments across invoices, maintaining consistency even in highly complex multi-entity structures. |
| PO Matching | Automated engines execute two-way and three-way matching, cross-referencing invoice details against purchase orders and goods receipt notes (GRNs) at line levels. | Enforces purchasing compliance automatically, flags overbilling instantly, and ensures teams only pay for what was contractually authorized and received. |
| Workflow Routing | Intelligent routing trees evaluate variance thresholds and financial policies to dynamically direct bills to the correct organizational approvers. | Removes manual internal tracking and prevents cycle bottlenecks, enabling approvers to act via email without platform training. |
| Duplicate Detection and Risk Control | Continuous transaction screening prevents fraud and overpayment by flagging duplicate invoices and anomalies early. | Provides a critical, proactive security layer that instantly triggers duplicate invoice detection, identifying payment anomalies and potential overpayments. |
| Payment Execution and Compliance | Automated sanctions screening (OFAC, AML) runs for every payee before funds are released. AI flags behavioral anomalies and connections to blocked entities across the payment network. | Ensures no payment executes without compliance clearance, enabling proactive fraud prevention rather than reactive cleanup after the wire clears. |
| Reconciliation and Financial Close | Real-time ERP sync automatically posts every transaction to sub-ledgers. AI-powered reporting generates custom close reports from natural-language prompts. | Accelerates financial close by 50% or more. The month-end stops being a reconciliation scramble and becomes a validation exercise. |
Learn the Three Essential Readiness Areas Before Adopting AI
Our webinar experts noted that automation acts as an environmental amplifier. If an organization introduces AI into a broken operational environment, it accelerates existing inefficiencies. Before launching an AI initiative, they recommend that leaders evaluate three foundational readiness pillars.
1. Process Clarity
AI can only scale in AP when the underlying workflows are standardized. If approval chains are inconsistent, thresholds are unclear, or exceptions are handled ad hoc, finance teams should expect unreliable outputs. Start with one defined use case, choose tools that support it, and put the right internal processes and team enablement in place before expanding.
2. Data Readiness
AI-powered models depend on the quality of the data they consume. Moving away from manual, paper-based workflows to localized, structured digital datasets is essential, but if underlying vendor registries, primary data, or banking information are messy or scattered across disconnected spreadsheets, the resulting outputs will be flawed.
3. AI Governance Framework
Organizations must establish clear operational boundaries regarding automation guardrails. Before deploying accounts payable software, finance leaders need to answer these questions:
- What data points is AI authorized to recommend?
- What routine transactional parameters can move with touchless execution?
- Where must human review checkpoints be enforced?
AI tends to amplify whatever is already going on in your current environment: good, bad, indifferent. If things are not going well, you’ll see where problems are very quickly.
Alexandra Cancio, Principal at RSM
Maintain Human Oversight
The most secure operating model leverages a structured human-in-the-loop framework in which AI handles high-volume, repetitive processing, while humans supervise the outputs.
Certain strategic, high-risk operational moments must always have human oversight:
- Data and banking modifications: Any supplier request to alter banking details, routing info, or payment details should be flagged and manually verified to eliminate phishing and fraud vectors.
- High-value or non-routine transactions: Invoices that exceed established financial tolerance variances or deviate from routine payment history require human review.
- Compliance-sensitive outflows: Final payment releases, international cross-border compliance routing, and onboarding overseas entities should have human sign-off.
The goal is not to remove humans from AP; it’s letting AI handle routine work so humans can focus on judgment, risk, and strategy.
How Mid-Size Organizations Can Get Started
Businesses can achieve operational lift by taking an incremental approach to accounts payable automation. Rather than attempting a sweeping tech overhaul, leaders should identify specific localized friction points as AI pilot cases, such as:
- Digitize supplier onboarding and strengthen vendor management by deploying a self-service portal. This shifts the administrative burden of W-9/W-8 tax collection and banking registry maintenance to the suppliers themselves.
- Target repetitive tasks starting with basic invoice header capture and predictive GL coding for recurring, fixed-amount vendor bills.
- Optimize approval workflows by replacing shared drives, chat channels, and manual email follow-ups with email-based approval loops and automated reminders.
Evaluating an AI-Powered AP Platform
When evaluating potential tech partners, finance executives must prioritize business outcomes, compliance standards, and systemic transparency over abstract feature lists. A modern, AP automation platform should fulfill several non-negotiable architectural requirements:
- Explainable logic: The platform must provide transparency, allowing users to drill down into the model’s reasoning and see confidence scores rather than operating as a black box.
- Fixed audit trails: Every automated recommendation, human override, approval action, and system sync must leave a permanent, time-stamped record.
- ERP connectivity: Automation layers must connect directly to the core system of record (e.g., NetSuite, Sage Intacct, or Microsoft Dynamics) via a prebuilt API to avoid fragmented workflows or manual file exports.
- Stringent financial controls: The platform must inherently reinforce role-based permissions, automated TIN matching, regulatory list screening (e.g., OFAC), and strict segregation of duties. Controls should be enforced at the platform level, not layered on after the fact.
[Finance leaders] should focus on outcomes, not AI features.
Brendan Madigan, Chief Financial Officer at Limited Run Games
Move Toward a Strategic Finance Future
Ultimately, deploying AI for finance teams is about empowerment. Maintaining a human-in-the-loop approach empowers staff to focus their energy and talent on critical exception management rather than manual data entry. By letting AI-driven automation shoulder the operational burden of high-volume transaction processing, finance teams are freed up to conduct high-value analysis that supports business growth.
When finance teams transition away from data entry tasks and step into higher-value strategic roles, they focus on cash flow optimization, working capital planning, risk management, and proactive business advisory. Implementing intelligent accounts payable workflows amplifies their strategic value.
Modern accounts payable software should offer native automated guardrails, such as proactive duplicate invoice detection, to catch errors before they hit the ledger.
Learn how modern AP automation platforms combine AI, controls, and human oversight.
