For many finance leaders, the allure of general AI assistants is seductive. The promise of easier automation. Flashy demos. Managing basic tasks. However, accounts payable is a high-stakes, edge-case-filled domain. Generic tools often fail when faced with unusual invoice layouts, missing data, blurred scans, or cross-document relationships.
That’s why specialist AI agents – built for AP workflows – are gaining traction. They fill the skills gaps left by general AI assistants. Only escalating to humans when they lack confidence or hit guardrails. At their best, they increase productivity and accuracy way beyond what rule‑based systems or human data entry can match.
What specialist AI agents bring to accounts payable
Let’s get one thing straight. These tools aren’t repackaged chatbots. They’re intelligent digital coworkers, or virtual junior colleagues, that…
Understand document context
AP is more than reading invoice lines. It requires interpreting embedded details – multiple languages, supporting documents, PO references, tax rates. Specialist AI systems learn to link those elements. They distinguish between header items and line items. They flag missing signatures or mismatch VAT codes.
Retain cross‑document awareness
Often, understanding comes from piecing together several documents – invoices, purchase orders, receipts, delivery notes. Specialist AI agents can see these connections and apply them. They reduce errors by rejecting invoices without matching POs or with inconsistent pricing.
Handle multimodal inputs
Invoices might be text-rich PDFs, handwritten notes, embedded images. Multimodal AI systems process text, images, and numeric data in parallel. They outperform text-only models, like standard ChatGPT, which may struggle with complex scanned tables or poor-quality handwritten notes.
“84% of companies who have fully automated their AP report increased cash flow and savings.”
Accounts Payable and Receivable Trends: What’s Next in Automation
Measurable efficiency gains from customers
Rossum’s cloud-native platform is powered by specialist AI agents that adapt to our customers’ operations, systems, and rules…
- Veolia, a leader in optimized resource management, achieved a 90% reduction in manual workload. And 87.5% time saved per document.
- Fugro, a geotechnical, survey, and geoscience services leader, rolled out Rossum across four shared service centers in just three months. With average invoice processing time dropping from two minutes to 35 seconds.
Inertia in AP isn’t harmless. Every minute wasted on manual handling increases cost, delays payment, and introduces risk.
Processing speed is just one angle. When teams aren’t bogged by data re-entry, they can focus on exceptions, vendor relationships, and cash strategy.
According to the survey we sent to lend weight to our Document Automation Trends 2025 report, primary motivators for automation adoption reveal a focus on operational excellence…
- Efficiency gains – 43%
- Cost reduction – 32%
- Improved decision making – 27%
These findings highlight that operational efficiency and cost control remain the top drivers for automation.
Forrester’s 2024 Automation Predictions supports these findings, saying that businesses increasingly see automation as a strategic must-have rather than a cost-cutting tool.
Evaluating efficiency – What counts when choosing a specialist AI tool
Next up, how finance leaders should evaluate vendors. Focus on three key competencies…
1. Document context understanding
Look for solutions trained on real-world invoice data that adapt to variations. Avoid template‑only systems that struggle when formats shift.
2. Cross‑document awareness
Ask how the tool links incoming invoices to POs, delivery records, and previous payments. Can it spot duplicates or missing attachments?
3. Structured + unstructured data handling
Many invoices include unstructured notes, line items shifted across images. Tools must merge optical character recognition (OCR), natural language processing (NLP), and layout analysis. Ask vendors to demonstrate on your real invoices.
Questions to include in your RFP
When preparing an RFP for an intelligent document processing solution, include the following questions…
- What proportion of invoices pass through fully autonomously, and what percentage require human review?
- How quickly does the system learn from corrections? Does performance improve over time without retraining?
- Can it extract data from attachments like packing slips or combine them with invoice information?
- Does it integrate with our ERP software – SAP, Oracle, NetSuite, etc. – including support for PO matching, GL coding, and multi‑entity configurations?
- How does it handle exceptions? Can it route issues to the right person, escalate overdue invoices, or apply pre‑defined tolerance rules?
- What BI and analytics tools are included? Will you get visibility into cycle time, discrepancy rates, and employee efficiency?
Before writing your RFP, draw up a list of vendors you’ll approach. There will be homework on your side. Understand your processes and operations. Do you need template-free, language agnostic, document agnostic? Check out customer reviews, product review sites, free demos and trials.
“If your team is still personally reading documents and copying and pasting data, there’s really no time to waste here. Every finance leader must have an AI automation plan in place now, or you risk falling too far behind your savvy competitors. Where to start? There’s so much hype and confusion around GenAI and AI Agents, so you need to ask the vendors: how will your AI solve my problem? Partner with the one who replies, tell me first about your processes and people. Then we’ll talk about the AI.”
Dan Lucarini – Senior Industry Analyst, Deep Analysis
Why multimodal AI rules the roost
Multimodal AI processes visual layout, text content, and numerical patterns together. It simplifies complex documents in ways that a text-only model can’t. Here’s how it outperforms…
- Scanned pages with handwritten annotations
Text-only AI is unable to extract or interpret handwriting - Tables crossing pages
The system must see column alignment, merging cells, and reading structure - Embedded stamps or signatures
Required for invoice approval – must be recognized visually
Multimodal agents process documents in a more human-like way by simultaneously analyzing text, layout, and visual cues. Not relying on flimsy templates that break with format changes.
How to avoid common implementation failures
Building specialized AI tools doesn’t guarantee success. The following obstacles can often trip teams up…
- Change management problems
People reject solutions that disrupt their routines - Past failures
Teams are burnt by pilot projects that never launched - IT bandwidth constraints
Implementation stalls without infrastructure support
To succeed, finance leaders need strategic planning…
Build a governance team
Educate finance, operations, IT, and procurement about your automation goals. Get input early from AP experts. Make sure stakeholders understand how the tool affects daily tasks.
Audit typical invoice scenarios
Map out document types, currencies, vendor formats, attachment types, approval processes. This helps you identify where automation will deliver the fastest ROI.
Integrate with ERP early
Don’t treat integration as an afterthought. Ensure seamless PO matching, GL coding, vendor validation, and approval routing. Real efficiency comes when data flows end to end.
Check out What is ERP? The Definition of Enterprise Resource Planning to understand more.
Pilot extensively, then scale
Start with a sample of high-volume, mid‑complexity invoices. Measure KPIs such as automation rate, error rate, throughput time. and time saved. Use that to refine rules and train your team before increasing to greater volumes.
The cost of doing nothing
Rossum’s Calculating the Cost of Doing Nothing eBook outlines hidden financial losses…
- Extended invoice cycle time leads to poor cash flow and missed discounts
- Manual data entry error risks lead to duplicate payments and compliance issues
- Team time wasted on low‑value tasks instead of strategic finance work
- IT maintenance for legacy systems drains resources
Failure to act hits operational performance year after year. However, structured AI deployment delivers time savings, improved control, and healthier vendor relationships.
A PwC report found that AI could contribute up to $15.7 trillion to the global economy by 2030. With most of this value coming from its ability to enhance human capabilities.
Our document automation survey revealed that 66% of finance leaders see automation as an aid, not a threat. With success determined by balancing automation with human expertise for strategic decision making.
A roadmap for finance leaders
CFO, AP manager, or transformation leader, here’s your benefit-led plan of action…
- Quantify your current AP costs
Measure invoice volume, average process time, manual intervention rates, and error rates. For example, 10,000 invoices at two minutes each translates to 333 hours of work monthly.
- Evaluate solutions using the validation framework
Filter vendors using questions around automation rate, exception handling, integration, and learning capabilities.
- Run a live pilot program
Automate a bundle of invoices from a big vendor or region. Track performance over 60–90 days. Get actionable metrics for time saved, such as straight-through processing rate, error reduction, etc.
- Prepare your team before rollout
Provide training. Adjust roles so that team members handle exceptions and approvals, not data entry.
- Reinforce control and analytics
Enable dashboards to flag trends – delayed approvals, duplicate invoices, expensive vendors.
Conclusion
Generic AI assistants do have their place. They handle chat queries and perform simple tasks. But when it comes to accounts payable – the force behind vendor relationships and cash flow – specialist AI agents make the difference.
They understand invoices, link them to other documents, extract complex data, and integrate seamlessly into ERP systems.
If you’re evaluating AP automation, ignore marketing claims and focus on proof. Ask about context understanding, multimodal capability, ERP integration, and continuous learning. Run a pilot, adjust how your teams work, and start using analytics.
Buzzwords don’t get results. Performance does. Specialist AI agents deliver outcomes that generic tools will never be able to do.
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