The robotic process automation (RPA) pilot went smoothly. It was meant to be a big step forward for Accounts Payable (AP), freeing up the finance team to spend less time on manual data entry and more time on higher-value tasks.
That was until new suppliers came on board and some existing vendors updated their invoice layouts. Suddenly, the exceptions queue was longer than the list of approved invoices.
AI-native AP automation limits the need for manual intervention by reading invoice data in context. Instead of pulling data from predetermined fields, AI-native tools understand what each field actually means, so new or updated layouts don’t cause them to break.
Key takeaways
Robotic process automation for Accounts Payable uses software bots that mimic user behaviour, such as clicks, keystrokes and navigation, to follow a defined, rule-based script. For example, RPA might be used to log into a system, copy data from a field in one document and paste it into another system as many times as requested or until all fields are completed.
Accounts Payable automation using RPA saves time and effort on repetitive manual tasks like downloading invoices from vendor systems and transferring invoice data into an enterprise resource planning (ERP) system.
But the same capabilities that allow the software to effectively automate these tasks can prove its limitation. RPA depends on a set of rules to follow, so it can only identify invoice data when the fields are exactly where they are expected to be. When invoices deviate from their expected pattern, the software creates exceptions that must be manually reconciled.
For this reason, RPA rarely works alone. The reality is often RPA invoice processing and traditional optical character recognition (OCR) bolted together. AP teams manually create a new template for each new invoice design, which template-based OCR uses to turn invoice data into structured fields that the RPA software can pull into the ERP system.
AI-native AP automation is designed around AI models that interpret document content and its context. It goes beyond following fixed rules and scripts, understanding what each data field means and the role it plays. So it can lift the right information even if the invoice design deviates from the initial template or rule.
For example, AI-native AP automation recognises the difference between supplier, invoice and purchase order (PO) numbers and can discern the difference between an invoice and a credit note.
This intelligent automation is built for the variation that is so common in AP. Vendors don’t create invoices to fit neatly in your ERP, and AI-native AP automation adapts to this, working to each invoice design in context to remove the need for manual template creation and reduce exception handling.
The term intelligent document processing (IDP) refers to AI tools that extract and classify data from documents, no matter how it's structured, with little or no human intervention.
AI-native AP automation uses IDP at the first stage, lifting key invoice data without manual template creation. The tool then uses AI agents to decide the next steps, for example, matching invoices to purchase orders and routing data into the ERP or flagging rare exceptions for human checks.
Many AP tools now advertise themselves as being AI-powered. But there’s a difference between being developed with AI from day one and incorporating a machine-learning element into an existing tool.
And this difference can be key for the finance team, because it determines the level of manual support and intervention needed to achieve the desired outcome. If the tool still requires a template for each new invoice design or supplier, the benefits to the AP team are limited.
AI-native tools need no templates or fixed rules to interpret and pull invoice data, which is where the real time and cost savings come from.
The easiest way to visualise the differences between RPA and AI-native AP automation is to compare how they perform under key use cases and considerations.
| Consideration | AI-native AP automation | RPA |
|---|---|---|
| How it works | Reads and interprets invoice data | Replays scripted clicks and rules |
| Unstructured and semi-structured invoices | Natively handles variations | Requires structured input |
| New supplier or design | No templates or per-supplier setup | Often a developer ticket for new rules or templates |
| Exceptions | Resolves routine variation, escalates rare edge cases with context | Stops and queues any exceptions |
| Maintenance | Minimal | Bots can break with invoice or system changes |
| Scalability | Scales with volume | Requires more bots and greater upkeep |
| Configuration responsibility | AP team in natural language | RPA developers or automation centre of excellence (CoE) |
| Improving over time | Touchless rates improve as agents are configured | New rules from developers |
| Human requirement | Review flagged decisions only | Clear the exceptions queue |
| ERP integration | ERP-agnostic, API-based | Screen-level, fragile on upgrades |
Is your current set-up struggling to deal with invoice changes or regularly breaking? See how Xelix Capture handles your most complex supplier invoice today.
RPA projects rarely fail outright, but they often stall. Simple, structured invoices from existing suppliers flow effortlessly until rules and systems change or the manual workload becomes bottlenecked. Most stalling points fall under one of the following four categories:
What happens: It only takes a minor change, such as a supplier moving the invoice number or line items running onto a second page, before the rules given to the bot no longer match the document in front of it. At this point, the bot either stops extracting data or even starts pulling incorrect data without flagging it to the AP team.
What it costs AP: Hours spent correcting the work or, worse, incorrect data flowing downstream to the pay run.
How AI-native AP differs: By interpreting the data in context, AI-native AP automation can recognise when a field has moved or adjusted in layout, and pull the correct data.
What happens: Exceptions are flagged to the AP team, which is fine when these instances are rare and easily reconcilable. But as the vendor base grows, so does the exceptions queue.
What it costs AP: Increasing time spent reconciling errors and creating new templates erodes the time and cost savings associated with using an automated system.
How AI-native AP differs: Variation is expected and resolved automatically, with only true edge cases being flagged to the AP team, with context attached to reduce the time spent researching the issue.
What happens: Changes to ERP systems, screen layout changes and new suppliers can all create the need for bot fixes that require specialist input from developers or the automation CoE, rather than the AP team.
What it costs AP: At this point, the AP team is downstream of these other teams, bottlenecked until the issue is resolved. This creates downtime and cost implications that were never in the original business case.
How AI-native AP differs: With no templates or rules to manage, and a tool that’s less exposed to screen-level changes, there are fewer manual interventions and less downtime.
What happens: RPA proves effective for suppliers with simple, structured invoices. As more variables come into play, the process is no longer truly automated, as each supplier needs its own templates and rules. Innoflexion notes that rule-based bots limit touchless processing rates to a ceiling.
What it costs AP: Each new supplier set-up covers a smaller volume of invoices and so generates a lower return on investment (ROI), while the touchless rate stalls below target.
How AI-native AP differs: The ROI on AI-native AP scales with invoice volume, as variable layouts are covered from the start.
So, how do RPA and AI-native AP automation compare in the categories that matter most?
Technically speaking, RPA replicates the rules it’s given with 100% accuracy, every time. But this doesn’t mean the outcome is 100% accurate.
A more representative way to measure accuracy is through field-level accuracy (how often the extracted value is correct) and touchless rate. Xelix Capture delivers 99% extraction accuracy.
The cost of implementing RPA goes beyond just the vendor licence. Per-bot licences, as well as ongoing maintenance and additional template OCR costs, can see spending increase beyond the initial scope. And every new supplier or invoice redesign can mean additional costs.
AI-native AP automation is typically offered as a subscription rather than on a per-bot licence. And it comes with a low change cost, as variations are expected and managed automatically.
Scale follows the same pattern. With RPA, more invoices and more suppliers usually mean more bots and more upkeep. AI-native AP automation takes on higher volumes without the same rise in set-up work.
To understand which model will deliver the highest ROI for your organisation, compare the following key metrics:
| Consideration | Explanation |
|---|---|
| Cost per invoice | Covers people, licence and maintenance costs |
| Touchless rate | Reveals how much additional human input and work is required |
| Maintenance hours (per month) | Shows the cost of keeping automation running |
RPA and AI can work together to improve extraction accuracy and ROI. The two tools aren’t in conflict, they solve different problems.
AI is able to interpret and understand invoice data in context before RPA takes the clean, structured data and follows predetermined rules to move it through ERP systems.
RPA systems were never inherently flawed and they aren’t outdated. Many organisations are simply relying on RPA tools alone to perform tasks they were never built to do. But when you incorporate an AI layer that is able to provide the RPA with data it recognises, the tool can work accurately and efficiently.
For example, Xelix Capture is our invoice intake layer. It sits in front of the ERP and downstream AP systems and sends them clean, structured data. That can mean fewer bots doing simpler jobs, with only those that move this clean data still needed.
AI-native AP automation software needs less set-up and maintenance than RPA invoice processing.
Pre-trained AI models already understand invoices, so they don’t need per-supplier training or large data sets. Xelix Capture, for example, works out of the box. It needs no training and no per-supplier setup, and touchless rates improve as agents are configured.
While RPA needs developers and an automation CoE to validate every new rule or fix, AI-native tools need little maintenance. The AP team configures the tool in natural language, without specialist IT skills, which reduces the development backlog that builds up when exceptions are flagged.
AI tools are not simply left to their own devices. A human-in-the-loop (HITL) design means people are only involved in certain cases, where it matters most. This might include resolving flagged exceptions or any invoices that don’t meet pre-defined confidence thresholds, as well as general approvals.
Any invoice processing software is naturally going to handle sensitive vendor and finance data. Ask vendors these questions early in your evaluation:
Xelix holds ISO 27001 and SOC 2 certification, and data is encrypted, GDPR compliant and stored in the EU, US or UK.
Transitioning from RPA to AI-native AP automation doesn’t have to be daunting. AI-native software becomes the invoice intake layer, sitting in front of the ERP and downstream AP systems. When you compare tools, ask which ERPs they integrate with, how they connect and what testing and control review the switch will need.
Follow these five steps for a low-risk transition:
AI’s role in AP goes beyond invoice intake. The ability to interpret document content and detect patterns can also support other functions across the AP process, including:
AI automation can be applied across the AP process to save time spent on manual tasks such as keying and chasing, freeing the finance team to spend more time on control and judgement tasks.
AI-native AP automation works alongside existing RPA invoice processing tools to improve accuracy and send clean, structured data downstream to your ERP.
Xelix Capture, our invoice intake layer, delivers 99% extraction accuracy. Agents configured in natural language resolve exceptions before invoices reach the ERP, laying the foundation for a higher touchless rate.