The AI-Powered RFP Series: Governing The Next Generation Of AP Automation
Ardent Partners will be publishing our 2026 AP Automation and Payments Technology Advisor report in December. This report is designed to help AP, finance, and P2P leaders navigate the AP Automation and Payments solution provider market. We’ve been publishing this report since 2019 report (our previous version available here).
To keep up with the rapid innovation taking place in the industry, we reworked the provider survey, the evaluation criteria, and the report. Accounts Payable is undergoing a foundational shift from rule-based workflow automation to AI-driven cognitive decisioning, forcing organizations to rethink how AP technology is evaluated, selected, deployed, and governed.
My point is that traditional RFPs, built around features, checkboxes, and deterministic outcomes are not designed to protect organizations from AI risk including performance risk, model inaccuracy, or vendor over-claim. This 3 part series reframes the AP technology sourcing process around evidence, transparency, data integrity, and sustained model performance, equipping finance and procurement executives with a modern blueprint for AI-ready vendor evaluation.
Part 1 – The AI Mandate: Redesigning Your RFP for Statistical Truth
The first article examines why the conventional RFP construct fails when applied to machine-learning-driven platforms, where outcomes are probabilistic and accuracy varies by data quality, variance, and operational context. Rather than asking vendors whether they “have AI,” AP leaders must test how the model behaves, how it explains decisions, how it manages risk, and how performance is proven, not simply promised.
Traditional AP automation RFPs were built for rules-driven systems. They focused on binary capability tests: Can the platform match POs? Does it handle three-way match? Can it route multi-entity approvals? But AI-enabled AP platforms are not deterministic engines, they are probabilistic statistical systems whose performance varies based on data quality, model design, and continuous learning. This means that the core success metric has changed: the priority is not whether the system can perform a process, but whether it can consistently produce accurate results at scale with traceable reasoning.
1) Define and Evaluate the AI Philosophical Approach
A modern RFP must evaluate how intelligence is embedded into the platform: as add-on features, domain-trained models, or platform-level cognitive infrastructure. The vendor must disclose its model lineage, training approach, data governance design, and “explainability.” For example, Bottomline, a leader in B2B payments, including secure payment capabilities and extensive fraud- and risk-mitigation controls, should be evaluated on how its AI enhances transaction monitoring, strengthens compliance workflows, and supports traceability when high-risk activity is identified.
2) Test Explainability, Transparency & Trust Controls
The RFP should require full visibility into how an AI model reaches a conclusion, including the reasoning path, confidence score and versioning history. This is critical for audit, compliance, and internal controls. Require product demos showing model output transparency, not UI convenience.
3) Require Evidence-Backed Data Extraction Performance
Invoice capture accuracy is the first, and most important, AI performance gate. Ask vendors for document-level accuracy proof using your real data, not curated demo sets. Medius, widely recognized for its overall AI strength and its machine-learning-driven invoice capture, automated matching, and exception-handling capabilities, should be benchmarked using identical data sets to assess model behavior and ensure buyer-specific, evidence-based scoring.
4) Proof-of-Accuracy Testing Must Be Contractual
Require a pre-award accuracy performance test, using anonymized buyer invoice data, tied to contractual performance thresholds (example: 90%+ post-training classification accuracy after 90 days). Do not accept third-party benchmark claims, generic industry averages, or lab-tested models.
Conclusion
As AP leaders confront a technology landscape shifting from deterministic workflows to probabilistic intelligence, the RFP becomes the first—and most critical—governance mechanism for separating mathematical truth from marketing optimism. By demanding statistical evidence, model transparency, lineage disclosures, real-data accuracy testing, and contractual performance thresholds, procurement and finance teams ensure that AI-driven AP platforms are evaluated on measurable outcomes rather than feature-labeled promises. This new approach reframes the RFP as a validation instrument for trust, safety, and operational reliability in an AI-native AP environment.
In Part 2, we move beyond evaluating the AI itself and examine what happens after the model is selected—how AP leaders architect cognitive workflows that blend autonomous agents, human oversight, escalation logic, and explainable decision paths. We’ll break down how each layer of intelligence should be governed, monitored, and continuously improved, ensuring that AI not only performs accurately in isolation but operates responsibly at scale across the entire AP lifecycle.
