In speaking to finance professionals and CFOs at SAP’s recent Innovation for Finance event, it's clear that many share the same challenge right now. They know AI could transform their operations, but they're overwhelmed by where to start and worried about getting it wrong. Those attending aren’t alone — we regularly work with CFOs who are struggling to find their AI starting point.
The procurement-to-payment (P2P) process offers an opportunity. Straightforward on the surface, the reality is that P2P can be complex and overwhelming. It requires managing hundreds of vendors, each with different invoice templates, across multiple countries, business units, and product lines.
The solution? AI. P2P has the risk blend of a value-add process to transform with some logical wins to resolve your AI dilemma. Moreover, using AI to solve your P2P challenges will give your team a high-value scenario to start their AI journey with confidence.
In this blog, we'll explore why even well-automated financial processes still create costly headaches for CFOs and show you how a few fundamental AI activities can transform your P2P operations from a persistent problem into a strategic advantage. Read on to start building the foundation for broader AI adoption across your organization.
You may think your financial processes are pretty sound. Especially if you’ve already applied automation in several key areas, like purchase requisition creation, flexible approval workflows, three-way matching and variance posting.
These controls automations are sufficient from a compliance perspective and will tick off a smooth audit. But more and more companies are seeing AI's potential and don't want to be left behind.
From a financial standpoint, incorrect or duplicate invoices are still getting paid, and fraudulent invoices that enable outside parties to intercept vendor information are still being processed. When money is paid to a party that it shouldn’t have been, it can be very difficult to reclaim, especially when the amount runs into the millions which we have heard more than we should.
Operationally, processes across purchase requisitions, approval workflows, and invoice data entry are still quite manual and soak up valuable staff time, particularly when hundreds or thousands of these must be processed every week. On top of that, these manual processes are slow and prone to human error. This can be within the physical inventory warehouse management checks or even within Finance and Payables teams getting invoices to the point of payment.
All of the above leads to the P2P process costing far more time, money and resources than it really needs to.
Not only can AI help solve these bottlenecks and pain points, but financial processes like P2P represent the ideal starting point for AI deployments.
This is because they should be based around well-defined, structured processes that can be enhanced incrementally and clear demonstrable ROI through reduced error rates and improved efficiency. The controls enhancements are comparatively easier to implement than other complex automations and they can be applied at every stage of the P2P process:
Purchase requisition
Intelligent requisitioning can both predict when new PRs are needed and automatically generate them and flag them for approval (if required), while smart approval routing can determine required approval levels based on risk, budget impact and business rules. All this can reduce staff admin burden and delays and maintain appropriate oversight.
Purchase order
AI can detect anomalous data points in quantities, prices, and delivery terms; detect non-contractual arrangements; and forecast potential fulfilment delays based on vendor history and product complexity. This cuts the risk of costly ordering mistakes, operational disruptions, and disputes and contractual breaches with suppliers that can cause long-term damage.
Receipt of goods/services
Image recognition covers a range of areas, from verifying item quantities on receipts, through detecting surface-level package damage, to enhancing three-way matching. This boosts processing speed and accuracy, reduces the need for manual reconciliation, and lightens the workload on staff.
Invoice processing
Processing invoices can be transformed through OCR and automated capture, duplicate detection, fraud detection, and predictive tools that can assess the likelihood of invoices being approved. These capabilities collectively protect data and funds, vastly speed up invoice processing, and improve vendor satisfaction by ensuring they’re consistently paid in a timely, accurate manner.
Payment processing
Smart payment scheduling enables early payment discounts against cash flow requirements, and dynamic approval workflows can find the best payment routing depending on approver availability, response times and accuracy history. This allows the business to maintain an optimal cash position while protecting processing standards regardless of urgency or staff availability.
Of course, all of this needs to be deployed in line with regulatory and compliance demands, which are tightening all the time with more AI-specific legislation on the way.
New and recent developments in the pipeline include:
UK AI Regulation Bill (2025): pro-innovation, light-touch approach with voluntary frameworks
UK AI Intelligence Bill (Pending): aiming to establish a statutory framework for AI regulation and ethical AI principles
EU AI Act (2024): a more stringent, comprehensive risk-based framework, which will impact all companies that operate or interact with those in EU territories
Indirect impacts: UK Corporate Reform Provision 29 and US SOX both require AI in financial reporting to be auditable and traceable, and with human oversight
To stay ahead of compliance requirements, organizations should adopt three critical governance principles from the start:
Document: First, document all AI models with clear business purposes and approval trails. The approval trail should align to a set policy/ framework for safe AI usage.
Oversee: Second, maintain human oversight and test AI implementations alongside existing processes to ensure operations remain correct and explainable, e.g. compare the deviations between the AI model and your as-is process before committing to the implementation of the AI model.
Review: Finally, conduct regular reviews of the output of the AI. Pay specific attention to the exceptions produced by the AI model to identify if the model no longer fits business purposes and ensure tools remain aligned with their intended purposes and your Risk Management framework.
We know that many finance teams are concerned about using AI, whether due to resistance to change, the endless choice of tools, a lack of internal expertise, or worries about governance or building a business case. But financial processes like P2P represent the perfect opportunity to explore AI's potential in controls and build confidence for broader implementation across the organization.
If you feel you need help along the way, Turnkey’s expertise in financial controls transformation, complex automation, and regulatory requirements can be invaluable. We can guide you through your AI journey with all the building blocks you need to make your deployment a success, including governance framework design, process re-engineering, and audit alignment.
To find out more and to discuss your specific requirements, get in touch with the Turnkey team today.