Transaction risk intelligence
Automated supplier statement reconciliation
Supplier risk intelligence
Managed AP recovery audit
Supplier file performance review
Forensic risk analysis
Consulting and bespoke reporting
You’ve automated your AP processes, but things don’t feel linked up.
You’ve done the work and things are running smoothly. You’ve even started using AI in earnest.
And yet.
Getting a clear picture of your P2P performance still means logging into disparate systems. Chasing a discrepancy means your team jumps between platforms. Asking a straightforward question often means asking for someone to pull a report, or waiting until month end.
Why is getting the information you and your team need still so hard?
Here’s what’s happened. Organisations have built up a stack of capable technologies. But they are limited in their integrations, often featuring an AI that only works on their product. They don’t communicate effectively with each other.
What are the options?
Let’s re-wind.
For a long time, there have been two options.
The first: Choose the best tool for each job. Each is best-in-class at its function, and your team dips in and out as the work demands.
The second: Find a platform that does as much as possible under one roof. For many organisations, that simplicity works well.
But both approaches come with trade-offs that finance teams know well.
Disparate systems: Each piece of software becomes a walled garden of sorts – it looks neat and functional on the inside. But it’s isolated, and it’s making your process clunky. Those in management feel this most acutely. Three systems to resolve one discrepancy is an inefficiency that takes time, and time (as we know) is money, especially in finance.
The all-in-one platform: This tends to fall short in terms of depth. A platform built to cover AP, risk management, supplier management, reporting, and more faces an inherent challenge: genuine breadth and genuine specialisation are difficult to achieve simultaneously. The functionality is there, but the nuance often isn’t: The sophistication of duplicate payment detection, the granularity of supplier risk scoring, and the precision of exception management.
So organisations find themselves weighing up two imperfect options. And the question of which trade-off they can better live with often doesn’t have a clean answer.
What the leading organisations are doing differently
With the advent of AI, there’s a new option emerging. One that doesn’t require you to abandon either approach, but instead gives you greater flexibility in how you work across them.
Using your AI platform (whether that’s Copilot, Claude, Gemini or ChatGPT) as a connected layer across your existing systems means you can keep the specialist tools that do their jobs well, while removing much of the friction that comes with moving between them. The AI becomes the thread that runs through your workflow, rather than another login to manage.
The finance teams pulling ahead aren’t necessarily using more AI, but are using it more comprehensively. Teams further along this curve are managing entire workflows through integrations that let AI move across systems: pulling from multiple data sources, updating multiple documents, consulting multiple reference points, all from a single instruction.
What connected AI looks like in finance
Imagine a CFO opening Copilot and asking:
- “Show me our riskiest suppliers by spend this month.”
- “What’s our average days to pay, and what are the biggest blockers to bringing the number down?”
- “How does our duplicate payment rate compare to last quarter?”
Instead of emailing the AP team for information, or waiting on a report at the end of the month, they can get real time data, not a report that was accurate three days ago. And that can go straight into a high level report for the board, or build straight into your strategy documents.
This is what AI connectivity (with an MCP connector) makes possible. And, it’s the same thing that many AIs already use to connect to the Microsoft ecosystem.
And we can see the benefits clearly in how FISCAL are already using this technology. We’ve made benchmarking data available via an MCP connection for our team, enabling them to find and share helpful performance information with customers, without needing to train on a new system, log into a separate system or search for the information.
Two users. One platform. Different ways in.
Now, this doesn’t upend how you use your technology. Instead, it gives you and your team more options.
The AP clerk’s work is the foundation everything else rests on. Paying suppliers, checking transactions, amending discrepancies, reconciliation — that’s expert work, done by people who understand the detail. Here, the AP team still works in their dedicated platforms: ERP, Risk management etc.
However, there are many team members who don’t truly need to use the platform day-to-day. The CFO and Finance Controller no longer have to route through the AP team, or search dashboards inside the software, to get a read on the numbers. They can access the intelligence directly through the AI tools they already use, with no training or logins required.
For AP managers and clerks, that’s good news. Less time fielding report requests. More time for the analytical work that the platform is there to support.
The platform becomes accessible from another door. The AP team have the flexibility to work however suits them best, either within their own AI processes or within the software.
So, it’s not about replacing the software necessarily, but about using it in a different way depending on the results you need. And senior finance leaders can reach the same intelligence through the tools they already trust, without creating more work for the people who know the detail best.
The AI governance question
There’s one more dimension worth considering: governance.
Explainability matters more than many leaders currently account for. If AI flags a payment as high-risk, or clears one it shouldn’t, you need a defensible audit trail. “The system recommended it” won’t satisfy an auditor — or a board.
When AP software processes data through its own AI, the governance trail runs through the vendor. There’s significant due diligence involved before you can agree to that arrangement.
When your already-approved AI accesses that intelligence via MCP, you’re operating within governance structures you already control and understand. The vendor isn’t in the AI processing chain at all. As CFOs face increasing scrutiny over responsible AI use, that distinction matters. Rather than adopting a new AI governance question, you’re answering it with a tool you’ve already approved.
Your finance AI stack – where to go next
The future of finance is connection. We’ve seen in the past how silos don’t work, and that communication and collaboration is key to creating a streamlined P2P process.
The choice between specialist tools and all-in-one platforms isn’t going away. Both will continue to suit different organisations, different team sizes, and different levels of process complexity.
But the question of how your team accesses the intelligence those tools hold is changing. Connected AI gives finance teams a way to make their existing stack work harder, without asking anyone to compromise on the tools they rely on most.
How connected are your current finance processes?
How FISCAL can help
FISCAL’s P2P risk management platform has AI connectivity built in. Finance teams can access FISCAL’s intelligence through Microsoft Copilot, Claude, and other AI tools or use the platform as usual.
