The era of AI for AI’s sake is officially over. The boardroom has lost its patience with flashy science projects and wants to see cold, hard cash. As Basware’s Jason Kurtz puts it, and I couldn’t agree more, “We’ve reached a tipping point where boards and CEOs are done with AI experiments and expecting real results. AI for AI’s sake is a waste.” It seems the C-suite is finally asking the right question: not what can AI do, but what should it do to boost the bottom line?
For a long time, the answer has been underwhelming. We’ve had a decade of so-called ‘digital transformation’ where finance teams were sold on automation that, frankly, wasn’t very smart. It was good at following rigid rules but would fall over the second an invoice had a slightly different format. Now, a new approach is quietly taking over, and it’s delivering the goods. We’re talking about agentic AI accounts payable systems, and the numbers are starting to look very interesting indeed.
The Brains Behind the Brawn: What Is Agentic AI, Really?
So, what’s all the fuss about? Let’s get one thing straight: agentic AI is not just another name for the automation you already know. Think of traditional automation as a very obedient, but very limited, factory robot. You program it to put widget A into slot B, and it will do that perfectly, a million times a day. But if widget C comes down the line, the robot freezes. It has no ability to reason, learn, or adapt.
Agentic AI, on the other hand, is like giving that robot a brain. These are autonomous agents, software programmes that can perceive their digital environment, reason about what needs to be done, and take independent action to achieve a goal. For an accounts payable (AP) team, this means an agent can receive an invoice, understand its context, cross-reference it with a purchase order, check for compliance, flag discrepancies, and even approve the payment—all without a human needing to step in for every single decision. This is the leap from simple “if-this-then-that” rules to genuine autonomous workflows.
The real magic here is their ability to handle exceptions. A traditional system might kick out 30% of invoices for manual review because of tiny errors. An agentic system can often figure out the problem on its own, learning from past corrections to become smarter over time. This is a fundamental shift in how we approach AP optimisation.
The 80% Payday: Why Autonomous Workflows are Winning
Now for the part that gets the Chief Financial Officer’s attention: the finance automation ROI. A recent report, highlighted by Artificial Intelligence News, found a stark difference in returns. While general AI projects delivered a respectable 67% return on investment, those using agentic AI hit an impressive 80%.
Why the gap? It comes down to autonomy. Standard AI often just provides insights, leaving a human to do the actual work. It’s like a sat-nav telling you there’s traffic ahead but expecting you to find the new route. Agentic AI is the new route. It doesn’t just identify a problem; it executes the solution. This reduces the need for manual intervention, frees up skilled finance professionals for strategic work, and accelerates the entire procure-to-pay cycle.
Of course, this doesn’t happen in a vacuum. The effectiveness of these intelligent accounting systems is entirely dependent on the quality of the data they’re trained on. Rubbish in, rubbish out. An agent trying to make sense of a messy, inconsistent data swamp will be just as useless as a human. This is where companies with vast, clean datasets have a massive, almost unfair, advantage.
Accounts Payable: The Perfect Lab for a Smarter Machine
Why has accounts payable become the central battleground for this technology? A recent study shows that a massive 72% of finance leaders are earmarking AP for their first agentic AI accounts payable deployment. The reason is simple: it’s the perfect testing ground.
AP is a function built on structured processes, repetitive tasks, and clear rules. You have invoices, purchase orders, and payment terms—all things a machine can be trained to understand. With 46% of CFOs admitting they’re under pressure from leadership to implement AI, AP offers the quickest path to a tangible win. It’s a place where you can prove the technology works and deliver a clear ROI before attempting to unleash it on more complex areas like financial planning and analysis.
However, the path isn’t without its challenges. The same report from FT Longitude notes that 61% of finance leaders confess their initial AI agents were purely experimental and failed to translate insights into action. The key is moving from a tentative experiment to a fully governed, operational tool.
The Build-Versus-Buy Dilemma
This brings us to the great strategic question facing every CTO and CFO today: do you build your own AI army or buy a ready-made one? The data suggests a split in thinking. For standardised processes like AP, 32% of organisations prefer to buy an embedded AI solution from a vendor. It makes sense—why reinvent the wheel when a specialist has already perfected it?
However, for areas where a company hopes to create a unique competitive advantage, 35% lean towards building their own AI in-house. The choice is a strategic one. Building offers customisation and control but comes with enormous cost, time, and talent challenges. Buying offers speed and expertise but might not fit every unique business process perfectly.
Ultimately, successful deployment isn’t about the tool itself, but the governance around it. It’s telling that 46% of finance leaders refuse to deploy AI agents without strong safeguards in place. The smart approach is incremental autonomy. You don’t just switch the machine on and hope for the best. You start by having agents flag issues for human review, then allow them to handle low-risk tasks, and gradually increase their decision-making authority as trust is built and performance is proven.
Learning from the Data Giants
To see this in action, look at a company like Basware. They aren’t just selling software; they are selling intelligence refined from a network of over two billion invoices. An AI model trained on such a colossal, real-world dataset has a profound understanding of context. It knows what a normal invoice from Supplier X looks like, making it incredibly effective at spotting fraud or errors in an invoice from that same supplier.
This data advantage creates a powerful moat. A new start-up could design a brilliant AI agent, but without the data to train it on, it’s like a genius who has never read a book. Basware’s system isn’t just executing tasks; it’s applying cumulative wisdom to every transaction, moving finance from a world of manual task completion towards one of strategic oversight.
The Autonomous Future of Finance is Here
The conversation around agentic AI accounts payable isn’t about a distant future; it’s about a very real and present strategic shift. The pressure is on, the technology is mature enough, and the ROI is undeniable. We are moving beyond automation that simply mimics human keystrokes to systems that can reason and act.
The real prize isn’t just about making AP more efficient. It’s about transforming the finance function from a cost centre focused on processing transactions into a strategic hub that provides real-time, data-driven intelligence to the business. When your team is no longer bogged down in chasing paper, they can focus on optimising cash flow, negotiating better supplier terms, and managing risk.
So, the question for every finance leader is no longer if they should adopt this technology, but how. Are you still tinkering in the experimental sandbox, or are you ready to deploy governed, autonomous agents that deliver tangible results? What’s holding your organisation back from making the leap?
