Finance is rapidly moving from a technology-assisted, manual discipline to an autonomous one. AI will soon allow us to orchestrate all our finance operations centrally and help us to embrace an environment that supports real-time transactions and demands real-time compliance.
But AI won’t so much create this new autonomous world as provide the final stepping stone. At Serrala, we’ve been automating finance for a long time. In fact, “finance automation” as a process began as early as the 1960s with the introduction of the first mainframe computers. The pace of change has accelerated in recent years. But the trend is one that’s familiar to us all.
Autonomous finance: where we are now
Over the last few years, we’ve seen a rapid increase in just how much of the operational side of finance can be automated.
Accounts payable remains one of the most manual areas of finance, yet it also offers the greatest potential for automation because of the high strategic importance of AP to a healthy supply chain. The effort required to capture invoice data, manage approvals, post transactions, and meet fraud controls has long driven AP teams toward automation. Since the pandemic, the focus has shifted from efficiency alone to a balance of speed and flexibility.
AI is already hard at work here, helping to ensure high rates of straight-through processing (Serrala customers typically see 70% straight-through processing rates, and higher still with preferred suppliers). Machine learning approaches allow for further improvements by ensuring the right invoices are flagged for additional handling, and smart integrations with other software tools make for smoother processes.
Accounts receivable is more of a mixed bag: some areas, like cash application, can already be automated in their entirety—Serrala customers can typically achieve 99% automation rates here.
Other functions, like credit assessment and management and the overall collections process, remain almost entirely manual. But this isn’t due to a lack of potential for automation so much as a lack of understanding among many teams that these processes can be automated.
But allowing these functions to continue working in a manual operating environment can expose your organization to significant risk. An inefficient credit approvals process represents a direct risk in the form of either lost business or bad debt. A slow collections process can have serious consequences for your working capital position, exposing the organization to financial risk that can escalate quickly.
This means automation, particularly AI-assisted automation, is not just a “nice to have” or a back-office transformation initiative. It is essential for gaining a clearer understanding of customer risk and reward profiles, improving visibility into collections and AR team performance, and generating actionable insights to strengthen working capital for strategic growth.
We’ve seen our AI-powered solutions cut DSO by 10%, automate up to 100% of collections tasks in real time, and reduce bad debt by 10% or more. These results don’t come from futuristic capabilities, but from applying proven principles to specific parts of the collections workflow.
Payments lag behind AR and AP thanks to the relative complexity associated with handling tech solutions, intermediary services, bank connectivity, and increasingly stringent real-time compliance overheads—even as digitization creates theoretical improvements and efficiencies. But recent advancements like ISO 20022 allow for data enrichment that makes global automation possible, provided companies are willing to confront the need for an entirely new mentality about payments and the architecture required to handle them.
In our experience at Serrala, what trips many organizations up when they’re designing an automated global payments function is that they leave a great deal of potential on the table by continuing to think of automation as an “efficiency” play, rather than as a component of a strategy that (much like AP automation) supports stronger supply chains and overall working capital allocation for stronger growth prospects.
Treasury management functions remain highly fragmented. But the technology exists to automate everything from bank account and asset management through liquidity management and more complex enterprise functions like ROBO, POBO, intercompany lending, and more.
Autonomous finance: where we’re going next
And all of this is without much help from AI.
AI-assisted workflows won’t in and of themselves automate the basic operational functions of finance. But they will make it possible to fully automate them.
We’ll see finance shift rapidly over the next few years from a reactive function that chases down documents and tries to predict the future based on the past, to a proactive function that runs 24/7 with true “lights out” capabilities based on accurate real-time data. That means processes will run without direct human input, relying only on occasional checks and audits to function properly.
86% of finance teams plan to heavily leverage AI by 2026, and over half are already running pilots. What exactly “heavily” means is up for debate, but Gartner estimates a similar number (90%) will have at least one AI-enabled tool in their tech stack by the same time.
And we’ve been told to expect about a 600% increase in adoption of “agentic AI” by the end of 2026—that’s a jump from 6% of organizations to 44%. By “agentic” here we mean an LLM that integrates with other solutions to perform a specific task with human oversight. The most promising area for agents lies in reconciliation tasks. Large enterprises like Microsoft are already using reconciliation agents experimentally to perform bulk record-matching tasks in minutes that would take human operators days or weeks.
These capabilities are expected to become standard by the beginning of 2027.
By 2028, it’s likely that tightly integrated AI agents will play a key role in AP and AR, but also in the operational components of FP&A and reporting, freeing up about a day per week for finance teams and creating a far more proactive working environment.
AI is of course a key ingredient in autonomous finance. But without shrewd adoption of the right tools in the right places, you won’t achieve autonomous finance.
You’ll simply achieve a much more complex tech environment alongside frustrated people and broken processes.
The main reason to fully automate processes across finance operations is to guarantee working capital intelligence for CFOs and other leaders: complete real-time insight into cash flows, reserves, and the best possible paths forward in terms of planning and investment. And as anyone with experience of digital transformation projects can tell you, this isn’t something you can achieve simply by buying new technology.
Autonomous finance: how to get there
Bridging the gap is about far more than simply onboarding AI tools and flipping the on switch. It’s a complex change management endeavor that’ll require careful planning and execution.
At Serrala, we’ve been working alongside global organizations to automate finance processes for decades. Our more forward-thinking customers are already incorporating autonomous finance principles.
Our combined experiences show us that completing the journey requires:
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Unimpeachable data sources
Many organizations have a long way to go before they’ll be able to effectively leverage the most advanced AI tools now available. This isn’t through any fault of their own—it’s simply a reality of technical debt. Legacy systems typically weren’t built with the kind of connectivity required for all-purpose data transformation in mind, and the information they hold will need to be carefully curated, cleaned, archived, and migrated to new systems to fuel the new autonomous reality.
For AI to produce the kind of results we need for autonomous finance, it requires data sources that are clean, properly tagged, and machine-readable. The ideal state is the integration of the entire finance environment with other systems of record on a shared central data layer. This allows for the greatest levels of visibility, and for the best possible training outcomes to guarantee AI-assisted processes run as smoothly as possible.
Fortunately for organizations not able to achieve this yet, AI itself can be a powerful ally in cleaning and purging your databases to raise your data lakes to the necessary quality levels to achieve good outcomes.
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A high level of “dumb” or traditional automation
This is a necessary component of the high-quality data streams. Most of what we think of as “automation” is just getting all the information we need into a single platform where we can carry out the necessary steps of a workflow without having to enter and re-enter data from multiple different places.
If we don’t have this kind of foundation, we can’t expect AI to help further automation rates.
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Robust governance frameworks
AI can do incredible things. But it isn’t a magic bullet, and it’s certainly not without risks. We’ve recently seen some highly publicized (and highly embarrassing) examples of companies unleashing AI-powered tools on their live systems… only for the AI tool to cheerfully delete their entire codebase or ERP master data store.
Strong governance is vital to make AI processes and the risks associated with them legible, auditable, and controllable. Your technical and non-technical leadership will need to understand exactly what deploying AI across different parts of the finance ecosystem can potentially mean for your customers, vendors, their data, and for the security of the organization.
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Careful workflow curation
Although agentic workflows have the capacity to revolutionize how your teams work, we should remember that AI agents aren’t capable of full autonomy and they certainly aren’t omniscient.
Like all LLM-based tools, they can make mistakes. This means that any AI-assisted workflow should be designed not as a black box where the magic computer simply does the task for you, but as a clear “on-rails” sequential process where the AI handles each step in order before escalating to a human overseer at clearly defined checkpoints.
We call this “keeping AI in the loop” and “keeping humans in the loop”. Structuring workflows so that computers and people do the jobs they’re best suited for. Without this kind of arrangement, new tools are much more likely to slow us down than bring gains to efficiency and productivity.
How can Serrala help your organization cross the finishing line and achieve autonomous finance?
At Serrala, we’ve been helping CFOs and their teams automate finance operations for over 40 years. It’s our job to help you integrate new technologies within your organization in a way that makes sense both for your challenges and their capabilities.
We’ve developed our newly released Serrala Finance Platform with AI-readiness and autonomous finance principles in mind.
A single working capital intelligence hub that provides the foundation to bring AR, AP, payments, and treasury workflows together in a flexible system that adapts to individual organizations’ digital transformation roadmaps while enabling AI integration in every single process.
Most importantly, the Platform is designed to make the kind of data clarity and quality necessary to leverage AI for autonomous processes simpler for organizations to achieve. We leave storage to your ERP—after all, that’s what it’s designed for—and allow every part of the finance department to access it to automate workflows, accelerate decisions, and deliver measurable ROI.
This allows us to provide the integrated architecture to deploy all AI use cases in a way that ensures total and seamless operational efficiency while still providing complete choice and flexibility as to which parts of the offering your organization uses (and pays for) at every stage of your transformation journey.
This means our solutions empower your organization to start with the workflows you need today, then expand as your needs grow and your transformation plans mature.
If you’re ready to learn more, check out our full report on the Road to Autonomous Finance here, or get in touch with a Serrala expert today to book a demo.
