Axel Rebien is the CEO of Serrala.
What if the most valuable member of your finance team isn’t a person, but an algorithm? And, what if I told you that an algorithm could increase your ROI by 100% or more with less complexity, increased visibility and higher predictability? Intriguing, right?
I spent most of my career as a CFO, surrounded by people who measure risk in decimals and sleep better when surprises round to zero. That discipline keeps companies solvent, but can also keep us from seizing the tools that will define the next decade of value creation. Chief among them is agentic artificial intelligence (AI).
Agentic AI is a revolution, but also an evolution. The revolution is that systems now act autonomously across the finance workflow, reading invoices, predicting cash flow and recommending capital-allocation moves. The evolution is the steady improvement that follows each deployment: the model learns, your data estate matures and what seemed daring on Monday is table stakes by Friday. The time is now.
Start With AI (Not With People)
Traditional projects begin with head-count plans. I prefer to flip the order: AI first, people second. Let’s think about agentic AI first and then human beings. It sounds harsh until you admit that human error is still the costliest line item in finance.
An algorithm trained on millions of transactions is less likely to err than the tired analyst eyeballing column AB at 11:58 p.m. Human beings will make more mistakes than agentic AI does, so why not let the machine take the first pass?
You Don’t Hire AI; You Onboard It
When leaders say, “We’re short on talent,” I point to the dashboard. Your next analyst is already here. You don’t need a recruiter—you need a login. Recruiting a skilled treasury professional can take nine months and six figures. Switching on an agentic module for working-capital management can happen in a weekend, at a fraction of the cost.
We need to rethink how we work. AI isn’t a candidate waiting to be hired; it’s a tool ready to be onboarded. When you fail to bring it in, you’re passing up one of the most immediate opportunities to reshape your operating model and delaying innovation in the process. Configure, train, release and let your people focus on the exceptions that still need a human brain.
Readiness Is A Choice
One of the most common objections I hear is, “We’re not ready yet.” But the reality is, readiness is a decision, not a prerequisite. Every organization has the ability to begin, regardless of where they are on the maturity curve. The key is to start small and stay focused. At Serrala, we often begin with targeted use cases like cash-application matching or short-term forecasting. These are low-risk, high-impact areas where agentic AI can demonstrate value quickly. You don’t need a fully built road map to take the first step; you just need to start walking.
Demand Use Cases
Selecting a vendor should feel like interviewing a new hire: Show me your work. Show me the touchless-matching ratio, the reconciliation speed, the forecasting accuracy. Hand them a month of real invoices and ask for a live demo. If the AI can’t outperform your team, walk away. You are not buying marketing hype; you are buying output.
The Two Real Constraints And Why They’re Manageable
Data-privacy regulation looms large, and budgets never feel generous. Yet both hurdles shrink when you run a proper ROI. In most pilots, the annual platform fee is lower than one fully-loaded salary, while savings (reduced working-capital requirements and error-correction time) often break even within the first year.
Governance is solvable, too. The CFO’s best friend on this journey is the CTO. Align early on data pipelines and audit trails. The result is a robust control environment with a digital mind doing the repetitive work and a human mind signing off on it.
The Mindset Shift In Practice
Risk aversion runs deep in finance. We conduct scenario modelling, sensitivity tables—anything to avoid surprises. Yet clinging to manual processes is itself a risk. When an algorithm flags a variance in seconds that would take an analyst 30 minutes, that is not magic; it is statistics at machine speed.
We treat the AI as a colleague. It gets a figurative seat at the monthly cash-flow meeting, and the team questions its outputs just as they would challenge any analyst. That dialogue builds trust faster than slide decks.
How much can agentic AI really do?
Today, agentic AI can take over what I call the “lower level of things,” such as coding invoices, matching line items and scrubbing bank statements. Tomorrow, it will suggest moving surplus cash across entities to minimize borrowing costs. The goal is 100% automation of manual data collection, with humans focused on interpreting and acting on the output.
Looking Ahead
I picture a finance stack where agentic AI orchestrates the entire working-capital cycle: dynamic discounting triggered automatically, excess cash swept to the best instrument, currency exposures hedged in real time. The human team becomes a board of governors that steers strategy while AI does the lifting.
Final Call To Action
To my fellow finance leaders: You just need to find the right level of AI. Start with one pain point, insist on measurable output and scale from there. Agentic AI doesn’t replace us; it accelerates us. And acceleration is exactly what our organizations need in an economy that punishes hesitation.
The revolution has started, and the evolution depends on us. Onboard your digital colleague today, measure relentlessly and let the data convince the skeptics. The upside to implementation includes smarter decisions, clearer forecasting and a tangible boost in performance that would take years to replicate via traditional methods. In finance, timing is everything, and the time is now.
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