With agentic AI, your finance ops will never look the same again

We're entering the biggest shift in finance since the spreadsheet.

Whether you run a venture-backed startup, a scaling SME, or a global enterprise, you’ve felt it by now: AI is everywhere. Our finance team at Airwallex have been using it to speed up workflows and automate the busywork we’ve carried out for years.

AI helps us process invoices and reconcile faster. But solving for speed alone isn’t enough. Across the industry, finance teams are still wrestling with fragmented systems, messy data, failed payments, and cross-border complexity.

That’s why‌ the next phase of AI matters. Rather than just generating insights, AI can now think, act, coordinate across workflows, and drive outcomes without handholding.

And this shift is accelerating fast: Deloitte predicts more than 60% of large enterprises will deploy agentic AI at scale by 2026, while Gartner expects 40% of enterprise applications to embed task-specific agents in the same timeframe.

Preparing for agentic workflows now simply gives your teams more room to move later. You’re not forcing big changes overnight, and you’re not locking yourself into decisions too early. What you’re doing is avoiding the situation most finance leaders dread: scrambling to rebuild your stack when the pressure’s already on. Teams that wait will still get there, but often with less choice, tighter timelines, and more risk than they’d like.

AI is no longer only about gleaning insights

When I first started exploring AI in finance, it was mostly about generating insights to save time: summaries, forecasts, and reports. We leaned on AI to build dashboards, spot trends, and detect anomalies.

But what I’ve seen in practice is becoming far more powerful: AI is starting to act on those insights, connecting data to decisions. Agentic AI is bridging the gap between insight and action. You set the rules and AI decides how to get to the outcome. It can plan, decide, and act using your systems, APIs, policies, and data, without needing a prompt at every step.

Finance teams are already using AI to guide decisions and automate workflows across areas like expense management, reconciliation, and forecasting. For example, AI can detect anomalies in expenses and automatically route them for review.

Soon, routine processes across the finance stack that used to eat up days will be running themselves, freeing your teams to focus on decisions that really matter. Imagine accounting closes that nearly run themselves, treasury decisions that respond in real time to market shifts, forecasts that continuously update, and compliance checks that proactively flag risks. And finance teams? We oversee and make judgement calls.

In future, humans and agents will run finance ops together 

I see teams increasingly using AI to power their workflows, streamlining spend management, month-end closings, and variance analysis. In future, AI could be making decisions and acting on your behalf.

Agents will pay your vendors without constantly pinging you 

AI is already helping finance teams manage expenses more efficiently. It can suggest how to categorise transactions, check them against policies, and flag anomalies before the month‑end close. For example, Endowus, a Singapore-based wealth management platform, uses Airwallex’s AI-powered spend management to speed up approvals and reconciliations.

As AI becomes more capable, agents could start understanding your policies, vendor relationships, and approval rules well enough to act independently by your rules. For example, you could set rules and let agents handle routine spend autonomously. An agent could pay approved vendors within limits, escalate unusual transactions, or adjust payment timings based on vendor risk signals and your cash flow priorities. Offloading these repetitive checks would give your team more time for cases that actually need human judgement.

Agents will handle your month-end close for you

Closing the books at month-end used to take up so much time. Now, AI is speeding this up by automatically matching transactions, flagging inconsistencies, and suggesting adjustments. Reconciliation that used to take days can now happen in hours, freeing finance teams to focus on interpreting results rather than chasing them. 

Over time, AI will begin to understand how entries connect across accounts, systems, and reporting cycles. That creates the foundation for agents to eventually run the entire close process end-to-end. They might gather data across systems, post routine entries, resolve discrepancies, and generate draft financial statements. Instead of stitching together data manually, your team would oversee the process and review outputs, freeing up weeks of work every month.

Agents will drive variance analysis so you can act faster

Variance analysis used to mean manually digging through spreadsheets to understand why numbers differ from budgets or forecasts. AI has accelerated this process: scanning large datasets, spotting unusual patterns, and surfacing potential drivers behind changes in revenue, costs, or margins. Teams can begin their analysis with likely explanations already highlighted.

Agents could eventually take this a step further by investigating changes autonomously. Instead of presenting static charts, they might test hypotheses, account for external factors like market shifts or supplier delays, surface the most likely explanations, and recommend actions. Your team would be able to move faster from understanding what happened to deciding what to do next, shortening the decision loop.

Why infrastructure is your first port of call

The role of finance doesn’t disappear as AI continues to improve and take on more operational work. If anything, it becomes more strategic. AI could flag anomalies in accounts payable, optimise cash management, and execute transactions autonomously. But finance teams still set the policies, define the guardrails, and make the judgement calls that matter most.

Getting the most out of agentic finance starts with the systems behind it. A network of agents will need a unified infrastructure to see across your stack and run your finance operations autonomously.

What that looks like is a programmable infrastructure, where every transaction, policy, and workflow can be accessed and run by AI. Open, granular APIs become the building blocks that agents can use to pull data, trigger actions, and connect workflows across your stack.

That works even better when your entire finance operations are on a vertically-integrated, global platform, where infrastructure, software, and AI work as one. At Airwallex, we’re already using AI to route payments optimally, manage real-time risk and fraud decisions, and automate financial workflows. With APIs, real-time data access, and global capabilities built into one platform, we’re uniquely positioned to support agentic workflows at scale. 

When autonomy becomes the norm, you won’t be racing to catch up. You’ll already be running on infrastructure built for it.