Agentic AI and CFOs: Turning Finance Workflows into Autonomous Systems

Key takeaways

• Agentic AI is AI that can plan, act, and adapt toward a goal with limited supervision—moving from “answering questions” to actually doing work across your finance stack.¹

• For CFOs, the biggest near-term gains aren’t sci-fi robots; they’re end-to-end workflows: close and consolidation, cash forecasting, AP/expenses, and FX/treasury that require far less manual effort.² ³

• The real unlock comes when you pair agentic AI with clean data, clear guardrails, and existing systems—including your ERP, banking, and cross-border payment rails.² ⁴

• Xe fits into this picture as the execution layer for cross-border money: agentic AI can watch positions and propose actions, and Xe actually moves funds, manages FX, and feeds reliable currency data into your agents.

What is agentic AI, really?

If generative AI feels like a very smart intern that answers questions, agentic AI is more like a digital team member with a to-do list.

In simple terms, agentic AI systems can:¹ ⁸

• Understand a goal (“prepare month-end variance analysis for EMEA”)

• Break it into steps (pull data, compare to budget, flag anomalies, draft commentary)

• Take actions across tools (ERP, FP&A, BI, email, collaboration tools)

• Adapt based on feedback (fix mis-classified items, update logic, learn from corrections)

Instead of waiting for prompts, agentic AI proactively executes workflows based on rules, data, and events—often in the background.

That’s why so many finance thinkers talk about “autonomous finance” or “digital employees”: the point isn’t just insights, it’s doing.² ³

Why CFOs should care now

CFOs have lived through every wave of finance tech: ERPs, RPA, cloud FP&A, dashboards. Most of those focused on speeding up individual tasks.

Agentic AI is different in a few key ways:² ³ ⁴

1. End-to-end, not step-by-step
   Instead of automating a single step (like extracting data from invoices), agents can orchestrate the whole process—receiving invoices, matching to POs, routing approvals, posting entries, and even chasing exceptions.

2. Judgment at scale
   Agents can combine rules, historical patterns, and LLM reasoning to make “small” judgment calls: which anomalies to escalate, what explanations to propose in commentary, which vendors to prioritize based on risk and terms.

3. Live, adaptive workflows
   McKinsey describes agentic AI as a shift from static workflows to live systems that adjust in real time to changing data, priorities, and constraints.³ Instead of monthly “batch” finance, CFOs get a function that stays in motion.

4. From cost center to co-pilot
   When routine analysis and reconciliations are handled by agents, finance teams can spend more time on forward-looking questions: pricing, capital allocation, market expansion, M&A scenarios.

Put simply: agentic AI is one of the first tools that feels aligned with how CFOs already think—systems, controls, rebates of manual work, better decisions—rather than just adding another dashboard.² ⁴

What agentic AI can actually do for finance

Let’s skip the hype and talk about concrete workflows that are already moving from pilot to production.

1. Close, consolidation, and reporting

Month-end is still where a lot of finance teams feel the most pain: checking balances, chasing explanations, and assembling commentary. Agentic AI can help by:

• Monitoring trial balances and flagging unusual movements as they happen

• Suggesting accruals or reclassifications based on prior periods and rules you define

• Drafting first-pass variance explanations using ERP and BI data

• Tracking close checklists, nudging teams on overdue tasks, and updating status automatically² ³

None of this replaces controllers or FP&A teams. Instead, it lifts a lot of the copy-paste and chase work off their plates, so human attention goes to material issues, not mechanical ones.

2. Cash and working capital

For many CFOs, the most stressful question is simple: “What does our cash really look like right now, and 13 weeks out?”

Agents can sit across AP, AR, payroll, and bank data to:

• Continuously update short- and medium-term cash forecasts

• Surface the impact of changing DSO, big invoices, or delayed collections

• Propose levers—like slowing non-critical payments, prioritising collections, or using surplus cash for pre-payments or hedging⁴

Instead of a spreadsheet that’s stale by the time it hits your inbox, you get a living forecast that responds as reality changes.

3. AP, expenses, and T&E

Spend control is an area where agentic AI can create quick wins without touching your most sensitive decisions.

In AP and T&E workflows, agents can:

• Read invoices or receipts, check them against policy, and propose GL coding

• Route approvals to the right people based on amount, category, and risk

• Highlight suspicious patterns, like duplicate invoices or unusual vendors

• Maintain a clean audit trail without endless email chains

The outcome is simple: fewer manual touches per transaction, and more time spent on the exceptions that really matter.

4. Cross-border payments and FX exposure

For global finance teams, foreign currency is both a necessity and a risk. Agentic AI can help by:

• Watching upcoming payables and receivables in different currencies

• Alerting you when FX moves push you outside your budgeted range

• Suggesting actions: when to convert, whether to use a forward or a limit order, or how to net flows across entities¹ ³

But while an agent can recommend when and how to move money, it still needs robust rails to actually do it. That’s where a specialist like Xe comes in—more on that shortly.

Governance: staying in the CFO comfort zone

All of this sounds powerful—and potentially risky. Most CFOs are excited and uneasy in equal measure. The latest research aimed at finance leaders has a consistent theme: agentic AI is promising, but governance has to come first.² ³

A few questions CFOs are already using to frame their thinking:

• Data: What data will agents have access to? Is it accurate, current, and governed?

• Reach: Which workflows are we comfortable letting agents run end-to-end, and where do we insist on human sign-off?

• Auditability: Can we reconstruct what an agent did, and why, if auditors or regulators ask?

• Security: How are credentials, personal data, and financial information protected when agents act across systems?

• Fairness & bias: If agents are involved in underwriting, pricing, or approvals, how do we monitor for skewed outcomes?

For now, most experts recommend a “human on the loop” model: agents do the heavy lifting, but humans define policies, approve key actions, and can override or pause behavior when needed.² ³

A practical roadmap for CFOs

You don’t need a giant AI lab to get started with agentic AI. You do need a structured approach.

1. Start with one or two real problems

Make a short list of workflows that are:

• Repetitive and rules-heavy

• Spread across multiple systems

• Important, but not extremely high-risk

Examples: variance analysis, cash forecast updates, AP triage, or preparing management commentary. Choose one anchor use case where success will be meaningful but manageable.

2. Map the process in human language

Before anyone writes a line of code, document:

• What triggers the process

• What steps are involved today, and who owns them

• What “good enough” looks like (accuracy, timeliness, completeness)

This becomes the blueprint for your first agent, and it’s where controllers, FP&A leaders, and auditors should all have a say.

3. Fix the data and connections

Agents amplify whatever data you give them. If your chart of accounts is inconsistent, entities are out of sync, or bank and ERP data don’t match, tackle that first.

Make sure agents will have secure, governed access to the systems they need: ERP, HR/payroll, banking/treasury, and key spreadsheets or planning tools.

4. Design guardrails before automation

For your first use case, decide:

• What the agent can do automatically

• What requires explicit human approval

• How you’ll monitor performance and shut it down if needed

That clarity keeps risk in check and gives your team confidence to collaborate with the new “digital colleague” rather than fighting it.

5. Pilot, measure, then scale

Run a contained pilot—maybe in one region, business unit, or period. Measure practical things:

• Time saved

• Cycle time reduction

• Error rate or rework

• How useful teams found the outputs

If it works, expand that use case or move to the next one. If it doesn’t, treat it as learning rather than a failed transformation.

How Xe turns agentic AI decisions into real-world results

Agentic AI can spot trends, recommend actions, and even draft policies—but when it’s time to move money, manage FX, or run payroll across borders, you still need infrastructure you trust. That’s where Xe fits into a CFO’s agentic AI story.

Reliable rails for global payments

When an agent decides it’s time to pay an overseas supplier or move funds between entities, it needs a clean way to act. Xe’s international payments platform lets you send money in 100+ currencies with transparent costs and full tracking—so agent-driven workflows still run on top of governed, auditable rails.

Real-time FX data for smarter agents

Agents making decisions about timing, hedging, or budget thresholds need live and historical FX data—not a static rate pasted from a website. The Xe Currency Data API feeds accurate, market-reflective rates directly into your models and agents, helping them reason with reality instead of stale or ad-hoc numbers.

Deep integrations with your finance stack

Agentic AI becomes more valuable when it’s embedded in the tools your team already uses. Xe’s ERP integrations connect cross-border payments and FX tools to systems like your general ledger or order-to-cash platform, so agents can help initiate and reconcile activity without breaking your audit trail.

Programmatic access via payments APIs

If you want agents to do more than “suggest” and actually initiate approved payment runs, Xe’s Payments API gives you a programmable way to create quotes, simulate costs, and submit instructions—always inside the limits, user roles, and approvals you define.

Keeping humans firmly in control

Finally, any agent that can trigger payments or hedges needs to live inside the same control framework as your team. Xe’s user roles let you define who (or what) can draft, approve, or release transactions—so agents become part of your governance model, not an exception to it.

FAQ

Is agentic AI just RPA with better branding?

No. RPA follows rigid scripts: “click here, copy that, paste there.” Agentic AI combines planning, memory, and integrations so it can respond to changing conditions and choose different paths to reach a goal—closer to a junior team member than a macro.¹ ³

Will agentic AI replace my finance team?

In the near term, it’s much more realistic to expect smaller, more leveraged teams—not no teams. Routine tasks (reconciliations, basic variance commentary, simple approvals) will likely be handled by agents, while humans focus on judgment calls, strategy, and stakeholder management.² ³

Where should I avoid using agents at first?

Most advisors suggest avoiding your most sensitive areas—like complex revenue recognition, tax positions, or high-risk approvals—as first pilots. Start with high-volume, lower-risk workflows where any errors are visible, reversible, and easy to learn from.²

What skills do my finance team need?

You don’t need everyone to become data scientists. You do need people who can map processes, define policies and guardrails, interpret agent outputs, and work closely with data/IT. Think “process architect + critical thinker” rather than “coder”.⁴

How does Xe fit if we’re just starting our AI journey?

You don’t need full agentic AI in production to benefit from Xe. Many CFOs start by using Xe to improve FX visibility and cross-border payments today; when you later introduce agents, they plug into infrastructure and data feeds you already trust.

Citations

¹ IBM — “What is agentic AI?” — (2025)

² Gartner — “Agentic AI Will Transform Finance: Here’s What CFOs Should Do Now” — (2025)

³ McKinsey & Company — “Seizing the Agentic AI Advantage” — (2025)

⁴ Rydoo — “Agentic AI: how can CFOs prepare for the next era of autonomous finance” — (2025)

⁵ Microsoft — “Get started with agents for finance: Learnings from 2025 Gartner® CFO & Finance Executive Conference” — (2025)

The information from these sources were taken on December 9, 2025.

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Disclaimer:
This blog is for informational purposes only and does not constitute financial, legal, tax, accounting, or technology advice. Agentic AI capabilities, regulations, and best practices are evolving quickly. Before making decisions about AI adoption, payments, or FX risk management, consider your own circumstances and consult qualified professionals where appropriate.