How Finance Leaders Can Apply Agentic AI Across Core Processes

It’s an all-too-common story: endlessly chasing data across your ERP, intercompany ledgers, procurement systems, and spreadsheets. It takes forever to track down all the information you need to take the next steps, which look something like manually reconciling transactions across four subsidiaries and three currencies. 

At every step, you feel insurmountable pressure because the volume of work is high, but the room for error is low. 

Say you’re attempting to close a balance sheet, and you discover a $47K discrepancy between your AP subledger and general ledger. Without agentic AI, you’d most likely need to diagnose the issue, reach out to multiple team members, and manually document the mistake and correction.

But agentic AI is designed to support multi-step, tool-using workflows with approvals and evidence — actions like pulling data from your ERP, cross-referencing the two entities, and identifying duplicate payments. Afterward, it can route to the AP manager impacted, with full evidence attached and a log of the entire investigation for auditing purposes. 

When Deloitte surveyed 4,000+ participants across the finance, accounting, and tax landscape, 60% reported that data quality, reconciliation, and transfer pricing documentation posed the greatest challenges to their organizations. 

That’s why many in the finance field see agentic AI as a valuable opportunity to address these operational challenges. Unlike generative AI (genAI), which answers questions and generates content, agentic AI is designed to execute multi-step workflows across systems.

What is agentic AI for finance?

Agentic AI in finance is an AI system that is able to plan multi-step workflows, use enterprise tools via APIs, handle exceptions, route approvals, and log evidence — all within finance-grade controls.

While genAI excels at summarizing and generating content, agentic AI is a more advanced, goal-driven system of AI agents that can autonomously execute multi-step finance workflows end-to-end.

Agentic systems are designed to handle work like receipt matching, policy enforcement, exception handling, and employee communication. They’re also capable of orchestrating procurement-to-payment steps, such as validating vendor info, flagging anomalies, and initiating payment workflows.

Agentic AI can be especially useful for finance versus other AI tech because it can enforce controls in motion, monitoring anomalies, tracking system activity, applying policies consistently, and maintaining detailed logs to support audit readiness.

Why agentic AI is gaining traction in finance teams

Finance is beholden to the high expectations of internal and external stakeholders. Executives want faster closes, auditors demand evidence documentation, and marketing and sales are chasing vendor approvals.

Deadlines seem to compress each quarter, compounded by a 5.3% productivity squeeze from workload increases and tightening budgets.

Traditional RPA and workflow tools often break when a single factor changes, but agentic AI is designed to adapt to evolving processes. 

Finance’s multi-system, cross-team workflows make this adaptability critical. Closing a variance means:

• Pulling financial data from your ERP
• Checking it in FP&A
• Routing the exception to the right accountant
• Documenting everything for auditability 

AI agents are capable of running that coordinated workflow automatically, end to end, without anyone having to chase approvals or toggle between five different tools.

Key benefits of agentic AI for enterprise finance

The tech stack as we know it today has become incredibly fragmented. Each tool is marketed to buyers as a solution that manages its respective area of expertise. Now your ERP is in one place, your P2P tool is in another, and your expense and treasury systems are somewhere else completely.

Every one of those handoffs between systems is a place where processes can stall. With agentic AI, teams can often begin to bypass these typical hurdles, like the constant context switching and toggling between tools, using APIs that make it easier for work to move forward.

AI agents are able to routinely pull data from one system to act in others. Take an expense report submission. An agent might pull the transaction from your expense system and validate it against your travel policy in the document management system. Then it could check the employee's budget in FP&A, route it for approval, and notify the employee.

With the right setup and integration strategy, teams can maintain control while creating a more consistent triage and exception process: an expense over the limit gets flagged, your policy is applied, and then manager sign-off is required. A receipt that doesn't match the PO gets routed to AP with the discrepancy highlighted.

A platform that logs every action automatically creates an audit trail. When auditors ask questions, you can pull up what the agent accessed, what it decided, and who approved. The logging happens as the work gets done.

The finance tech stack that enables agentic workflows

Finding the right technology for specific applications has never been an issue. Anyone who has dealt with vendor selection knows the market is oversaturated with tools.

Your finance team already has what it needs:

• ERP to track transactions
• P2P to manage service providers and invoices
• Expense tools to handle employee spending
• Treasury portals to keep a pulse on cash spend
• FP&A systems to optimize budgets organization-wide 
• BI platforms to consolidate reports into actionable insights
• Document management to store policies, records, and other important information

The major obstacle at play is that none of these point solutions connect to one another. Depending on the number of tools you’ve invested in, you may have dozens of logins, interfaces, and constant copy-pasting. AI agents have the potential to move between tools flawlessly via APIs.

Agentic AI is known for thriving within a fragmented stack, becoming an orchestration layer that stitches your financial systems together. 

Traditional tools like workflow engines, iPaaS, and RPA can connect systems, but they run fixed scripts. Something changes, or an exception hits, and they stop what they’re doing. 

Agents are designed to reason through issues, interpreting what you're trying to do, checking context across systems, and deciding next steps based on what they find.

So agentic platforms like Moveworks can function as the orchestration layer between point solutions. Moveworks sits on top of the systems you already have and doesn’t force you to rip out your ERP or P2P platform. You’re simply adding a membrane built to connect, reason, and execute workflows across your tech stack — in finance and beyond.

Agentic AI use cases in enterprise finance

The following use cases show how agents can potentially be used to handle finance work across your tech stack, orchestrating everything from invoice tracking to policy enforcement. 

We’ll look at how each workflow operates, what controls are built in, and how it can support faster and more informed finance operations. 

Providing context-aware answers to finance-related FAQs

Finance teams answer the same questions constantly: 

• What's the expense limit for meals?
• When does my vendor get paid?
• What do I need to onboard a supplier?
• What’s my budget for this new hire?

Instead of routing these questions to a human every time, an agent can pull answers directly from your connected policy repository, ERP, or FP&A tool — wherever the authoritative data lives.

If an employee follows up with a task, like "Why was my expense denied?", the agent can pull the transaction, identify the policy rule that triggered the denial, and provide suggestions on what to do next: resubmit, escalate, or request an exception. 

Key Measurements: Ticket deflection, reduced support load, faster resolution

Intelligent routing and escalation of purchase request approvals

Budget cycles and close periods turn approval chasing into a coordination nightmare. 

Instead, an agent could:

• Identify any pending approvals in your procurement system
• Notify approvers with context (what's requested, applicable policies, budget remaining)
• Route it through the proper network based on thresholds 

So a request above $50K could be automatically routed to the VP. A request pending for three days might be automatically escalated, with a run-down of the complete history. Agents are capable of supporting enforcing controls at every step, whether it’s applying approval thresholds or logging the approver’s identity for audit.

Key Measurements: Faster approval cycles, reduced procurement delays, improved spend governance 

Real-time invoice status tracking and bottleneck resolution

It’s not unusual for enterprise businesses to spend a significant amount of time tracking down invoices. That normally looks like manually digging through your ERP, checking approval queues, and trying to figure out what's blocking progress. 

But an agent could retrieve status directly from your ERP and AP systems, identify the obstacle, and either resolve it or route it to the right person.

If there are missing approvals, an agent might pinpoint the next approver and send a reminder. When it uncovers a matching issue, the agent could flag the discrepancy for review. Every interaction gets logged, so your finance team can track patterns as they happen.

Key Measurements: Reduced AP inquiry load, shorter approval latency, improved vendor satisfaction

Streamlining corporate card requests with policy-aware automation

Instead of filling out a form, submitting, and hoping it’s seen by the right people, an AI agent can support corporate card requests end-to-end:

• Gathering the required information (business justification, cost center, employee role)
• Validating it against your corporate credit card policy
• Routing the approval request to the appropriate manager
• Tracking issuance status and updating the employee when the card ships

The full audit trail captures who requested the card, why it was approved, and when it was issued.

Key Measurements: Faster card issuance, reduced finance admin load, improved policy compliance

AI-assisted expense review and policy enforcement at scale

Enterprise finance professionals handle hundreds of expense reports each cycle, checking each one for out-of-policy spend, missing receipts, or duplicate submissions. 

An AI agent could surface the items that need attention before a human ever opens the queue. Did the same vendor charge twice in one day, or was the hotel rate on the latest off-site significantly above market? The agent can help highlight unusual patterns before recommending approval or denial.

But it's up to the reviewer to see the flag, read the reasoning, and make the call. This human-in-the-loop approach means the agent assists but doesn't execute autonomously. The decision is logged with the reviewer's identity, timestamp, and rationale.

Key Measurements: Reduced review time, improved policy adherence, lower error leakage

On-demand expense report summaries and spend insights

Finance teams are constantly pinged with spend analysis questions about things like travel budgets and consulting expenses. "Summarize my team's travel spend this quarter" sounds simple, but the reality of pulling together that answer across systems, cost centers, and time periods can turn it into a big ask.

AI agents are capable of retrieving the expense data, generating a structured summary, and creating follow-up tasks. Every summary can include traceable source references, allowing the requester to verify numbers or drill into specifics.

The agent doesn't dump a spreadsheet and make someone figure out what matters. It’s designed to help synthesize the information, highlighting what's significant so the requester can act on it faster.

Key Measurements: Faster response time, reduced finance analyst time, better spend visibility

End-to-end purchase requisition workflows with built-in controls

Purchase requisitions touch multiple systems and require coordination and decision-making across teams. First, an employee submits a request, then someone validates it against policy and budget. Next, it gets entered into procurement, submitted to the vendor, and tracked through fulfillment. 

An agent could orchestrate the full lifecycle:

• Streamlining intake and validation
• Submitting the request to your procurement platform
• Routing approval based on organizational hierarchy and dollar threshold
• Tracking the status through your service management system, so the employee knows where it stands

But the key design element here is controls that are built into the workflow, not bolted on afterward. With the right solution and controls, budget gets checked before a request moves forward. Finance gets audit-ready logs that capture the full chain of events, including request details, policy checks, and approver actions.

Key Measurements: Faster requisition-to-approval time, reduced procurement admin effort, improved spend control and policy compliance

What strong ROI looks like for agentic AI in enterprise finance

The most defensible ROI case for agentic AI in finance is the potential to deliver two advantages at the same time: speed and risk reductions. So you’ll want to measure both operational efficiency and control maturity. 

On the efficiency side, track whether teams are shifting time from manual preparation toward review, analysis, and exception management. Not only is increased capacity important, but so are team productivity, responsiveness, and bandwidth.

For control maturity, monitor how well agents are able to enforce policies, prevent errors, and produce audit-ready evidence with little to no manual effort. 

Generally, you’ll want to start at the individual workflow level, whether that’s invoice tracking, expense review, or purchase approvals, then roll up to shared services or finance-wide impact once individual workflows prove stable.

Where to measure impact first

Start measuring your AI efforts with the metrics finance leaders already track and report, such as:

• Close cycle time
• Approval latency
• Exception aging
• Invoice cycle time
• Touchless processing rate 

But quality metrics also have a real impact on your bottom line. So look at KPIs like rework rates — are they dropping because more errors are being caught upfront instead of during reconciliation? Lower error leakage and improved policy adherence are two other potential indicators of measurable impact from agentic AI.

It’s also critical to evaluate what these efficiency gains mean for your teams. If AI agents save your AP team 20 hours per week previously spent answering invoice status questions, what strategic initiatives can you redirect that time to now that capacity is freed up? 

ROI should show your C-suite what the finance team can accomplish when they aren’t buried in routine inquiries that don’t have to require constant, hands-on human intervention.

How to maximize ROI over time

If you haven’t started your journey with AI yet, a simple assist mode can be a good place to start. Use AI agents to surface information, recommend actions, and help teams move faster — without autonomous workflow execution. 

Once this proves successful, you can start adding approval-based execution and let agents take action after human sign-off.

Finally, you can graduate to constrained automation, where agents execute within defined parameters for routine cases, without requiring approval each time (exceptions should still route to human experts). 

To encourage employee adoption, meet them where they already work, embedding agentic AI access in the web browsers or collaboration tools (Slack, Teams) they use every day. Lower friction typically means higher utilization, leading to increased agent transactions and ROI.

Operationalize agentic AI in finance with Moveworks

The use cases and workflows above need a platform that can execute them at enterprise scale — without rebuilding your entire tech stack.

Moveworks is built to be that orchestration layer.

The Moveworks AI Assistant acts as a conversational front door to finance work, helping employees submit expenses, request corporate cards, or ask policy questions right in their web browser, Slack, or Teams. More than just an intake channel or chat system, the assistant is designed to activate governed, cross-system workflows across your existing point solutions.

Agent Studio brings in those governed orchestration capabilities, enforcing custom guardrails and connecting your ERP, procurement, expense, treasury, and service systems through secure plugins. 

The Reasoning Engine sits behind these solutions to interpret intent (what the user is trying to accomplish) and plan execution across systems within policy constraints.

Enterprise Search adds the key ability to surface relevant data across both structured and unstructured sources through an AI-native, permissions-aware search layer, without switching contexts.

Instead of replacing your finance tech stack, Moveworks is designed to make it work together. Delivering an extensible plugin ecosystem, centralized governance and monitoring, and identity-aware automation aligned to access controls, Moveworks is built to be enterprise-ready out of the box. 

Transform your finance operations with agentic AI — explore Moveworks for Finance today.