How Enterprises Can Boost Finance Efficiency and Accuracy With Agentic AI

The end of the month is coming fast, and the numbers still don’t add up. Your team is racing through reconciliations, approvals, and last-minute exceptions, even as the inbox fills and forecasts shift. Leadership wants faster closes, greater accuracy, and tighter controls — all without adding headcount.

Traditional automation was supposed to help. But the moment a workflow spans ERP, procurement, payroll, and expense systems, those tools hit their breaking point. One missing field, one unexpected vendor format, one approval routed to the wrong queue — and suddenly finance is back to stitching together data by hand, pulling context from multiple systems, and explaining delays that shouldn’t exist.

More enterprise finance teams are turning to agentic AI because the old model simply can’t keep up. They need systems that are designed to reason across workflows, help flag issues earlier, and support keeping work on track with less manual intervention — giving finance professionals room to breathe again, and space to focus on the decisions that actually drive the business forward.

Why enterprise finance needs intelligent automation

In enterprise finance, even simple processes rarely stay simple. A single invoice might touch procurement, AP, ERP, and a reporting tool before it’s fully reconciled. 

Automation helps move pieces of that process along, but it often breaks when inputs change or approvals shift. That’s when teams have to step back in to troubleshoot and validate or patch up gaps, like approvals stuck in the wrong queue or missing cost-center information. 

This is where intelligent automation can make a real difference, thanks to its ability to understand workflows as a whole and adapt in real time. Enterprise teams are already using it to finally overcome sticky pain points like:  

High operational complexity and manual processes

Most finance workflows don’t live in one place. An invoice might start in procurement, get processed in AP, approved in ERP, reconciled in a reporting tool, and finally show up in a weekly close report. 

Traditional automation tries to connect these processes with rules and handoffs: if data appears in system A, move it to system B. That works — until something changes. When it runs into a missing field, a new approved rule, or a different vendor format, the automation either breaks or quietly skips steps. 

At that point, finance becomes the integration layer. Teams spend hours pulling data from multiple tools, reconciling matches, and micromanaging workflows that were supposed to run on their own. Processes slow to a crawl, reducing operational agility.

Intelligent automation plays a role in shifting this model by reasoning across the workflow itself and pulling context from each system to flag or even resolve issues before they turn into manual rework. 

Growing pressure for accuracy, compliance, and transparency

As audit requirements tighten, even small gaps become high-risk. A missing approval or mismatched expense entry can force teams to trace transactions across ERP, payroll, and expense systems just to prove what happened and why.

Traditional automation doesn’t solve this well, since it focuses on task execution, not accountability. Scripts move data forward, but they don’t verify intent or enforce policies consistently. 

Intelligent automation handles this differently. Finance professionals are using it to help validate data and support applying controls as workflows run, while maintaining detailed records of every action taken. Rather than reconstructing audit trails after the fact, they’re able to get built-in transparency as workflows run. 

Rising demand for strategic insights 

Finance teams are expected to deliver forecasts, scenario models, and real-time budget impact analysis, but the underlying data still lives across disconnected systems. Building a single cash flow projection often means days of pulling historical spend and reconciling actuals, while also explaining inconsistencies. 

Traditional tools help aggregate data, but they don’t reduce the effort behind it. Spreadsheets still require manual validation, and automation still depends on clean, predictable inputs that rarely exist in real life. 

Intelligent automation is already helping change this dynamic for the better by pulling and validating data automatically across systems. Rather than spending time double-checking numbers, teams can focus on interpreting them, shifting from reactive reporting to strategic decision-making.  

What is agentic AI? And why does it matter?

Most automation in finance today is task-based. You automate a report or an approval step or a data sync. Each piece works in isolation, but the overall workflow still depends on people coordinating everything in between. 

Agentic AI enables a different approach.

Rather than automating individual steps, agentic AI is designed to work cohesively to deliver outcomes. When paired with a conversational interface, agentic AI has the ability to understand a goal (you type in “close the books, process reimbursements, prepare a forecast,” or similar), then it determines the steps required, which applications to involve, and can carry that work across multiple systems, adjusting as conditions change.

This is what separates agentic AI from traditional RPA or conversational assistants. RPA follows scripts. Chatbots answer questions. Agentic AI can coordinate work from end to end, and adapt dynamically.  

In practical terms, that means an AI system that can go beyond responding to prompts or moving data and actually:

• Understanding the user’s goals and business context
• Pull context from ERP, payroll, procurement, and expense tools. 
• Decide what actions are needed next.
• Execute those actions within existing systems. 
• Verify that the workflow actually finished. 

So it’s an ideal fit for structured, repeatable finance processes, like month-end close, AP/AR cycles, and compliance reporting — work that’s predictable enough to automate but still too complex for traditional tools.

The goal is to minimize human intervention while improving accuracy and reducing cycle times, freeing up finance teams for more strategic work. Agentic AI can play a big role in achieving that efficiency. Not by layering more tools onto your stack, but by reducing the manual operational work that slows processes down, which naturally improves:

• Autonomy: The system can take action without constant human oversight.
• Orchestration across systems: It coordinates work across ERP, payroll, procurement, and more.
• Consistency:  Processes run the same way every time, reducing errors.
• Reliability at scale: Finance workflows operate smoothly even as volume grows.

That’s a fundamental shift for finance teams: fewer brittle workflows, fewer handoffs between systems, and less time spent acting as the integration layer between tools. 

8 top use cases for agentic AI in finance 

Finance teams are often stuck managing repetitive tasks across different systems, but it doesn’t have to be that way. Below, we’ll highlight some real-world finance use cases that show how agentic AI can lighten the load and speed up workflows. 

See agentic AI in action and learn how to supercharge your finance operations. 

1. Invoice routing, approval, and status updates

Invoices often get stuck waiting for approval, slowing close cycles and frustrating both finance teams and vendors. With the right setup and integrations, agentic AI can largely take over this routine work, automatically verifying entries, routing invoices based on spend threshold or cost centers, and nudging approvers when action is needed.

When employees can check the status of any invoice in real time, without opening a ticket or chasing someone down, they can keep momentum going. The system helps keep every step logged and auditable, so teams maintain control and compliance, even as approvals move faster.    

The benefit: Finance teams often experience fewer bottlenecks, shorter close cycles, and reduced errors, freeing time for analysis, forecasting, and strategic initiatives, like cash flow optimization or budget planning. 

2. Enhancing forecasting and FP&A workflows

A finance manager scrambles to reconcile data from multiple systems to prepare a monthly forecast. Pulling actuals, checking inputs, and assembling models can take days, leaving little time for scenario planning or analysis. 

So the manager lets agentic AI take over the heavy lifting, validating inputs, consolidating data, and generating draft forecasts with variance explanations in minutes. Teams can also run “what if” scenarios, like modeling the impact of a 15% T&E reduction, while the AI highlights key drivers and explains insights in plain language. 

The benefit: FP&A teams tend to gain faster, more accurate forecasts and can spend more time on high-value work, like resource allocation. Decision-makers get actionable insights sooner, paving the way for more confident, data-driven strategies.

3. Automating expense management, approvals, and reimbursements 

Picture an employee waiting weeks for a travel reimbursement while their expense report sits in review. That’s exactly what tends to happen when finance staff have to manually match receipts, verify policy compliance, and track down missing details. 

When agentic AI automates these processes instead, it can provide a clear summary with next steps, reviewing each receipt against the transaction record, confirming it follows company policy, and alerting a human reviewer to any unusual items. 

With agentic AI securely connected to the full tech stack, low-risk reports can be automatically processed, and employees can check the status of their reimbursements at any time, without sending emails or opening tickets. 

The benefit: Many teams see reduced compliance risks and faster reimbursement cycles, with fewer manual reviews overall.

4. Self-service procurement-to-payment (P2P) operations (POs, requests, and catalogs)

Imagine a manager waiting days for a purchase request to be approved while workflows stall across multiple systems. Manual PO creation, budget checks, and approvals slow the process, delaying projects and tying up finance and procurement teams. 

Done right, agentic AI can automatically handle the entire P2P cycle: creating purchase requests or orders, validating vendor information, and flagging unusual entries for review. 

Payment workflows can start immediately. Employees can ask for open POs or specific vendor activity and get real-time answers, pulled directly from tools like Coupa. 

The benefit: Teams experience faster approvals, improved budget compliance, and greater operational transparency, while employees get more streamlined request-to-purchase experiences. 

5. Employee-facing finance helpdesk automation

An employee isn’t sure which cost center to use or whether a SaaS subscription is reimbursable. They send an email or open a ticket, and the finance team spends time digging through ERP records to respond. 

Instead, agentic AI systems can provide instant, accurate guidance, retrieving cost center codes, budget ownership, spend-to-date, and projecting allocations, all through natural-language prompts. 

Employees can get immediate answers to questions like:

• Can I expense this software?
• Are parking fees reimbursable?
• What’s the per diem for London?

The benefit: Finance teams handle fewer repetitive inquiries, reducing errors and speeding up transaction processing. Employees get faster guidance, while leadership gains clearer visibility into spending and better compliance.  

6. Real-time spend visibility and budget inquiries 

A budget owner gets pulled into a meeting with minutes to spare and needs an accurate view of current spend: what’s left in the marketing budget, where money’s flowing, and which projects are starting to go over budget. The data exists, but it’s scattered across reports and dashboards. 

Agentic AI has the ability to bring that visibility together in real time, giving leaders a clear, current picture of budgets, vendor spend, and forecasted overruns without manual reporting or last-minute data pulls. Rather than chasing numbers, teams get a reliable source that reflects live financial activity across ERP and FP&A systems. 

The benefit: Finance and business leaders gain faster, more confident decision-making. Teams can spot risks earlier to course-correct before budgets slip, turning financial visibility into a strategic advantage.  

7. Collections and receivables follow-up

A collections rep prepares for an outreach call by piecing together past invoices, email threads, and payment notes from different systems. By the time they have a full picture, the opportunity to act — or escalate the right accounts — has already narrowed. 

Agentic AI helps bring that context together before outreach begins, consolidating payment status, prior communications, and open issues into a single, clear view. It can also support timely, policy-aligned outreach for overdue invoices, so reps can focus on high-value accounts, invoices nearing escalation, and customers with a strong likelihood of payment.  

The benefit: Collections teams can more effectively prioritize the right accounts and reduce delays in cash collection. Finance leaders gain better visibility into receivables risk and predictable cash flow.   

8. Strengthening compliance, controls, and audit readiness 

It’s the week before an audit, and finance is pulling screenshots, approval records, policy docs, and system logs from half a dozen systems. Everyone’s careful, but the scramble leaves room for mistakes and missed context, making it harder to tell a clean story to auditors. 

An agentic AI platform can continuously monitor transactions and activity as work happens, not weeks later. Policy exceptions surface early, while duplicate or unusual entries stand out. Vendor activity that falls outside expected patterns gets flagged before it becomes an audit issue. 

Every action is logged along the way, creating a reliable record of who did what and when. 

The benefit: When auditors request evidence, approval trails, policy context, and transaction details should already be connected and easy to assemble, often resulting in stronger controls and cleaner audits. 

Implementing an agentic AI platform in enterprise finance 

Finance leaders evaluating agentic AI are ultimately deciding which systems to trust with core financial workflows. These platforms interact with sensitive data, initiate actions, and influence how work moves through the organization.

That’s why the distinction between systems that suggest next steps and those that can execute them matters. An effective platform can carry multi-step processes forward on its own — pulling the right data, acting inside connected systems, and recording each decision along the way. 

This shifts evaluation criteria: security models, governance frameworks, and operational reliability become foundational rather than checkboxes. Finance teams need to understand exactly how decisions are made and where authority lives. 

Choose a platform with deep integrations and secure access

Agentic AI only works as well as the systems it can connect to. In finance, that means having seamless access to core platforms without creating new risks around sensitive information. 

Most finance teams live in their ERP systems, like Workday, SAP, or Oracle, but their everyday work often demands data from:

• Procurement and AP tools like Coupa or Ariba
• Expense platforms like Concur or Navan
• Payroll systems like ADP or Workday Payroll
• Forecasting and planning warehouses like Snowflake or BigQuery

A strong AI-driven platform works to bring these environments together behind the scenes, supporting workflows that require cross-system data lookups, updates, approvals, and reconciliations — without forcing teams to jump between tools. 

That depth of integration is foundational for allowing automation tools to move work forward while respecting access controls and data boundaries. 

Ensure enterprise-grade governance, auditability, and controls 

When artificial intelligence supports workflows inside financial systems, trust matters as much as speed. Leaders need confidence that every step is visible, traceable, and aligned with internal controls.

The right platform supports explainability by design, with built-in controls to enforce role-based permissions, log every action, and keep AI agents operating within defined boundaries. You should be able to see what happened, who approved it, and why it moved forward. 

In addition to aligning with enterprise security standards like SOC 2 or ISO 27001, solutions from strong vendors should provide least-privilege access and mechanisms to prevent agents from operating outside authorized systems or policies. 

Red flags show up quickly. Opaque decisions, missing audit trails, weak policy enforcement, and broad agent autonomy can make it hard to explain outcomes to auditors. 

In finance, automation only scales when governance keeps pace with execution.  

Evaluate the platform’s ability to assist and autonomously complete tasks 

Finance teams don’t need another assistant that stops at suggestions — they need support that carries work across the finish line. 

This often looks like a platform that can discern intent, gather the right data, act securely within connected systems, and confirm the outcome. That means moving from “here’s what to do” to actually doing it. When AI can complete tasks on its own, work can keep flowing without constant check-ins or hand-offs. 

Plan for scalability across large, complex organizations 

Finance teams need a platform that grows with them, handling global teams and high-volume workflows. 

A strong agentic AI solution can adapt as processes change or the business expands, so you don’t have to redesign workflows for every new region or team. Unlike rigid automation tools, agentic AI workflows can scale to accommodate different tasks and volumes while maintaining accuracy and consistency. 

Take the next step toward autonomous finance operations 

When finance teams have to chase approvals across systems and resolve the same exceptions every month, the work slows down — and so does the business. Agentic AI helps shift that reality by reducing the operational overhead that keeps teams from focusing on strategy.

Moveworks supports that shift by combining an AI assistant with autonomous AI agents designed to help execute multi-step workflows, resolve common issues, and surface insights from your finance systems. Rather than stitching data together or troubleshooting broken processes, teams get clearer paths to completion and more predictable operations.

Finance organizations often see faster close cycles, stronger compliance support, and reduced manual effort — all while enabling teams to direct more time toward planning, forecasting, and analysis. With a unified experience for search, action, and cross-system orchestration, the platform is designed to help finance teams operate with greater accuracy and agility as demands increase.

Ready to automate your finance operations? Explore Moveworks’ AI solutions for finance teams.