5 Use Cases for AI Agents in Finance: Drive ROI With a Strategic AI Pilot

Finance teams are drowning in manual processes while being asked to provide more strategic insights than ever. This practical guide presents five proven use cases of AI agents for finance that directly address some of the biggest operational challenges — such as invoice processing bottlenecks and cash flow visibility gaps — with clear implementation guidance to help CFOs and finance directors choose their transformational AI agent starting point.

In brief:

• AI agents for finance automate manual processes and deliver strategic insights, helping finance professionals move from repetitive tasks to value-added decision-making.
• Intelligent invoice processing and accounts payable automation with AI agents reduce errors, accelerate approvals, and improve cash flow visibility.
• Automated accounts receivable and collections management streamline payment tracking, follow-ups, and dispute resolution for healthier cash flow.
• Smart expense management powered by AI agents enforces policy compliance, speeds up reimbursements, and provides real-time spending visibility.
• AI-driven financial reporting, analytics, and budget management enable dynamic forecasting, real-time variance monitoring, and proactive resource optimization.

Financial directors know what it’s like to stare at a huge pile of invoices and reconciliations while also being asked for a real-time budget forecast. Finance teams get bogged down in repetitive tasks like processing invoices and generating reports, and they also face growing pressure to provide strategic, forward-looking insights.

At the same time, artificial intelligence (AI) agents for finance are ready to automate manual tasks and uncover new insights, and finance leaders feel the pressure to adopt this new technology just to keep up.

The key is to start with a project that can deliver quick wins and help your team build trust in AI. This blog post will show you five specific ways to use AI to solve common, time-consuming finance problems.

Centric Consulting’s Financial Services Co-Lead Matt Cotter says, “This isn’t just about automation. It’s a strategic transformation opportunity for finance to lead the business forward.”

5 Use Cases for Agentic AI in Finance

Let’s explore five proven use cases of AI agents that address some of the biggest operational challenges in finance.

Use Case #1: Intelligent Invoice Processing and Accounts Payable Automation

The Problem Finance Teams Face

Accounts payable (AP) is ready for a major change. Unfortunately, AP sometimes slows down every other department because it’s bogged down with manual data entry, long waits for approvals, and a lack of clear, concrete data. All of these issues compound to result in lost time and money and create strained relationships with vendors.

All this makes AP the perfect place to start your journey with transformational agentic AI for finance and accounting because it involves high-volume, manual processes with these pain points:

• Manual Bottlenecks: Processing invoices manually creates severe delays in approvals and payments.
• High Error Rates: Human errors happen, and manual work in the general ledger is a recipe for mistakes that can be expensive to fix.
• No Cash Flow Visibility: Without a real-time insight into payments, it’s difficult to understand what the business can fully spend.
• Strained Vendor Relationships: Slow payments or missed deadlines damage relationships with key suppliers.

How AI Agents Solve These Problems

AI agents bring a whole new level of accuracy and efficiency to AP. AI agents for finance automate entire invoicing processes from start to finish with:

• Automated Invoice Capture: Agents can pull data from any invoice, paper scan, an email, or a PDF with high accuracy so no one has to type information by hand.
• Intelligent General Ledger Coding: AI can automatically add codes to everything by looking at a vendor’s history and other invoice details.
• Dynamic Approval Routing: Invoices get sent to the correct people automatically based on rules you decide: amount, department or project.
• Payment Optimization: Agents can look at your payment terms and suggest the best time to pay. This helps you get early payment discounts and manage your cash flow more effectively.

How to Get Started

• Analyze the highest-volume vendors or a specific expense for a proof-of-concept pilot.
• Integrate AI agents across existing systems and approval processes.
• Start small and scale up. Allow AI agents to learn and build trust with human teams.
• Closely monitor metrics around invoicing processing, data accuracy, and error rate.

Cotter notes, “A lot of people worry about losing human oversight, but agentic AI for finance isn’t about replacement. It’s about letting the AI handle the repetitive work so your team can focus on the exceptions and strategic decisions that truly require their expertise.”

Use Case #2: Automated Accounts Receivable and Collections Management

The Problem Finance Teams Face

While AP automation gets a lot of attention, your accounts receivable (AR) process significantly impacts your cash flow. It’s the lifeblood of payments running into your business. Manually keeping track of everything leads to inconsistent follow-ups and spending a ton of time fixing disputes that tie up your money and exhaust your team.

Some of the problems you face with manual AR process are:

• Manual Collections: AR teams spend countless hours manually tracking down outstanding invoices and chasing payments.
• Inconsistent Follow-Up: Collection efforts are erratic, leading to late payments.
• Limited Risk Insight: It’s difficult to spot customer payment trends and see potential risks ahead of time.
• Time-Consuming Cash Handling: Matching incoming payments to invoices is time-consuming, especially at the end of the month close.

How AI Agents Solve These Problems

AR doesn’t have to be a reactive manual job. AI agents make it easy to be proactive and lead by data. They can be tailored to handle different customer types, from small businesses with few invoices to large clients with complex payment structures. They can adjust their approach for historically problematic payers versus those that are consistently good.

AI agents help with:

• Better Payment Tracking: AI agents pay attention to payment timelines and how your customers are paying, giving you a real-time view of your cash flow.
• Automated Follow-Ups: AI sends payment reminders and collection notices automatically.
• Streamlined Resolutions: When a customer questions a charge, the agent automatically finds the right documents and sends them to the right people.
• Automated Cash Application: Agents can match incoming payments to outstanding invoices, even handling partial payments and other exceptions.

How to Get Started

• For fast results, start with your biggest customers or those with the oldest balances.
• Map your current collections process and communication templates to train the AI agent.
• Set clear rules for when you escalate an issue or when to approve a write-off.
• Monitor your days sales outstanding (DSO), how effective your collections are, and your customer satisfaction to measure your progress.

Use Case #3: Smart Expense Management and Policy Compliance

The Problem Finance Teams Face

Expense reports are a headache for everyone. The entire process of submitting, reviewing, and receiving reimbursement is often slow and frustrating, and it’s prone to compliance issues. These issues create friction within your teams and generate a significant amount of administrative work. They include:

• Slow Reimbursements: Manual expense reports processing means unhappy employees waiting for their money.
• Inconsistent Rules: It’s hard to make sure everyone is following the same spending policies across departments and employees.
• Slow Validation: Finance teams waste valuable time by manually checking receipts.
• Low Spending Visibility: Without real-time data, it’s hard to get a clear picture of what’s being spent and where budgets are off track.

How AI Agents Solve These Problems

Expense management tools powered by AI automate the entire process, making it faster, simpler, and ensuring rules are followed. They help with:

• Faster Receipt Processing: Employees can snap a picture of a receipt, and the AI agent will automatically pull out key information, like vendor, date and amount.
• Instant Policy Enforcement: The agent can check submitted expenses against company policy, flagging anything that is not in compliance and prompting employees to fix errors before they can submit.
• Easy Categorization: AI automatically puts expenses into the correct categories and assigns the correct project codes, saving time and improving accuracy.
• Better Approval Routing: Expense reports are automatically sent to the right manager for approval based on things like department and amount.

How to Get Started

• Look at your most common expenses first: travel, meals or office supplies.
• Put current expense policies in a clear, structured format for easy AI readability.
• Make the submission process mobile-friendly to make it easy for employees.
• Track key metrics like your processing times, compliance rates, and employee satisfaction to prove the return on investment (ROI).

Use Case #4: Intelligent Financial Reporting and Analytics

The Problem Finance Teams Face

The real value of the finance team comes from the ability to provide smart business insights. Unfortunately, they’re stuck in a loop of manually pulling data together and creating reports. Finance is more than a payment processor, but it often gets relegated to busywork and back-office functions. This results in:

• Manual Data Consolidation: Teams spend days pulling data from various systems (ERP, CRM, spreadsheets) for basic financial reports.
• Variance Analysis: Manually finding variances between actual spend and budgets is a painstaking process, ending in no answers.
• No Real-Time Insights: By the time reports are ready, the data is yesterday’s and no longer accurate for strategic decision-making.
• Error-Prone Processes: Manual calculations and complex spreadsheet formulas create errors.

How AI Agents Solve These Problems

AI agents for finance can automate the financial reporting process, keeping the team focused on decision-making and growth. This automation leads to:

• Automated Data Consolidation: Reconciliations are performed in real time by automatically pulling financial data from multiple sources.
• Smarter Variance Analysis: AI can spot differences and provide initial explanations by connecting financial data with operational data. A drop in revenue can be flagged and linked to the recent dip in sales activity.
• Dynamic Report Generation: Agents create standard reports on demand with custom views for different teams and employees.
• Predictive Insights: Analyzing historical trends creates an opportunity to predict future performance and spot potential issues before they become major problems.

In a real-world example, JPMorgan Chase’s Contract Intelligence (COIN) platform is an AI agent that automates the bank’s legal document review, drastically reducing the amount of human time spent on basic data review.

How to Get Started

• Focus on the monthly reporting process for just one team.
• Figure out where all your data currently lives and what reports need to be created.
• Create report templates and decide what counts as big enough to be flagged.
• Keep track of the time you save, errors in reports, and employee approval.

Use Case #5: Automated Budget Management and Forecasting

The Problem Finance Teams Face

Traditional budgeting is a slow, rigid process. Your marketing department is halfway through the year and working with a budget that was set based on last fall’s projections, but now a key advertising channel is no longer performing as expected. Teams need a more flexible way of planning and forecasting to guide the company because those Excel spreadsheets aren’t cutting it anymore.

Traditional budgeting and forecasting has its downsides:

• Lengthy Budget Cycles: Creating and finalizing budgets by hand takes weeks or even months.
• Static Forecasts: It’s hard to quickly adjust annual budgets when business conditions change.
• Disconnect Between Planning and Performance: Budgets are often created in a bubble, not connected to what’s happening in day-to-day operations.
• Reactive Management: Finance teams often just react to budget differences instead of proactively making the best use of resources.

How AI Agents Solve These Problems

AI agents enable a more dynamic and intelligent approach to financial planning and analysis, including:

• Dynamic Budget Creation: Agents can create multiple budget scenarios based on historical data and economic indicators.
• Real-Time Variance Monitoring: Agents continuously track what’s happening against the budget and flagging differences.
• Automated Forecast Updates: Forecasts are updated in real time based on current data such as sales trends and changes in operations.
• Spend Optimization: AI agents identify spending patterns to discover opportunities to move money around and ways to save costs.

Cotter says, “AI doesn’t replace financial controls — it strengthens them. It makes human oversight more powerful, not less necessary.”

In a real-world example on stock trading, Renaissance Technologies uses AI agents to identify patterns and anomalies to automate trading. Their data also shows the best results came from a mix of human intelligence and machine learning (ML).

How to Get Started

• Focus on the predictable budget categories: payroll or facility costs.
• Connect the AI agent with your existing tools and systems.
• Define clear rules surrounding budget deviation.
• Track the accuracy of forecasts and budget cycle speed to measure improvement.

Now that you’re familiar with detailed AI agent use cases, let’s discuss a simple framework for getting started.

Choosing Your Starting Point With AI Agents: The Finance Leader’s Decision Framework

To figure out the best first case for your company, analyze volume versus impact, data and system readiness, and change readiness. It’s not enough to just hop on the AI bandwagon. You’ll want to have clear guidelines, change management action plans with data governance, and a concrete strategy in place.

Quick decision guide:

• If the month-end close is painful: Start with intelligent financial reporting.
• If your AP team is drowning in invoices: Begin with intelligent invoice processing.
• If cash flow is unpredictable: Focus on AR and collections management.
• If expense reports are a constant backlog: Deploy smart expense management.
• If budgeting takes too long: Implement automated budget management.

Transform Finance Into a Strategic Function

True business growth occurs when finance steps out of the shadows and becomes a strategic function. When it comes to implementing AI, don’t wait for the perfect solution. Identify clear business objectives, create a plan, and launch a small pilot program. Prove the value of AI agents for finance to demonstrate the benefits of data-driven transformational AI.

Are you ready to explore how AI agents can fit into your business but aren’t sure where to start? Our AI experts can guide you through the entire process, from planning to implementation. Talk to an expert