Finance AI: Transforming Financial Operations & Decision-Making

For today’s CFOs, leading a business can feel like steering a ship through a relentless storm — markets are choppy, tariffs are cracking the hull, and the margin horizon keeps slipping out of view. That’s why many are turning to AI to navigate this uncertainty and steer their businesses into more stable conditions.

Notably, 40% of CFOs in the annual Coupa Strategic CFO Report named AI investments as their top growth strategy for the next six to 12 months, and for good reason. It has the power to automate repetitive tasks, surface hidden patterns in cash flow and revenue trends, optimize decision-making around forecasting, and so much more.

What is AI in finance, and how does it work?

AI uses a combination of techniques, such as machine learning algorithms, natural language processing (NLP), and deep learning, to automate time-consuming tasks, improve the accuracy of financial data, and provide valuable insights to make smarter financial decisions. The following are just a few examples of how these technologies work in finance departments:

• Machine learning algorithms learn from data to identify patterns, predict outcomes, and make recommendations. These algorithms are trained on robust datasets and can be used in critical areas such as fraud detection and risk management.
• Natural language processing (NLP) can understand and extract meaning from financial data. NLP can be used for tasks like analyzing financial documents and automating report generation. For example, NLP can automatically read purchase orders and invoices, identify important information like supplier names, dates, and amounts, and then extract that into a structured format. It can also analyze text within invoices to identify suspicious transactions.
• Deep learning can be trained on historical data or image recognition, which can be applied to fraud detection by analyzing financial documents. For example, deep learning algorithms can analyze historical data on supplier performance, past disruptions, and industry trends to predict the likelihood of future problems. This allows you to prioritize risk assessments and focus resources on the best-performing suppliers.
• Generative AI (GenAI) refers to a class of artificial intelligence algorithms that produce new and original data and content, such as text and images, by learning patterns from existing data. As it relates to finance, it’s like having a highly sophisticated virtual assistant that can analyze vast amounts of financial data, identify patterns, and generate valuable reports, contracts, or documentation autonomously.
• Agentic AI leverages multiple AI technologies to automate complex, multistep tasks without much input. It’s AI that doesn’t just analyze, but acts. This might look like an AI agent interpreting spend data in real time, handling invoice and payment processing, flagging suspicious patterns on a few, and then stopping those invoices on behalf of the finance team. Or, directly within a spend management platform, the finance team might ask an agent to draft reports for the upcoming quarterly meeting. These routine tasks are executed autonomously, freeing teams to focus on more strategic, higher-order work. While agentic AI technology is still developing, it’s positioned to become widely accessible in the near future.

Given these applications, many finance teams may already be using AI within their systems and processes without even realizing it.

Where finance leaders stand on AI today

CFOs’ confidence in AI is growing. According to our report, based on a survey of 500 CFOs and finance leaders across North America and Europe, nearly 50% currently use AI in finance and procurement processes. Even more, 74% believe in their ability to govern AI at their company. This shift likely stems from the tangible benefits of deploying the technology so far, such as eliminating manual processes and making it easier for employees to spend compliantly.

For finance leaders today, it’s not about whether they should invest in AI, but how fast they can scale and extract the most value from it. There’s a growing sense that those who don’t start strategically using the technology now will fall behind competitively. So it’s no surprise 100% of finance leaders plan to make AI investments within the next year.

Most of those investments will go towards evaluating direct and indirect spending to identify cost savings opportunities (39%), improving financial planning and forecasting (39%), and eliminating manual processes (38%).

“As AI capabilities advance across the enterprise technology landscape, finance leaders face a critical strategic question: how to evaluate and orchestrate these tools to maximize organizational impact,” says Michael Agresta, Chief Financial Officer at Coupa. “The answer isn’t deploying AI indiscriminately, but creating a deliberate framework that identifies where intelligent automation and predictive analytics deliver the most meaningful transformation.”

What’s holding CFOs back from full AI adoption?

Integrating AI isn’t just about access to tools — it’s about overcoming real technical roadblocks, from fragmented data systems to growing cybersecurity and compliance risks. Layer in persistent economic uncertainty, and every dollar invested in the company must clearly demonstrate measurable ROI. That’s why CFOs can no longer do it alone.

More and more CFOs are partnering with IT to drive true business transformation. The numbers back it up: Nearly 45% of CFOs are aligned with their CIO. These two functions have historically operated in silos. But in this new era, cross-collaboration is the key to getting the most value from AI and building a foundation for sustainable growth.

Examples of AI in finance

AI is already reshaping the day-to-day work of finance teams. Take Vanquis, a leading bank in England, that uses AI to streamline and improve invoice processing rates. “Automating tax coding and digitizing invoicing by 500% enables the accounts payable team to focus on more strategic tasks like query resolution,” says Andrew Labeth, Systems Manager.

While AI-driven invoicing is a common practice nowadays, the technology is also being used in many different ways, including:

Detecting fraud and non-compliant buying

Finance leaders use AI to reduce financial risks by automatically identifying and detecting suspicious activities and errors before they impact the bottom line and the company’s reputation. For instance, payment transactions that deviate from a user’s normal spending pattern or transactions originating from unusual locations are flagged for further investigation.

Fraud detection software continuously analyzes and recognizes patterns across transactions, external vendors, and internal data to spot irregularities that employees might miss, such as:

• Duplicate invoices
• Price changes after issuing a PO
• Incorrectly issued tax and duty policies
• External regulation mismatches
• Fraudulent expenses
• Incorrect timesheets
• Non-compliant purchases

Automating contract analysis and data extraction

Contract reviews are critical for CFOs, but they’re often time-consuming and prone to manual error. AI-driven solutions that use NLP and machine learning algorithms automatically extract crucial financial data from contracts, including payment terms, obligations, and compliance requirements for faster approval.

AI can also streamline invoice processing. It works by receiving invoices from email, scanned PDFs, digital forms, or even paper via optical character recognition (OCR), converting key information into structured data, validating it against internal data (like a PO), and routing it for approval.

This automation enables finance teams to quickly access large volumes of contracts and invoices with higher accuracy. By reducing manual intervention, AI saves time for more strategic work, minimizes errors, reduces compliance risk, and improves the consistency of financial reporting.

Analyzing spending and forecasting

AI enhances spend analysis by automatically categorizing transactions and surfacing insights that might get buried in thousands of line items. Instead of manually reviewing spending or relying on outdated classifications, CFOs see where money flows in real time, where waste occurs, and how spend trends are shifting over time.

When it comes to forecasting, machine learning can analyze a wide range of financial and operational data to create more agile and accurate forecasts. It analyzes patterns and updates projects based on internal and external data, such as:

• Sales performance
• Revenue history
• Expenses and payroll
• Inventory
• Seasonality
• Inflation rates
• Interest rates
• Consumer sentiment

Combine this intelligent forecasting with accurate spend classification, and CFOs have a powerful end-to-end view of future financial outcomes and the spending behavior shaping them.

Modeling “what-if” scenarios

Finance leaders can proactively anticipate challenges and make more confident decisions using AI for scenario planning and forecasting. Working with their supply chain team, they can run potential scenarios — such as new tariffs or shifts in demand — using a digital modeler to understand their impact on costs and the bottom line. Decision-makers can then evaluate trade-offs and choose the best course of action with full visibility into the potential outcomes.

Benchmarking against peers

It’s always good to know if you’re improving processes internally. But how do you know if you’re keeping up with best-in-class companies?

Finance leaders use AI to benchmark their organization's performance against peers and industry standards by analyzing large datasets and key performance indicators (KPIs). This real-time comparison highlights areas of underperformance or success and provides prescriptions to guide improvements in cost reduction, resource allocation, and process efficiency.

In today’s competitive environment, these insights are crucial for proactively adapting strategies. For benchmarking to be truly valuable, it should be grounded in objective, real-world data — not just self-reported surveys. The most reliable insights come from vast, diverse datasets that capture actual behaviors and outcomes. Coupa AI benchmarks your performance against real-world data from more than 10 million buyers and suppliers, so you get the best insights grounded in truth.

Benefits of AI in finance

AI offers several advantages for finance teams, such as allowing them to do more with less and complete tasks with precision. Some of the key benefits include:

Increased efficiency and reduced costs

AI automates repetitive and manual tasks like data entry, invoice processing, and generating financial reports. This frees up resources and staff for more strategic activities and analysis. Coupa’s latest Strategic CFO Report states that eliminating manual processes is the top AI use case for finance leaders.

Enhanced fraud detection and risk management

AI helps finance teams prevent financial losses and protect customer information by quickly analyzing transactional data in real time to identify suspicious transactions or patterns that might indicate fraudulent activity.

Currently, 32% of finance leaders use AI to detect fraud and non-compliant behavior. Thanks to AI analyzing historical data, market trends, patterns, and shifts at incredible speeds, leaders can make smarter decisions through predictive analytics to minimize risk.

Faster and more accurate decision-making

This ability to analyze vast amounts of data enables AI to identify patterns that finance teams might miss, helping them make more informed decisions. Finance leaders gain instant visibility into cash flow, margin performance, spend trends, and errors. Instead of waiting for end-of-the-month reports, they can adjust their strategies faster to maximize results.

Streamlined regulatory compliance

AI simplifies regulatory compliance by handling routine tasks such as data collection, classification, and reporting. It even automates and ensures the company's compliance with regulations such as GDPR, SOX, and CCPA.

While intelligent intake and orchestration workflows make it easier for employees to submit requests and spend within policy, every action is also logged to create a reliable and accountable audit trail.

Better industry benchmarks

AI-powered insights enable businesses to benchmark their performance against a global community to reduce risks, increase efficiencies, and improve margins. Currently, nearly 30% of finance leaders use AI to benchmark their performance against peers.

Moving toward Autonomous Spend Management

CFOs are using current AI capabilities in finance and procurement to prepare for the next frontier: Autonomous Spend Management. This is when an AI agent-native layer of intelligence handles routine, repetitive tasks automatically across the entire source-to-pay cycle. But autonomous does not mean without human intelligence. Finance leaders will be needed to guide the strategy and set parameters for these agents. In fact, 67% of executives are considering autonomous agents as part of their AI transformation strategy.

This technology and the future frontier of spend management will further streamline reporting, enhance decision-making, and enable autonomous task execution.

Risks and challenges associated with AI in finance

While AI holds enormous promise for finance, it also brings a set of real and pressing challenges. For one in five CFOs, outdated systems and siloed departments remain the biggest barriers to digital transformation success, according to the Strategic CFO Report. This makes it difficult to unify data, integrate new tools, or scale AI effectively.

For external risks, cybersecurity (40%) and data protection (38%) are among the top concerns for finance leaders. It’s important that sensitive company and customer information isn’t compromised by AI misuse or weak governance. These risks and challenges are all the more reason for finance teams to partner closely with IT.

AI is only as good as the data it’s trained on

While AI provides various benefits and is a central component of financial maturity, not all AI should be treated the same. The right data is crucial because AI solutions designed to support smarter decisions are only as good as the data they’re built on.

Data scraped off the internet or based on surveys, collected for a few months or from a limited number of customers, and run through public large language models (LLMs) will train AI-driven solutions that reflect these limitations. Companies must understand the data organizations use and look for providers that use proprietary, secure, and confidential data that have been safely and ethically sourced for years, from thousands of customers and millions of suppliers.

AI won’t replace jobs — it’ll change them

As AI becomes more embedded in finance, it’s not eliminating roles. It’s evolving them. In last year’s Strategic CFO Report, hiring ranked as the lowest growth priority. This year, hiring now ranks as the third-highest priority, signaling a shift in how finance leaders are thinking about their teams. The message is clear — AI is only as powerful as the people who use it.

Finance teams need to adapt and grow alongside the technology to maximize AI's potential. This means developing new skills beyond traditional accounting or spreadsheet modeling. Some of the most in-demand skills will be:

• Data literacy and financial analytics to interpret and act on AI-generated insights
• Automation rule-setting capabilities to define the parameters for AI fraud-detection tools
• Cross-functional collaboration with IT, procurement, and operations to embed AI across the business
• Training agility to adopt new tools, processes, and ways of thinking with confidence

AI-powered finance: Coupa’s AI-native Total Spend Management platform for all finance needs

In addition to providing the benefits above, the Coupa AI-native Total Spend Management platform equips finance leaders with comprehensive data visibility and control by unifying the organization’s supply chain, inventory management, contracts, procurement, invoicing, and automated payments in one place. The easy-to-use platform boosts user adoption, enabling more collaboration for smarter decisions across the entire team and organization.

Coupa’s AI agents for finance

Coupa’s Navi™ agent portfolio enhances the Coupa platform and makes finance teams smarter and more agile. Take generating reports at the speed of thought. The Navi™ Operational Reporting Agent can pull the most relevant data and retrieve documents to create custom data table views and reports instantly. How about finding out which suppliers have shown the most significant increase in cost over the past two quarters? An employee can ask the question directly to the Coupa Navi™ Analytics Agent and get an answer without needing to dig into the numbers themselves. Plus, get instant answers to source-to-pay questions based on your company’s buying policies and documentation with the Coupa Navi™ Knowledge Agent. Coupa Navi™ agents support and optimize every stage of the S2P process with continuous innovation and intelligent automation.

Coupa AI is different

For more than 19 years, we have trained our AI on data from more than $8 trillion of global transactional spend across a network of 10 million buyers and suppliers. Coupa’s AI-native platform provides intelligent insights and recommendations to take the guesswork out of decisions for smarter operations.

Training our AI solutions on this proprietary “community intelligence” ensures our AI provides precise, tailored KPIs and recommendations that account for each organization’s unique business rules, supplier dynamics, market factors, and operational constraints. This contextual awareness is pivotal in driving tangible, transformative value across the source-to-pay cycle, enhancing margins and improving profitability.