How accounting teams use AI to get more work done everyday

AI brings measurable benefits to corporate accounting departments, from reducing manual work to providing strategic insights that drive informed business decisions. Here are some of the most significant ways AI can improve accounting operations.

Time savings and efficiency

Accounting departments spend hours on repetitive tasks that AI can now handle automatically. Invoice processing, data entry, and transaction categorization can be completed in minutes through automations. Automating these processes allow accounting teams to close their books faster and reduce the overtime hours typically required during month-end periods.

For instance, with the typical accounts payable process, an accountant receives dozens of invoices daily, manually enters data from each one, codes them to the proper accounts, and routes them for approval. With AI automation, these same invoices get processed in seconds through OCR invoice processing that extracts relevant data, machine learning algorithms that apply consistent coding rules, and workflow automation that handles routing.

Improved accuracy

​​Human error is inevitable when processing large volumes of financial data manually. Transposed numbers, incorrect account codes, and duplicate entries create mistakes that require time-consuming corrections later in the accounting cycle.

The best automated accounting software eliminates the potential for manual errors by applying the same logical rules to every transaction. When the software processes an invoice from a specific vendor, it matches the expense to the right account every time, using the same tax treatment and approval workflow. This consistency extends to all related processes, from expense categorization to journal entry creation.

Companies using AI for transaction processing can see error rates drop significantly, reducing the time spent on reconciliation adjustments and audit corrections. Clean data also improves the reliability of financial reports, giving you confidence in the numbers to make business decisions.

Real-time insights

Traditional accounting operates on monthly cycles, with financial information becoming available weeks after the books close. AI accelerates this timeline by processing and categorizing transactions as they happen, creating up-to-date financial dashboards that reflect current business activity. Ultimately, this allows managers to spot trends and issues while they can still take corrective action. A sudden spike in travel expenses, an unusual pattern of vendor payments, or a deviation from budget targets becomes apparent within days rather than weeks.

Real-time processing also improves cash flow management. Companies can monitor their current cash position, track outstanding receivables, and anticipate upcoming payment obligations with current data rather than relying on weeks-old reports.

Strategic focus

When AI can take care of routine processing tasks, finance and accounting teams can spend more time on activities that impact business performance. Instead of manually entering invoices or reconciling accounts line by line, accountants can analyze spending patterns to identify cost-saving opportunities or investigate variances and recommend operational improvements.

This shift changes the role of accounting within organizations. Finance teams become business partners who provide insights and recommendations rather than looking backward and maintaining historical data. They can participate in strategic planning, evaluate investment opportunities, and help other departments make data-driven financial decisions.

AI tools have functions across corporate accounting departments, streamlining everything from transaction processing to complex financial analysis. The applications below show how artificial intelligence can address the pain points that accounting teams face daily, making financial processes more efficient and accurate.

Automating accounts payable and invoice processing

When vendors send invoices, AI can process them with little or no human intervention. Once an invoice is uploaded or forwarded to a dedicated email address, OCR invoice processing captures details like amounts, dates, and vendor information, while machine learning algorithms automatically code these transactions to the appropriate general ledger fields.

The best AP automation software makes this workflow seamless. As invoices arrive electronically or through scanning, AI extracts the relevant data, matches it against purchase orders when available, and routes approved invoices through your existing approval hierarchy. This eliminates the manual data entry that delays payment processing. Instead of invoices sitting in inboxes waiting for manual processing, AP automation allows them to move through your within hours.

Expense management

Employee expense reports are administrative burdens for both staff and accounting teams, as reports need to be manually created, reviewed, and approved. AI automates expense reporting by automatically capturing transaction details from corporate credit cards and generating expense reports that require little or no employee input. The best expense management software can identify merchant categories, extract receipt information from photos, and flag expenses that might violate company policies before employees submit them. When employees do need to add details like business purposes or attendee names, AI can suggest likely options based on historical patterns.

Brex's AI-powered spend management software can categorize corporate card transactions as they occur and map them directly to the correct general ledger accounts. This real-time processing means expenses are already organized and coded when accounting teams begin their monthly close procedures, eliminating hours of categorization work.

Accounts reconciliation

Monthly accounts payable reconciliation processes traditionally require accountants to manually match transactions across different accounts and identify discrepancies. AI-powered Two-way matching saves time and reduces errors.

AI integrated into spend management platforms can change this by automatically comparing transaction records and flagging expenses that need attention as they happen. Machine learning algorithms also get more accurate over time by learning how accountants have handled similar transactions in the past. They can suggest likely matches between bank statements and general ledger entries, identify duplicate transactions, and even recommend journal entries to resolve common reconciliation issues.

As a result, reconciliation tasks that consume several days of focused work can often be completed in a few hours of review and approval. This efficiency allows accounting teams to close their books faster and be more confident in the accuracy of their financial records.

Compliance monitoring

AI provides continuous oversight of financial transactions, scanning for patterns that might indicate errors, fraud, or policy violations. Unlike periodic reviews, this monitoring happens in real time as transactions are processed, increasing the odds that any fraudulent or unapproved spending is caught before it becomes a significant issue.

AI-powered monitoring can detect unusual spending patterns, duplicate vendor payments, or expenses that exceed policy limits. It also monitors for more subtle issues like transactions that occur outside normal business hours or payments to vendors not in the approved vendor database. For companies with strict regulatory requirements, AI monitoring provides documented oversight that helps demonstrate compliance with internal policies and external regulations.

Forecasting

Traditional financial forecasting relies on historical data analysis that accountants perform manually, often using spreadsheets and limited statistical methods. Although this process has worked for decades, businesses no longer need to rely on manual financial forecasting.

By analyzing extensive datasets, AI can identify subtle patterns and trends that human analysis might overlook. AI forecasting tools analyze multiple variables simultaneously, considering factors like seasonal trends, economic indicators, and company-specific patterns to predict future cash flows and expenses. As a result, finance teams can anticipate cash shortages months in advance, identify spending trends that might affect budgets, and make more informed decisions about corporate cash management.

Successfully implementing AI into your corporate accounting processes requires careful planning and execution, but with the right solution, it shouldn’t be overwhelming. The following steps serve as a guide for integrating AI into your existing workflows while minimizing disruption.

Step 1: Identify opportunities and pain points

Start by evaluating your current accounting processes to find the most time-consuming tasks. Look for repetitive processes that follow predictable patterns, such as invoice data entry, expense receipt processing, or routine journal entries. Tasks with standard, repeatable procedures typically benefit the most from AI.

Track and document how long these processes currently take and the errors that commonly occur. For instance, monitor how many hours your team spends each month categorizing expenses or how often transaction coding mistakes require correction during reconciliation. This will help you find the tools that best meet your business's needs and quantify improvements after implementation.

Focusing on processes that directly affect your accounting cycle timeline can have the biggest impact. If manual invoice processing delays your accounts payable workflow or if expense categorization extends your month-end close, these areas should be priorities for automation.

Step 2: Research and select the right AI tools

Evaluate AI solutions based on your specific needs rather than general feature lists. A B2B payment automation tool might excel at OCR processing but lack the approval workflow capabilities your organization needs. Similarly, an expense management software might offer excellent mobile receipt capture but integrate poorly with your existing ERP software.

Request demonstrations that use your actual data rather than vendor-prepared examples. This approach reveals how well the AI handles your specific transaction types, vendor formats, and coding requirements. Pay attention to accuracy rates during these demonstrations and ask about training periods required for the AI to reach optimal performance.

Consider integration requirements carefully. AI tools that operate in isolation create data silos and additional manual work. Look for solutions that can connect directly with your general ledger, share data with other accounting applications, and export information in formats your team already uses.

Step 3: Pilot the AI solution on a small scale

Start with a limited implementation that automates only one process or department. For instance, automate expense processing for a single team before expanding to the entire organization, or implement AI invoice processing for one category of vendors while maintaining existing workflows for others. Once implemented, monitor performance and track it against your baseline measurements. Track processing times, error rates, and staff time savings throughout the pilot period and document any issues.

Gather feedback from your employees who interact with the AI pilot daily. Their insights about usability, accuracy, and workflow integration can help you find improvement opportunities that aren't apparent from performance metrics alone.

Step 4: Train your team and manage change

Once you decide to implement an AI solution, offer your employees comprehensive training. Your training should help your team understand how the AI makes decisions, what types of exceptions it might flag, and how to verify its accuracy to help your team use the platform more efficiently. And while conducting the training, address concerns about job security. Explain how AI will handle routine tasks while creating opportunities for staff to take on more analytical and strategic responsibilities.

Once your team is prepared to use the new solution, update your existing procedures to incorporate AI workflows. Define how exceptions should be handled, establish approval processes for AI-generated entries, and create audit trails that meet your compliance requirements.

Step 5: Monitor performance and continuously optimize

Track key performance indicators that matter for your accounting operations. These might include month-end close timeline, accounts payable KPIs, staff overtime hours, or other procurement KPIs. Regular measurement helps you quantify impact and identify areas that can be refined and improved.

As a part of this process, collect feedback from your team about how the solution is performing and its usability. AI tools often include settings that can be adjusted to better match your organization's preferences, and providers often issue updates adding new capabilities that might benefit your workflows. Once your initial implementation is successful, plan for gradual expansion. Apply the same pilot-and-train approach to additional processes, using lessons learned from your first implementation to accelerate subsequent rollouts.

Although AI can benefit accounting departments, there are obstacles that require planning and management. Being aware of these challenges can allow you to prepare realistic timelines, allocate appropriate resources, and set proper expectations for AI adoption.

Upfront investment and training

Bringing AI into your accounting processes can require significant financial and time investments. New tools often need integration with existing accounting software, data migration from legacy applications, ongoing technical support to maintain optimal performance, and staff training. Accounting teams also need to learn how to operate new AI tools and interpret their outputs, handle exceptions, and maintain quality control over automated processes.

In many cases, you’ll need executive support to overcome the initial challenges. Finance leaders should prepare business cases that demonstrate long-term return on investment, including quantified benefits like reduced processing time and improved accuracy. Starting with high-impact areas where AI can show quick wins helps justify continued investment in broader automation initiatives.

Data security

Financial information is some of the most sensitive data in any organization, making security a significant concern when implementing AI tools. Cloud-based AI platforms process transaction data on external servers, creating potential vulnerabilities that don’t exist with on-premise accounting software.

To address these concerns, accounting departments need to work closely with IT and compliance teams to evaluate vendor security standards before implementation. This evaluation should include encryption standards, access controls, data backup procedures, and audit trail capabilities. Many software vendors undergo SOC 2 audits and maintain compliance certifications, but accounting teams should verify these credentials independently.

Simultaneously, establish clear data governance policies by defining which financial data can be processed by AI tools, how long vendors can retain information, and what happens to data if you discontinue the service. These policies protect sensitive information while allowing your business to automate processes with AI.

Regulatory compliance

Another challenge for integrating AI tools into your processes is determining that they’re in compliance with internal and external policies. Your tools must operate within existing accounting standards and regulatory requirements, which can be complex depending on your industry and jurisdiction. Automated transaction coding, for example, must follow GAAP principles, while AI-generated journal entries need proper documentation for audit purposes. The challenge is even greater for companies subject to SOX compliance, industry-specific regulations, or international accounting standards.

Building your processes to maintain human oversight of AI decisions helps address compliance concerns. Establish review processes where accountants verify AI outputs, especially for material transactions or unusual items. Document these review procedures to demonstrate adequate internal controls during external audits.

Human oversight

Some accounting staff may resist AI implementation due to concerns about job security or skepticism about reliability. Address these concerns through transparent communication about how AI will change job responsibilities rather than eliminate positions. Emphasize that AI handles routine tasks while creating opportunities for staff to focus on high-impact advisory work.

During implementation, remember that AI tools augment accounting teams rather than replace them entirely. Over-reliance on AI can lead to missed errors or inappropriate handling of complex transactions that require professional judgment. Be sure your employees understand their role.

Implementing AI can require training beyond the software itself. Accounting professionals may need to develop new skills in AI oversight, data analysis, and technology management. Providing training and career development opportunities helps staff adapt to these evolving role requirements and execute their responsibilities more effectively.

AI is changing corporate accounting by making processes faster, smarter, and more efficient. Accounting departments are using AI to eliminate time-consuming manual tasks, reduce human error in data processing, and expedite financial analysis that drives business decision-making.

The future of accounting will likely involve even more sophisticated AI capabilities. Conversational AI assistants will be able to handle routine inquiries from other departments, while advanced predictive analytics could identify financial risks and opportunities months before they materialize. Machine learning algorithms will become more accurate at handling complex transactions, and AI-driven audit procedures may supplement traditional compliance reviews. Accounting professionals who adapt to this change will find themselves in increasingly valuable positions as business advisors rather than transaction processors.

For accounting teams considering AI adoption, the solutions available today make this implementation worthwhile. Even modest solutions in single workflows can generate significant efficiency gains and provide valuable experience with AI tools. Begin by identifying your most time-intensive manual processes, research solutions that address those specific needs, and implement changes gradually to build confidence and expertise.

From automated invoice processing to real-time financial insights, AI provides immediate opportunities for accounting teams to improve their operations. Companies implementing AI in their accounting workflows can see big reductions in processing time, fewer errors in their financial records, and an accounting staff that can focus on strategic analysis. Whether supporting invoice processing best practices, streamlining expense management, accelerating reconciliation processes, monitoring compliance, or improving financial forecasting, AI tools address the specific challenges that accounting teams encounter daily.

Companies that embrace AI in their accounting operations will have competitive advantages, from faster financial reporting and more accurate data to accounting teams focused on driving business value rather than processing paperwork. With proper planning and realistic expectations about implementation challenges, accounting departments can successfully integrate AI tools to work smarter and more strategically in the years ahead.

For businesses looking to add AI to accounting processes, Brex offers a unified spend management platform that can automate and accelerate your financial management. Brex combines corporate credit cards with AI-powered expense management tools, creating automated workflows that eliminate manual data entry and accelerate financial reporting. When employees make purchases using Brex cards, transactions are automatically categorized, reviewed, and coded to appropriate general ledger accounts using Brex AI. And when you receive invoices, Brex’s bill pay platform automates the entire accounts payable workflow, from processing invoices to executing payments, preventing manual bottlenecks that traditionally extend payment cycles.

AI is integrated across Brex, automating many of the manual processes that slow down accounting teams. The platform accelerates accounting operations by providing accounting rule suggestions that automate GL coding decisions, shortening the tail end of transactions that typically need manual attention. Additionally, Brex AI improves forecasting by analyzing budgets, spend limits, and utilization patterns to offer actionable insights on spend optimization, helping finance teams make more strategic decisions about resource allocation.

Sign up for Brex today to accelerate your accounting processes with AI.