How AI is transforming spend management

If you’re spending hours chasing down receipts, categorizing expenses, reviewing purchases, and reconciling transactions at month-end, adopting another platform or feature may feel like more hassle than it’s worth. But today’s AI-powered spend-management software is designed to reduce manual effort, and it can help streamline your company’s spend management processes.

In this article, we’ll explore how AI is changing spend management, where it creates measurable value for growing businesses, and when human oversight is still needed.

AI spend management refers to the use of machine learning and generative AI to automate and improve the way businesses track, categorize, control, and analyze company spending. It goes beyond basic automation and can include tools that learn patterns, make predictions, and even generate insights using natural language. It can help finance teams reduce manual data entry, identify unusual spending faster, improve budgeting decisions, speed up approvals, and surface insights that would otherwise take hours to uncover.

Automation has been part of finance software for years. Traditional spend management automation relies on fixed rules. For instance, if a transaction matches a specific vendor or amount, it could automatically get routed or tagged a certain way, such as sending expenses over $500 to a manager for approval or categorizing all Uber receipts as “Travel.” 

AI adds another layer of intelligence. Instead of following fixed rules, AI-based spend management tools can analyze spend patterns and adapt over time. For example, it may recognize that purchases by one department should be categorized differently than similar purchases made by another department. Over time, it can get better at predicting expense categories, flagging unusual transactions, and adapting to how your business actually operates. Instead of requiring someone to manually update rules every time spending patterns change, the system can continuously improve based on your company’s spending data.

As a finance leader or founder, here are several different ways that you can use AI to make your spend management systems smoother and more powerful. 

Automated expense categorization and data entry

Manual workflows can create all sorts of bottlenecks — and the process of manually categorizing expenses can be one of the biggest time sinks for finance teams. The process often involves all sorts of human errors to wade through. Employees might upload receipts with incomplete information, for instance, or transactions might have coding errors. 

AI spend management software can significantly reduce this workload. Optical character recognition (OCR) lets AI read receipts and invoices, extract vendor names, dates, and totals, categorize expenses, suggest general ledger (GL) codes, and match receipts to transactions. This eliminates the need for manual data entry and, in turn, reduces errors and results in faster month-end close. Your finance teams can review AI recommendations and approve, when needed.

Real-time anomaly, fraud, and duplicate-spend detection

Traditional expense audits catch anomalies or duplicate transactions retroactively. This usually happens when someone reviews transactions weeks or months after the transactions happened. Since these manual reviews are time-consuming, your team may also end up putting them off. 

By continuously monitoring spending activity and identifying purchases that deserve a closer look, AI tools can shift this process from being reactive to proactive. AI tools can flag potential fraud or unusual transactions in real-time before money leaves your business — such as an employee card being used in a way that doesn’t match their role or historical spending patterns — and block them immediately.

Predictive budgeting and cash flow insights

AI doesn’t just help you understand what has happened in your business; it can help forecast what’s likely to happen next. By identifying patterns — such as seasonal spending trends, a rise in software subscriptions, and which departments tend to spend during certain time periods — AI spend management software can predict cash flow needs, flag departments or projects that are likely to go over budget, and surface trends that might not be obvious from a static spreadsheet. 

For startups and small businesses managing tight cash flow, this kind of predictive insight can be extremely valuable. Instead of finding out that your team went over budget after the end of the quarter, AI can flag the trend while there’s still time to adjust. Automated cash-flow projections and forecasts can also help you make more informed decisions before problems arise. 

Generative AI for policy questions, reporting, and analysis

Generative AI is also making financial information easier to access and understand. Instead of sifting through reports and manually digging through data, for instance, finance teams can ask plain-language questions like, “How much did we spend on software subscriptions last month?” or “Which department is closest to going over budget?” AI is able to draw out and present answers immediately based on underlying data. 

Employees can also use AI to make sense of complex policy documents by asking questions like, “Can I expense this?” or “What is our policy for travel expenses?”. Or when reviewing technical reports, they could ask, “How can we stay within budget this quarter?” This can be especially helpful to finance teams, since it can make financial information easier for the rest of the organization to understand and, therefore, reduce how often they’ll need to field repetitive questions.

How AI can improve approval workflows and policy enforcement

Approval workflows are prone to becoming bottlenecks as companies grow. For example, managers might get wrapped up in reviewing routine purchases that follow company policy while more important requests wait in line. 

AI spend management software can help streamline approval workflows by identifying and separating low-risk transactions that meet pre-defined policies and highlighting exceptions that require additional review. For example, you could train your AI system to auto-approve a recurring software subscription from a known vendor, but have a one-time purchase from an unfamiliar vendor get routed for additional approval, even if both involve the same cost. This kind of approval workflow can reduce friction for routine spend while maintaining oversight where it actually matters.

There was a time when finance tools were only designed for large enterprise organizations, but that's changing quickly. Today, AI spend management is making enterprise-level finance tools more accessible for startups and small businesses. 

Several trends are shaping how small companies are adopting these tools, as detailed below. 

Built-in AI features

Small businesses are increasingly choosing platforms with AI capabilities that are built into the core product, rather than purchasing separate AI tools that need to be integrated. 

Lower barrier to sophisticated forecasting

Predictive forecasting capabilities that were once only available to larger enterprises are now more accessible, thanks to AI spend management software. So, small businesses and startups can now benefit from these powerful features, too.

AI as a force multiplier for lean teams

For startups without a full finance department, AI-powered spend management features — like anomaly detection, categorization, and reporting — reduce the burden on small teams or sole founders and deliver meaningful time savings.

AI spend management tools are powerful, but they aren’t without limitations. Understanding the risks is just as important as understanding the benefits. Here are a few things to consider.

AI can miss context that humans understand intuitively

AI can’t replace human judgement. But it can only flag things for humans to judge. A flagged transaction might look unusual to an algorithm but make complete sense to a team member who understands the business context and the need for a special, one-time transaction for a new initiative, for example. 

Bias and training data limitations

AI models are only as good as the data they’re trained on. If your company’s historical data is incomplete or has errors, like incorrect expense categories or missing documentation, AI could potentially replicate or amplify those issues, rather than correct them. 

False positives or false negatives

Some legitimate transactions could get flagged unnecessarily, whereas other genuinely problematic transactions might not trigger an alert at all.

Plausible-sounding incorrect answers

Generative AI can sometimes make up or misquote information that sounds pretty professional. Always double-check AI-generated information, use your judgment, and don’t assume it’s accurate.

Compliance and audit requirements still require human oversight

Regulatory and audit standards typically require human review and accountability, even when AI assists with detection and categorization. Think of AI as an assistant, rather than an autonomous decision-maker. 

Ideally, AI spend management tools are able to reduce the manual work that finance teams need to do, but they should never completely replace human oversight and expertise. AI tools can often handle repetitive work, surface useful insights, and speed up everyday workflows, but your team should supervise any AI tools and remain responsible for oversight, as well as making final calls and financial decisions. 

The businesses that are getting the most value from AI spend management are the ones using AI to eliminate busywork, so finance leaders and founders can spend more time on strategic decisions. If you’re evaluating AI spend management, start by identifying where manual work is slowing your company down. Then, look for tools that can automate those tasks.

Explore Mercury’s expense management tools to see how AI in spend management can simplify your financial workflows while letting you stay in the driver’s seat.