Finance automation: What you need to know.

A modern finance organization oversees more financial data and drives more business decisions than ever before. To succeed, finance and accounting teams need to fully embrace automation. Learn what finance automation is, how it can limit repetitive tasks and processes, and the ways it can benefit your organization.


Learn about intelligent finance automation.

Automating traditionally manual finance processes can seem like a daunting task for any company. Explore how to take the first steps toward intelligent automation by following these links.


What is finance automation?

Finance automation is the use of software and other technology to automate finance tasks that were traditionally performed manually. Automation targets the repetitive parts of financial operations, such as account reconciliations, accounts payables, and accounts receivables, resulting in cheaper and more efficient business processes. As a result of automation, businesses reduce manual errors, enabling employees to shift their focus to more meaningful work. 

Modern conversations around finance automation typically center on digital transformation, but businesses have been automating finance processes for decades. The replacement of manual ledgers with electronic databases in the 1970s gave way to robotic process automation (RPA) in the early 2000s—legacy software bolt-ons that were able to emulate simple human interactions in real time. 

Now, as businesses continue to rely on outdated finance processes, newer finance automation software enables a more intelligent finance function. With technologies like artificial intelligence, businesses can automate labor-intensive areas that have remained manual, empowering finance teams to use their skills in more productive ways. Rather than reducing the need for human workers, “intelligent automation” plays to the strengths of both sides of an augmented workforce: computers process huge volumes of financial data, identifying anomalies and generating predictions, while humans apply common sense reasoning and business context. 

Here are three key terms associated with intelligent automation and AI in finance:

  • Artificial intelligence (AI) is technology that enables machines to perform tasks traditionally seen as requiring human intelligence. These tasks include problem solving, performing data analysis, and generating content.
  • Machine learning (ML) refers to any computer system designed to learn and adapt without direct need for human intervention. By simulating our ability to learn through iteration, ML enables computers to use algorithms to find patterns in data and predict future outcomes.
  • Natural language processing (NLP) is a machine’s ability to interpret and generate human language, speech, or text. NLP supports speech recognition, language translation, sentiment analysis, text summarization, and chatbot responses.

“AI is not going to replace CFOs. But CFOs who use AI will replace those who don’t.” 

—Erik Brynjolfsson, Senior Fellow, Stanford Institute for Human-Centered Artificial Intelligence


What finance processes require increased automation?

ERP and finance applications are evolving at a faster and faster pace. To remain competitive, CFOs—and their departments—must find ways to leverage new and innovative technologies, while simultaneously addressing social, economic, security, and privacy challenges. The modern CFO oversees not only cash flows and balance sheets but also major enterprise-wide technology transformations. Knowing what finance operations need to be modernized and how, is a key part of steering those initiatives.

The finance function is also facing major talent shortages. The American Institute of Certified Public Accountants estimates that roughly 75% of CPAs reached retirement eligibility in 2020. To continue to attract fresh talent, businesses must assess their existing business processes, ascertain where automation can eliminate inefficiencies, and create technology-driven roles more suited for younger generations. 

Businesses looking to the future of finance automation should separate by function the areas in need of further automation. In the next section, we explore six use cases that highlight how intelligent automation in finance can dramatically improve operational efficiency.

Accounts payable

The procure-to-pay process is one of the more manually intensive processes for both accounting and procurement teams. A cumbersome purchase order process, late payments, incorrect settlements, and other human errors place strain on important partnerships in the procure-to-pay cycle. Worse, these issues can lead to delays in the close process for finance teams, causing a chain reaction of further inefficiencies.

With important factors like marketplace reputation depending on regular, reliable payments, businesses must eliminate any room for error. Here are several ways in which intelligent automation can support your accounts payable professionals:

  • When creating a purchase order, NLP technology can provide real time recommendations based on prior purchase orders. This not only reduces the need for time-consuming manual tasks but also minimizes categorization errors and increases accounting accuracy.
  • One of the more tedious tasks in the procure-to-pay process is invoice processing. Machine learning technology enables users to bulk upload and scan invoices, before routing them to accounts payable specialists. Workday software can further detect urgent invoices and prioritize them for processing accordingly. 
  • With intelligent work queues finance teams can adjust workloads based on AI-generated data and recommendations. The resulting lower invoice processing costs and expedited processing times have a major impact on an organization’s bottom line.

Accounts receivable

Invoicing customers and collecting payments is a crucial part of managing receivables but is also monotonous. Streamlining these processes through automation can improve customer relations and day sales outstanding (DSO) dramatically. 

More automation is especially needed in manual “cash application,” a process in which customer payments are matched to invoices. When existing rules don’t process a significant number of payments, receivables clerks are required to manually track and match payments to invoices. In fact, research shows that accounts receivable teams spend over 50% of their time on manual transactions. Despite this, in 2023 accounts receivable teams across 18 industries reported that 10% of their payments were over 90 days late. 

Intelligently automated financial processes provide an efficient and scalable fix to these issues. Machine learning algorithms in Workday calculate the probability that a payment matches an invoice—including scenarios in which a payment covers multiple invoices. Over time, the accuracy of those matches increase, reducing the need for manual intervention and user-defined rulesets, and freeing employees for high-value tasks. 


High benefits, low adoption.

An overwhelming number of CEOs believe AI can realize major business benefits, yet only a tiny percentage have embraced widespread adoption. That makes the opportunities represented by intelligent automation huge.


of CEOs believe there would be some immediate business benefit from implementing AI.


of CEOs would classify their AI adoption as mature.

General ledger

While the term “general ledger” brings to mind debits and credits posted manually to a journal, journal entries have been automated for decades. Accounting areas like account reconciliation, retained earnings, and other rule-based tasks have all benefited from finance process automation. However, embedding AI into the system of record opens the door to more intelligent automation. 

Accounting teams have traditionally been consumed with daily activities that ensure the accuracy of transactions being processed. The proactive steps enabled by intelligent automation, such as surfacing journal anomalies as they occur, free up employees to focus on higher priority actions during the close. Here are three examples where greater automation helps shift employee focus from data collection to taking action: 

  • Automatically generate an in-the-moment view of your consolidated financial performance
  • Unite data from any source for a more comprehensive analysis, and quickly generate financial statements
  • Interrogate accounting transactions with AI, identifying anomalies and data trends


At companies where processes aren’t intelligently automated, managing payroll can be complex. Payroll professionals spend too much time manually tracking down employee information that is critical for payroll processing, leading to time-consuming audits of payroll records. By leveraging machine learning to detect anomalies, intelligent automation reduces the risk of human error, ensuring transactions flow faster with increased precision.

According to a study undertaken by Workday and PayrollOrg, 56% of payroll professionals said an automated solution that updates employee data changes into payroll in real time was the most valuable emerging technology for their function. By embracing a cloud system with automated capabilities and connected finance, HR and payroll data in your company can:

  • Enable continuous payroll processing, accompanied by automatically-generated insights
  • Automate how payroll is processed across different departments
  • Generate flexible audits of payroll data, with real-time financial reporting and analytics

Expense reimbursement

Payroll is only one aspect of the employee-employer contract, however. Expense management, the process of tracking, reporting, and reimbursing expenses, can be remarkably labor-intensive, especially when employees submit expense reports manually with paper receipts. The backlog this creates has a negative impact across other finance functions. As such, the potential value of streamlining the expense-to-reimburse process through automation is massive.

Employees and employers benefit immediately when expense receipts can be scanned rather than entered manually. Scanned receipts can be reviewed automatically by AI for anomalies, and subsequently tagged for errors, ranging from misclassified expense items to duplicate expenses. By identifying errors and prioritizing them in order of urgency, Workday ensures finance teams can work efficiently and precisely.

Planning and forecasting

As markets across the globe continue to evolve rapidly, access to accurate forecasts will be increasingly valuable. With businesses processing massive amounts of financial data every day, it's possible to generate more precise forecasts than ever before. The only difficulty lies in analyzing the huge volumes of data. Accordingly, planning is one area in which intelligent finance automation already generates significant business value.

By automating parts of the planning process, businesses can quickly surface insights that would otherwise have remained buried. Here are three ways in which Workday AI is evolving the process of financial planning:

  • Anomaly detection provides users with immediate alerts by identifying data outside the normal bounds. Users can identify discrepancies and quickly catch and eliminate errors early in the cycle.
  • Outlier reporting compares forecasts, budgets, and what-if scenarios against AI-driven forecasts to identify accounts with notable differences. That comparison serves to flag potential mistakes while further securing confidence in the accuracy of existing plans.
  • Predictive Forecaster uses historical data in combination with machine learning algorithms to make stronger predictions. Businesses can further enrich their predictions by incorporating external datasets, including labor market statistics, economic trends, and even the weather.

Intelligent automation and the role of the CFO.


What are the benefits of finance automation?

While modern finance systems have already automated many manual processes, AI enables the automation of tasks that previously required human intervention. According to Workday research, 80% of decision-makers agree that AI is a requirement to keep their business competitive. But what advantages does intelligent automation give finance departments over rival organizations?

This global survey from Workday and FSN found that 70% of finance professionals identified improved process efficiency as a major priority for AI and ML. A further 67% prioritized reducing mundane and manually intensive work, while 65% wanted to improve speed to insight. Fortunately, these are all areas where AI shines. Here are five key benefits of intelligent finance automation:

  • Reducing opportunities for human error (and catching them when they occur)
  • Increasing overall operational efficiency by minimizing repetitive manual tasks
  • Empowering employees with access to real-time insights and analytics
  • Improving confidence in proposed financial budgets through regular AI-testing
  • Creating more time for employees to focus on higher value activities that bring greater benefit to the organization 

Workday research shows that 98% of CEOs believe there would be some immediate business benefit from implementing AI. However, only 1% would classify their adoption as mature. Without proper AI implementation and integration, many businesses struggle to encourage widespread adoption, limiting the value to employees. That’s why we've embedded Workday AI at the core of our platform, ensuring organizations can reap the benefits much earlier in the product lifecycle.


The path to finance automation with Workday

Taking the next step toward more intelligent finance automation with AI can be daunting for businesses of every scale and location. This 2023 Workday study found that 77% of business leaders believed uptake of AI and ML would increase at their company if the perceived risk was lower. Accordingly, having a trusted partner is critical when taking the first step with intelligent automation.

That same Workday research found that more than 9 in 10 (93%) decision-makers believe it is important for a human to assist AI when making significant decisions. At Workday, we follow that same principle. Our commitment to responsible AI ensures that our AI is developed with safety and security in mind, and that our customers have visibility into how our AI reaches its conclusions. The best approach to AI always keeps a human in the loop for important decisions.

With more than 65 million Workday users globally, our approach to automation is constantly evolving to meet user needs. To learn more about how finance automation can help your business overcome crucial hurdles, discover how Workday enables an intelligent approach to finance.

“It’s time for CFOs to embed trust in their data, so everyone at the company can understand how AI is being used and how important it is.” 

—Zane Rowe, Chief Financial Officer, Workday

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