Artificial intelligence (AI) is transforming accounting processes and, therefore, the work of accountants and auditors, while also delivering significant benefits to businesses. We can expect this trend to continue and possibly accelerate. AI has the potential to assist accounting teams in solving many of their perennial challenges and elevate their role within organizations. Knowledge is power, so let’s explore the key technologies in accounting AI, their benefits and challenges and how to leverage them for business and professional success.

What Is AI in Accounting?

Like most modern AI uses, AI in accounting is powered by a set of closely related technologies: machine learning (ML), deep learning, natural language processing (NLP) and generative AI. Taken together, these technologies represent a professional game-changer for accountant teams and accounting processes alike. AI tools can analyze vast data sets, identify patterns, make predictions, extract and synthesize structured and unstructured data, and can automate tasks, such as data entry, transaction reconciliation and financial reporting. AI is particularly useful in accounting because it can intelligently handle much of the heavy lifting, resulting in increased team productivity, better accuracy, cost savings and improved support for business decision-making.

Key Takeaways

  • AI is revolutionizing accounting processes with its ability to help automate tasks, analyze vast amounts of data and predict trends, which is exciting to some and concerning for others.
  • Business benefits of AI include reduced costs and improvements in efficiency, accuracy, scalability, client service, fraud and anomaly detection, and decision support.
  • AI lets accounting teams concentrate on consultative and high-value activities, rather than transactional tasks. This transition is quite valuable but may require changes in skills and mindset.
  • Many of the current challenges involved with AI are likely to resolve as the technology evolves and becomes more mainstream.
  • Leading-edge accounting software embeds AI functionality that makes use of the accumulated data.

AI in Accounting Explained

AI is used by accounting teams across industries, for purposes such as bookkeeping, tax preparation and financial audits. The “big four” public accounting firms — Deloitte, EY, PwC and KPMG — have already tapped into AI to transform their financial audit processes and internal workflows, such as managing audit reviews and approvals. Internal accounting and audit teams at companies of all sizes are beginning to follow suit or will be soon.

For instance, auditors can eliminate transaction sampling — and the risks associated with that traditional practice — because AI can quickly analyze an entire accounting data set. In another example, accountants are enhancing the audit process by using AI to identify unusual or anomalous transactions as part of planning and risk assessment stages, rather than uncovering them as part of field work. Meanwhile, smaller accounting firms are ramping up more slowly, using AI for research, tax-return preparation and bookkeeping services, but they’re expected to accelerate their pace.

Overall, accounting teams at companies of all sizes and industries are implementing AI to make their processes more efficient, improve accuracy and support decision-making. This can be achieved in many ways, including adopting automation for touchless invoice processing, supporting more frequent and comprehensive forecasting, and making data analysis and scenario planning easier and faster. As a result, staff time can be reallocated to tasks that require more thoughtful finesse, such as strategic planning. This is welcome news for chief financial officers (CFOs) and financial controllers who have been trying to evolve their accounting and finance teams to be consultative and provide high-value insights that move the business forward, rather than to focus mostly on reporting.

Key Technologies in Accounting AI

Many technologies are formally or informally referred to as AI. The four closely related technologies mentioned earlier — ML, deep learning, NLP and generative AI — form the foundation for AI in accounting, as they do for most other current applications of AI. They are the engines that enable AI to do more with data, faster and with greater precision. Robotic process automation (RPA) and optical character recognition (OCR) are older technologies that don’t use AI but are sometimes integrated with AI capabilities or confused with AI. They’re included in the following list to help set the record straight.

1. Machine Learning

ML allows a computer to “learn” on its own, without being specifically programmed. It relies on algorithms that evolve with experience to form descriptive, predictive and prescriptive suggestions based on data. ML supports smart automation and can identify patterns from massive volumes of data that humans simply can’t process at the same rate. For example, ML can create sales forecasts using historical point-of-sale data down to the SKU level. ML can also analyze transaction patterns and flag anomalies indicative of potential fraud, thereby enhancing internal controls. Additionally, ML aids in bookkeeping by automatically assigning general ledger expense codes to invoices.

2. Deep Learning

Deep learning is a type of ML built on neural network architectures — multilayered networks of artificial neurons encoded in software. Although AI systems that predate deep learning have, for many years, achieved success in areas such as image recognition, NLP and predictive analytics, newer deep-learning-based AI systems consistently outperform them. Deep-learning technologies improve the performance of all the accounting functions mentioned in the preceding ML discussion.

3. Natural Language Processing

NLP reads and interprets words instead of numerical data. Older NLP technologies continue to be useful, but newer NLP capabilities are almost always built on deep learning. NLP can summarize volumes of text, such as financial news or customer feedback, into structured data that enriches accounting functions. For example, NLP’s ability to synthesize tens of thousands of customer reviews could inform the reserve estimates for future product returns or alert for potential inventory sellouts. Contract analysis is another application of NLP in accounting. It involves reviewing and extracting key points from contracts and legal documents to ensure adherence to financial agreements and highlight risks. NLP can also assist with accounting compliance by examining regulatory documents from government, industry, tax and accounting authorities.

4. Generative AI

Generative AI is the surprising capability of certain deep-learning AI models to rapidly create content in response to text prompts. Generative AI models represent a significant advance in AI because they not only understand natural language, but they also can generate it. Generative AI tools can synthesize knowledge from many sources and contribute to problem-solving across multiple domains of expertise. In accounting, for example, generative AI can produce first drafts of reports and financial statements. It also can assist accountants and auditors in exploring a business’s financial data to identify opportunities to improve financial efficiency.

5. Robotic Process Automation

RPA software is not AI technology but is sometimes thought to be because it can be coded to perform certain tasks that previously required human workers. But RPA works only with structured data and is most appropriate for rules-based transactions — although this is changing rapidly as RPA continues to be integrated with ML. But because RPA’s key benefits are speed and consistency, even without ML it has many applications in accounting. RPA supports automated accounts payable (AP) systems, for example, which can be used to match supplier invoices with purchase orders, compare travel expense items with company policies and reconcile accounts. On the accounts receivable side, RPA can validate customer invoices by ensuring proper authorization, pricing and descriptions of products/services and then automatically posting them to revenue accounts in the accounting system. RPA bots that collaborate with ML-based AI technologies are far more capable for accounting applications such as intelligent invoice processing, fraud detection and automated compliance checks.

6. Optical Character Recognition

OCR is a non-AI technology that converts text from scanned or digital documents into machine-readable text. Newer ML-based NLP systems easily perform the same function, which is sometimes still called OCR since businesspeople understand what that terms means. Both the old and new versions of OCR functionality eliminate manual data entry, which saves time and reduces the potential for human error. Additionally, they enhance document storage by digitizing documents and making them searchable. This capability is typically included in better accounting software, especially for invoice processing as part of automated AP.

Benefits of AI in Accounting

AI’s benefits hit all the crucial notes for accounting: accuracy, efficiency and scalability. Further, AI adds unmatched speed. The following are some key advantages of AI in accounting.

  • Increased efficiency: When AI is used to handle the volume of routine tasks, accounting teams can spend more time on value-adding work. This balance helps increase overall productivity and makes better use of the accounting staff’s expertise and experience.
  • Enhanced accuracy: Automation, such as coding general ledger transactions, minimizes manual errors, helps improve accuracy and reduces rework, such as fixing misclassifications.
  • Improved decision-making: AI helps get better information into decision-makers’ hands quickly. AI-assisted analysis can also be more comprehensive, drawing up data from the entire organization to surface deeper business insights.
  • Enhanced fraud detection: Data analysis that identifies anomalies and outliers is a primary way to detect potential fraudulent transactions. AI’s ability to quickly examine massive data sets greatly expands those efforts. As a result, the use of AI in antifraud programs is expected to triple over the next two years, according to a 2024 survey by the Association of Certified Fraud Examiners.
  • Cost savings: Automating routine tasks helps companies save money by reducing the amount of time staff spend on rote tasks, as well as through process improvements that, for example, minimize or eliminate late payments, doing the same for associated fees and penalties.
  • Scalability: Because AI automation handles many rote tasks, such as calculations, cross-checking and data entry, it helps accounting processes scale along with the business. This is particularly beneficial given the ongoing shortage of accountants, attributed mostly to burnout.
  • Better compliance: The compliance challenge in accounting is about putting processes in place so that transactions are handled properly (in accordance with laws/regulations/standards) and then identifying errors — data errors, errors in interpreting GAAP, reporting errors. AI-based predictive analytics gives accountants both a wider and a finer net to catch those errors and, therefore, reduces potential compliance risks. Early detection of anomalies in accounting data is the best defense against compliance issues. Additionally, AI may be able to monitor relevant external sources for changes in regulations.
  • Improved client services: AI tools help the accounting team provide better and more consistent service to internal departments and external clients and partners. Communications, such as email, can be made more professional through the use of generative AI. And more accurate information paves the way for better customer service and avoids embarrassing interactions, such as sending dunning notices to a customer who has already paid their bill.

Challenges of AI in Accounting

As with the introduction of any new tool, AI in accounting is not without a learning curve, and its use must be modeled and encouraged from the top down. Only then can meaningful efforts be taken to overcome the other challenges listed here.

  • Initial costs: The initial costs of AI can be a barrier to adoption, even when long-term savings and benefits are expected. These costs include software licenses, possible integration costs and training for IT staff and employees. However, AI capabilities are often deployed as an integrated part of business applications that companies already use and so have no separate cost. Cloud-based SaaS applications that offer “free,” integrated AI features can minimize upfront costs and simplify adoption. This approach also allows staff to use AI easily within their familiar workflows, reducing the need for additional training.
  • Skills gaps: The skills gap between accounting and AI expertise is another obstacle for companies looking to adopt the technology into their finance functions, especially among smaller companies that have fewer resources and less technological capacity. Further, the skills gap can differ significantly among employees of different generations, requiring thoughtful and ongoing training programs.
  • Regulatory concerns: Accounting is subject to many layers of regulation from various standards-setting bodies. As with any other tool, ensuring that AI keeps up with these changes can be difficult. In addition to financial regulations, AI that accesses sensitive information is subject to data privacy and security regulations. Embedding AI into accounting systems that are continually updated to reflect the latest changes in regulation can go a long way toward solving these issues.
  • Integration issues: Legacy systems may not be capable of integrating with AI software, either entirely or only with costly customization. Additionally, anemic or inaccurate data degrades the quality of AI output, so siloed, unverified or incomplete data in existing systems needs to be cleaned in order to achieve the full benefits of AI and avoid faulty results. Using cloud-based integrated business accounting software suites with embedded AI capabilities can avoid this issue.

AI Trends in Accounting

It’s an exciting time for accounting teams as they become more familiar and comfortable with AI and the technology becomes more ubiquitous in their everyday work. Leading software companies are baking AI into their offerings, such as enterprise resource planning (ERP), accounting and financial systems, to enrich functionality. As AI continues to gain momentum, here are several trends to be aware of.

AI as a Capable Assistant/Adviser

AI capabilities are emerging as a kind of adviser for business accountants, enhancing their ability to provide valuable insights and make informed decisions. Because AI enables modern accounting software to analyze far more data than previously possible, it can bring new patterns and trends to accountants’ attention, which helps them provide strategic guidance to their organizations. AI can also suggest ways to increase the accuracy of forecasts, optimize tax strategies and automate compliance checks. And AI systems never tire of generating new and different financial scenarios to assist accountants in exploring the nuances of their forecasts and recommendations.

AI as a Competitive Differentiator

It’s fairly easy to grasp how AI can be a competitive advantage for business functions like sales and marketing — just think of those “you may also like” suggestions from online retailers. But what about the accounting function? Consider the relationship between more accurate forecasts and the optimization of inventory and labor scheduling. AI can analyze historical data and market trends to help staff develop more precise demand forecasts, so that businesses can stock the right amount of inventory items at the right times. This helps reduce losses from obsolescence and minimizes holding costs.

Enhanced Predictive Analytics

Many companies use predictive analytics to estimate what would happen if certain conditions were met using probability, quantitative analysis and modeling techniques. It’s hard, complicated work. However, now that AI can autonomously identify patterns in data and develop predictive models, more businesses can use these models to help forecast outcomes, such as revenue and cash flow. Additionally, AI takes business intelligence and predictive analytics to another level by incorporating unstructured data, such as social media posts, customer service calls, videos, images, emails and external web pages. This information helps improve the quality of the predictions by including such variables as customer behavior and market trends. In turn, more business leaders are expected to use AI-enhanced predictive analytics to gain superior insights and make better-informed decisions.

Real-Time Data Analysis

AI-supported systems can automate real-time data analysis, performing it much faster and more accurately than humans. This not only improves the speed and quality of reporting, but it also enables quicker action, which can make all the difference from a competitive point of view. Real-time analysis also plays a critical role in improving customer service, fraud detection and forecasting. Given these significant benefits, real-time data analytics is expected to become a staple of modern accounting software.

Integration of AI with Blockchain

Blockchain technology’s record-keeping feature makes it a natural fit for accounting and auditing. Blockchain organizes records in a way that makes changing a transaction entry impossible, which is important for overall data governance, reliability and compliance and is particularly helpful for the auditing process. AI can quickly analyze large amounts of data and examine blockchain transactions to identify any abnormalities or alterations. For example, when used together, AI and blockchain can increase the efficiency of auditing financial transactions, leading to quicker, less labor-intensive audits.

Examples of AI in Accounting

AI is helping to transform the way accounting teams work by streamlining and enhancing various functions. Across the board, there is evidence that AI is driving improved efficiency and accuracy. Some specific examples of AI in accounting are explored below.

  • Forecasting: AI can be used to analyze large volumes of historical data and identify patterns that help predict future trends and outcomes. This predictive financial analysis aids in forecasting cash flows, revenue, expenses and other financial metrics based on the insights derived from the data analysis. AI-based forecasting models get better as more data becomes available, providing more accurate projections than traditional statistical forecasting methods.
  • Scheduling: AI can assist with scheduling resources, such as staff and inventory, based on periods that are projected to be busy or slow. It can also help schedule and centrally monitor tasks, including cash collections, department calendars and the monthly accounting close.
  • Managing cash flows: By predicting sources and uses of cash using data from multiple systems, including AR and AP, AI can generate more accurate cash flow estimates. This helps businesses better understand their cash position, potentially improving investment returns and reducing unnecessary borrowing costs.
  • Automating workflows: AI can bring a higher level of sophistication to workflow automation. Beyond simply routing tasks, AI can decide what needs to be routed vs. what can be automatically accepted based on past experience or certain rules. This helps reduce the workload for staff and the potential for items to fall through the cracks. Automated processes often include travel and entertainment expense reports, invoices, account reconciliations and audit workpaper review.
  • Composing emails and inbox management: AI can review and sort received emails by category, topic or priority and flag those that require a reply. It’s also helpful for when users compose email replies, evaluating grammatical accuracy, tone and form. Beyond that, AI-powered email assistants can automatically extract relevant information, update customer relationship management (CRM) systems and draft a reply to questions.
  • Invoice processing and expense management: AI can automatically identify and record the relevant data from supplier invoices, as well as match purchase orders, delivery documents and receipts. This helps increase productivity, improve accuracy and accelerate payment processing.
  • Data analysis: AI excels at pulling together data from disparate locations and creating reports at a level of depth and speed that is unmatched by humans. For example, AI can generate an analysis that shows variances to budgets, as well as comparisons to internal and external benchmarks. With that legwork completed, accounting staff can spend more time understanding the issues and developing action plans, rather than creating the analysis.
  • Business communication: Whether communicating with customers, investors or colleagues, AI can help increase efficiency and enhance relationships. For example, AI can gauge sentiment in customer emails, route them to the correct AR or customer service clerk for a proper reply and then help compose appropriate email responses.
  • Project management: AI can be used for project management at several stages of an accounting project. First, AI can organize all project documents and maintain version control, which is especially helpful for iterative projects, such as developing a capital budget. Next, the technology can transcribe and summarize notes from project meetings to help keep everyone on the team up to date. Then, using predictive analytics, AI can flag potential project overruns before they happen, such as when implementing a new automated billing system. Finally, AI can generate progress reports that keep the CFO and team on track to complete the project.

Learn How AI-Powered ERP from NetSuite Helps You Manage Your Business Better

AI is a powerful tool that enhances the output and efficiency of accounting departments. Its ability to gather, analyze and synthesize data makes it a game-changer, but AI tools require clean, reliable and complete data. This is the primary advantage of pairing AI with an ERP system that combines accounting and operational data from all parts of the business. NetSuite ERP incorporates NetSuite AI functionality, giving businesses quick access to all the potential benefits.

For example, NetSuite Bill Capture uses AI to eliminate manual data entry in invoice processing and automatically performs three-way matching of purchase orders, shipping documents and supplier invoices. This helps reduce errors, increase productivity and foster healthy relationships between accounting teams and suppliers. Similarly, NetSuite Analytics Warehouse provides faster access to analysis and reporting, using AI capabilities. Accounting teams can access real-time data from within NetSuite systems and other sources, including CRM and ecommerce platforms, to gain deeper, quicker and more actionable business intelligence. The included customizable dashboards and intuitive report builder put the power of AI into accountants’ hands with a short time to value.

Businesses stand to gain significant benefits from integrating AI into accounting processes. Key among them are cost savings, scalability, enhanced accuracy, improved compliance and fraud detection, better forecasting and customer service, and enhanced decision support. For accounting teams, AI’s automation of routine and time-consuming tasks frees them to focus on higher-value activities that make sure of their expertise for strategic analysis, problem-solving and strategic decision-making. AI also empowers them by analyzing massive datasets, using predictive analytics and assisting with project management and business communications. It’s a seismic shift that’s exciting and new, with the potential to help accounting teams achieve new levels of influence and success.

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AI in Accounting FAQs

Will AI replace accountants?

Artificial intelligence (AI) may have the potential to replace low-level accounting clerks as it automates routine tasks, such as data entry. However, accountants with the right skills and mindset will continue to be valuable assets to their organizations. AI will be an important tool used by accountants as they evolve from being transactional to more value-based.

How is AI being used in accounting?

There are many use cases for artificial intelligence (AI) in accounting, including helping to improve forecasting, scheduling, invoice processing, business communications and workflow management. AI can also help assess audit risk and work as an alternative to audit sampling.

Is AI going to replace accounting?

Accounting won’t go away or be replaced by artificial intelligence (AI). However, AI is changing the way in which accounting work is performed and enhancing modern accounting software.

Which accounting firms use AI?

Accounting firms use artificial intelligence (AI) as part of various client services, including bookkeeping services, tax preparation and financial audits. The “big four” public accounting firms — Deloitte, EY, PwC and KPMG — have been leading the way. Small and midsize accounting firms, meanwhile, have begun using AI for research, tax return preparation and bookkeeping services.

Can GenAI be used in accounting?

Generative artificial intelligence (GenAI) is a form of artificial intelligence that can produce unique content, more closely mirroring human capabilities. The key difference between GenAI and traditional AI is the former’s ability to generate its own output, whereas traditional AI operates on predefined responses or learned patterns. GenAI can be used in accounting as another progression to streamline operations, enhance communications and extract valuable insights from large datasets.