AI in accounting: A virtual colleague, not competition

Summary

While some see AI in accounting as a threat, it can actually be a valuable tool to help support and streamline your work. Through machine learning, natural language processing, generative AI, and automation, artificial intelligence can take on the tedious tasks of data entry and report generation as well as support strategic decision-making. As AI tools become more capable and more embedded in everyday accounting software, it's important to understand both what they can do and the need for human understanding, judgment, and oversight.

Artificial intelligence (AI) is more than just a buzz-worthy topic. Many of us use AI in some form every day, especially for personal use. Maybe you're using ChatGPT or Claude to plan a trip or asking Siri for the name of a book you can't remember. But you can also use AI in accounting to reduce your workload and complete projects more efficiently and thoroughly.

And I'll admit, while I first viewed using AI in accounting with skepticism, now I look at it with optimism for myself as a former practitioner turned professor and for my students entering the profession. It's on track to become a tireless assistant who can save us from late nights we've all seen too often.

But how? In this guide, we'll walk through the full picture: what AI is, the types of AI you're likely to encounter, how accountants and auditors are using it today, how to use it well and responsibly, and what it means for the future of accounting careers.

Your Guide to AI in Accounting

This page is your starting point. When you're ready to go deeper on a specific topic, these guides pick up where this one leaves off:

• The fundamentals: What Is Emerging Technology in Accounting?
• How the profession is changing: Accountants and AI: Understanding the Impact on the Profession
• AI in audit: The Risks and Benefits of AI in Auditing
• Choosing tools: What Are the Best AI Tools for Accountants?
• Getting hands-on: A Beginner's Guide to Copilot AI in Accounting
• Managing risk: What to Know About the Risks of AI in Accounting
• Governance and controls: What to Know About COSO AI Guidance
• Your career: Can AI Replace Accountants? Future-Proofing Your Career
• Exam prep: Using AI to Pass the CPA Exam

What Is Artificial Intelligence?

AI is the capability of a machine to imitate intelligent human behavior, specifically the ability to learn, understand, and apply knowledge and skills that would typically require human intellect.

There are four classifications of AI capabilities:

• Reactive: Extremely limited and can only react to established rules and does not have the ability to learn (Examples: motion sensors or a programmable thermostat)
• Limited memory: Can access and use memory to learn but is limited by how much data it can retain and use to make decisions (Examples: image recognition, self-driving cars, and the generative AI tools most of us use today)
• Theory of mind: AI that could consider, understand, and predict human emotions, intentions, and behaviors. (Still largely in the research stage; today's chatbots can simulate this kind of understanding but don't truly possess it)
• Self-aware: AI that is conscious and aware of its own existence. (No examples; this is purely hypothetical)

For accountants, a more practical way to think about today's AI is by what it does. Most of the tools you'll encounter fall into two broad groups:

• Generative AI: Creates new content such as text, summaries, formulas, code, or images in response to a prompt. Tools like ChatGPT, Claude, Gemini, and Microsoft Copilot are built on large language models (LLMs) trained on enormous amounts of text.
• Agentic AI: Goes a step further by planning and carrying out multi-step tasks across systems with limited direction, such as pulling bank data, reconciling it against the ledger, flagging exceptions, and drafting entries for a human to review.

Generative AI is already part of most accountants' daily work. Agentic AI is newer and moving quickly, with accounting software vendors and large firms rolling out "AI agents" for tasks like reconciliations, close management, and audit workflows. We break down the differences in more detail in What Are the Best AI Tools for Accountants?

While artificial intelligence capabilities are incredibly advanced, your input is key to its success. Without critical thinking and professional judgment to create prompts, set up tests, and run automation, using AI in accounting will cause more problems than it solves. Whether you're a leader, practitioner, or educator, you must understand AI's capabilities and when and how to implement them.

Types of AI in Accounting and How to Use It

With this in mind, let's look at different types of artificial intelligence and examples of how to use AI in accounting. AI is one part of a broader wave of tools reshaping the profession, alongside cloud systems, data visualization, and big data analytics. For a look at the full landscape, see What Is Emerging Technology in Accounting?

Machine Learning

Machine learning is a branch of AI that allows computers to gain knowledge, predict outcomes, and make decisions autonomously by creating algorithms and models capable of learning patterns and relationships from data. Accountants can use machine learning to:

• Run risk analysis: Better understand relationships between variables
• Detect anomalies: Review potential fraudulent scenarios and identify unusual activity based on hypothetical and real situations
• Forecast: Project financial performance
• Plan for taxes: Determine optimal tax savings for a client through a review of deductions, credits, and tax laws

Natural Language Processing

Natural language processing (NLP) is the ability of a computer to understand, interpret, and utilize written and spoken human language, such as predictive text. Natural language generation (NLG) is a branch of NLP that focuses on producing language that simulates human speech and writing and can engage with the user, such as through a chatbot or content creation.

Accountants can use NLP for:

• Document classification and information extraction: Find relevant language in lengthy documents for regulatory and compliance procedures, saving time on manual review
• Text summarization: Summarize financial documents, reports, or transcripts to identify key information
• Transcribing information: Verbally record inventory counts
• Receivable confirmation: Create, send, and follow up on receivable confirmations during audits

Accountants can use NLG to:

• Financial statement generation: Generate drafts of financial statements and required disclosures
• Client communication: Draft personalized communications to clients about tax or financial matters
• Audit documentation: Generate standardized audit documentation based on test results
• Summarize information: Use your manually created notes and findings to build summaries

Becker used machine learning, natural language processing, and natural language generation to aid in creating Newt™, our AI assistant and study tool. Built using Becker's curriculum, exam review students can ask questions and get reliable information immediately!

Generative AI

Generative AI brings machine learning and NLP together into tools you can talk to in plain language. Instead of learning a new piece of software, you describe what you need, and the tool drafts it. That makes generative AI the easiest entry point for most accountants, and also the place where professional judgment matters most, because its output can sound confident even when it's wrong.

Accountants can use generative AI to:

• Draft and edit: Write client emails, memos, engagement letters, and internal policies for your review
• Research and summarize: Condense new standards, tax law changes, or lengthy contracts into key points
• Build in Excel: Generate formulas, clean and standardize data, and explain unfamiliar spreadsheets
• Prepare for conversations: Outline talking points for a client meeting or anticipate questions on a variance analysis

If Microsoft 365 is your firm's platform, Copilot is often the first generative AI tool accountants use day to day. Our Beginner's Guide to Copilot AI in Accounting walks through practical Excel, Word, and Outlook examples.

Computer Vision

Computer vision allows computers to "see" and comprehend visual data, enabling them to analyze images and videos. Machine vision is a branch of computer vision used to interpret and understand images focusing on industrial automation and inspection.

Accountants may use computer vision (including optical character recognition or OCR) in the following ways:

• Extract information: Pull key data from supporting documentation
• Digitize documents: Make paper records machine-readable and editable
• Count inventory: Detect and classify inventory in images or video for accurate counting and documentation
• Strengthen controls: Implement biometrics as internal controls

Accountants can also use machine vision in managerial accounting for quality assurance and process management.

Automation and AI Agents

Automation (including Robotic Process Automation) employs software bots to automatically manage repetitive, rule-based, and mundane tasks typically performed by humans. Intelligent automation enables automation to utilize other AI capabilities, such as machine learning, NLP, and computer vision.

Accountants can use intelligent automation for:

• Completing data entry
• Creating sample selections for testing
• Creating amortization schedules
• Performing audit confirmations
• Generating reports and emails
• Performing routine audit tests and reconciliations

For example, intelligent automation using OCR could match purchase orders, receiving documents, and invoices (three-way match) before invoice payment, regardless of the origination channels or format.

AI agents are the next step in this progression. Traditional automation follows fixed rules you set in advance; an agent can decide which steps to take, use multiple tools along the way, and adjust when something doesn't match. In practice, that might look like an agent that gathers month-end support, prepares reconciliations, flags unexplained differences, and routes them to the right person. The responsibility for reviewing and approving that work still sits with you, which is why governance over agentic tools is quickly becoming a priority for firms and finance teams.

A Practical Example of AI in Accounting

Let's look at how AI in accounting can streamline a common financial accounting scenario. Watch the video below for the steps and read on for more detail!

You have a 42-page hard copy of a lease agreement for a warehouse your company is renting and have been asked to classify it and create an amortization schedule. You could pour yourself that fifth cup of coffee and begin reading or embrace AI to save time and improve accuracy with these steps:

1. Use OCR to digitize the lease agreement.
2. Allow NLP and machine learning techniques to interpret, understand, and categorize the document's details and identify payment schedules, lease durations, and other critical financial clauses.
3. Generative AI can reference FASB standards to summarize the agreement and note how the lease should be classified.
4. Generative AI can then use the extracted data to create a detailed amortization schedule automatically and map out the payment timeline and amounts based on the agreement's terms.

Now, you can review the output rather than create it.

That review step is where your expertise earns its keep. You'll still want to confirm the classification against ASC 842 yourself, tie the payment terms back to the source document, and check the discount rate and any renewal or purchase options the AI may have misread. Increasingly, purpose-built lease accounting tools and AI agents can run steps one through four with very little prompting, which makes a disciplined review process even more important.

How Accountants Use AI Today

AI has moved from experimentation to everyday use in a short period of time. Becker's own research found that a majority of certified accounting professionals already use AI in their work, though most commonly for writing and communication rather than technical accounting tasks. 

Adoption usually starts with lower-risk tasks and expands as confidence and firm policies mature. Common uses today include:

• Communication: Drafting client emails, improving tone, and summarizing meetings
• Research: Getting a fast first pass on a technical question, a new pronouncement, or a tax law change before verifying it against authoritative sources
• Spreadsheet work: Writing formulas, cleaning data, and surfacing trends or outliers in large datasets
• Workflow automation: Reducing repetitive steps in the close, AP, and reporting processes
• Advisory support: Building forecasts, scenario models, and narrative explanations that help clients make decisions

What's changing most is where AI sits. Rather than a separate chatbot you open in a browser, AI is increasingly built directly into the accounting, ERP, and productivity software you already use. That makes it more convenient, but it also means AI may be shaping your work in ways that aren't always obvious.

The bigger shift is in how firms operate. As AI takes on more compliance and transactional work, many firms are redirecting capacity toward advisory services. For a deeper look at adoption trends, training gaps, and how sentiment toward AI is evolving across the profession, read Accountants and AI: Understanding the Impact on the Profession.

Realizing the Potential of AI in Accounting Through Responsible Adoption

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How Auditors Use AI

Auditing may be where AI in accounting's potential is most significant, because so much of the work involves reviewing large volumes of data and documents for patterns, exceptions, and risk. Financial statement, compliance, and internal auditors are applying AI across the engagement:

• Planning and risk assessment: Identifying the areas of the financial statements and disclosures that merit the most attention, which helps reduce over-auditing low-risk areas
• Journal entry testing: Flagging significant or unusual entries by analyzing patterns across the full general ledger
• Substantive testing: Reading bank statements, contracts, and invoices, and in some cases testing 100% of a population rather than relying on a sample
• Documentation: Linking evidence across workpapers and drafting standardized documentation from test results
• Internal audit and controls testing: Monitoring transactions continuously and testing controls more frequently than a traditional annual cycle allows

The largest firms are moving fastest. Several have announced agentic AI capabilities embedded across their audit platforms, designed to help teams orchestrate tasks and access updated guidance throughout the engagement. 

Regulators and standard setters are paying close attention. COSO has published guidance on applying its Internal Control Integrated Framework to generative AI, giving organizations and auditors a familiar structure for governing these tools; we cover it in What to Know About COSO AI Guidance. The PCAOB has also opened a public conversation about how its standards should address AI in accounting, and the UK's Financial Reporting Council has issued guidance on generative and agentic AI in audit. 

For a closer look at examples, benefits, and audit-specific risks, read The Risks and Benefits of AI in Auditing.

Tips for Using AI in Your Accounting Work

Knowing what AI can do is only half the equation. Getting reliable value from it depends on how you use it. These practices apply whether you're testing a general assistant for the first time or rolling out an AI agent across a team.

Start with Approved Tools

The best AI tool for accountants is one your firm or organization has approved. Approved tools have typically been vetted for data security, privacy, and contractual protections around how your data is stored and used. Free consumer versions of AI assistants may not offer those same protections, so never enter client, employee, or proprietary data into a tool that hasn't been cleared for that purpose.

Start Small and Build from There

There's no need to automate your entire close on day one. Pick one recurring, lower-risk task, such as summarizing a lengthy document or drafting a routine client update, and use AI for that. As you learn where the tool is reliable and where it struggles, expand to more technical work.

Write Better Prompts

The quality of AI output depends heavily on the quality of your input. Strong prompts tend to include:

• Role: Who the AI should act as (for example, "an experienced audit senior")
• Context: The relevant facts, standards, or background
• Task: Exactly what you want it to produce
• Format: How you want the response structured (a table, bullet points, a memo)
• Constraints: What to avoid, what to cite, and when to say it doesn't know

Keep a Human in the Loop

Treat every AI output as a draft from a capable but inexperienced team member. A useful rule of thumb is that AI can get a task most of the way there, while you review, revise, and finalize the rest. Verify figures, tie conclusions back to source documents and authoritative guidance, and never send AI-generated work to a client or include it in a workpaper without your review.

Document How You Use AI

If AI contributed to an analysis, a workpaper, or a deliverable, note which tool you used, what you asked it to do, and how you validated the result. Good documentation protects you and your firm and makes your work easier to review, defend, and reproduce.

Keep Learning

AI tools change constantly, and so do the expectations around how professionals use them. Continuous learning, through CPE, firm training, and hands-on practice, is the best way to stay confident and current. For guidance on choosing the right tool for the task, see What Are the Best AI Tools for Accountants?

Using AI Responsibly: Understanding the Current Challenges

When AI can pass the CPA Exam, it seems almost limitless in its capabilities. But even though AI is changing the way accountants can and will do their jobs, there are still many limitations. Let's explore the challenges of AI in accounting and how to overcome them.

A Lack of Critical Thinking

AI, whether for accounting or any other use, can't "think outside the box." It lacks the self-awareness and emotional intelligence needed for complex, judgment-based decisions. So, while it can augment human creativity and analytical capacity, it's not able to be creative or analytical on its own and is highly dependent on the quality and breadth of its training data. Instead of AI driving any innovation, humans must understand and evaluate the new challenges and concepts first.

Not All Accounting AI Is Created Equal

Not all AI is created equally, nor are the companies that employ it. When you think about artificial intelligence processes, you probably think of advanced systems and software programs that integrate effortlessly with huge quantities of high-quality data, creating crisp reports through intuitive, well-designed dashboards.

But the reality is that the integration of AI in accounting processes often reflects the scale of the firm. Large organizations can adopt advanced, high-cost AI technologies more readily than mid-sized or small firms. This disparity could widen the technological divide, with smaller firms using inferior AI solutions that become outdated, rely on low-quality data, or don't fully integrate with existing hardware.

Reliance on Inaccurate Data

Accountants have always had to deal with poor data quality; however, poor AI may make a challenging situation worse. The effectiveness of accounting AI largely depends on the quality of the training data. Poor-quality data can lead to significant inaccuracies and errors, which are particularly problematic in accounting. If the data is outdated, irrelevant, or unreliable, this can undermine the integrity of the outcome.

Generative AI models that have these data issues become less effective at solving more complex or judgment-based problems. For example, AI models can produce "hallucinations," where information is created without a source but presented as fact. Accountants must be proficient in data wrangling and professional skepticism to mitigate these risks and ensure data relevancy, accuracy, and integrity.

Data Privacy and Confidentiality

Accountants handle some of the most sensitive information an organization has. Entering client or company data into an unapproved AI tool can expose that information, violate confidentiality obligations, and create regulatory risk. Before using any AI tool with real data, confirm that it's approved, understand how it stores and uses what you enter, and follow your organization's AI policy. Keep in mind that bad actors are using AI too, through more convincing phishing messages and deepfake fraud attempts, which raises the stakes for strong cybersecurity habits.

Governance and Controls

As AI tools become more autonomous, organizations need clear policies on who can use which tools, for what purposes, and with what review. That includes knowing where AI is already in use (including "shadow AI" that employees adopt without formal approval), assessing the risks of each use case, and designing controls around them. Many organizations are turning to established frameworks to do this; COSO's guidance on generative AI maps its internal control principles directly to AI-specific risks.

Lack of Human Touch

AI can simulate human interactions but cannot replace the human touch required in sensitive situations, such as negotiating with clients or managing employee relations.

Using AI in accounting also raises significant concerns regarding transparency and the traceability of decision-making processes, necessitating a thorough understanding and scrutiny of AI tools by accountants. If you can't explain how an output was produced, you shouldn't rely on it for a conclusion you'll need to defend. As technology utilized in AI grows and changes, employees and management must be flexible and adaptable to overcome challenges in the inevitable learning curve.

For a full breakdown of each risk and practical ways to manage it, read What to Know About the Risks of AI in Accounting.

AI and the Future of Accounting Jobs

An additional challenge about AI in accounting comes from potential resistance from accountants, especially if they perceive that AI developments may threaten job security. Fortunately, fears of obsolescence are largely unfounded. Like past technological advances, AI is unlikely to displace the accounting profession. Instead, the shift offers improved efficiency, more time to devote to critical and strategic decision-making, and enhanced opportunities to provide value-added services in AI implementation, evaluation, and compliance with evolving regulations.

The outlook for the profession supports this. The U.S. Bureau of Labor Statistics projects continued employment growth for accountants and auditors over the next decade. 

How Roles are Changing

What AI will change is the shape of accounting work, especially at the entry level. Tasks that once filled a new staff accountant's first years, such as data entry, basic reconciliations, and document gathering, are increasingly handled by technology. That shifts early-career professionals from doing every step by hand to supervising, reviewing, and interpreting what the technology produces.

The skills that matter most are shifting with it:

• Professional judgment and skepticism: Knowing when an output is wrong or missing context
• Data and technology fluency: Understanding how AI tools work, where they're reliable, and how to validate them
• Communication and advisory skills: Turning numbers into insight clients and leaders can act on
• Ethics and accountability: Owning conclusions that carry professional and legal responsibility

Clients rely on CPAs for advice that drives major financial decisions, and auditors sign opinions that carry significant liability. That accountability can't be delegated to an algorithm. For more on how firms are upskilling talent and how to future-proof your own career, read Can AI Replace Accountants? Future-Proofing Your Career.

AI and the CPA Exam

AI is changing how future CPAs prepare, too. Advanced AI models have been reported to pass the CPA Exam, which says as much about their research ability as anything else. For candidates, AI can be a helpful study partner for quick explanations and clarification, as long as the answers come from a reliable source. General AI tools may pull from inaccurate information or hallucinate, which is why Becker built Newt to source its answers exclusively from Becker's curriculum. Learn more in Using AI to Pass the CPA Exam.

Considering Accounting AI as a Colleague

In embracing AI, accountants are not handing their roles over to machines but are instead stepping into the future of accounting, one where their expertise is augmented by technology, not replaced. The future belongs to those who embrace AI, not as a replacement, but as an indispensable ally.

Accounting AI Software

Now that we've discussed what it is, the types of AI in accounting you're likely to encounter, and some of the challenges and value it offers, let's look at the most common software options to support AI in accounting.

Excel is a comfortable place for most accountants because it is both familiar and powerful, but you may be seeing changes in its functionality through AI capabilities. While it isn't going anywhere anytime soon, the exponential growth in AI capabilities from new startups in software has driven existing programs, including Excel, to add AI components to remain competitive.

As it pertains to the accounting profession, users benefit from AI software that can do the following:

• Automate processes
• Prescribe decisions
• Predict outcomes
• Detect anomalies
• Document activities
• Transcribe calls
• Digitize, extract, and understand documents
• Populate templates
• Understand and apply accounting-based rules and concepts, among other possibilities

Based on the variety of needs, it is likely that most firms will or already do employ various software, including proprietary tools. Many of the accounting platforms you already use, from general ledger and ERP systems to AP automation and audit software, now include built-in AI features. To remain relevant and efficient, accountants must familiarize themselves with a variety of AI tools that automate processes and enhance decision-making capabilities.

Key AI software includes (but is definitely not limited to):

• Microsoft Copilot: Integrates with Microsoft 365 products, including Excel, Word, and Outlook, to generate data insights, formulas, and drafts through natural language prompts
• ChatGPT by OpenAI: Offers extensive text analysis, summarization, and calculation capabilities, making it an effective tool for interactive and interpretative tasks
• Claude by Anthropic: Supports long-document analysis, drafting, and data work, making it useful for summarizing contracts, standards, and reports
• Tableau AI Analytics: Creates data visualizations and dashboards, can analyze data and identify patterns, and enables anomaly detection and cluster analysis to support fraud detection
• Alteryx AiDIN: Enhances the Alteryx analytics platform with AI capabilities to automate and optimize data workflows
• IBM watsonx: Provides robust data analysis and natural language processing solutions to support complex decision-making
• UiPath: Combines RPA with AI and agentic capabilities to improve process efficiencies and discover new operational opportunities

Before adopting any of these, check with your firm or organization about which tools are approved and how they may be used with client or company data. For a deeper look at tool categories, including accounting software with built-in AI, AP automation, and audit and compliance tools, see What Are the Best AI Tools for Accountants?

The integration of AI in audit and accounting not only challenges accountants to adapt and innovate but also equips them with the tools to perform at higher levels of efficiency and strategic insight. By embracing AI, accountants can transcend traditional roles and drive the profession toward a tech-savvy future. The journey will require perseverance, adaptability, and a commitment to continuous learning and ethical standards.

Relevant AI Training and CPE from Becker

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FAQs About AI in Accounting

What is AI in accounting?

AI in accounting is the use of technologies like machine learning, natural language processing, computer vision, generative AI, and intelligent automation to perform or support accounting tasks. Common applications include data entry, reconciliations, document review, anomaly detection, forecasting, research, and drafting communications.

Will AI replace accountants?

No. AI is automating many routine tasks, but accounting, audit, and tax work depend on judgment, accountability, and client relationships that AI can't replace. What AI is changing is the nature of the work, especially at the entry level, with more emphasis on review, analysis, and advisory skills. Read more in Can AI Replace Accountants?

What is the difference between generative AI and agentic AI?

Generative AI creates content, such as a summary, a formula, or a draft email, in response to a prompt. Agentic AI plans and carries out multi-step tasks across systems, such as preparing a reconciliation and flagging exceptions, with less direction from the user. Both require human review.

Is it safe to use ChatGPT or other AI assistants for accounting work?

It can be, as long as you use a tool your organization has approved and follow its policies. Never enter confidential client or company data into an unapproved tool, always verify outputs against source documents and authoritative guidance, and document how AI contributed to your work. See What to Know About the Risks of AI in Accounting for more.

How can accountants learn to use AI?

Start with hands-on practice on low-risk tasks using approved tools, then build structured knowledge through CPE and certificate programs focused on AI for accounting and auditing. Our Beginner's Guide to Copilot AI in Accounting is a good place to begin.