AI in Accounting: What It Changes for Accountants and Finance Teams

Short answer: AI in accounting today means software that extracts data from documents, matches transactions, flags unusual entries and drafts text or analysis for a person to check. It speeds up the repetitive middle of the job. It does not replace judgement, sign-off or accountability, and its output still has to be tested like any other evidence.

Below: what the survey data shows, which tasks suit AI, what standard setters expect, and the skills worth building.

Last updated: October 2026

What is AI in accounting actually doing today?

In most finance teams, AI is doing automation and error detection, not decision-making. The most common uses are smarter process automation, anomaly detection and analytics, with generative AI used mainly for drafting and summarising.

A Gartner survey of 121 finance leaders, run in June 2024, ranked the uses. Intelligent process automation was used by 44% of finance functions, anomaly and error detection by 39%, analytics by 28%, and operational assistance and augmentation by 27% [Source: Gartner, finance AI survey, September 2024].

In practice that looks like this:

• Document capture: reading supplier invoices, receipts and bank statements, then proposing the account code.
• Matching: pairing bank lines with ledger entries, and invoices with purchase orders and receipts.
• Anomaly flags: scanning every journal or expense claim for outliers, duplicates and odd timing, instead of a sample.
• Drafting: first versions of variance commentary, policy summaries, client emails and board-pack narrative.

Each is a pattern task with a right answer a person can check, which is why it works.

How widely are finance teams using AI in accounting?

Use is now mainstream but shallow. Most organisations are piloting or using AI in parts of the process; few have adopted it widely or have a defined strategy for it.

Gartner found 58% of finance functions using AI in 2024, a rise of 21 percentage points on 2023 [Source: Gartner]. KPMG’s survey of 1,800 companies in 10 countries found 72% piloting or using AI selectively in financial reporting, but only 10% with wide adoption [Source: KPMG Malaysia, July 2024]. Thomson Reuters’ 2025 study of 2,275 professionals found only 22% reporting a visible, defined AI strategy in their organisation [Source: Thomson Reuters, June 2025].

Read these as direction, not precision: samples and definitions of “using AI” differ, and several authors sell AI-enabled services. The consistent message is that adoption is ahead of skills and governance.

Which accounting tasks is AI good at, and which still need a person?

AI is strongest where the task is high-volume, rule-based and checkable, and weakest where the answer depends on judgement or accountability. The table below is our own working framework for sorting tasks.

Anomaly and error detection is already used by 39% of finance functions [Source: Gartner], while the ACCA lists hallucinations, inaccuracies and bias among the integrity concerns with AI output [Source: ACCA, Enabling finance insight, July 2026]. That split is the logic behind the three columns.

A practical test: if you cannot say how you would check the output, the task is not ready for AI. For Lebanese tax, a chatbot may quote an outdated rate; as our guide to VAT in Lebanon shows, the current law is the reference.

Where does AI in accounting fail?

It fails on accuracy, explainability and controls. Generative tools can produce confident, wrong answers, and many finance teams do not yet have the skills to catch them.

In the ACCA and CA ANZ survey of 1,600 finance professionals, 93% said they were concerned by the integrity and verifiability of AI-generated insights, and 72% reported only basic or no GenAI skills [Source: ACCA, July 2026]. Ainslie van Onselen, Chief Executive Officer of Chartered Accountants Australia and New Zealand, put it plainly:

“AI is now a core part of the finance toolkit, but it’s not a shortcut. CFOs and finance teams need to use it to sharpen judgement and generate real value, not just speed up old processes.” (ACCA news release, July 2026)

The failure modes most often cited are:

• Hallucinated facts: invented standards paragraphs, tax rates or citations that look plausible.
• Silent data gaps: a model summarising an incomplete extract as if it were the full ledger.
• Weak audit trail: no record of which prompt, version or data produced a number.
• Confidentiality leaks: client or payroll data pasted into a public tool.

Each is a control problem first, which is where accountants already know how to lead.

What do auditing standard setters and ethics boards say about AI?

They have not banned it, and they have not lowered the bar. The message is that technology-assisted work must still produce relevant, reliable evidence, and that the accountant remains responsible for the result.

The PCAOB amended AS 1105 and AS 2301 in June 2024 to clarify auditor responsibilities when audit procedures involve technology-assisted analysis, effective for audits of fiscal years beginning after December 15, 2025 [Source: Deloitte DART, June 2024]. The stated aim is to reduce the risk of an opinion issued without sufficient appropriate evidence.

On ethics, the IESBA’s technology-related revisions to its Code took effect on December 15, 2024. They add guidance on confidentiality, professional competence and due care, and complex circumstances, and apply to any technology [Source: IESBA].

Companies expect auditors to follow. In KPMG’s survey, 64% of companies expect auditors to review the control environment around their use of AI in financial reporting more closely [Source: KPMG]. For internal auditors and controllers, that makes AI governance part of the job; our comparison of CIA vs CMA covers which path leans more toward controls and assurance.

What skills should accountants build to work with AI?

The valuable skills are data literacy, sceptical review, controls design and clear communication. Prompting matters less than proving the answer is right.

Thomson Reuters’ respondents expect AI to save professionals an average of 5 hours a week within a year [Source: Thomson Reuters], while 41% of ACCA and CA ANZ respondents are upskilling in their own time [Source: ACCA]. Gartner also warns that CFOs are finding AI talent hard to source [Source: Gartner].

A sensible skills list for 2026:

1. Strong technical accounting. You cannot review an AI draft of an IFRS note without knowing the standard; see our IFRS Mastery course.
2. Data handling. Cleaning extracts, understanding completeness and reconciling AI output back to source.
3. Analysis and storytelling. Turning flagged anomalies into decisions, the focus of Financial Analysis for Decision Makers.
4. Controls and governance. Approval rules, logs and segregation of duties for AI-assisted processes, core to the financial controller role.
5. Audit thinking. Testing technology-assisted evidence, covered in Modern Auditing with AI Technology.

How Balance Brightness can help

We teach AI in accounting as a practitioner’s skill, not a sales pitch. Our Finance Manager and AI Track works through these tasks with live coaching: where AI saves time and where a manager must step in. Auditors and internal auditors can add Modern Auditing with AI Technology, and analysts can build the interpretation side with Financial Analysis for Decision Makers.

For whole teams, our corporate finance training can be shaped around your own close, reconciliation and reporting process; see training for organisations. Sessions are led by Mahmoud Chehade, drawing on 30 years in practice and more than 500 professionals trained, in person in Hamra, Beirut, or online. Individuals who want a tailored plan can book one-on-one finance coaching.

Frequently asked questions

Will AI replace accountants?

The evidence points to a change in the work rather than its disappearance. Surveys show AI taking on extraction, matching and first drafts, while accountability for estimates, judgements and sign-off stays with qualified people. Standard setters have reinforced this: the PCAOB’s 2024 amendments and the IESBA’s technology revisions both keep responsibility with the professional using the tool. The roles most exposed are those made up mainly of data entry. The roles that grow are review, analysis, controls and advisory work, which all need someone who understands both the accounting and the limits of the technology producing the first draft.

Is it safe to use ChatGPT or similar tools for accounting work?

It can be, for low-risk drafting, if you follow your organisation’s policy and keep confidential data out of public tools. The IESBA’s revised Code puts specific weight on confidentiality and on professional competence and due care when using technology. Treat any figure, rate or standard reference from a general chatbot as unverified until you have checked it against the primary source, such as the standard itself, the tax law or the ledger. For client or payroll data, use approved enterprise tools with logging, and record what the tool produced and how you checked it.

What is the easiest place to start with AI in a small finance team?

Start with a process that is high-volume, repetitive and easy to check, such as invoice data capture or bank reconciliation matching. Measure the time spent today, run the tool alongside the existing method for one or two closes, and compare exceptions. Keep a person reviewing every unmatched item and every flag. Gartner’s data shows process automation and anomaly detection are the most common entry points for finance functions, partly because the output is easy to test. Once that runs reliably, move to drafting tasks such as variance commentary, again with every number traced to the ledger.

Do I need to learn programming to work with AI in finance?

No. Most accountants will use AI through accounting software, spreadsheets and enterprise assistants rather than code. What matters more is data literacy: knowing where a figure came from, whether an extract is complete and how to reconcile output back to source. Some coding, for example in Python or SQL, helps if you move into analytics or audit data work, but it is a bonus rather than a requirement. Strong technical accounting, clear analysis and good controls design will do more for most careers than a programming course on its own.

The bottom line

AI in accounting is already useful for capture, matching, anomaly detection and drafting, and it is still unreliable without skilled review. The accountants who gain most will pair solid technical knowledge with sceptical testing and good controls. If you want a structured way to build those skills, explore the Finance Manager and AI Track or contact us to talk through your team’s needs.

Sources

1. Gartner: Gartner Survey Shows 58% of Finance Functions Using AI in 2024
2. ACCA: Enabling finance insight (ACCA and CA ANZ research news release)
3. IESBA: Technology-related Revisions to the Code
4. Deloitte DART: PCAOB Clarifies Requirements Related to Use of Technology-Assisted Analysis
5. KPMG Malaysia: 99 percent of organizations expected to adopt AI in financial reporting
6. Thomson Reuters: The AI adoption reality check (Future of Professionals 2025)