In the ever-evolving world of accounting, artificial intelligence (AI) has become both a promising assistant and a potential point of concern. As financial professionals grapple with increasing volumes of data, compressed timelines, and stringent compliance demands, AI tools have emerged to alleviate the burden. But as accountants well know, not every answer can be found in an index — judgment, nuance, and economic substance still reign supreme. So, is AI a helper or a hazard when it comes to applying GAAP and IFRS standards? The answer lies in understanding what AI can do, what it cannot, and how to wield it wisely.
AI’s Capabilities in Technical Research
At its core, AI excels at information retrieval and summarization. For instance, an accountant can prompt a generative AI tool to:
- Identify which ASC Topic governs lease accounting (ASC 842)
- Summarize the key requirements under IFRS 9 regarding expected credit losses
- Generate a checklist of disclosures under ASC 606 for revenue recognition
This kind of functionality streamlines technical research, especially when time is short or when sifting through lengthy codification. It offers speed, accessibility, and breadth, making it particularly valuable for junior staff or early-phase analysis. However, surface-level summaries are not the same as applied understanding. AI can cite the rule, but it can’t always grasp the rule's spirit.
Nuance and Context: Where AI Falls Short
Accounting isn’t just about what’s written — it’s about why it’s written and how it applies to unique scenarios. As the AICPA cautions:“Generative AI may answer a straightforward question (like the codification reference for lease accounting) correctly, but if asked how to apply that standard to a specific transaction, it may produce an inaccurate answer.”Unlike a trained CPA, AI doesn’t reason through the economic reality of a transaction. It lacks the ability to:
- Interpret intent in contract terms
- Evaluate substance over form
- Exercise professional skepticism
This is particularly dangerous when AI tools are confidently wrong, a phenomenon known as hallucination — where the system generates plausible but incorrect information.
Problem Areas: Where AI Might Stumble
Revenue Recognition (ASC 606 / IFRS 15)Determining whether revenue is recognized over time or at a point in time hinges on interpreting performance obligations, contract modifications, and transfer of control. AI might:
- Misidentify performance obligations
- Fail to assess contract-specific risks
- Recommend aggressive recognition, as seen in PwC’s AI case study, where human review corrected an over-assertive AI interpretation
Lease Accounting (ASC 842 / IFRS 16)Leases are notorious for their gray zones, such as:
- Classifying lease vs non-lease components
- Determining whether a lease is finance or operating
- Measuring right-of-use assets and liabilities
AI may oversimplify these assessments, or worse, invent criteria that don’t exist.
Estimations and Judgment
From bad debt reserves to warranty provisions, accounting estimates require a balance of:
- Historical data
- Company-specific trends
- Conservative bias
- Regulatory expectations
AI might propose numbers based on extrapolated trends — but without grounding in company context or prudence, such estimates could breach financial reporting principles.
GAAP vs. IFRS Confusion
Unless explicitly guided, AI may mix frameworks:
- Applying IFRS treatment to a GAAP question
- Using U.S.-specific SEC interpretations in global contexts
This poses serious risks in multinational organizations or during cross-border transactions.
Regulatory Compliance
AI is only as current as its training data. If the tool hasn’t ingested latest amendments, FASB updates, or SEC interpretations, it could offer outdated guidance. This includes:
- Missing 2025 FASB changes
- Ignoring new IFRS clarifications
- Failing to flag emerging disclosures required by regulators
Audit Trail and Accountability
Auditors now ask: If AI was involved, how do we verify its conclusions?Best practice involves:
- Retaining the original AI prompt and response
- Documenting the human review and rationale
- Citing relevant codification sections or authoritative guidance
- Ensuring transparency and traceability in the accounting file
This process aligns with audit trail expectations, where every financial statement line must be justifiable.
What the Regulators Say
Global standard-setters are starting to weigh in. The IFRS Foundation noted that feeding AI unstructured data can lead to misidentification of financial elements, while structured taxonomy tags improve accuracy. The PCAOB and AICPA are studying the impact of AI on audit quality, with likely guidelines or oversight mechanisms to emerge. This is a signal to firms: don’t wait for the rules — get ahead of them with strong internal controls.
When AI is Helpful vs. Harmful
Appropriate Use Cases:
- Summarizing standards (e.g., ASC 606 overview)
- Drafting boilerplate disclosures
- Generating checklists for compliance
- Cross-referencing standards during reviews
- Creating initial drafts of financial report sections
Inappropriate Use Cases:
- Making final accounting determinations
- Judging economic substance
- Estimating material financial figures
- Overriding auditor or CFO interpretation
- Substituting for technical accounting expertise
As one expert put it: “AI can jump-start accounting processes, but it still requires a driver at the wheel.”
CFO and Controller Tips: Use AI as a Second Pair of Hands, Not a Second Brain
Finance leaders should adopt explainable AI — tools that not only generate outputs but also highlight their logic paths, such as:
- Referenced Codification sections
- Key phrases from contracts
- Cited examples or use cases
If an AI suggestion cannot be explained in plain terms or can’t be supported by authoritative literature, it must be flagged, reviewed, or discarded.
Conclusion: AI and Accounting Can Coexist — If We Set the Rules
AI is not here to replace the accountant — it’s here to support them. It can shave hours off technical tasks, spark new insights, and catch overlooked requirements. But left unchecked, it can just as easily introduce misstatements, compliance gaps, or audit flags. “AI is a powerful aide for technical accounting, but not a rule-maker. The finance team must ensure that AI’s work passes the same gauntlet of professional skepticism as any junior accountant’s work. With proper controls, AI can accelerate the grind of compliance without compromising accuracy – but without those controls, it could just as easily introduce errors.” In next week’s final article, we’ll bring together the full series and explore the road ahead: How can finance professionals responsibly integrate AI into their careers — and thrive in the AI-assisted future?
