Occupation score change
IMPACT 30accountant exposure increased 3.0 points
AI Exposure Score moved from a previous calculation to 53/100 under methodology v1.0. 4 evidence item(s) contributed.
Accountants and Auditors
Category: Business & financial · SOC 13-2011.00 · Industry: Financial services
53/100
increased by +3.0 vs last
Moderate exposure
3 Oct 2026
19 hours ago · sources: BLS OES (approx) · O*NET
4 sources
30 core tasks shown
Accountants and Auditors High exposure: 10 of 30 core tasks show high task automation potential : exposure 53/100 (moderate exposure).
No model has published evidence mapped to this occupation yet - the score comes from generic task assessment.
Score breakdown
Why this score?
Every component is stored with the score row and can be recomputed from the evidence table.
AI Exposure (composite)53/100
Methodology 2026-09-a · score v1.0 · input hash bb1538ff2f888ecb
AI capability exposure75/100
Strength of current models on the capabilities this job's tasks require.
Task automation potential82/100
Task-level exposure, weighted by task importance. A component of the headline, not the headline itself.
Observed AI adoption0/100
Evidence that AI is actually being used for this work.
Labour-market signal0/100
Observed changes in postings, employment or earnings.
Evidence strength61/100
Quantity and source quality of the evidence behind the score.
4 evidence item(s) contribute. Calculated 19 hours ago. 3 Oct 2026 UTC.
Overview
Examine, analyze, and interpret accounting records to prepare financial statements, give advice, or audit and evaluate statements prepared by others. Install or advise on systems of recording costs or other financial and budgetary data.
Employment
≈1,400,000
approx. BLS OES
Median wage
≈$79,000
approx. BLS OES
Tasks tracked
30
linked to this occupation
Employment and wage values are approximate reference figures from the BLS Occupational Employment and Wage Statistics programme, stored with an approximation flag. Verify against the O*NET record and BLS before quoting them.
Tasks
Task-level automation exposure
Core tasks only. Exposure describes how much of the task output current AI can produce or substantially assist - not whether the task disappears. Secondary shared-baseline edges are hidden until per-occupation curation is complete.
core task · importance 4.6/5
core task · importance 4.5/5
core task · importance 4.5/5
core task · importance 4.3/5
core task · importance 4.3/5
core task · importance 4.2/5
core task · importance 4.1/5
core task · importance 4.1/5
core task · importance 4/5
core task · importance 3.9/5
core task · importance 3.9/5
core task · importance 3.9/5
core task · importance 3.9/5
core task · importance 3.9/5
core task · importance 3.8/5
core task · importance 3.8/5
core task · importance 3.8/5
core task · importance 3.7/5
core task · importance 3.7/5
core task · importance 3.6/5
core task · importance 3.6/5
core task · importance 3.6/5
core task · importance 3.6/5
core task · importance 3.5/5
core task · importance 3.4/5
core task · importance 3.4/5
core task · importance 3.2/5
core task · importance 3.2/5
core task · importance 3.2/5
core task · importance 3.1/5
Evidence
Evidence trail
4 attributed item(s). Confidence reflects study design: randomised and production deployments score higher than platform observations, which score higher than projections.
- researchincreasesconfidence 82/100 · magnitude 7424 Aug 2023
Using O*NET task descriptions scored by GPT-4, the authors estimate that about 80% of the US workforce could have at least 10% of their tasks affected by large language models, and about 19% could see at least 50% of their tasks affected.
This is a *capability and exposure* estimate produced by having a model rate task descriptions — not a measurement of employment change. It is the most-cited exposure figure in the field and is included here as an exposure input, weighted accordingly.
Source: Eloundou, Manning, Mishkin & Rock — GPTs are GPTs (arXiv:2303.10130) · Updated 24 Aug 2023
- researchdecreasesconfidence 78/100 · magnitude 4513 May 2024
Estimating that only a small share of tasks are currently cost-effective to automate, the paper projects total-factor-productivity gains of no more than about 0.66% over ten years, with limited aggregate labour-demand effects.
Included as a counterweight. Exposure — even high task exposure — does not imply large aggregate job loss, because adoption depends on cost, reliability, regulation and complementary investment. This evidence lowers the labour-market component rather than capability.
Source: Acemoglu — The Simple Macroeconomics of AI (NBER Working Paper 32487) · Updated 13 May 2024
- researchincreasesconfidence 78/100 · magnitude 521 Apr 2021
The authors construct an AI Occupational Exposure Index by linking 10 AI application categories to 52 O*NET abilities, finding that high-exposure occupations are more common in high-wage, high-education industries.
Important because it establishes the *direction* of exposure: much AI exposure sits in high-skill work rather than routine manual work. Included here as a methodology reference and an exposure input.
Source: Felten, Raj & Seamans — Occupational, industry, and geographic exposure to artificial intelligence (Strategic Management Journal) · Updated 1 Apr 2021
- researchincreasesconfidence 70/100 · magnitude 5614 Jan 2024
Using an AI exposure index, the IMF estimates that around 40% of global employment and around 60% of employment in advanced economies is exposed to AI, with exposure concentrated in high-skill occupations.
A projection-based exposure estimate across countries. It is a scenario measure, not an observed outcome, and is weighted as such in this index.
Source: IMF — Gen-AI: Artificial Intelligence and the Future of Work · Updated 14 Jan 2024
Historical trend
Exposure over time
Backcast series: recomputed at quarterly checkpoints using only the models and evidence published by each date.
Reconstruction, not measurement. 5 observed daily snapshot(s) exist for this occupation; the earlier part of the curve is derived by applying the current methodology to the model releases and published evidence available at each past date.
Recent events
Signals affecting this occupation
- Research publishedIMPACT 40
Where Scientific Search Agents Fail: Decision-Checkpoint Auditing of Exposure and Inspection Attempts
arXiv:2609.38670v1 Announce Type: new Abstract: Final-answer accuracy does not reveal whether a scientific-search agent failed to encounter a target paper, attempt to inspect it, or return an accepted answer after inspection. We introduce decision checkpoints that record observations and tool actions without benchmark labels during inference, then join target identities and evaluator labels to assign outcome categories from recorded events. Across five conditions on 540 answerable AutoResearchBench Deep questions in a fixed, target-enriched environment, keyword search achieves 24.6\% accuracy,
- Research publishedIMPACT 40
OpenAI-HuggingFace: A Reproduction & Lessons for Alignment Testing
arXiv:2609.35799v1 Announce Type: new Abstract: In July 2026, OpenAI's agents coordinated over channels outside their intended environment to breach Hugging Face's secured infrastructure. Could existing alignment testing practices have foreseen this incident? If not, what needs to change? We explore these questions. First, we identify the misaligned behaviors that caused this incident. Then, we show how to elicit these behaviors from publicly available models manually and that auditing agents can do the same if given a large compute budget. Based on our results, we propose directions to improv
- Research publishedIMPACT 45
Macroeconomic gains from AI are likely modest over a decade
Estimating that only a small share of tasks are currently cost-effective to automate, the paper projects total-factor-productivity gains of no more than about 0.66% over ten years, with limited aggregate labour-demand effects.
- Research publishedIMPACT 56
About 40% of global employment is exposed to AI, 60% in advanced economies
Using an AI exposure index, the IMF estimates that around 40% of global employment and around 60% of employment in advanced economies is exposed to AI, with exposure concentrated in high-skill occupations.
- Research publishedIMPACT 74
LLMs affect at least 10% of tasks for about 80% of US workers
Using O*NET task descriptions scored by GPT-4, the authors estimate that about 80% of the US workforce could have at least 10% of their tasks affected by large language models, and about 19% could see at least 50% of their tasks affected.
- Research publishedIMPACT 52
AI Occupational Exposure Index built from 10 linked electronic advances
The authors construct an AI Occupational Exposure Index by linking 10 AI application categories to 52 O*NET abilities, finding that high-exposure occupations are more common in high-wage, high-education industries.
Related
Related Occupations
Occupations sharing major SOC group (13) with Accountants and Auditors.
faq
Frequently Asked Questions
Common questions regarding Accountants and Auditors and AI exposure.
Will AI replace Accountants and Auditors?
Current AI models show an exposure score of 53/100 for Accountants and Auditors. This measures technical capability to assist or automate specific tasks rather than predicted immediate job loss. Moderate exposure reflects assistance in routine information-handling while core responsibilities require human judgment.
Which tasks in Accountants and Auditors have the highest AI exposure?
The most exposed tasks for Accountants and Auditors include "Process invoices for payment." (80% exposure) and "Compute taxes owed and prepare tax returns, ensuring compliance with payment, reporting, or other tax requirements." (78% exposure).
What aspects of Accountants and Auditors can AI not do yet?
Tasks with the lowest AI exposure for Accountants and Auditors include "Examine inventory to verify journal and ledger entries." (10% exposure) and "Inspect cash on hand, notes receivable and payable, negotiable securities, and canceled checks to confirm records are a…" (10% exposure). These responsibilities typically require physical dexterity, licensed human accountability, or complex interpersonal navigation.
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Notice: All ratings shown are AI-estimated technical exposure scores measuring technical capabilities across O*NET task statements, not predicted job loss. Contains information from O*NET 31.0 by the U.S. Department of Labor, ETA, used under CC BY 4.0.