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Artificial intelligence is taking over repeatable research, preparation, summarization, bookkeeping, document review and correspondence in professional services. Accountants and auditors are not disappearing; their work is moving toward judgment, risk interpretation, client communication and supervision of machine-generated results. Tax adoption is currently ahead of audit adoption, while regulators and firms are demanding stronger privacy, security, explainability and audit trails before AI handles higher-risk decisions.
What is changing first
AI is entering workflows at the point where large volumes of structured or unstructured information must be found, classified, compared or drafted. A language model can summarize a regulation, extract figures from invoices, propose a response to a client question or flag an unusual transaction. It cannot, by itself, accept professional responsibility for an opinion, tax position or control conclusion.
Adoption is accelerating. Enterprise generative-AI use in tax, audit and accounting firms reached 21% in 2025, compared with 8% in 2024, according to the Thomson Reuters Institute. In the same 2025 research, 89% of tax-firm professionals and 92% of corporate-tax practitioners said generative AI could be applied to their work. Support for applying it rose to 71% at tax firms and 75% in corporate tax departments, from 52% and 60% respectively in 2024.
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The pattern is uneven by service line. Tax and accounting teams have many repeatable research and preparation tasks, so they are deploying AI faster. Audit teams must connect outputs to sufficient, appropriate evidence and retain a defensible record of how conclusions were reached; that makes broad deployment slower.
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Where AI is being used in tax and accounting
| Use case | Share identifying it as a GenAI application | Typical human responsibility |
|---|---|---|
| Tax research | 77% | Confirm the jurisdiction, effective date and primary authority; review citations and apply judgment. |
| Tax-return preparation | 63% | Validate source data, elections, calculations and filing requirements before submission. |
| Tax advisory | 62% | Evaluate alternatives, business consequences, uncertainty and client-specific facts. |
| Accounting and bookkeeping | 57% | Approve account mappings, reconciliations, adjustments and exception handling. |
| Document summarization | 55% | Check that material terms, dates, exceptions and missing information were not omitted. |
| Document review | 53% | Set review criteria, investigate flagged items and preserve the underlying evidence. |
| Auditing | 36% | Design procedures, assess evidence and sign conclusions; the model is not the auditor. |
These percentages come from Thomson Reuters’ 2025 survey and describe reported application, not guaranteed accuracy or autonomous production. A practical evaluation should score each proposed tool on task fit, source traceability, review effort, data sensitivity, integration and measurable time saved.
Tax research and question answering
AI can search a controlled body of legislation, guidance, rulings and firm knowledge, then produce a concise answer. The safe pattern is retrieval with citations rather than an uncited general-purpose response. A reviewer should open every cited authority, check that it applies to the client’s country and tax year, and record unresolved ambiguity. Prompts and outputs should be retained when they influence a filing or advice memo.
Return preparation and compliance
Extraction models can read invoices, trial balances and prior returns; systems can suggest classifications, populate workpapers and identify missing schedules. Staff still need to test totals, reconcile to the general ledger, verify elections and inspect unusual transactions. Automation is most valuable when it routes exceptions to a named reviewer instead of silently accepting a low-confidence result.
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AI can propose account codes, match transactions, draft reconciliations and explain movements between periods. Firms should define materiality thresholds and require approval for journal entries, revenue recognition, related-party items and manual overrides. A time-saved claim is meaningful only when measured against rework, error rates and the time needed to review exceptions.
Tax advisory and client communication
Once routine preparation is compressed, professionals can spend more time on forecasting, transaction planning, business consulting and explaining trade-offs. Drafting tools can prepare a client email or meeting brief, but a practitioner must ensure that the language reflects the client’s facts, does not imply certainty where law is unsettled and states assumptions and deadlines.
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How AI is changing audit
Audit deployment remains more limited but is evolving. In outreach dated July 22, 2024, the Public Company Accounting Oversight Board found that firms were using generative AI mainly for administrative and research activities while exploring applications in audit planning and performance. Firms emphasized supervision, data privacy and security.
Near-term audit value is therefore operational: organize evidence, classify contracts, summarize meetings, compare disclosures, identify duplicate or missing documents and scan populations for risk indicators. These capabilities can shorten the path from a large data set to a focused procedure, but a flagged item is not an audit finding and a generated explanation is not evidence.
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A controlled audit workflow
- Define the purpose. State the assertion, population, period and decision the tool supports. Do not begin with an open-ended request to “find fraud.”
- Load approved data. Use the engagement’s authorized repository, apply access controls and document data lineage. Remove unnecessary personal or confidential information.
- Run a reproducible procedure. Version the prompt, model, rules, filters and data snapshot. Record the run date and user.
- Investigate exceptions. The engagement team obtains source documents, interviews personnel and performs the required test; it does not treat a model score as sufficient evidence.
- Review and sign off. A qualified auditor checks completeness, accuracy, bias and contradictory results, then records the conclusion and rationale in the workpapers.
This approach preserves professional responsibility while using AI to reduce searching and sorting. It also makes a later inspection possible because another reviewer can reconstruct what the system did.
Financial reporting and the new advisory market
KPMG’s 2024 survey found that 62% of US companies used AI to a moderate or large degree, 58% had piloted or deployed generative AI, and 52% used AI in financial reporting. Every surveyed US financial-reporting leader expected to pilot or use AI in financial reporting within three years, and 97% expected to pilot or use generative AI.
Those leaders also expected auditors to evaluate control environments and provide insight into AI-governance maturity. That demand creates work beyond traditional implementation: AI governance design, model and data controls, third-party assurance, privacy reviews, disclosure support, implementation oversight and operating-model redesign.
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As KPMG US Vice Chair–Audit Scott Flynn put it, “It’s clear that financial reporting leaders are rapidly accelerating investments to use AI and GenAI to not just gain efficiencies but create more value for their organizations by predicting trends and identifying emerging risks.” The control question is whether those predictions can be explained, monitored and challenged before they affect reported numbers.
Why the professional role is shifting, not vanishing
AI is strongest at pattern matching, retrieval, transformation and first drafts. Professionals remain accountable for interpreting incomplete facts, resolving conflicting authorities, assessing uncertainty, negotiating with clients and regulators, and deciding whether evidence supports a conclusion. Those duties become more important when a system produces a plausible but wrong answer.
Thomson Reuters’ 2025 findings show the expectation of impact: 79% of tax, audit and accounting professionals anticipated a high or transformational effect from AI, yet only 14% reported having a defined AI strategy. An Australian corporate risk manager described the opportunity as reducing low-value tasks and compressing the time from identifying a problem or opportunity to delivering value. The gap between enthusiasm and strategy is where firms need operating rules, training and measurement.
Skills that gain value
- Writing precise, testable instructions and recognizing when a prompt is underspecified.
- Checking primary sources, citations, calculations and data lineage.
- Understanding model limitations, confidence signals, bias and error modes.
- Explaining technical outputs to clients, boards and audit committees.
- Designing controls, escalation thresholds and evidence-retention procedures.
- Combining domain judgment with data, workflow and change-management skills.
Governance controls every firm should establish
Purpose and risk classification
Maintain an inventory of approved use cases. Classify them by potential harm: administrative drafting may be low risk; a filing position, audit conclusion or financial-reporting entry is high risk. Require a stronger reviewer, evidence standard and approval path as risk increases.
Confidentiality, privacy and residency
Do not paste client records into a consumer service without contractual and technical approval. Define permitted data types, retention, encryption, identity management and geographic processing requirements. Restrict access by engagement and log exports. Vendor contracts should state whether customer data is used for training and how it is deleted.
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Explainability and source traceability
Prefer systems that return citations, document snippets, confidence or uncertainty indicators and a link to the underlying record. Store the input, output, model version, knowledge-base version and reviewer decision for material work.
Testing and monitoring
Test representative cases, edge cases, different jurisdictions and deliberately adversarial inputs before release. Re-test after a model, prompt, data source or workflow change. Monitor accuracy, false positives, omissions, drift, override rates and incidents rather than relying on a vendor’s generic benchmark.
Human review and escalation
Name the accountable professional, define when review is mandatory and prohibit the system from filing, posting or issuing an opinion without approval. Escalate low confidence, conflicting sources, novel transactions, suspected fraud, personal-data exposure and material errors.
Third-party assurance and continuity
Assess vendors’ security reports, subprocessors, incident-notification terms, service availability and change controls. Keep a fallback process for outages or model withdrawal so a filing, close or audit deadline does not depend on one service.
How to select and measure an AI implementation
Compare approaches using the same questions rather than choosing on a fluent demonstration:
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| Decision area | Questions to answer |
|---|---|
| Task fit | Does it support research and preparation, or is the proposed use judgment-intensive advisory that needs a different control design? |
| Review burden | How long does a qualified person need to verify a result, and who handles exceptions? |
| Data protection | What information leaves the firm, where is it processed, and how is it retained? |
| Evidence | Are citations, calculations, prompts, versions and audit trails available? |
| Integration | Can it connect safely to practice-management, tax, ERP and reporting systems without uncontrolled copying? |
| Governance | Can the firm test, approve, monitor and obtain assurance over the model and its provider? |
| Outcome | Will the project improve cycle time, cost, revenue, error detection or client value after review time is included? |
Start with a bounded pilot such as summarizing a defined document set or proposing bookkeeping classifications. Establish a baseline, run the old and new process in parallel, sample outputs for material errors, and set a stop condition. Scale only when quality, security and reviewer capacity are demonstrated.
Track measures such as turnaround time, hours per return or audit section, exception rate, correction rate, citation completeness, data incidents, client response time and revenue from new advisory work. A productivity gain that creates more review or rework is not a gain.
Business impact and what the numbers mean
Wolters Kluwer reported in 2025 that 72% of firms used AI weekly, 77% planned to increase investment and 65% offered advisory as a key service, up from 49% in 2024. Thomson Reuters reported average worldwide revenue growth of 21.3% in 2024 and an average profit increase of 25%. These are industry survey results, not a promise that an individual firm will achieve the same outcome; they indicate that investment and advisory expansion are occurring together.
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A practical adoption roadmap
- Set the policy. Define prohibited data, approved tools, accountable owners, retention and incident reporting.
- Map the work. List high-volume tasks and rank them by data quality, risk, reviewability and expected value.
- Choose one measurable pilot. Specify the baseline, success threshold, sample size and human-review procedure.
- Build the evidence trail. Capture sources, prompts, versions, outputs, overrides and approvals.
- Train the team. Teach confidentiality, verification, hallucination detection, citation checking and escalation.
- Review with risk and quality leaders. Test edge cases and decide whether to expand, redesign or stop.
- Scale through controlled integration. Connect only the systems and data needed, with role-based access and monitoring.
Bottom line for firms and clients
AI is already changing audit, tax and advisory services by removing clerical effort and accelerating access to information. Tax research, preparation and bookkeeping are the most mature uses; audit applications are developing under heavier evidence and supervision requirements. The winning operating model pairs automation with accountable professionals, protected data, traceable sources, tested controls and candid communication about uncertainty. Firms that build those foundations can redirect time toward higher-value advice without outsourcing their judgment to a model.
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