Generative AI for Auditing:
Transforming Efficiency, Quality, and Trust
The audit industry faces mounting pressures due to regulatory scrutiny, growing complexity of financial data, talent shortages, and escalating operational costs. Traditional auditing practices that rely on labor-intensive manual reviews, inherently selective transaction sampling, and siloed document-centric workflows no longer can address these challenges.
Generative AI (GenAI) emerges as a transformative technology for augmenting auditors’ capabilities. It offers near real-time deep insights into data, streamlines routine tasks and processes, and enables comprehensive risk coverage, while helping mitigate the risks of regulatory non-compliance, financial restatements, and reputational damage.
Forward-thinking audit executives are already adopting GenAI to meet today’s challenges, strategically positioning their organizations for future demands. All of the Big Four audit firms – Deloitte, PwC, EY, and KPMG – are investing billions of dollars in GenAI to modernize their audit processes and practices.
This whitepaper explores how generative AI is transforming the audit industry, delivering measurable business value. It details key GenAI use cases, strategic insights, and a practical roadmap for GenAI adoption.
The auditing profession is facing a critical talent gap. In the US, more than 300,000 accountants and auditors have exited the field, representing a 17% decline from its peak in 2019. The pipeline of new CPAs is also shrinking, with accounting graduate numbers falling at the steepest rate in decades. As a result, firms are contending with rising labor costs, burnout, and mounting operational burden. Many are now turning down new engagements due to insufficient staff.
For auditing firms, expectations for audit depth and coverage are increasing, yet fee structures remain flat or constrained. At the same time, staffing shortages and rising compensation costs are putting additional strain on margins. It leaves little room for inefficiency as firms are expected to deliver more value, faster, and with fewer resources. Manual, labor-intensive processes are not scalable or sustainable, especially as audits grow more complex and data-heavy.
In its latest inspections, the PCAOB found that 46% of reviewed audits contained significant deficiencies, often due to inadequate testing of estimates, insufficient evaluation of internal controls, or incomplete documentation. Even among the Big Four firms, the deficiency rate more than doubled in just two years. These findings demonstrate growing regulatory – and business – expectations for rigor, consistency, and depth in the way audits are executed, end to end.
Auditors have to deal with petabytes of complex data – from financial transactions to contracts, policies, board minutes, emails, and other internal documents that are scattered across disconnected systems in diverse, inconsistent formats. Traditional manual methods cannot keep pace, leaving key risks of compliance issues getting buried in the noise. The inability to efficiently access and use insights without exhausting auditing teams or compromising audit quality poses a real challenge.
Real-World Example of GenAI Application
A mid-sized auditing firm is struggling to staff new engagements. With a shrinking talent pool and rising labor costs, the firm has had to turn away new clients and stretch its remaining teams thin, leading to missed deadlines and signs of burnout.
To ease the pressure, the firm deploys generative AI to automate routine audit tasks:
- Draft standard workpapers
- Summarize control walkthroughs
- Answer technical accounting questions on demand
Now, their auditors spend less time on repetitive document work and more time on risk-focused analysis. Generative AI allows the firm to take on more work without increasing headcount, while improving both consistency and morale across auditing teams.
Traditional audit practices are reaching their limits. Firms are under pressure to do more with less, to maintain quality, manage risk, and meet regulatory expectations despite staffing shortages and growing data complexity.
As a result, many audit firms are beginning to adopt generative AI as a practical tool for augmenting their employees, to streamline workflows, relieve pressure on teams, reduce manual effort, and improve audit quality and consistency.
- GenAI helps address staffing shortages by automating document-heavy tasks, compliance checks, and data analysis, to help audit teams handle more work without increasing headcount.
- GenAI assistants can streamline and scale routine tasks, and support complex document reviews, enabling auditors to refocus towards strategic, higher-value analytical work.
Field Example
Deloitte’s DARTbot makes it easy for auditors to instantly access complex accounting standards, reducing hours spent researching guidelines, and boosting productivity across audit teams.
- GenAI enables auditors to analyze full populations of transactions, to reduce the risk of oversight and improve the auditor’s ability to detect anomalies, outliers, or misstatements that might otherwise go unnoticed.
- GenAI enables auditors to analyze full populations of transactions, to reduce the risk of oversight and improve the auditor’s ability to detect anomalies, outliers, or misstatements that might otherwise go unnoticed.
Field Example
PwC’s assistants powered by AI/ML & GenAI automatically verify financial disclosures, proactively flagging and reporting inconsistencies and missing elements, enabling the company to reduce regulatory risks and improve audit outcomes.
- GenAI can rapidly analyze massive high-volume datasets across ledgers, subledgers, and audit trails, enabling teams to identify hidden risks and patterns at a scale traditional sampling cannot match.
- GenAI can process and extract insights from documents, emails, and other unstructured sources, bringing critical context and evidence into the audit process that might otherwise be overlooked.
Field Example
KPMG uses AI & GenAI to score every transaction within client general ledgers, finding potential anomalies for deeper investigation. Instead of sampling a tiny fraction of transactions, auditors can review entire datasets for anomalies or high-risk items.
As generative AI becomes embedded in audit workflows, its role is shifting from optional add-on to full-fledged knowledge worker assistant. Early GenAI solution deployments across the Big Four have already demonstrated tangible results. PwC reports that teams regularly using GenAI tools are seeing 20-40% productivity gains, while one pilot at a financial institution showed a potential reduction of up to 8,000 audit hours per year. These are not theoretical benefits – firms that use GenAI are already scaling capacity, increasing consistency, and reducing time spent on documentation, compliance checks, and repetitive scenario testing and analysis.
More importantly, GenAI is starting to redefine how auditors engage with data, standards, and risk. Tools like Deloitte’s DARTbot and EY’s AI-driven disclosure checklists show how GenAI can turn static knowledge bases into real-time, contextual Q&A applications, improving the speed and accuracy of knowledge work.
Independent assessments of whether financial statements are presented fairly, in accordance with accounting standards such as GAAP. These are subject to PCAOB (AICPA) inspection and carry significant regulatory weight.
Focused on evaluating adherence to specific laws, internal policies, and external regulations such as SOX, HIPAA, ERISA. Common in highly regulated sectors like financial services, insurance, healthcare, and life sciences.
Conducted by in-house teams or third parties to assess risk management, control frameworks, and governance processes. Often driven by management objectives and used to inform strategic decisions across the enterprise.
Evaluate the effectiveness and efficiency of business operations, processes, and systems. Operational audits often involve diverse data sources and require auditors to interpret both qualitative and quantitative performance metrics.
1. Generative AI-powered Audit Documentation and Compliance Management
- Automated drafting of audit workpapers and control narratives
- Real-time disclosure verification against applicable accounting standards
- Intelligent checklists to guide compliance with PCAOB requirements
- Early detection of missing or inconsistent financial reporting elements
Real-World Scenario
An audit team preparing year-end disclosures for a public company uses GenAI to complete financial statement checklists. As the auditor uploads draft statements, the GenAI assistant flags a missing revenue recognition disclosure and suggests language aligned with ASC 606. What would have taken hours of manual review across multiple source documents is resolved in minutes, ensuring completeness and consistency, avoiding late-cycle revisions, and reducing the risk of a PCAOB deficiency.
2. Full-Population Transaction Analysis and Real-Time Risk Detection, Driven by AI/ML & GenAI
- End-to-end review of 100% of transactional data
- Anomaly scoring based on behavioral and contextual risk patterns
- Plain-language explanations of flagged transactions
- Prioritization of high-risk entries for further investigation
Real-World Scenario
During a quarterly audit, a team utilizes GenAI to analyze a full year of journal entries from a multinational client. The system flags a cluster of weekend entries just below the approval threshold – transactions that would have been excluded in a standard sample. The GenAI assistant explains the anomaly in natural language, providing details on the timing, dollar threshold, and lack of historical precedent. The auditors escalate the issue early, uncovering a control weakness that could have led to a material misstatement.
3. Intelligent Assistants for Knowledge Management and Real-Time Research
- Natural-language querying of accounting, auditing, and regulatory guidance
- Instant answers sourced from vetted, up-to-date technical libraries
- Consistent interpretations of audit policies across teams, delivered in real time
- Dramatic reductions in time spent on manual research and document navigation
Real-World Scenario
During a client walkthrough, an audit manager encounters an unusual revenue contract structure. Instead of pausing work to search manuals, the team asks questions to a GenAI assistant trained on the firm’s methodology. Within seconds, they receive guidance on how ASC 606 applies to the transaction type, complete with links to relevant internal memos and examples. This helps the team proceed without delay, reducing disruption and ensuring consistent application of standards.
4. Automated Scenario Analysis and Proactive Audit Planning, Powered by AI/ML & GenAI
- Continuous risk monitoring across structured and unstructured data sources
- AI/ML-generated scenario modeling based on operational and environmental signals
- Near real-time adjustments to audit plans based on early risk indicators
- Enhanced visibility into business or control changes impacting audit scope
Real-World Scenario
While planning the audit of a global manufacturing client, GenAI is deployed to analyze the prior year’s financials alongside board meeting minutes and operations updates. It flags multiple mentions of delayed ERP system rollouts and recent staff turnover in the finance team that are indicators of potential risk. The audit team revises its plan to increase focus on IT controls and cutoff testing early, reducing the likelihood of rework or missed issues later in the cycle.
Early adopters of generative AI already demonstrate tangible operational and competitive advantages. Firms delaying GenAI integration risk long-term disadvantages in efficiency, quality, and client trust.
Provectus guides audit organizations through a structured practical adoption process, delivering measurable results in audit quality and consistency, regulatory compliance, and process efficiency. Our approach includes GenAI workshops, readiness assessments, and prototype developments, to strategically position your firm for sustained success.
Johnson Lambert LLP is a CPA and consulting firm focused on serving distinct industry niches.
As audit demands grew more complex, the audit firm identified an opportunity to modernize its processes and scale operations with generative AI. Their auditors were spending between 60 to 80 hours per audit, extracting, normalizing, and validating financial tables from reports. This manual process was time-consuming, costly, and prone to human error.
By implementing a GenAI solution for report processing, Johnson Lambert transformed its audit workflow. The firm’s auditors could automatically extract, organize, and validate financial insights from complex reports, reducing time and effort without compromising accuracy. GenAI enabled the firm to move into higher-value, client-focused activities.
Taking the first steps toward adopting GenAI can feel daunting, but it does not have to be complicated.
Provectus offers a clear, proven path forward with a structured program available through AWS Marketplace. Our subject matter experts and technical team guide your organization through every step, to help you quickly adopt and implement the most impactful GenAI use cases.
Our approach encompasses two phases:
