AI in auditing is no longer a future possibility. It is a competitive advantage that mid-size CPA firms are using right now to deliver faster, more accurate engagements. At a recent FinTech conference in Las Vegas, our team shared how adopting artificial intelligence in our audit process has changed the way we work, the quality of results we deliver, and the way clients and peers see our firm. What started as a conversation opener at networking meetings quickly became the defining theme of every interaction: AI is reshaping auditing, and firms that adopt it early are pulling ahead.
This article answers one question that every accounting leader is asking right now: how does AI in auditing actually save time and improve quality, and what does it take for a mid-size firm to capture those gains? The short answer is that targeted automation of repetitive evidence review, paired with sound professional judgment, delivers measurable results within a single quarter.
What We Learned at a FinTech Conference About AI in Auditing
In March 2025, Tim Porter, CPA, CISA, joined over 5,000 professionals in the FinTech industry at a major conference in Las Vegas. The event brought together CEOs, founders, CISOs, and account executives to collaborate, share visions, and expand professional networks. For a 150-person regional CPA firm based in Cleveland, Ohio, it was an opportunity to benchmark our technology adoption against the broader industry.
The conference offered a matchmaking meeting app designed to accelerate networking. Over two days, Tim completed 20 speed-dating-style meetings with industry leaders. After exchanging elevator pitches, conversations consistently turned to a single topic: how AI for CPA firms is creating measurable advantages in audit efficiency and quality.
What surprised many attendees was the scale of AI adoption at a regional firm. Several contacts expected a much larger organization based on the technology being described. The takeaway was clear. Firms that invest early in artificial intelligence audit tools can deliver quality comparable to large firms at fees typical of smaller ones.
That positioning matters because the audit profession is under steady pressure from regulators and standard setters to improve quality and documentation. The AICPA has been explicit that technology and data analytics are central to the future of audit and assurance services, and its guidance on automated tools reflects how quickly expectations are shifting. You can review the AICPA’s current audit, attest, and quality management standards directly through its audit and quality management standards resources.
How AI and OCR Automate Audit Evidence Review
One of the most time-consuming tasks in any audit engagement is reviewing evidence provided by the client to determine whether a control activity was designed and implemented correctly. For our associates, this process traditionally took between 1 and 15 minutes per control. Multiplied across 75 to 300 controls per engagement and 20 to 50 engagements per associate per year, the total adds up to roughly 1,900 hours annually per associate spent on evidence review alone.
In Q4 2024, the firm implemented a tool that combines Optical Character Recognition (OCR) with artificial intelligence to automate large portions of this workflow. OCR is a technology that converts scanned documents and images into machine-readable text, enabling AI systems to analyze their contents. Here is how the process works:
- Document upload and conversion. Files provided by the client are automatically converted to PDF format and processed through OCR to extract text content.
- AI-generated summaries. Once the document is readable, an AI model creates a summary of the key information, saving the auditor from reading every page manually.
- Query-based analysis. As auditors query specific questions about the document, such as whether a particular control setting is enabled, the AI uses the relevant content to provide answers along with source citations.
- Annotated documentation. The PDF is annotated with answers to each query, which streamlines the review process and automates the test procedure write-up that auditors previously composed by hand.
The results have been significant. To date, the firm has observed a 30 to 60 percent reduction in associates’ time spent on evidence review per engagement. That reclaimed time allows auditors to focus on higher-level analysis rather than repetitive document inspection.
Importantly, source citations and annotated working papers also strengthen the audit trail. Clear, traceable documentation supports the evidence requirements set out in professional standards, including the auditing standards maintained by the PCAOB. When every conclusion ties back to a cited source within the evidence, review and inspection become faster and more defensible.
Why AI Improves Audit Quality, Not Just Speed
AI in auditing and accounting delivers more than efficiency gains. It raises the quality bar for engagements. Speed is important, but accuracy and coverage are where artificial intelligence audit tools create their deepest impact.
Consider user access testing, a common audit procedure that involves matching usernames to individuals and then evaluating whether each person’s access level is appropriate for their job title and responsibilities. Performing this analysis manually on a large user population is slow and error-prone. AI assists auditors by analyzing larger data sets more quickly and with greater accuracy than manual review allows.
By handling the data-intensive portions of the audit, AI frees auditors to apply professional judgment where it matters most. Instead of spending hours cross-referencing spreadsheets, auditors can focus on identifying anomalies, assessing risk, and advising clients on control improvements. The net effect is a higher-quality audit that covers more ground in less time.
This shift also benefits clients directly. Faster turnaround times reduce disruption to their teams, and more thorough analysis catches issues that a time-constrained manual process might miss. For a mid-size firm, the ability to offer large-firm rigor at regional-firm pricing is a genuine differentiator that AI makes possible. It also pairs naturally with adjacent services such as risk advisory services, where the same analytical capabilities support broader control assessments.
What Early AI Adoption Means for Mid-Size CPA Firms
The conference reinforced a pattern visible across the FinTech industry: early adopters of AI for CPA firms are gaining an outsized advantage. Firms that wait for the technology to mature further risk falling behind competitors who are already building institutional knowledge around AI-driven workflows.
For a 150-employee firm, the benefits extend beyond individual engagement efficiency. AI adoption signals to clients, prospects, and referral partners that the firm is forward-thinking and invested in delivering modern audit services. It also helps with talent retention. Associates who spend less time on repetitive tasks report higher job satisfaction and develop analytical skills faster.
The practical lesson is straightforward. Firms do not need thousands of employees or enterprise-scale budgets to adopt AI in auditing. A targeted implementation, starting with the highest-volume and most repetitive tasks like evidence review, can deliver measurable return on investment within a single quarter. From there, expanding AI into areas like user access testing, data analytics, and risk assessment builds on a proven foundation.
The implications also reach into adjacent practice areas. As AI tools mature, the same techniques that accelerate audit evidence review can support accounting services more broadly, from transaction analysis to compliance documentation across the industries a firm serves. The firms that win will be those that treat AI as a discipline to build rather than a feature to buy.
How to Start Adopting AI in Your Audit Practice
Adoption works best when it is sequenced rather than rushed. Begin with a single, well-defined, high-volume task where the manual effort is large and the inputs are repetitive. Evidence review is an ideal first candidate because the time savings are immediate and easy to measure against a known baseline.
Set a clear baseline before deployment so the gains are verifiable. Track average minutes per control, controls per engagement, and engagements per associate, then compare those metrics after the tool is in place. Documented, defensible numbers protect the firm during peer review and inspection and make the business case for further investment.
Finally, invest in training and governance alongside the technology. Auditors must understand how the model reaches its conclusions, how to verify cited sources, and where human judgment remains mandatory. AI does not relieve a firm of its professional responsibilities, and the auditing standards that govern those responsibilities, including those issued by the PCAOB, still apply in full. Treated this way, AI becomes a durable capability rather than a one-time experiment.
Frequently Asked Questions
How is AI used in auditing?
AI in auditing automates repetitive tasks such as evidence review, document analysis, and user access testing. It uses technologies like OCR and natural language processing to extract information from documents, generate summaries, and answer auditor queries with source citations. This allows auditors to spend more time on professional judgment and risk analysis.
Can small or mid-size CPA firms afford AI audit tools?
Yes. Mid-size firms with as few as 150 employees are already implementing AI in their audit processes. Many AI tools are priced per engagement or per user, making them accessible without enterprise-scale budgets. Firms typically see a return on investment within one to two quarters through reduced associate hours.
How much time does AI save during an audit?
Firms that have adopted AI-driven evidence review report a 30 to 60 percent reduction in time spent per engagement on document review tasks. The exact savings depend on the number of controls tested and the complexity of the evidence, but the efficiency gains are consistent across engagement types.
Does AI replace auditors?
No. AI handles data-intensive, repetitive portions of the audit workflow, such as reading documents, extracting data, and matching user access to job roles. Auditors still perform all professional judgment, risk assessment, and client advisory functions. AI makes auditors more effective, not redundant.
What is OCR and why does it matter for auditing?
OCR stands for Optical Character Recognition. It converts scanned documents, images, and PDFs into machine-readable text. In auditing, OCR is the first step in AI-powered evidence review because it makes client-provided documents readable by AI systems, which can then summarize, search, and annotate them automatically.
How does AI improve audit quality beyond saving time?
AI improves audit quality by analyzing larger data sets with greater consistency than manual review. It catches patterns and anomalies across thousands of records that a time-constrained human reviewer might miss. The result is more thorough coverage, fewer overlooked issues, and stronger documentation, all of which raise the overall standard of the engagement.
