AI audit tools: What to look for and what to avoid

Today's audit firms face a perfect storm of challenges: mountains of financial data, evolving regulations, limited staff capacity, and clients who expect faster, more insightful results. To stay competitive, traditional methods are no longer enough.

That's where AI comes in — promising greater efficiency, deeper insights, and stronger audit quality. But not every audit technology that claims to be "AI-powered" actually delivers. The market is crowded with solutions that sound impressive yet fall short in practice.

Knowing how to choose the right AI audit tool directly impacts your firm's ability to maintain compliance, uphold professional judgment, and remain competitive. This white paper offers a clear framework for evaluating and selecting an AI audit solution, including essential features to look for and common pitfalls to avoid, so you can harness this powerful technology for a true strategic edge.

The AI audit landscape: Promise vs. reality

The right AI audit tool can elevate your firm's audit workflow from repetitive tasks to data-driven insights. However, while AI offers immense potential, the reality is far more complex. Understanding the gap between promise and reality is critical if your firm seeks to utilize AI effectively.

The current state of audit AI adoption

According to the Future of Professionals Report 2025, AI has the potential to save professionals an average of five hours of work per week, or roughly 240 hours per year. This kind of time savings, when applied across an entire audit team or firm, can be transformative. It also opens the door to focusing on higher-value work like advisory services and complex analysis.

While many express a strong interest in AI, implementation often falls short of expectations. Challenges include insufficient training, resistance to change, and misaligned technology purchases that fail to integrate with existing workflows. The result is an implementation gap, where the enthusiasm for AI far outpaces actual return on investment (ROI).

The consequences of failed implementations can lead to wasted resources, decreased staff morale, and disruptions in client service. For audit professionals already working under tight deadlines, a tool that complicates rather than streamlines processes can become a costly burden.

The ideal AI audit tool should:

• Integrate seamlessly into workflows to meet time-sensitive deadlines without compromising quality
• Provide transparent, auditable, and traceable logic to satisfy regulatory compliance
• Enhance risk management and professional judgment
• Deliver faster, more insightful data for clients while maintaining human oversight and expertise

The marketing hype problem

As the popularity of AI grows, vendors are racing to position their products as AI-powered. This race has resulted in what many professionals call "AI washing," where basic automation or workflow tools are rebranded as artificial intelligence.

While automation can certainly provide efficiency gains, it is not the same as AI-driven insight or advanced analytical capability. When firms cannot distinguish between the two, they risk purchasing tools that fail to meet their expectations.

Even when vendors offer impressive demonstrations, many firms often encounter a feature-versus-function disconnect. Tools that perform flawlessly in a controlled demo environment may falter under the pressure of real-world audit demands.

What audit professionals actually need

What’s needed is an AI audit tool designed to address the core pressures of their work.

Time-sensitive deliverables demand that AI audit tools should integrate seamlessly into your firm's existing workflows. Efficiency gains are only valuable if they can be realized consistently and at scale.

AI tools must also offer transparent, auditable processes that meet regulatory demands. "Black-box" systems that obscure how they generate outputs can expose your firm to risk, particularly if regulators demand explanations. The most valuable AI audit tools are those that provide clear, traceable logic aligning with compliance standards.

AI can also significantly improve how your firm identifies, assesses, and mitigates risk by automating detection, enhancing decision-making, and enabling real-time analysis. This capability allows you to focus your audit procedures on high-risk areas and minimize time spent on low-risk ones. AI improves audit quality by pulling out unusual transactions and catching items normally missed by humans.

Additionally, client expectations play an increasingly important role in shaping AI adoption. Clients want to see that their audit firms leverage modern tools to deliver faster, more insightful results. Firms that strike this balance — using AI to enhance efficiency and insight while maintaining the human touch — will gain a competitive advantage. The key lies in adopting AI audit tools that empower you and your staff, aligning technology adoption with the trust and expertise your clients rely on.

Essential criteria for evaluating audit AI tools

As more and more AI audit tools saturate the market, firms face a critical decision point: which tools deliver real value and which fall short of expectations? Choosing wisely can mean the difference between improving efficiency and quality — or adding complexity and risk.

Audit-native vs. generic AI

Audit workflows are fundamentally different from those in legal services, consulting, or other professional domains. Audits are not just about document review or data processing; they are about applying structured methodologies, adhering to strict professional standards, and producing evidence-based conclusions that withstand regulatory scrutiny.

Generic AI solutions, even those marketed as AI-powered, often lack a specialized understanding of the audit profession. Forcing audit workflows to fit the technology instead of the technology fitting audit workflows can be a costly and inefficient approach.

So how do you know if your AI solution has an audit-native design? Let's take a look.

Key evaluation points:

• Audit methodology integration. The first question to ask is whether the solution understands audit procedures and standards. A truly audit-native AI solution aligns with your firm's established methodologies. It should support standard steps, such as risk assessment, evidence gathering, and substantive testing, rather than analyzing documents in isolation.
• Risk-based approach. Audits are designed to focus attention on the areas of greatest risk. An AI solution should mirror this principle by identifying high-risk transactions, accounts, or processes and directing your firm's effort accordingly. Tools that treat all data as equal miss the essence of auditing, which is to apply judgment and resources where they matter most.
• Evidence compilation. Documentation is the backbone of audit credibility. AI solutions must support built-in evidence compilation and automatic work-paper generation. This feature not only saves time but also ensures that every AI-assisted step is traceable and properly documented for review.
• Professional standards compliance. Perhaps most importantly, tools must align with regulatory and professional requirements. Any AI solution used in audits must meet standards such as GAAS, PCAOB rules, or ISA compliance. If a tool cannot demonstrate conformity, it risks introducing noncompliance into the audit process.

Optimize your workflows with audit-native design

Thomson Reuters CoCounsel Audit is an agentic AI solution that improves audit engagements by providing verifiable outputs and streamlining workflows. It automates repetitive tasks, such as analyzing trial balances, summarizing PDFs, and preparing risk assessments, allowing your firm to focus on higher-value activities and complex judgment calls.

Explainable AI and transparency

As your firm incorporates AI into your workflows, one principle stands above all others — transparency. Auditors must operate under strict professional standards requiring evidence, reasoning, and defensibility of every conclusion.

The black-box problem

One of the biggest challenges in adopting AI for audit is the so-called "black-box problem.” Many AI systems generate outputs without providing clear explanations of how they arrived at their conclusions. For auditors, this is unacceptable. Professional standards demand that every conclusion be supported by evidence and reasoning that can withstand scrutiny from regulators, clients, and oversight bodies. If an AI tool cannot explain its logic, it undermines the very foundation of audit assurance.

So, how do you ensure your AI solution is both explainable and transparent? Let's take a look.

Critical features to assess:

• Source attribution. Every AI-generated output should include clear citations to the source data, which allows auditors to validate results and provides the transparency needed to defend audit conclusions. Without attribution, your firm cannot reasonably rely on AI outputs.
• Reasoning transparency. Beyond citations, AI tools should make their reasoning processes traceable. If the system flags a transaction as high risk, auditors need to understand why. Was it based on unusual frequency, size, or context? This level of detail allows your firm to exercise judgment rather than blindly trusting AI outputs.
• Audit trail generation. Automatic documentation of AI-assisted procedures is another critical feature. Every AI action should be logged and easily retrievable, forming part of the audit trail to ensure compliance with documentation requirements and support peer review.
• Human oversight integration. AI should never replace professional judgment. The best tools include clear handoff points where auditors can review, approve, or override AI suggestions.

Data-driven audits that are explainable and transparent

Thomson Reuters Audit Intelligence enables transparent, data-driven audits that are explainable and transparent. The suite of AI-powered solutions enhances transparency, efficiency, and trust in the audit process, aligning with trusted PPC audit methodologies and leveraging advanced analytics.

Audit Intelligence empowers firms to streamline workflows, improve risk analysis, and confidently deliver high-quality audits — all without disrupting proven practices.

Workflow integration and user experience

Adopting AI in audit isn't just about selecting the most advanced technology. It's about ensuring that the solution enhances, rather than hinders, the way your audit firm works.

The adoption challenge

Because auditors operate under tight deadlines, they cannot afford technology that slows them down or forces unnecessary adjustments. No matter how powerful, an AI tool that disrupts established workflows or requires your team to abandon proven methodologies will face resistance.

How do you know if the AI solution will seamlessly fit into your firm's workflow? Let's take a look.

Integration checklist:

• Native integration. The tool should work within your existing audit software and processes rather than requiring your firm to switch platforms or duplicate effort.
• Learning-curve assessment. Be sure to evaluate how quickly your team can get up and running with the tool and that your chosen vendor offers the support you need. If it takes months of training to achieve basic proficiency, the ROI diminishes. The best AI tools deliver value quickly, often through intuitive interfaces and role-specific functionality.
• Scalability. Audit engagements can vary widely in size and complexity, from small business audits to large, multinational engagements. Your AI tool must scale accordingly, delivering consistent performance across different contexts without requiring extensive customization.
• Multi-user collaboration. Auditing is a team effort. Your AI solution should support collaboration by multiple users, ensuring that workpapers, insights, and risk assessments are accessible to the entire engagement team.

The most comprehensive online solution for efficient and accurate audits

Thomson Reuters built its Cloud Audit Suite for seamless integration with the way your firm already works. Its comprehensive, cloud-based platform connects directly to your existing audit workflows — no need to overhaul proven processes or switch between multiple tools. Real-time collaboration, automated engagement management, and secure online confirmations all streamline your user experience, letting your team complete audits faster and with greater confidence, wherever they work.

Data security and compliance

For audit firms, data security is a core requirement. Your clients' sensitive financial information must be protected against breaches, misuse, or regulatory violations.

How do you evaluate whether your AI audit tool is up to the job? Let's take a look.

Non-negotiable requirements:

• Enterprise-grade security. Features such as end-to-end encryption, secure APIs, and compliance with global security standards are non-negotiable. Any AI tool that cannot meet enterprise-grade security standards should be eliminated from consideration immediately.
• Role-based access controls. Audits involve teams with varying levels of responsibility. Your software should provide granular permissions to ensure that only authorized users can access sensitive information. These controls safeguard confidentiality and support accountability.
• Data residency. Your firm should know where client data is processed and stored. Different jurisdictions impose different data residency and privacy requirements, and tools must provide clarity on how these obligations are met.
• Regulatory compliance. Certifications such as SOC 1, SOC 2, and ISO 27001, along with detailed audit logs, are critical indicators of your technology vendor's commitment to security and compliance. Without them, your firm can expose itself to regulatory and reputational risks.

Robust security measures that ensure data protection

Enterprise-grade security for highly regulated business environments is a core feature of CoCounsel Audit. By embedding security into the architecture from day one, it ensures that your firm can adopt AI with confidence, knowing that client data remains protected and you meet compliance requirements.

Audit software evaluation checklist

Key questions to ask vendors

Choosing the right AI-powered audit solution can feel overwhelming, with countless platforms promising efficiency, accuracy, and compliance.

This checklist provides a structured set of questions to guide your conversations with vendors, helping you cut through the noise, compare options effectively, and identify the software that best fits your firm's needs today — and scales with you into the future.

Audit-native design:

• Does the tool explicitly support GAAS, PCAOB, or ISA audit standards?
• Can it integrate with my firm's existing audit methodology?
• How does it handle evidence compilation and work-paper generation?
• Does it apply a risk-based approach to prioritize high-risk areas?

Explainability and transparency:

• Does every AI output include clear citations to source data?
• Can I trace the reasoning behind flagged risks or anomalies?
• Does the tool automatically generate an audit trail of AI-assisted procedures?
• Where are the clear handoff points for human oversight and judgment?

Workflow integration and user experience:

• Will the tool integrate natively with my firm's current audit software?
• How steep is the learning curve and how quickly will staff reach productivity?
• Is it scalable across both small and complex multinational engagements?
• Does it support collaboration for multiple auditors on the same engagement?

Data security and compliance:

• What certifications — SOC 1, SOC 2, ISO 27001, etc. — does the tool meet?
• Does it offer role-based access controls suitable for audit team structures?
• Where is client data stored, and does that align with regulatory requirements?
• How is sensitive data encrypted, both in transit and at rest?

Common pitfalls and red flags

While there is no doubt that artificial intelligence promises significant efficiency gains for the audit profession, the path to realizing those benefits is far from straightforward.

To safeguard investments and avoid unnecessary disruptions, it's important to recognize the most common pitfalls and red flags that appear when evaluating audit AI tools.

The ‘one-size-fits-all’ trap

One of the most frequent mistakes audit firms encounter is adopting AI platforms that were never truly built for the audit environment.

Many vendors in the broader professional services market their tools as equally effective for legal, consulting, tax, or audit practices. This one-size-fits-all approach should immediately raise concerns for firms that understand the unique regulatory, procedural, and workflow demands of auditing.

Warning signs include:

• Vendors that promote nearly identical solutions across multiple professional services without meaningful distinctions
• Demonstrations that lack audit-specific terminology, case studies, or examples tied to actual audit engagements
• Efficiency claims framed in generic terms without describing how the tool applies to audit risk assessments, sampling strategies, or evidence gathering

Overpromised performance metrics

Another recurring red flag is inflated performance metrics. In the race to win attention, some AI vendors present claims of extraordinary time savings or accuracy improvements that collapse under scrutiny.

Warning signs include:

• Vague claims of efficiency without clarifying whether it applies to document review, risk assessment, or another activity
• A lack of customer references from firms of similar size, client base, or industry specialization — testimonials from law firms or corporate finance teams are not substitutes for audit-specific case studies
• Performance metrics that are mathematically implausible or inconsistent with the complexity of audit work — promises of 90% or greater time savings across the board should be treated with skepticism

A more realistic approach is to evaluate tools based on specific, context-driven performance outcomes. For example, testimonials from Thomson Reuters clients have demonstrated measurable reductions in document processing time by automating evidence collection and organizing audit workpapers. These improvements are tangible, repeatable, and directly tied to defined audit tasks.

Rather than relying on sweeping promises, demand precise metrics grounded in practical use cases.

Integration nightmares

Even when an AI tool is well-designed for audit, implementation can fail if it does not fit seamlessly into existing workflows.

Many audit firms operate within complex software ecosystems, from engagement management systems to data analytics platforms. Introducing a tool that requires a complete overhaul of these workflows can be disruptive and costly.

Warning signs include:

• Tools that require firms to abandon or significantly modify established methodologies, forcing auditors to retrain around entirely new processes
• A lack of API connectivity or interoperability with widely used audit platforms, creating data silos and manual transfer workarounds
• Vendor lock-in scenarios where data and processes become trapped within the AI tool, limiting the firm's flexibility to adapt or switch providers in the future

That said, adopting the latest technology doesn't mean you have to disrupt or replace your current audit workflow. Thomson Reuters is paving the future of audit in a practical, guided way that aligns with your process and complies with professional standards.

Moreover, integration-ready platforms reduce the learning curve for staff, allowing your audit team to realize value faster.

Measuring true ROI and impact

When firms evaluate the return on investment for AI audit tools, they often focus narrowly on time savings. While efficiency is important, a more complete ROI framework must consider quality improvements, client satisfaction, and the long-term impact on staff engagement.

Taken together, these dimensions provide a truer picture of AI's role in creating lasting value for your audit practice. Let's take a look.

Go beyond time savings with quality and risk metrics

Efficiency gains are often the most visible benefit of audit AI. By automating sample selection, document processing, and multidocument comparisons, auditors can reduce hours of manual work. However, efficiency alone does not capture the full ROI story.

AI-powered tools also improve audit quality by reducing human error, enhancing risk identification, and ensuring consistency in the application of professional standards.

Client satisfaction is another vital element. Faster engagement turnarounds, coupled with the deeper insights derived from AI analysis, can lead to stronger client relationships and a competitive edge in the marketplace.

Perhaps most importantly, AI can play a crucial role in staff retention. Staff members who see technology as an enabler are more likely to remain engaged. By offloading repetitive tasks, AI enables your team to focus on higher-value work that leverages their judgment and expertise.

Real-world results

As an early adopter of Thomson Reuters Audit Intelligence Analyze from Thomson Reuters, RBSK Partners has reported measurable improvements in both efficiency and quality.

"Our firm can definitely see how AI will increase our efficiency. When using Analyze, I would say time savings was probably the number one thing. Not only did we save 20 to 30 minutes just selecting the sample, but the sample size was cut in half, which reduced testing time — and client data gathering time — by half as well. And this is just for receivables," said Austin Seale, CPA, RBSK Partners.

This testimonial highlights the compounding effect of AI — reducing sample size does not just save time during selection, it also cuts downstream work during testing and client data collection. These savings not only improve project margins but also free up staff capacity to manage additional engagements or focus on more complex audit areas.

Additional measurable results include:

• Efficiency gains. Sample selection time reduced by 20 to 30 minutes with 50% smaller sample sizes, 60% to 80% faster document processing, and 40% to 60% time savings in multidocument analysis.
• Quality and risk improvements. AI-assisted review reduces human error while data-driven sampling and anomaly detection enhance risk identification.
• Client impact. Faster engagement turnaround and more actionable insights lead to improved client satisfaction through efficient, accurate audits.
• Staff benefits. Automation of repetitive tasks enables focus on judgment-based work while enhancing professional skills and supporting retention.

Assess long-term value

Short-term efficiency is important, but sustainable ROI requires a longer-term view. One critical factor is scalability.

AI tools must deliver consistent performance across different engagement types and firm sizes. A tool that works well for a mid-sized audit should also be effective for a large enterprise engagement, and vice versa.

Training and adoption costs are another long-term consideration. That's why it's essential to evaluate the total cost of ownership, including staff training, system integration, and support. A solution that minimizes disruption to existing workflows will pay dividends over time by reducing hidden costs associated with change management.

Finally, be sure to choose a technology partner committed to continuous improvement, ensuring that the AI evolves in tandem with regulatory changes and professional needs. A forward-looking roadmap demonstrates that the technology investment will not only deliver immediate ROI but also continue to create value as the firm grows.

The path forward: AI designed for audit, not just adapted for it

The audit profession's AI transformation is inevitable, but success depends on selecting tools built for the realities of auditing.

As you evaluate AI audit tools, prioritize these four guiding principles:

1. Audit-native design over generic AI capabilities. Tools should align with GAAS, PCAOB, and international standards — not just automate general document review.
2. Transparency and explainability. AI must provide reasoning and traceable outputs that auditors can defend to regulators and clients.
3. Workflow integration from day one. Disruption to established audit software and methodologies can derail adoption, so seamless integration is a must.
4. Realistic ROI metrics grounded in customer results. Overpromised performance damages trust; firms need evidence-based benchmarks from peers.

By embracing an audit-native solution now, your firm will not only improve efficiency but also position itself as a trusted advisor in an increasingly technology-driven marketplace.

Aligning technology with firm strategy

The long-term value of AI adoption will ultimately depend on alignment between a firm's strategic goals and the capabilities of its chosen solutions. Scalability across engagement types, compatibility with proprietary methodologies, and a clear vendor roadmap are all critical considerations.

The wrong choice can lock a firm into costly, inflexible systems, while the right choice can open new pathways for growth.

That is why your firm should approach AI adoption not as a one-off software purchase, but as an ongoing partnership. Vendors must demonstrate not only current functionality but also a vision for future enhancements that align with an evolving audit profession — and your firm's strategic vision.

Your next step

The path forward is no longer about asking if AI should be part of your practice — it's about deciding how to implement it effectively.

The firms that thrive will be those that choose AI audit tools designed with auditors in mind, not retrofitted from generic software. Prioritizing audit-native design, explainability, seamless workflow integration, and proven ROI ensures that AI becomes a driver of efficiency rather than a source of disruption.

Thomson Reuters Audit Intelligence and CoCounsel Audit embody these principles, setting the standard for audit technology with data-driven insight. Together, these audit tools help firms reduce the time spent on repetitive tasks, strengthen audit efficiency, and deliver insights that clients value most — all while safeguarding compliance and security.

This is what responsible AI adoption looks like: tools that empower professionals to focus on judgment, analysis, and client engagement, while the technology handles the heavy lifting.

Discover how our AI-powered audit tools can transform your audit processes, enhance efficiency, and ensure compliance.