Finding an advantage at the edge: What AI can do for auditing

Understanding AI’s role in modern auditing

AI is reshaping auditing by automating routine tasks, accelerating risk identification, and providing deeper insights — all while empowering auditors to deliver higher-quality engagements with greater efficiency.

To understand AI’s role in audit, it’s helpful to first define a few foundational concepts:

• Machine-learning (ML). Algorithms that learn from historical data to detect patterns, flag anomalies — for example, duplicate payments or unusual transactions — and support risk assessment.
• Robotic process automation (RPA). Automation of repetitive, rule-based human tasks, common in industries like automotive and client bookkeeping.
• Natural-language processing (NLP). Enables AI to extract relevant information from unstructured documents like contracts, invoices, and workpapers.
• Generative AI. AI models that can summarize, synthesize, and generate narrative insights based on large volumes of audit data, streamlining documentation and client communication.
• Agentic AI. Emerging AI that autonomously performs tasks and enhances decision-making.
• Computer vision. AI that interprets and processes visual information like images and videos.
• Predictive analytics. Forecasts that use historical and real-time data, helping auditors evaluate the reasonableness of management’s projections or identify potential red flags.

These technologies, when integrated into audit workflows, enable staff to quickly access and analyze full populations of client data down to the transactional level. Risk areas that once required hours of manual review can now be flagged instantly, freeing auditors to apply their expertise where it’s most impactful.

Enhancing audit functions and elevating human expertise

Rather than replacing auditors, AI serves as a tool that augments human expertise. For example, AI supports assisted decision-making in risk identification, assessment, and audit procedure design. This parallels the use of clinical decision-making in medicine, offering auditors data-backed insights to complement their judgment. This capability reduces the reliance on manual processes and allows auditors to focus on more strategic, high-impact tasks.

“Auditors can even apply AI and machine-learning capabilities to enable assisted decision making in the subjective areas of the audit, such as risk identification, risk assessment, and deciding how to design audit procedures in response to risks,” continues Lanke. “This tactic is very similar to how clinical decision support is quickly evolving in the medical profession and can bring to hand the judgment skills that auditors typically develop and refine only through years of experience and training.”

Let’s look at how AI enhances, rather than disrupts, each phase of the audit lifecycle:

• Data ingestion and preparation. AI automatically extracts and organizes client data from various systems, making it easier to start analysis quickly and securely.
• Data analytics. AI automates the analysis of large datasets, revealing patterns and anomalies that may otherwise go unnoticed.
• Risk assessment. AI can identify unusual patterns and high-risk transactions, helping auditors prioritize testing and tailor procedures to the client’s risk profile.
• Audit planning and resource allocation. AI recommends procedures and allocates resources based on client characteristics and industry benchmarks.
• Continuous auditing. AI enables real-time data monitoring for ongoing assurance, rather than relying solely on point-in-time reviews.
• Document review and analysis. AI uses NLP to analyze complex textual data, such as contracts and financial statements, reducing time spent on manual review.
• Decision support. AI assists auditors in making informed professional judgments by surfacing precedent cases, regulatory guidance, and relevant analytical insights.
• Fraud detection. AI algorithms identify irregularities and potentially fraudulent activities within transactional data, allowing auditors to focus on high-risk areas.

The human-AI partnership

At the core of this transformation is the need to strike a balance between leveraging AI for efficiency and maintaining the vital role of professional judgment. Auditors bring context, experience, and ethical reasoning to every engagement; capabilities that AI alone cannot replicate. When used appropriately, AI supports auditors by automating routine tasks, surfacing relevant insights, and expanding the scope of analysis, enabling professionals to apply their judgment more effectively and consistently.

“Generative AI can be applied in numerous ways to deliver greater value from the audit,” continues Daniel. “An example is using generative AI to analyze and synthesize data from audit workpapers and client documents to surface valuable insights, thereby identifying both problems and opportunities. Another example is using data and AI to benchmark clients’ business metrics against similar businesses and provide critical insights to help clients run their businesses more effectively.”

AI also strengthens one of the profession’s core values — professional skepticism. By rapidly analyzing complete data sets, AI can flag unusual patterns, inconsistencies, or anomalies that might go unnoticed in traditional sampling methods. This gives auditors a broader, more objective view of potential risks and allows them to ask better, more informed questions. Rather than replacing the auditor’s critical thinking, AI provides a powerful layer of support that enhances the auditor’s ability to challenge assumptions and exercise professional skepticism with greater precision.

It’s important to distinguish between decision support and decision replacement. AI tools are designed to assist, not to make final audit determinations. They can suggest areas of focus, assess trends, or highlight outliers, but they do not replace the judgment and accountability of the auditor. This distinction is essential to preserving the integrity of the audit process and ensuring that AI serves as a tool in the hands of qualified professionals — not a substitute for them. Responsible AI use means treating outputs as inputs for consideration, not conclusions to accept blindly.

Finally, one of the most practical benefits of AI is its ability to reduce cognitive load and optimize focus. By eliminating time-consuming manual work — such as data entry, reconciliation, or document review — AI frees auditors to spend more time on analysis, strategy, and communication. This allows professionals to work more efficiently and with less fatigue, leading to better audit outcomes and greater job satisfaction.

In this way, AI not only improves performance but also redefines the nature of the work, allowing auditors to focus on the areas where they add the most value.

Considerations for audit firms when implementing AI

As audit firms move to integrate AI tools into their workflows, strategic planning, obstacle awareness, and ethical foresight are essential to long-term success. Implementing AI is not simply a matter of adopting new technology; it requires a thoughtful evolution of processes, mindsets, and responsibilities. Let’s take a look.

Strategic planning

Proactive strategic planning ensures your audit firm can unlock the full potential of AI technology while maintaining the professional standards and human judgment that define quality audits. Here’s how:

1. Assess your organizational readiness. Before implementing AI, audit firms must take a critical look at their current environment. This includes evaluating the state of your technology infrastructure, the quality and accessibility of client data, and the digital proficiency of your staff. Understanding these foundational elements helps your firm identify where you are starting from, what gaps exist, and which internal capabilities can be leveraged. A thorough readiness assessment sets the stage for successful and scalable AI integration.
2. Set clear strategic priorities. With many potential use cases for AI, it’s important to prioritize initiatives that align with your business objectives and provide immediate value. Whether the goal is to reduce audit cycle time, enhance data analysis, or improve client reporting, narrowing the focus ensures efficient use of resources and faster ROI. You should also consider client expectations and regulatory requirements to ensure that AI applications not only streamline internal processes but also enhance service delivery.
3. Allocate resources effectively. AI adoption requires thoughtful investment — not just in software or platforms, but in people. Your budget should account for technology acquisition, system upgrades, and ongoing support. Equally important is allocating time and resources for training your staff, hiring specialists, and creating cross-functional teams to oversee implementation. Designating internal champions can help bridge the gap between technical experts and audit professionals, ensuring smoother integration across teams.
4. Build a proactive change management strategy. Implementing AI is not just a technical shift, it’s a cultural one too. Firms need a clear change management plan to overcome resistance to change, encourage adoption, and foster long-term engagement. This includes transparent communication about the role of AI, reassurance that human expertise remains central, and practical training to help teams integrate AI into their daily workflows.

Three critical obstacles for AI deployment in auditing

There are hurdles, of course, to implementing AI. The following are three critical challenges to be aware of — but can also be overcome with an evolutionary approach.

1. System disparity. To be effective, AI needs reliable, standardized data, and auditors must be able to ingest that data efficiently. However, companies use hundreds of different accounting systems, and these systems typically don’t talk to each other, leaving auditors to deal with disparate data sets. Currently, there’s not much incentive for the manufacturers of those systems to invest in the costly process of standardizing the data configuration. Eventually, they’ll likely have to, making this problem obsolete. But soon, system disparity is a reality that firms must deal with when implementing AI.
   However, the technologies needed to ingest, validate, and standardize data from multiple different systems continue to evolve and mature. So, finding the right partner to help with this part of the deployment is essential.
2. Staffing challenges. Many firms have more work than they can staff for, so they don’t have the resource bandwidth to execute a pervasive “change management” project. This transformation to AI can be complex. Plus, in auditing, new technologies must be adopted practically and with due care to ensure firms comply with professional standards and don’t jeopardize their peer review. For example, firms adopting new anomaly detection algorithms without understanding how they work or whether they can be relied upon put themselves at risk. Auditors cannot simply rely on black-box technology. The approach we suggest in the next section will solve this problem — and ease your staffing shortage overall.
3. Overwhelming disruption. Firms are being told that they must disrupt their entire audit process to adopt these new technologies and implement a massive, complex system that changes everything. It’s unfortunately true that this kind of transformational innovation always sounds great until you try it; then, it often becomes overly complex, overly cumbersome, and overly expensive.

But this doesn’t have to be the case with AI implementation. With the right tools and technology partners, firms can integrate changes seamlessly rather than go through major disruption. This partnership will allow them to allocate fewer resources and see incremental and immediate wins that propel the next deployment phase forward. Read on to find out how to eliminate these roadblocks.

Overcoming implementation challenges

How can firms overcome these challenges? Through a “beach entrance” approach to AI deployment.

Rather than a vertical approach of getting all the data upfront and then figuring out what to do with it — or diving headfirst into the deep end of the water — firms can take a cumulative approach, get specific data and address one piece of the deployment.

That approach involves quickly and efficiently solving one critical process, then quickly expanding to the next most impactful process. This cumulative approach eliminates the fear of overwhelming disruption and limited resources for the project.

For example, your firm can start with AI automation of confirming accounts receivable, which can be a straightforward deployment. You’ll see immediate efficiency and quality gains without transforming your entire audit methodology or going through a huge change management process. By doing this in nimble phases — like the water rising slowly as you walk into a lake — you’ll see efficiency gains that exceed your deployment costs and avoid a long, complex, costly deployment. Each of those cumulative gains will further validate the value of your implementation and help convince stakeholders to continue with the project.

“Our profession is still learning how to apply AI in a manner that creates actual audit efficiencies,” said Spradling. “There has been a lot of talk about applying AI to test 100% of populations and getting rid of sampling. While this makes for a great sound byte, the reality is that AI is not yet able to identify actual misstatements — it can only identify ‘potential’ anomalies — so it carries a significant risk of identifying false positives. If the AI capability identifies 30 potential anomalies, and the auditor spends 20 hours investigating those only to find that none are actual material misstatements, the auditor has spent significantly more time with no increase in either efficiency or quality.”

Spradling continued, “Another thing that auditors are being told is that they must become experts in things like data ingestion, data analysis, and data visualization. That is not really true. Just like every auditor doesn’t have to be an expert on valuing complex financial derivatives, auditors need trusted resources to rely on when tackling these issues. They can rely on a strong, established partner to do this for them so they can focus on client service and audit quality basics — things like professional skepticism and independence.”

Ethical considerations and guidelines

Trust is the cornerstone of any successful AI deployment, particularly in the audit profession, where integrity, objectivity, and confidentiality are non-negotiable. As your audit firm integrates AI into your processes, it is critical to uphold the highest ethical standards — and that begins with the secure and responsible handling of client data. AI systems must be designed and implemented with strong safeguards around data privacy, access controls, and cybersecurity. This includes ensuring client information is protected not only during data ingestion and analysis, but also throughout the AI lifecycle.

Beyond data security, you must also ensure that AI tools support and reinforce professional standards. This includes maintaining independence, exercising professional skepticism, and avoiding overreliance on algorithmic outputs. Auditors must be equipped to understand how AI tools generate insights and be able to explain and defend those outputs in line with audit documentation and regulatory requirements. Practical, ethics- aligned guidance is essential to ensure that AI is used to enhance — not compromise — the quality and credibility of your audit work.

Transparency and explainability are also vital. AI tools should be interpretable, providing auditors with clear rationale behind risk assessments, data trends, or anomaly flags. This enables your firm to maintain control over the decision-making process and ensure that clients and regulators can trust the basis for audit conclusions. Your firm must also be mindful of potential bias in AI models and take steps to validate the fairness and accuracy of AI-generated results.

Building a future-ready framework for audit firms

To thrive in a rapidly evolving audit landscape, firms must build a foundation that supports innovation while preserving quality and compliance. The following four building blocks can help your firm position itself for sustainable success:

1. Technology infrastructure requirements. A future-ready audit firm begins with a strong and scalable technology infrastructure. This includes secure, cloud-based platforms that support real-time collaboration, seamless data access, and integration with AI tools. Infrastructure should be flexible enough to support remote and hybrid teams, while also maintaining rigorous data privacy and cybersecurity standards.
2. Process adaptation strategies. As firms introduce AI into their workflows, process adaptation is essential to ensure efficiency gains without sacrificing audit integrity. This involves reevaluating existing workflows to identify where automation and AI can eliminate redundancies, accelerate evidence gathering, and enhance risk identification. Importantly, adaptation doesn’t mean discarding proven methodologies — rather, it means embedding intelligent tools into familiar steps.
3. Quality control mechanisms. Maintaining audit quality in an AI-augmented environment requires new layers of oversight and governance. Firms must establish clear quality control mechanisms that monitor the accuracy of AI outputs, ensure alignment with professional standards, and track how AI recommendations are being used in the audit process. This includes audit trail documentation, model validation, and periodic reviews to detect bias or errors.
4. Performance metrics. To measure the success of a future-ready framework, firms must evolve their performance metrics beyond traditional productivity indicators. Metrics should capture both operational efficiency — such as time saved on data retrieval or document review — and qualitative outcomes — such as improved client insights or reduced audit findings. Tracking adoption rates, team satisfaction, error rates, and client feedback helps firms evaluate how AI is impacting overall audit quality. These metrics not only justify investment in AI but also help refine strategies for continuous improvement and long-term growth.

The new standard: AI plus human touch

The future of auditing isn’t about replacing people with machines — it’s about empowering professionals with intelligent tools that enhance their judgment, streamline their work, and elevate the value they deliver to clients. This human-AI partnership is redefining how audits are performed while staying true to the principles that define the profession.

By taking over time-consuming, manual tasks like researching standards, linking documentation, or identifying common risk areas, AI enables auditors to focus their time and energy where it matters most — exercising judgment, asking better questions, and delivering strategic insights to clients. This is the new standard in auditing; one where technology works in the background to elevate human performance at every stage of the engagement.

Thomson Reuters is leading this transformation with CoCounsel Audit and Audit Intelligence, two AI-powered solutions purpose-built for auditors.

CoCounsel Audit acts as an intelligent, embedded assistant, helping audit teams find relevant standards, map assertions to risk areas, and document decisions with confidence — all without leaving their workflow. It offers contextual guidance, reduces time spent on manual research, and supports auditors at every level by surfacing the most applicable information exactly when it’s needed.

Audit Intelligence complements this by providing a broader, data-driven perspective. By drawing from anonymized audit data across firms and industries, it reveals commonly identified risks and helps auditors benchmark their planning decisions. This collective intelligence supports more informed, proactive engagements and enables firms to stay aligned with evolving client expectations and industry trends — all while reinforcing consistent, high-quality outcomes.

Embrace the future of auditing today — equip your team with CoCounsel Audit and Audit Intelligence to unlock smarter workflows, deeper insights, and lasting competitive advantage. Let your firm lead the way in audit innovation with the right AI-powered solutions at your side.