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

Firms must begin work now to unlock the benefits of AI without disrupting core processes and priorities

Ready or not, the future is here

Artificial intelligence (AI) is changing every business and organization. By automating, accelerating, and enhancing business processes with data-based analysis, AI allows organizations to transform at scale and drive value. Auditing is no different: AI can provide highly efficient population analysis and machine learning for assisted decision making, showing how others in the profession made similar judgments in similar situations. This technology allows professionals to produce higher-quality audits and meet the auditor in the role of someone who uses data analytics to help clients drive growth.

A 2022 study found that investing in AI improves audit quality and reduces fees. The research revealed that a one-standard-deviation change in recent AI investments correlated with a 5% decrease in the likelihood of audit restatements and a 0.9% reduction in audit fees. On top of that, AI’s deeper insights into complex data sets can uncover valuable patterns and trends that enhance the reliability of audit reports.

“Our CEO has described the emergence of AI as being a transformative event for society comparable to the advent of the PC on every desk 30 or 40 years ago, the emergence of the internet, mobile, social media, and the cloud,” said Scott Spradling, Executive Director, Audit Innovation, Thomson Reuters. “We view AI as the next major technology phase — and it could easily be even more transformative than these historic events.”

However, auditing firms have been relatively slow to adopt this industry-changing technology. They’re still figuring out how to use it to tangibly drive new audit efficiencies, and because of staff shortages and heavy workloads, they don’t have the resources to experiment. Experimenting in the auditing space is a risk anyway since audits are complex and high stakes.

While firms have been told for years by other vendors — as well as the AICPA — that they need to make this transformation, firms are not necessarily seeing the urgency. They have plenty of work, they’re making more money than ever, and other firms aren’t rapidly shifting to more AI, so why should they invest time in this technology? The status quo is working just fine, they think.

However, the rapid pace of change in business coupled with evolving client expectations necessitates a timely response from firms. Clients want more insights from their accounting firms — including data-driven insights — to derive more business value. Auditors are well positioned to meet these needs if they have the tools to do so. If not, they risk falling behind the competition in both audit quality and relevance.

In other words, firms must evolve alongside the expectations of their clients so auditors can no longer afford to delay the adoption of AI capabilities into their workflows.

This white paper discusses how AI is transforming auditing, addresses the critical need for a practical and immediate approach to AI integration in the audit industry, examines the three most common challenges to implementation, and provides a practical and immediate approach to seamlessly marry traditional techniques with innovation.

Transformation doesn’t have to mean re-envisioning all processes

The evolution to more digital technologies, especially AI, can be scary because it initially seems to blow up the accepted way of doing things and radically shift trusted processes. But nobody wants that — least of all those leading your firm through this evolution. Throwing away the old playbook and implementing a new one will cause a counterproductive disruption when all you’re seeking is process improvement.

“A digital transformation isn’t necessarily a disruptive change to the future of audit,” says Steve Lindsey, Director of Product Management, Thomson Reuters. “It’s an evolutionary change involving continuous innovation where you’re using advanced technologies to become more efficient and do more with less, keep compliance with professional standards, and have greater confidence in the work that you performed. You still may confirm accounts receivable, but with these new technologies, you will do it much more effectively and efficiently. Seeking ways to do more with less has driven our profession for the last 30 years, and that’s what you’ll see with these new technologies.”

Said Spradling: “It’s sort of like changing from cable to streaming. It’s very similar underneath — very similar channels and programming — but there are more efficient ways to get to those channels and programs and interact with the technology. It can be confusing, so you don’t make the change lightly. It’s the same with changing how you do an audit, except in audit the stakes are much higher.”

One of the key findings from the Thomson Reuters 2023 State of the Professionals Report, involving 540 respondents from accounting firms of all sizes, is that efficiency has now replaced talent as the number one priority and top concern for firms. While talent is still a concern, efficiency is now supreme, so this is not a time for firms to take on highly disruptive change management projects.

But if done right, AI can help firms create efficiencies and cope with staffing shortages, free up auditors to focus on high-risk audit areas, provide the deeper business insights that clients want — and auditors want to provide — and drive profitability. So, the evolution and continuous innovation are certainly worth it if you can make the transition efficiently without complete disruption.

How AI is transforming auditing 

AI holds significant potential for various aspects of the auditing process, driving both efficiency and quality gains. With advanced data-ingestion capabilities, auditors can quickly and seamlessly obtain client data down to the transactional level and secure it in the cloud. Then, AI and machine-learning techniques can quickly analyze complete data sets and detect anomalies like duplicate payments and fraud indicators, allowing auditors to more deeply understand where the potential risk lies in the population.

Generative AI (GenAI) can be applied in numerous ways to deliver greater value from the audit. 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.

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. 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.

This evolution, characterized by continuous innovation, aligns with the shifting landscape of client expectations in the auditing industry. In today’s dynamic business environment, clients demand more than just financial compliance. They expect their auditors to leverage AI to offer a holistic view of their financial health, identifying strengths and weaknesses and suggesting strategies for improvement. By harnessing AI’s capabilities, auditors can quickly achieve both efficiency and quality gains. With the right technology partner, this can be done with a minimal learning curve and without disrupting existing processes.

Additionally, AI serves as an indispensable tool for addressing the subjective aspects of audits. In the past, auditors relied on their judgment skills, honed through years of experience and training, to navigate complex audit procedures. With the integration of machine learning, AI can now augment these skills. Auditors can leverage AI-driven insights to make informed decisions on how to design audit procedures in response to specific risks. This ensures a more robust and efficient audit process, allowing auditors to focus on higher-level strategic tasks. Clients value this blend of human expertise and AI-enhanced decision making as it leads to more reliable and insightful audit reports, aligning perfectly with their evolving expectations for thorough and data-driven auditing services.

Audit automation backed by AI-powered solutions with embedded research and professional standards throughout the engagement process allows even junior audit staff to find answers quickly and confidently.

Here are some additional ways in which AI can be used in the audit process:

  • Data analytics. AI-powered data analytics tools enable auditors to process large amounts of structured and unstructured data quickly and efficiently to identify patterns, trends, and anomalies.
  • Continuous auditing. AI automates the process of data collection, verification, and analysis on an ongoing basis.
  • Fraud detection. AI algorithms can analyze transactional data to identify potentially fraudulent activities or irregularities, allowing auditors to focus on high-risk areas.
  • Natural-language processing (NLP). NLP enables AI systems to understand and process human language. Auditors can use NLP to extract relevant information from contracts, financial statements, and other textual documents.
  • Predictive analytics. AI-powered predictive models can forecast financial outcomes, helping auditors assess the reasonableness of management’s projections and identify potential issues in advance.
  • Audit planning and resource allocation. AI can suggest appropriate audit procedures and resource allocation based on the client’s data and risk profile.
  • Risk assessment. AI’s advanced data analysis can identify correlations and patterns that humans may overlook.
  • Document review. AI can automate the review of large volumes of documents, making the process faster and more accurate.

Ideally, AI will decrease the amount of number crunching and data chasing that auditors must do. Manual and repetitive tasks can be automated, leaving auditors to focus on high-risk areas and the tasks that require judgment — tasks that AI can help with by presenting analysis and options based on what has worked before.

Three critical obstacles for AI deployment in auditing

There are hurdles, of course, to implementing AI for auditing. The following are three critical challenges that have emerged.

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 in the near future, 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 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, getting specific data and addressing “one slice” 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 the 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 soundbyte, 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.”

AI principles

Trust is an essential component of any AI deployment. You need to ensure that your customer data is kept secure. Audit staff implementing these new technologies need trusted, practical guidance to ensure they comply with professional standards — which means the need to work with a partner with an established track record of trust. That’s where Thomson Reuters comes in. As a long-trusted partner for auditing firms globally, Thomson Reuters is building the solutions for practical, pain-free AI deployment in auditing.


To move into the future, you must change how you do auditing — otherwise, you risk being left behind as other firms continually evolve. Continuous innovation is critical. This evolution can be daunting, but it doesn’t have to be overwhelming. With the Thomson Reuters approach, you don’t change everything at once, so you realize immediate quality and efficiency wins as you go along and adopt continual enhancements at the right time — and when your firm is ready. Our long-standing and deep expertise in auditing, technology, and data security ensures that our customers can trust us to take them into the future in a way that is both efficient and practical.

The Thomson Reuters Cloud Audit Suite incorporates assisted decision making that effortlessly identifies related risk areas by understanding the meaning of words instead of just exact word matches. It taps into anonymized data from other Cloud Audit Suite users to suggest other risks you may not have considered, so you can feel confident that you’ve considered all risks when completing your audit.

This technology means you achieve efficiency and quality gains immediately without going through a steep learning curve. Staff can continue focusing on clients — now even more efficiently and confidently. It’s like having an auditing assistant who never stops learning and never needs a vacation.

Welcome to the future.

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