Move from AI uncertainty to business advantage


Today’s clients expect more from their accountants. They want data-infused strategies for growing their wealth or business. They want expert advice on taxes and audits. They want an advisor who knows them well. The tools to provide these services are available for accounting professionals, but it’s not always easy to sift through the volume of information.

That’s where artificial intelligence (AI) can help. AI is permeating every aspect of the accounting profession; as accounting staff strives to meet changing client needs, it will continue to be vital for customer service and for shifting from reactive to proactive in serving clients.

In the on-demand webcast, “The future of audit: Keep your firm relevant and competitive through continuous innovation,” Scott Spradling, Executive Director - Audit Innovation with Thomson Reuters, says, “Our CEO recently 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 in mobile, social media, and the cloud. We view AI as the next technology phase — and maybe it will be even more transformative than these other events.”

Many firms are exploring their relationship with AI, acknowledging the likelihood that it is here to stay. However, few firms are fully making use of its potential because of privacy and quality concerns. Leading firms, though, are overcoming the initial hurdles to improve workflows, efficiency, and profitability.

“We’re going to see an increase in the number of interactions with AI, autonomous agents, and hybrid teams where it’s a mix of humans and their digital peers working in collaboration,” says Domingo Huh, Lead UX designer for Thomson Reuters Labs in “Staying up to speed with artificial intelligence in accounting,” a blog post. “The whole landscape of how we operate and how we collaborate is really going to change. I think we’ve just started scratching the surface. In the next year or two, we’re going to see seismic shifts.”

So, now is the time to adopt AI to stay ahead of the ever-evolving accounting landscape — and your competition. This white paper discusses how to do that by discussing:

  • How generative AI is changing the accounting industry
  • The need for the industry to embrace AI within their workflows
  • Initial hurdles to AI adoption and how Thomson Reuters Checkpoint Edge overcomes them

How generative AI is changing the accounting industry

AI uses computing power to mimic the human brain via “neural networks,” interconnected nodes or neurons in a layered structure that resembles the human brain. With this intelligence, AI can, for example, analyze data and identify patterns in large datasets.

Generative AI (GenAI) is a type of AI wherein the AI can produce something wholly new as a creative output, such as writing an e-book or scoring a musical composition. In the accounting field, a simple application of generative AI is the creation of first drafts of client correspondence, which automates repetitious work and creates more time for staff to focus on higher-value activities.

But GenAI goes far beyond automation. It can also:

  • Provide intelligent financial analysis
  • Give personalized insights and guidance
  • Enhance analysis and predictions
  • Support audits

However, a recent Thomson Reuters Institute study showed that tax research is the area where financial professionals see the most potential for generative AI to make an impact. Tax research can be challenging because there’s so much information from too many sources. Sifting through countless online resources for answers is not only time-consuming and highly inefficient but also leads to a greater risk of errors and misinterpretations.

AI creates a shortcut to finding the answers accounting professionals need. For example, AI can give junior staff members a great starting point when they’re asked to research a topic they’re unfamiliar with, and it can point them to additional information far more effectively than a Google search.

Significantly, while AI can take over tedious tasks, it will never replace the role of people. There is still a need for humans to make judgment calls in the profession. “I don’t believe for a second that generative AI will replace accountants, but it can absolutely transform the way accountants work,” says Nancy Hawkins, VP of Product Management at Thomson Reuters. “Accountants can use generative AI to be even more effective in their jobs. They can use it to automate or make lower-value work more efficient so that they can spend their time on the highest value work where they’re adding their knowledge and experience.”

Large-language models

GenAI uses large-language models (LLM) to produce content. An LLM is a deep-learning algorithm that can perform various natural-language processing (NLP) tasks. Because they use transformer models — a neural-network architecture that can automatically transform one type of input into another type of output — and are trained using massive datasets, they can recognize, translate, predict, or generate text or other context based on words that commonly go together.

LLMs have become increasingly popular because they have broad applicability for a range of NLP tasks, including:

  • Text generation
  • Translation
  • Content summary
  • Rewriting content
  • Classification and categorization
  • Sentiment analysis
  • Conversational AI and chatbots

The numerous advantages that LLMs provide to organizations and users include:

  • Extensibility
  • Adaptability
  • Flexibility
  • Performance
  • Accuracy

The most widely known and used LLM-based AI is ChatGPT.

The primary obstacles to AI for accounting

Artificial intelligence is exciting, and its impact on the profession is unprecedented. But, AI poses legitimate concerns for firms. One problem involves “hallucinations.” AI needs to be trained on large, complex, and disparate datasets. It’s trained to find words that often go together and present a rational response, but it’s not necessarily trained to return accurate, recent, or precise responses.

For the user, there’s no way of knowing what pieces of data the model based its response on and no easy way to vet the information the models give, which can lead to some severe problems. For example, a lawyer recently used ChatGPT to prepare a court filing, but the AI bot delivered fake cases that the attorney presented in court. The judge fined two lawyers and the firm for the mistake. For accounting professionals who rely on accuracy and preciseness, the results of fake information can be devastating to their firm and career.

Then, there are reasonable concerns about privacy and security. If employees input company data into the LLM, the AI could incorporate it into its learning model. That information could become a part of its knowledge base, and other users could see responses with proprietary data. This situation happened at Samsung, where workers unwittingly leaked top-secret data while using ChatGPT to help them with tasks. The company allowed engineers in its semiconductor branch to use ChatGPT to help fix problems with source code. To do so, the workers input confidential data, such as source code and data relating to its hardware. That information is now part of the data ChatGPT uses to generate content.

Governments are responding to this concern with new regulations. For example, in January 2023, the National Institute of Standards and Technology issued the AI Risk Management Framework (AI RMF) to provide guidance for using, designing, or deploying AI systems. The framework is voluntary, with no penalties for non-compliance, so state regulations take on an essential role in promoting privacy protections.

Given the potential for input information to be used to train an LLM or otherwise exposed to other parties, firms should take care to maintain client confidentiality and refrain from entering client data into LLM tools.

A third concern is that the AI may have an “unconscious bias” that impacts the accuracy or fairness of outcomes generated by the tool. AI models are susceptible to a range of biases arising both from the datasets they are trained on and the way they’re trained. These include availability bias — over-reliance on more prominent information — and confirmation bias — the tendency to disregard evidence that challenges a pre-established position. In legal, tax, and accounting contexts, any inbuilt bias that is allowed to skew results and outcomes has the potential to undermine the advice given to clients.

Thomson Reuters professionals recommend caution when approaching AI. “As a senior accountant, you would not be likely to just hand off some work to a junior colleague and then let it go out the door without looking at it yourself. It’s the same dynamic with AI,” says Des Brady, Senior Director for Inbound Product Marketing with Thomson Reuters. “We recommend it be used as a first draft, used as your assistant and your guide to the content and the answers you need. But we would never say you can just rely on this and send it to your client without even looking at it. We would always recommend a check.”

Mitigating the risks: The TR approach

There’s a way to eliminate these concerns — using a trusted partner for your AI, one with vetted data and proven content for the LLM to learn from. That’s the case with Thomson Reuters.

The foundational capabilities of Thomson Reuters are based on the skillsets of its people, its trusted content, and its rigorous approach to retrieval augmented generation (RAG). Thomson Reuters has been working with AI since 1991 when Chief Scientist Howard Turtle helped found one of its first R&D groups. Since then, the company has paired advanced data scientists with its large team of tax, accounting, and audit subject-matter experts.

Together, these two groups are rigorously training and testing a variety of large-language models with Thomson Reuters content, marrying authoritative insight with the LLM so people can trust the responses they get from Thomson Reuters Checkpoint Edge, a full-text database of federal, state, and international tax laws, regulations, and secondary research sources.

When Checkpoint GenAI launches in 2024, the RAG approach will restrict the LLM to working solely with this expertly created content, with the rigorous testing process reducing inaccuracies arising from bias or other sources.

“When somebody asks a large-language model a question within a Thomson Reuters environment, it’s not going out to pull information from across the web,” Brady says. “It’s pulling information from the strictly delimited areas of content that we have told it to answer a question from.” This ability is where the Thomson Reuters pedigree as a publisher and reputation — built on expert and trusted content — comes into play.

Adds Hawkins, “The quality that our users depend on today — the accuracy, the completeness, the currency — that’s the foundation of why generative AI within Checkpoint can also be trusted.”

Furthermore, as a leading provider of tax and accounting workflow solutions, Thomson Reuters draws on long experience in building information security into the tools it offers to professional customers, eliminating data privacy concerns.


AI is here to stay. Accounting firms that choose to deploy it can deliver significant benefits for themselves and their clients but must be careful and intentional in their approach. The productivity and customer-service benefits are genuine, but so are the concerns about the privacy of information and reliability of results.

Thomson Reuters is developing the tools to help firms use AI effectively. Checkpoint Edge already uses a highly sophisticated AI-powered algorithm to return targeted research results based on full-phrase, natural-language questions. Following the deployment of generative AI in 2024, it will be able not only to return search results but also to summarize findings, prompt follow-ups, and serve up next steps to the user in a single motion, radically re-imagining the tax research experience.

To get more information about the development of generative AI in tax and accounting research, enroll in the Thomson Reuters Checkpoint Edge generative AI engagement program. There, you can see early prototypes, stay regularly informed on progress, and influence the development of GenAI.

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