This blog was last updated on June 12, 2026

Transactions. Are they taxable or exempt? If taxable, at what rate? Does the customer’s intended use matter? Is there pending legislation that may change the answer? These are just some of the questions sales tax practitioners face on a daily basis. AI can cut time spent on researching legislation, laws, and regulations as opposed to starting the process manually from scratch. But can we trust it? 

Short answer: not fully. While AI tools have transformed the way we think about approaching research, its outputs should always be verified by a sales tax professional. So, rather than choosing one method over another, it is important to recognize the strengths of each and to apply them appropriately. Adopting a hybrid approach is often the most effective solution.  

Where AI Adds the Most Value in Tax Determination

AI tools are best for repetitive, highly manual tasks. For instance, instead of humans spending hours performing repetitive administrative tasks like sifting through and organizing data, AI can complete these types of tasks in a fraction of the time if not less. It also eliminates volume-based bottlenecks. For example, AI can easily test large volumes of transactions or look up tax rates in thousands of jurisdictions in a matter of minutes.  

AI tools are praised for being consistent and precise if given the proper instruction or prompt, uniformly applying rules and formulas without fatigue, which is especially beneficial in peak volume periods. It also helps eliminate human errors, which can easily creep in when performing manual tasks. Further, it can organize large amounts of data by automatically archiving previous tax research and even suggests trends or patterns in a specified format for further human analysis.  

Why Human Judgment Still Matters in Tax Compliance

Humans do what AI cannot. For example, the tax rates mentioned above still require validation against primary sources and, at times, direct confirmation from a tax authority. People are also far better equipped to handle new and complex scenarios, such as interpreting emerging business models with no historical precedent, evaluating products or transactions that do not fit neatly into existing categories, and addressing issues of first impression like the tax treatment of SaaS, NFTs, and cryptocurrency.

We can assess ambiguous issues, work through conflicting, unclear, or incomplete guidance, and make informed judgment calls when the law is silent. These are areas where human expertise is essential. The same is true even when applying a state’s “true object” test – a legal analysis used to decide whether a company-specific bundled transaction is really the sale of a taxable product or a non-taxable service. That kind of judgment should not be left to AI without human review. 

A good example is the recent Amazon v. South Carolina case. The issue was not just whether tax applied, but how to interpret statutory language written before marketplace facilitator laws took effect. The court had to decide whether Amazon’s role in third-party sales meant it was already “engaged…in the business of selling” under the state’s earlier law. Applying that kind of context-driven legal analysis is where human expertise matters most. Leaving it to AI could be risky. 

When the situation calls for nuance, people can take a more personal, context-driven approach. AI can surface information, but it can’t responsibly take and support a position – that requires human judgment and experience built over years of tax experience.  

When AI Gets It Wrong: Lessons From a Real-World Case

AI is certainly not a brand-new phenomenon, and in fact, has had its chance in the public sphere, with severe consequences. Arguably the most famous case of AI gone wrong is Mata v. Avianca, Inc. (2023). A New York attorney used an AI tool to conduct legal research for a personal injury case. The tool generated six fake cases, which even after the attorney asked the tool if they were real, it confirmed they were. The attorney submitted the brief with citations to these fake cases to federal court, and once the error was discovered, it resulted in the judge imposing a $5,000 fine on the attorney’s law firm, finding that the attorney had “acted in bad faith”.  

Recognizing the danger of using AI tools in an unchecked manner, the State Bar of Texas’s Professional Ethics Committee released Opinion 705 in February 2025, which explores the ethical considerations of using generative artificial intelligence in Texas’s legal field.    

While AI can be a powerful tool, legal experts are already recognizing the risks of heavily relying on AI without human intervention. 

Human-Guided AI: A Hybrid Success Story

MHA is a UK-based accounting firm specializing in audits and tax advisory services. They implemented AI in their business processes to cover some of the more administrative duties using optical character recognition. The tool they implemented, for instance, completed most of a tax return and left the last bit to an accountant to validate and complete the remainder of the return. It resulted in thousands of hours of work saved each year, freeing up time for accountants to be able to provide more valuable tax advisory services. You can read more about this success story here.  

Best Practices to Develop the Hybrid Model  

AI works best for high-volume, lower-complexity, and repetitive tasks. Humans should focus on higher-risk items and truly complex or novel issues. A strong starting point is to set clear escalation criteria for when AI output must be reviewed by a tax professional. 

Next, build-in continuous improvement. Regularly review AI-flagged transactions, use a clear process for deciding when escalation is needed, and refine the rules based on what you learn. 

Over time, AI can “learn” from human decisions, making it safer to delegate certain tasks. Issues that once seemed novel can become repeatable processes, allowing more work to shift to AI as predictability increases. 

Finally, include ongoing self-audits. Periodic spot checks of AI output improve accuracy and compliance while helping catch issues early. This matters because the business remains responsible for the outcome, even when work is delegated to AI. 

Balancing Automation and Expertise in Modern Tax Compliance

In a world where AI tools are becoming more accessible and commonplace, the role of tax professionals is evolving. A professional is increasingly less of a data processor and more of a trusted strategic partner. Freeing up time allows professionals to focus on complex problem solving, planning, and optimization.   

Rather than a choice between AI or human judgment in tax determination, the better question is, how do I leverage the tools available to me while still allowing for flexibility in case a complex or unusual scenario presents itself? AI is excellent at processing vast amounts of data quickly, consistently applying known rules, and maintaining meticulous records. Humans bring what AI can’t: expertise, contextual understanding, strategic thinking, and the ability to navigate ambiguity. In a world in which tax compliance grows more intricate, and transaction volumes increase, organizations that balance both resources are the ones who are best prepared.