4 Competitive Advantages of AI in Tax Research and Analysis

Mar 10, 2022

Billy Beane, Executive Vice President of Baseball Operations and Minority Owner of the Oakland Athletics, famously said “Adapt or die.” It was in reference to taking a data-driven approach to scouting players, building teams and winning baseball games – a radically different approach to winning then. 

Initially, it was met with plenty of resistance. The tactic seemed counter-intuitive, and it made a lot of experts question Beane’s leadership. But the results spoke for themselves and he transformed baseball. 

The emergence of AI can be seen in a similar way. There are clear competitive advantages that AI-powered tools offer and those that learn to embrace it will thrive. 

Right now, in many industries, the use of AI-powered tools is the norm because they effectively streamline workflows, provide valuable insights, cut down on time-consuming tasks and more. Rather than replace human beings, AI is enabling people to execute at a much higher level. 

In the world of tax research, Blue J is blazing the way forward with its AI-powered predictive platform. With the increasing complexity of tax legislation, and the speed at which new creative tax planning strategies are developing, tax practitioners need greater clarity into the law and the ability to uncover deeper insights faster than ever, all in order to provide the best possible outcomes for their clients. That’s where AI plays a critical role. 

More importantly, AI won’t go away. The biggest firms will find ways to innovate and leverage AI-powered solutions. They will shape tax research and analysis, and set a high bar for what clients expect from tax practitioners. Mid-sized firms, in order to keep their competitive edge, need to leverage the advantages of AI sooner rather than later. 

So how exactly does AI help tax practitioners with their work? We’re going to demystify that here. Rather than replace jobs, AI has practical, tangible applications for tax practitioners, helping them build better insights in less time. 

We’re going to cover 4 distinct advantages of AI in tax research and analysis. With AI, tax practitioners have the ability to quantify risks for clients, identify the best tax planning and business strategies, uncover blind spots, and identify the most effective litigation strategies. We’ll also discuss actual examples to show how the application of AI benefits tax practitioners. 

Interested in jumping ahead? Just click on the links below to a specific section.

Quantify Risk for Clients with AI

Tax practitioners understand the number of variables involved that drive a particular tax outcome, but may have difficulty predicting them on their own. Clients want clear, concrete answers, rather than “it depends”. 

What would be helpful to both a client and the practitioner is the ability to point to the precise facts an outcome actually depends on and backing it up with data. This becomes possible with AI-powered software. 

For example, in Ryder (a recent tax case), a taxpayer wished to assign his income to a related taxpayer. This was covered in our November 2021 installment of Blue J Predicts.

A tax practitioner with a fact pattern similar to Ryder could easily quantify the risk of an assignment and communicate it to their client in the following ways: 

1. The likelihood that the IRS or the courts will disallow the assignment of income with this particular proposed structure is more than 94 percent, based on a comprehensive analysis of 297 IRS or court rulings in which the assignment of income was an issue.

2. As a general matter, only 5.8 percent of the IRS revenue rulings and court decisions involving an assignment of income for services have found that the income was appropriately assigned to another taxpayer.

3. Significant changes will need to be made to the business arrangement for it to come close to successfully assigning the income to the related corporation, including significant changes to the structure of the agreements with the clients.

The tax practitioner can support their position with AI-backed data and mitigate risks for their client. The client is reassured that rather than just instinct, the tax practitioner has done a thorough analysis. 

Optimize Tax or Business Strategies with AI

With the help of AI, you can run through countless scenarios, understand how individual factors are driving them, and provide your clients with the most accurate insights. This is crucial when developing a tax strategy or business structure that will benefit them in the future.

Sometimes clients will want to explore every avenue of obtaining their desired tax outcome despite the low likelihood of that outcome. In other cases, clients may wish to pursue a business strategy without triggering unintended tax consequences. In both situations, clients expect their tax professional to point with confidence to the most relevant considerations that influence the tax consequences and provide an opinion on the most likely outcome.

To achieve this, a practitioner would need to predict the most likely outcome, understand how to weigh the individual factors driving that outcome, and how to make arrangements that align closely with the client’s needs. Exploring a multitude of options manually can be terribly time-consuming, and even with extensive effort, incomplete. Providing a way forward with scientific accuracy is possible once you leverage AI and predictive modeling. 

Using the past information of the courts, government and individual tax-papers, AI can project possible outcomes for the tax scenario, based on a number of different factors. It can then indicate how individual factors influence the outcome, and what would need to be revisited in order to drive different outcomes.   

Uncover Blind Spots with AI

Leveraging AI, tax practitioners can also uncover blind spots to improve their chances of successfully obtaining desired tax outcomes.

We can look at Aspro as an example, covered in our October 2021 installment of Blue J Predicts. Aspro sought to deduct from taxable income the money paid to shareholders as “management fees.” To deduct the management fees, under section 162(a)(1), Aspro was required to show that (1) the fees paid to the shareholders were for ordinary and necessary services performed for Aspro by or on behalf of the shareholders, and (2) the fees paid to the shareholders were reasonable in their amounts.

How would predictive software such as Blue J’s have helped in a case such as Aspro’s? Here are four insights practitioners could have uncovered:

1. An independent analysis should be conducted for the deductibility of fees for different services, rather than treating disparate and unrelated services performed by different entities in the same manner.

2. The IRS and the courts may assess whether the expense is “customary or usual” by investigating whether the taxpayer’s competitors would likely also incur this type of expense.

3. Successfully proving that other competitors would also likely incur a similar expense can increase the probability that the expense is found to be “ordinary and necessary” by up to 30 percent.

4. Presenting evidence that there is not a substantially more cost-effective alternative to the expense improves a taxpayer’s odds of claiming that the expense is ordinary and necessary.

Uncovering these insights prevents blind spots from occurring in the tax planning stage. If left unchecked, blind spots can expose the taxpayer to challenge by the IRS and may result in the taxpayer having access to limited documentation and evidence that would have otherwise have supported their position.

Optimize litigation strategies with AI

Finally, there may still be instances where a client’s position is challenged in court. While it can be straightforward to identify the relevant test that the courts will apply on any given legal issue, it may be challenging to know precisely which aspect of a test to prioritize in defending the tax position.

We can infer from the data collected from tax cases that some arguments are more persuasive than others because some factors of a legal test have a higher correlation with a particular legal outcome than others. For instance, this can happen in situations in which the courts look beyond the language of the agreement between the parties and assess the actions and behavior of the parties to understand the true nature of the arrangement. How the parties papered the transaction (such as the formal existence of a written contract) impact the outcome less than factors that more closely relate to the parties’ conduct and reasonably inferred intentions.

Supported by AI, practitioners can know exactly which facts and circumstances associated with a test are most persuasive to the courts. Practitioners can then prioritize their efforts accordingly and avoid spending precious time on factors that have a negligible effect on the overall outcome.

That’s exactly what we did in our June 2021 installment of Blue J Predicts

We analyzed the facts in Cross Refined Coal, and leveraged AI to identify two key factors deemed determinative of the appeal by the algorithm. Although many arguments were raised on appeal, Blue J’s algorithm identified two pivotal factors that are each likely to be independently determinative. In other words, losing on either of those factors will more likely than not cause the IRS to lose its appeal based on the particular set of facts in Cross Refined Coal.

In addition to uncovering the pivotal factors in a given situation, AI algorithms can go even one step further: They can identify how significant a given fact is in relation to the existence or absence of other facts. 

AI algorithms are capable of dynamically assessing the data both independently and in conjunction with other data points from hundreds of past decisions. Courts don’t use static formulae to decide cases, and the power of AI is that it can dynamically assess the variables. 

Looking ahead

The applications of artificial intelligence are boundless, and will only become more popular and sophisticated. Rather than replacing jobs, in many ways, AI will transform how jobs are done, the skills involved in doing them, and the precision with which they’re carried out. To learn more about the role AI and machine learning will play in tax analysis (and even other areas of law), you can check out the complete article penned by Ben Alarie (Osler Chair in Business Law at the University of Toronto and the CEO of Blue J) and Bettina Xue Griffin (senior legal research associate at Blue J) on Taxnotes.