Artificial intelligence is making it easier for people to understand and file their taxes—but it’s also increasing the risk of costly mistakes and highly convincing fraud.
Research and classroom insights from Caroline Bruckner, a professor of accounting and director of the Kogod Tax Policy Center at the Kogod School of Business, show that AI is simultaneously improving tax literacy and weakening safeguards that traditionally protected taxpayers.
This tension—a system that is more accessible but more vulnerable—is reshaping how individuals file taxes, how scams operate, and how accountants are trained.
The central finding in Bruckner’s work is straightforward: AI lowers the barrier to entry for tax engagement, but it does not guarantee correct outcomes.
Tools powered by AI now allow taxpayers to:
In a system where confusion is common, accessibility is meaningful. Many taxpayers historically lack even basic clarity around what they owe or why.
AI is beginning to fill that gap—but it does so unevenly.
Bruckner’s research highlights a critical limitation: AI can produce answers, but it cannot reliably account for nuance.
That creates three key risks:
These risks are especially pronounced for:
In these cases, small errors can lead to significant financial consequences.
One of the most immediate real-world implications of this research is the evolution of tax fraud.
AI has dramatically lowered the cost and effort required to produce high-quality scams. Fraudsters can now generate:
The result is not just more scams—but more believable ones.
Even experienced professionals can struggle to distinguish fraudulent messages from legitimate outreach, which marks a significant shift in risk.
Despite these concerns, Bruckner’s work also points to a meaningful opportunity: AI could help solve one of the most persistent problems in the US tax system—low tax literacy.
Many taxpayers approach filing with uncertainty or avoidance. AI tools are beginning to change that by:
Over time, this could lead to more informed and engaged taxpayers.
A key implication of this research is that AI is not replacing accountants—it is changing what expertise looks like.
The distinction is critical:
That responsibility becomes more important—not less—in an AI-driven system, particularly in:
As AI increases both access and error potential, the need for professional judgment grows.
At Kogod, these insights are directly shaping how accounting students are trained.
Instead of focusing primarily on technical execution, coursework now emphasizes:
Students still learn the fundamentals—but they also learn how to question, interpret, and validate information in an AI-assisted workflow.
That shift reflects the reality graduates will face in the field.
Taxes sit at the intersection of policy, technology, and everyday decision-making. As AI becomes embedded in financial systems, its influence extends beyond efficiency—it changes behavior, risk, and trust.
Bruckner’s research makes one point clear: AI is not simplifying taxes. It is transforming them.
For taxpayers, that means greater access—but also greater responsibility.
For businesses, it means new efficiencies paired with new risks.
For future accountants, it means a career defined not by automation—but by judgment.
And that is exactly the kind of problem Kogod prepares students to solve.