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by Aleksandra Bal June 5, 2026
Aleksandra builds tax technology solutions leveraging her unique background in taxation and computer science. With more than 200 publications and a track record as a recognized speaker, she is a leading voice on how AI is reshaping global tax.
AI tools can scan a jurisdiction for relevant rules and structure a tax argument in minutes, tasks that used to anchor a tax professional to their desk for hours. The efficiency gains are real, and they aren’t going away. But operating parallel to this speed is a dangerous counter-trend: AI tools have become so good at mimicking professional expertise that it is becoming harder to spot where the data ends and the hallucination begins.
Many AI outputs look convincing but do not hold up under scrutiny. The model sounds authoritative, but the answer is wrong. In most cases, this is not the result of asking the wrong question or selecting the wrong tool. It is the result of misunderstanding what AI language models are and are not capable of doing.
Tax rules depend on precise definitions, narrow thresholds, and jurisdiction-specific exceptions. A small error can change the outcome. And in a field where the difference between a correct and incorrect answer can carry audit risk, fluency is not a reliable signal of accuracy.
To help explain how AI should be used in tax research, we asked an expert from our parent company , Stripe, to provide some guidance. Aleksandra Bal is the Global Indirect Tax Technology Lead at Stripe, leading a team focused on developing tax technology solutions and managing indirect taxes across six continents.
Watch the full breakdown: In the five-minute video above, Aleksandra Bal explains why “fluency is not accuracy” and pulls back the curtain on why standard AI prompts fail in tax compliance.
We often assume that if an AI app has web search enabled, it’s reading current legislation in real time. But “can search” is not the same as “always searches.”
Different tools like Gemini, ChatGPT, Claude, and Perplexity all handle search differently. Some use it as a default, while others only trigger it when they decide they need current information. If a model thinks its training data is sufficient, it might skip the search entirely and fall back on pattern matching.
The danger is that looking like a correct answer and being a correct answer are two different things. A model might fluently describe a lodging tax in a specific county simply because it has seen similar patterns before, not because it actually verified that the tax exists today.
As tax professionals, we naturally want to quantify risk. This leads to one of the most common mistakes in prompting: asking the model to rate its own confidence.
You’ve likely seen it: “Provide the answer and then give me a confidence score from 1–100.” It feels responsible. But here’s the reality: the model doesn’t have a truth meter. It generates that confidence score using the same predictive text logic it used to write the answer.
To fix this, many teams turn to Retrieval-Augmented Generation (RAG), giving the AI specific documents to use as a reference. While RAG helps, it doesn’t eliminate hallucinations; it just changes how they happen.
We often try to command accuracy into existence. We use prompts like:
These sound like strict controls, but they are actually just stylistic requests. Telling a model to be 100% accurate doesn’t give it a new database of facts; it just encourages it to remove hedging language like “this may vary by jurisdiction”. It makes the model sound more certain, even when it isn’t.
Similarly, asking a model to review legislation often results in the model generating a section of text that looks like a legal analysis, but that analysis isn’t necessarily what drove the final conclusion. They are constructed in parallel, not as a verification step.
If we can’t control the path, how do we manage the risk?
AI can help tax professionals work faster, but it cannot replace the need for a reliable, verified source of truth. The three principles above are essential guardrails, yet they all require expertise, time, and vigilance to apply correctly. For businesses that need accurate, up-to-date tax calculations without the burden of constant manual verification, that guardrail is best built into the tools themselves.
One of the best ways to manage compliance continues to be using tax software, like TaxJar or Stripe Tax . Consider signing up for a free TaxJar trial or reaching out to the sales team . See how TaxJar can transform the challenge of sales tax management into a seamless part of your business operations.
Aleksandra holds a Ph.D., MBA, LLM, and several other degrees in taxation and computer science.
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