AI is powerful, but a cost segregation report is not the place to let a general-purpose model improvise. The risk is not that AI is useless. The risk is that it can sound persuasive while being wrong. A report that affects depreciation, tax filing positions, and CPA review needs more than fluent language. It needs a disciplined method, reliable source material, and property-specific facts tied to the owner's actual inputs.
Generic AI tools are especially risky because they often blend information from the open internet with patterns learned from examples. They can hallucinate citations, invent unsupported assumptions, and make confident statements about construction costs or tax treatment without a reliable basis. Even when the writing sounds professional, the facts may not apply to the property in front of you.
This is not only a problem with older or low-quality models. Even advanced systems can produce plausible but false answers, especially when the task requires domain-specific judgment, source discipline, and careful handling of uncertainty. In tax-sensitive work, a polished answer is not the same as a supportable answer.
Cost segregation is more constrained than ordinary writing. A study needs to classify building components, connect assumptions to recognized cost sources, and separate shorter-life property from long-life structural property in a way that a reviewer can follow. If a model guesses at a component or applies the wrong assumption, the output can look polished and still be weak.
Documentation is the other problem. A useful report should preserve why a number or classification exists. If the logic is hidden inside a black-box prompt, the user may have no practical way to explain it later. Property owners and CPAs need to know what the report relied on, what was assumed, and where the support came from.
Basis uses modern technology differently. We use software to structure the intake, apply repeatable logic, organize source-backed outputs, and make the experience clearer for the user. The product can benefit from advanced systems without asking the customer to trust an unsupervised chatbot with the tax-sensitive substance of the report. Technology should reduce friction and improve consistency. It should not replace source discipline.
Further reading
- OpenAI explains why language models hallucinate Why more capable models can still give confident wrong answers.
- Nature covers research on detecting hallucinations in large language models Why users cannot assume a generated answer is correct.
- IBM summarizes AI hallucinations How inaccurate or fabricated outputs relate to data quality and model behavior.
Stock photo source: Pexels