AI in Tax: Rules vs. Reasoning — Who Wins the Future?

Tax teams are adopting two very different kinds of AI. 

Rules-based AI (expert systems) encodes legislation into deterministic checks and calculations—perfect for things like eligibility tests, thresholds, carry-forward limits, and depreciation schedules where you need consistency, auditability, and clear justification. 

Generative AI (LLMs) excels at understanding messy inputs and producing language—summarising guidance, drafting client explanations, normalising documents, and exploring “what if” scenarios (e.g. salary vs. dividend, timing of asset sales) against the outputs of your rules engine.

We’ll compare both with practical examples and show a hybrid workflow that’s already emerging: data intake and validation with rules; conversational prep, scenario modelling, and client comms with gen AI; and human review with a full audit trail. You’ll leave with a simple decision framework, implementation tips (guardrails, approvals, references, and logging), and a roadmap to embed both approaches into next-year’s tax cycle.