Source: SiliconANGLE
Tax compliance is becoming a proving ground for agentic AI because errors carry immediate, quantifiable costs—missed deductions, audit flags, penalty exposure—rather than the fuzzy trade-offs tolerated in recommendation engines or chatbots. This changes the competition among AI vendors: companies building tax assistants must solve for deterministic correctness at scale, not just plausibility, which favors narrow, rule-based systems over broad foundation models and forces real accountability into AI deployment. The shift exposes a hard limit in how broadly general-purpose AI can substitute for domain expertise without material risk transfer.