A recurring source of friction in AI regulatory compliance is a gap between how a rule is written and the practical reality of how AI systems are actually built, deployed, and maintained inside a real organization. Regulatory bodies drafting AI rules frequently rely on legal and policy expertise without an equivalent depth of hands-on technical deployment experience, which can produce requirements that are reasonable in intent but difficult to implement as literally written.
Where this gap shows up
A requirement to document a model's training data in detail can be reasonable in principle but genuinely difficult to satisfy precisely for a system built on a foundation model whose training process the deploying company doesn't control or have visibility into. A requirement for a specific testing methodology before deployment can be well-intentioned but poorly matched to how iterative, continuously-updated AI systems actually operate in practice, where a rule designed around a single pre-deployment checkpoint doesn't map cleanly onto ongoing model updates.
Why this happens
Regulatory drafting processes typically involve public comment periods where affected companies and technical experts can flag implementation concerns, but the technical depth of that engagement varies considerably, and the final rule doesn't always fully incorporate the practical feedback received, particularly when technical nuance conflicts with a policy goal regulators consider a priority regardless of implementation difficulty.
What this means for companies navigating compliance
Organizations subject to AI regulation that's difficult to comply with as literally written are often left interpreting good-faith compliance in the absence of fully practical guidance, which creates real uncertainty about whether a reasonable, good-faith implementation will be considered sufficient if it doesn't match the rule's literal text precisely.
What better regulatory coverage should track
Coverage of AI regulation is more useful to affected businesses when it specifically flags where a rule's literal requirements diverge from practical technical reality, and tracks how regulators respond to that gap over time-through revised guidance, enforcement discretion, or amendment-rather than treating the rule's initial text as a stable, fully resolved final answer.