Generalist AI has been impressive and keeps on improving, but it is still far from enough to do proper regulatory work. Knowing where AI falls short is just as important as knowing where it excels.
Regulatory watch
If your job is to make sure nothing gets missed — every new guidance, every standard revision, every database update — a generalist AI is not the right tool. These models do not have real-time access to regulatory databases. They cannot guarantee exhaustiveness. And in regulatory watch, missing one update can have real consequences. This requires curated, continuously updated data sources, not a language model working from memory.
Accessing regulatory databases
Need to search FDA's MAUDE database for adverse events? Pull up 510(k) summaries for predicate devices? Generalist AI cannot do this, or at least not reliably. It often does not have proper live access to these systems. Worse: it will fabricate results that look convincing. This is one of the most dangerous failure modes — an AI that confidently gives you database results that do not exist.
One-shotting complex tasks
"Do my literature review" or "run a gap analysis" as a single prompt will give you something that looks right but is not reliable. These are multi-step processes where each step needs to be verifiable.
A Systematic Literature Review involves defining search criteria, executing searches, screening results, extracting data, and synthesizing findings. Each step needs to be precise and documented. AI can handle each one with human oversight at key decision points, but not all at once in a single chat message.
Same for gap analysis: checking documentation against requirements, flagging differences, scaling across multiple standards on a large document corpus. The AI does the comparison. The expert interprets the gaps and decides what to do.
Trusting outputs without verification
LLMs generate convincing text — that is the whole point. But in regulated industries, convincing is not the same as correct. They can hallucinate references, misinterpret requirements, or produce outputs that carry biases from their training data. If there is no verification step, no traceability, no human checkpoint, you are building on sand.
Handling sensitive data
Pasting proprietary documentation or patient-related data into a general-purpose chatbot is a real risk. Most generalist tools were not designed with healthcare data privacy in mind. If your workflow involves confidential information, you need to know exactly where that data goes and how it is stored.
So how should you decide when to use AI?
The pattern is clear: AI works when you break complex tasks into atomic, verifiable steps, with the right data sources connected and human oversight where it matters. It fails when you expect it to do everything at once and trust the output blindly.
This is exactly why we built Qalico the way we did — purpose-built AI for regulatory work, with the right guardrails in the right places.