u/ValentinaAtEntropy

I’ve been thinking about what actually needs to change before AI can become genuinely useful in regulatory work—not just another chatbot or isolated pilot.

Regulatory professionals often need to navigate hundreds of pages, compare different documents, verify requirements and document the evidence behind every conclusion.

The problem is rarely a lack of information. It is finding the right evidence, applying the correct criteria and producing a result that another professional can verify.

From the conversations I’ve had, seven requirements repeatedly emerge:

1. Evidence retrieval from approved sources The system should work with defined documentation such as regulations, SOPs, policies, contracts and technical records.
2. Structured document review It should follow a predefined analytical process rather than simply generate a plausible response.
3. Traceability Relevant findings should remain connected to their supporting evidence.
4. Process-specific configuration The AI should reflect the particular role, criteria, documents and expected outputs of the operation.
5. Meaningful human oversight Professionals should review ambiguity, approve consequential outputs and retain responsibility for final decisions.
6. Controlled information handling Access, data boundaries, retention and deployment requirements need to be considered from the beginning.
7. Operational integration The objective should be a repeatable process that teams can realistically use—not just an impressive pilot.

For me, this is the difference between using AI and operationalizing it in a regulated environment: combining speed with evidence, automation with oversight and innovation with control.

Full disclosure: I work at Entropy Systems, where we build configurable agentic infrastructure for enterprise operations. I’m sharing this because these are some of the most recurrent questions we repeatedly encounter when discussing real use cases with regulated organizations.

For those working in regulatory affairs: which of these requirements is the hardest to achieve in practice? Is anything important missing from the list?

There is a lot of discussion about how AI could transform Regulatory Affairs. I’m more interested in what is already happening in day-to-day work.

Regulatory Affairs involves documentation-heavy and analytical processes where accuracy, traceability and human accountability remain essential. AI may support some of this work, but its value depends on how it is integrated into real processes and where human judgment is retained.

For those working in Regulatory Affairs:

• Which tasks are already being supported by AI in your organisation?
• Is it being used for document comparison, information extraction, regulatory intelligence, dossier review or change assessment?
• Are you using general-purpose AI assistants or systems configured around your organisation’s processes and requirements?
• Which activities still require the most manual effort?
• What is currently limiting wider adoption: confidentiality, accuracy, validation, traceability, internal policies or lack of trust?

My view is that AI will not reduce the importance of regulatory expertise. Instead, it may make regulatory judgment, process design and output validation even more valuable—particularly when professionals remain in control of decisions and final outputs.

I’d be interested to hear what AI adoption actually looks like inside RA teams today—not just what the broader market says it should look like.