AI is moving quickly from experimentation to implementation across life sciences. For regulatory teams, that raises a bigger question than where AI can automate a task:
How can organizations use AI to make better decisions while maintaining the traceability, expert oversight, and confidence regulatory work demands?
That was a central theme when James Gianoutsos, Founder and CEO of Rimsys, joined Steve Gens, Founder and Managing Partner of Gens & Associates, on the Regulatory Executive Podcast.
Their conversation explored the changing role of regulatory intelligence, the introduction of Rimsys Market Access, the importance of transparency in regulatory AI, and what these changes could mean for regulatory teams.
Regulatory knowledge has been difficult and costly to scale
For global medtech organizations, regulatory knowledge has traditionally been highly dependent on people.
A company may have specialists responsible for particular countries or regions, along with local representatives, distributors, consultants, and other experts. Those people develop deep knowledge of local regulations, pathways, and expectations.
The problem is that the knowledge often remains distributed across the organization. And maintaining that market-by-market expertise can become expensive as a company expands its portfolio and geographic footprint.
As James described during the podcast, large manufacturers may rely on dozens or even hundreds of internal employees, third-party distributors, local representatives, and other specialists to understand what it takes to bring products into different markets. Smaller organizations may rely more heavily on outside consultants and partners. Either way, the model can be costly and difficult to scale.
But the cost isn't limited to what companies spend on expertise.
Steve described the workflow as a series of “start, stop” cycles: ask a local expert, verify whether the regulation is current, gather more information, and repeat. Each cycle consumes regulatory capacity and extends the time it takes to give the business a confident answer.
That creates another potentially more consequential expense: the cost of waiting.
When it takes months to understand what entering a market will require, commercial and product teams have less visibility into when revenue might begin, what resources will be needed, or whether the opportunity is worth pursuing. Regulatory teams spend valuable capacity assembling and validating information rather than applying their expertise to more strategic work.
Ultimately, scaling regulatory knowledge isn't simply an efficiency problem. It affects how quickly and confidently the business can decide where to invest.
The bigger question: Should we enter this market?
That challenge is central to Rimsys Market Access.
Most market-entry research begins with questions such as: How is the product classified? What's the regulatory pathway? What are the requirements?
Those questions establish whether a company can enter a market. But they don't necessarily tell the business whether it should.
Market Access is designed to help regulatory and commercial teams develop an earlier view of what market entry could involve, including classification, regulatory pathway, timeline, cost considerations, requirements and potential gaps.
For commercial and product leaders, that provides greater predictability before committing budget and resources.
For regulatory leaders, it means starting with a structured plan rather than assembling every market assessment from scratch.
Moving regulatory knowledge from individuals into a shared system
During the conversation, James described one of AI's most significant opportunities as moving knowledge that has historically lived in people's heads into software.
The goal isn't to eliminate regulatory expertise. It's to make that expertise more scalable.
Market Access works from a curated regulatory corpus, allowing market-specific knowledge to be applied across workflows without requiring every initial question to go through the individual who happens to know that jurisdiction. Outputs are designed to trace back to the underlying regulation, guidance, or law.
That can also change where regulatory professionals spend their time. Rather than focusing as heavily on finding information and assembling initial assessments, they can focus on reviewing the evidence, identifying gaps, applying judgment, and advising the business.
Steve described the concept as a subject-matter-expert assistant. James called it a workforce multiplier.
Trust requires opening the regulatory AI “black box”
Speed isn't enough when the information is being used to inform regulatory decisions.
James and Steve returned repeatedly to the importance of knowing where an AI-generated answer comes from. As Steve summarized it during the discussion, transparency builds trust.
Market Access is designed around that principle. Its outputs are grounded in a curated regulatory corpus and traceable to the specific regulations, guidance, or laws supporting them. That gives regulatory professionals the ability to verify an answer rather than simply accept it.
Human judgment remains essential as well. Market Access provides an upfront assessment, not a guarantee of a regulatory outcome. Reviewer interpretation and other factors can still affect what happens during the regulatory process.
The objective isn't to make uncertainty disappear. It's to make what is known, what the sources say, and where uncertainty remains much easier to see.
Connecting the market decision to execution
The discussion also pointed to a broader evolution in regulatory technology: moving from systems that primarily store regulatory information toward systems that help teams act on it.
That's where Market Access and Rimsys RIM play distinct but complementary roles.
Market Access is the planning layer. It helps teams determine whether, when, and how to enter a market.
Rimsys RIM is the execution layer. It manages the registrations, submissions, regulatory changes, and ongoing regulatory work required to act on that plan.
Together, they create a path from the initial market decision through regulatory execution rather than leaving planning and execution disconnected.
A new role for AI in regulatory work
One of the most interesting themes from Steve and James's conversation was that AI's impact may ultimately be less about replacing individual tasks and more about expanding what regulatory teams can accomplish.
Better access to market-specific knowledge can help professionals work across more markets, spend less time gathering information, and devote more capacity to the judgment and strategic work that requires their expertise.
The technology is changing quickly, but the fundamentals of regulatory work remain: reliable information, domain expertise, transparency, and human judgment.
The opportunity is to apply those fundamentals at greater scale while giving the business a clearer answer to the question that comes before execution:
Not simply, “Can we enter this market?”
But, “Should we?”
Listen to the full conversation
Hear the complete discussion between Steve Gens, Founder and Managing Partner of Gens & Associates, and James Gianoutsos, Founder and CEO of Rimsys, on the Regulatory Executive Podcast for more on regulatory AI, market-entry planning, transparency, and the changing role of regulatory teams.
