Rethinking Regulatory Information Management: Why RIM Must Evolve from System of Record to Control Tower

At Freyr One USA 2026, Bruno Machado Della Vecchia (Kenvue) and Sameer Siddiqui (Freyr Solutions) came together for a fireside chat on rethinking Regulatory Information Management (RIM). The conversation explored a question many regulatory organizations are currently grappling with: as portfolios grow more complex and AI enters everyday regulatory work, what does it take for a Regulatory Information Management System to deliver more than a record of what has already happened? 

Spanning topics such as data governance, AI-driven data quality, regulatory intelligence, multi-product portfolios, and the role of AI agents, the discussion revealed a recurring theme: RIM delivers value only when the data inside it can be trusted. Future-ready RIM will be built on enterprise-wide governance and a single place to act on trusted data. 

RIM is evolving from a data repository to a control tower

The discussion began with how regulatory leaders can approach a portfolio that spans medicines, devices, and consumer products across global markets, with the added complexity of mergers and acquisitions. The approach discussed rests on three pillars: data, governance, and automation. RIM is what makes all three possible. 

As regulatory functions have been pushed to become fully digital, the volume of data they create has grown sharply. The answer discussed was to reposition RIM so that it stopped being “just a storage data room” and became the control tower for the organization. Because regulatory acts as the gatekeeper between the company and the outside world, nearly all enterprise data passes through it at some point. Bringing that data together in one RIMS platform enables stronger governance and faster, compliant assessments across global markets. 

Data governance remains the biggest challenge in RIM transformation

When asked where the greatest effort lies in moving to a modern RIM, the speakers were clear: data governance. Regulatory teams may maintain strong governance within their own department, yet still rely heavily on data owned by other functions. 

One regulatory team that made this transition found regulatory data spread across the organization, much of it in spreadsheets. “It was like a nightmare,” one speaker recalled. Their approach to RIM data migration was methodical: map where the data lived, assess its quality, set governance rules for bringing it into the system, cleanse it, and only then migrate. 

The challenge was also framed as an opportunity. Data governance gives regulatory a reason to step beyond its traditional boundaries and act as a business partner to the wider enterprise, showing other departments that better data allows the whole organization to move faster. 

AI’s most immediate value in RIM lies in data quality

While much of the conversation around AI in regulatory affairs centers on content generation, the session offered a more practical perspective. The most valuable help AI has delivered so far has been in data governance: comparing RIM data with data held by other departments and raising red flags where they do not match. 

One example illustrated this. When a change control is created elsewhere in the company indicating that a submission may be required in a particular country, AI can cross-check it against RIM and flag it if the submission was never captured. Those red flags also proved persuasive internally. Some teams had not realized how poor their data was until they saw the inconsistencies and understood how they affected timelines for getting products to consumers. One speaker joked that his goal for the year had been to become “the most hated person” in the company, but it had also delivered better data. 

The speakers also explained why the challenge is so common. Products, applications, and submissions have different owners, and local teams update approvals after each submission. Updates are missed, people move on, and new team members need training, so many companies rely on manual governance teams that chase updates by email. There is “no silver bullet,” but AI can monitor these events automatically and flag what is pending. 

Connecting RIM with regulatory intelligence accelerates impact assessment

The conversation then turned to regulatory intelligence. The speakers described how connecting internal RIM data with external intelligence turns impact assessment into a matter of a few clicks. When a new regulation could affect a portfolio, teams can check it against RIM and quickly see the impact. The same connection works in reverse: when a new launch is planned, teams can search for the regulations that might affect its delivery. 

This capability is especially valuable in portfolios where the same product may be classified as a drug in one market and a cosmetic or device in another, and a single formula may map to several licenses. The session highlighted the role of Freyr’s knowledge manager, which builds a graph of these relationships rather than forcing every classification into a single standard. This allows teams to trace a license back to its product, or a formula forward to every registration. 

Multi-product portfolios demand flexible, consistently governed RIMS software

A question from the audience about managing products across continents brought the discussion to portfolio complexity. One approach shared was to maintain a single data source across regions and clusters, with the same governance rules applied regardless of product category. Consistent classification is equally important, because classifying the same product differently in different markets leads to gaps. 

Combination products illustrate the challenge well: a product may be classified as a device in one market and a medicine in another, while being treated as a pharmaceutical product at corporate level. Regulatory information management software originally built for pharmaceuticals and later extended to devices, consumer, or veterinary products often struggles to do the job. RIM must be configurable from the outset and designed as more than a product repository. 

Regulatory teams need one interface for system-of-record context and AI agents

The session also addressed a challenge familiar to most regulatory professionals. Every day, teams need to answer a health authority query, prepare a renewal, or check what a dossier committed. The information already exists in the system of record. The difficulty lies in bringing it together when it is needed. 

A video shown during the session demonstrated how Freyr’s Enterprise Intelligence Platform (EIP) and Workbench address this. A regulatory professional working on two submission sequences can pull both from the system of record into Workbench, compare details such as shelf life, and run an AI agent or skill on the result, all within a single interface. 

The speakers described an even broader vision: AI as a personal assistant for every regulatory professional, starting each day by asking an agent what needs attention, and using the same agents to assess the impact of a change control across markets. That would free regulatory teams for more strategic work and give them greater influence across the enterprise. 

Looking ahead: trusted data is the foundation of intelligent RIM

The fireside chat made one point especially clear: the move from system of record to system of intelligence depends less on new software and more on trust in the underlying data. Organizations that invest in enterprise-wide data governance, consistent classification, and connected regulatory intelligence will be best positioned to turn RIM into a true control tower, and to get real value from AI agents in day-to-day regulatory work. 

Turn your RIM into a control tower with freya.register. freya.register is the RIMS built for intelligence: it connects products, applications, submissions and registrations in one governed record, links them to regulatory intelligence, and puts AI agents to work on top. Explore freya.register and see what it can do for your portfolio.