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2023 Regulatory performance report
Today at Rimsys, we unveiled the 2023 MedTech Regulatory Performance Report, a new set of insights into the state of medtech regulatory affairs. Compiled based on interviews with 200 regulatory professionals and executives, the study provides a detailed look into how regulatory teams are staffed, their processes, the tools they use, and ultimately how they perform.
Why did we create this study? There were two driving factors behind the research. The first was a common theme that we heard from a number of our customers: Regulatory leaders don’t have clear data and benchmarks. They don’t necessarily know how long a new market submission should take, and how to plan for or assess the work of their teams. While other studies look at the medtech industry broadly or the state of the regulatory profession, this study tries to build a comprehensive resource for regulatory (and company) leaders.
The second factor was really for ourselves and the team at Rimsys. As a company building solutions specifically for medtech regulatory affairs, we wanted more insight into where companies were successful, where they struggled, and where we can add value.
What did we find? Regulatory teams perform a lot of hero work and rate themselves highly for their accomplishments. At the same time there is a lot of opportunity for process improvements, and companies that invest in digital transformation for regulatory affairs see better performance.
Regulatory professionals are superheroes
Regulatory teams are generally pretty small. Most companies have less than 10 full-time regulatory professionals. These small teams complete an enormous amount of work. Last year on average, RA teams completed 50 license renewals, 50 license updates, and 10 new market submissions. This is impressive output.
Digging a bit under the covers, we found that this output relied heavily on the support of external consultants. 90% of companies use consultants to keep pace with their regulatory workload. Front-line employees also struggle with burnout. They were much more likely to report feeling under-resourced than regulatory leaders.
But process problems persist
A lot of regulatory work remains extremely manual. 70% of regulatory teams spend half their time or more on repetitive administrative tasks. All of this manual work increases the frequency of errors and required rework. 61% of companies reported a major non-compliance incident in the past 2 years.
Manual work also makes it difficult to complete regulatory projects in a timely fashion. Teams completed a lot of projects, but each took a long time. Over half of all companies spend 4 months or more on license renewals, license updates, and new market submissions.
Moving regulatory affairs forward
As regulatory requirements become more complex, there’s a natural question about how teams will work moving forward. MDR & IVDR in Europe have significantly increased the regulatory workload required to bring and keep products on the market. Will organizations be able to keep pace with the same resources, tools, and processes?
No, and the performance report shows that medtech companies are investing to improve their regulatory capabilities. The majority of companies are planning to increase the sizes of their RA teams in 2023, and 40% expect to increase their investments in regulatory software. Companies are increasingly adopting specialized software to better support regulatory processes.
Dig into the survey results
The full survey results provide insights into more aspects of regulatory performance. They show that companies need to take a deeper look into their processes and how regulatory resources are allocated. There are two ways to learn more:
- Visit the survey page to see the full results (the survey whitepaper can be downloaded at no cost)
- Watch the recording of our webinar with PA Consulting. We discuss the survey results in more detail and share our regulatory predictions for 2023
Post-market surveillance for medical devices in the European Union
This article is an excerpt from Post-market surveillance for medical device in the European Union.
Table of Contents
- What is post-market surveillance?
- What classes of medical devices require post-market surveillance?
- Components of a successful post-market surveillance plan
- PMS data requirements
- Post-market surveillance system goals
- Required post-market surveillance reporting
- Embracing post-market surveillance as an integral part of your quality program
- Getting started with post-market surveillance
Post-market surveillance (PMS) is designed to monitor the performance of a marketed medical device by collecting and analyzing field use data. Article 10 of the EU MDR and IVDR requires all device manufacturers to have a post-market surveillance system in place. The main elements of the PMS are laid out in Article 83, and additional details for lower-risk and higher-risk devices are covered in articles 84 and85, respectively.
In general, a PMS system consists of both proactive activities and reactive, or vigilance, activities. While post-market surveillance and vigilance are sometimes used interchangeably, vigilance consists of separate activities that feed post-market surveillance programs.
Post-market surveillance systems are used to collect and analyze data not only about the manufacturer’s device but also about related competitors’ devices that are on the market. Data collected through PMS procedures is then used to identify trends that may lead to, among other things, quality improvements, updates to user training and instructions for use, and identification of manufacturing issues.
Note that “market surveillance” encompasses activities performed by a Competent Authority to verify MDR compliance, and should not be confused with the topic of this ebook,“post-market surveillance," which is performed by the manufacturer.
All medical devices marketed in the EU require some level of post-market surveillance, and all medical device manufacturers must implement a post-market surveillance system (PMS). The requirements of the PMS, however, vary and should be “proportionate to the risk class and appropriate for the type of device” (MDR Chapter VII). In particular, the type and frequency of reporting vary based on a device’s risk class.
A post-market surveillance plan (PMS) is an integral part of a manufacturer’s quality management system and provides a system for compiling and analyzing data that is relevant to product quality, performance, and safety throughout the entire lifetime of a device. The PMS should also provide methods for determining the need for and implementing any preventative and corrective actions. A PMS system should include and define:
Surveillance data sources
With the increased focus on proactive risk identification in the MDR, it is important to design post-market surveillance systems that actively acquire knowledge and detect potential risks. It is not sufficient to rely solely on spontaneous reporting by healthcare providers, patients, and other stakeholders.
In addition to information coming from Clinical Evaluation Reports and complaint and adverse event reporting, typical sources of surveillance data include:
• Social media networks: Because many of your stakeholders may be communicating on social media networks, it is important to employ social listening techniques and/or tools to identify issues and concerning trends as they develop.
• Industry and academic literature: Any studies, academic papers, and other literature that addresses similar devices or the specific use cases for which your device is designed should be evaluated. In particular, risk factors and adverse events identified with similar devices should be closely examined. It is also important to identify newer technologies that may affect the benefit-risk ratio and establish a new definition of “state of the art” for the device type.
• EUDAMED: While the European Database on Medical Devices (EUDAMED) is not yet fully functional, it is intended to provide a living picture of the lifecycle of all medical devices marketed in the EU. Manufacturers should take special care to consider information for similar devices made available through the EUDAMED system in the future.
• Registries: Patient, disease, and device registries can provide information that informs the clinical evaluation process which provides input into the post-market surveillance system.
Data analysis methodology
A well-defined data analysis methodology will accurately identify trends and lead to defendable decisions in the application of post-market experience. Once the necessary information has been identified and collected, and potentially cleaned of incomplete or otherwise unusable data, the data needs to be analyzed.
The goal is to identify meaningful trends, correlations, variations, and patterns that can lead to improvements in the safety and efficacy of the device. There are many data analysis tools available that can assist with:
• Regression analysis that will identify correlations between data (e.g. the device location/geography correlates to battery life).
• Data visualization that can be useful in spotting trends in the data.
• Predictive analytics, which can be particularly useful with large data sets, to identify future trends based on historical data.
• Data mining, which is also normally used with large datasets, to organize data and identify data groups for further analysis.
Benefit-risk indicators and thresholds
The MDR requires that medical device manufacturers not only demonstrate the clinical benefit of their device but also quantify the benefit-risk ratio. The benefit of a device must be shown to clearly outweigh the risk for it to gain market approval. Article 2 (24) of the MDR defines the benefit-risk determination as “the analysis of all assessments of benefit and risk of possible relevance for the use of the device for the intended purpose when used in accordance with the intended purpose given by the manufacturer.”
A PMS system should clearly define benefit-risk calculations and the data used to support them. Post-market surveillance activities are critical in order to re-evaluate and maintain the benefit-risk calculations and determinations of a device throughout its life. Information that is gained through a PMS system can lead to:
• Identification of new risk factors.
• Adjustments to risk frequency and/or severity values based on actual use data.
• Adjustments to established risk calculations based on new “state of the art” technologies becoming available.
• Adjustments to established benefit calculations based on actual use data.
While complaint handling and other feedback tracking are more often described as part of post-market vigilance systems, they play a role in the more proactive post-market surveillance processes as well. A PMS system should define ...
To continue reading this ebook, download the full version.
Day Zero Is Easy. Day One Is Where It Gets Hard
There is something I keep coming back to in these conversations.
You can go from idea to prototype incredibly fast right now. That is the day-zero problem, and AI has essentially solved it. You can spit out working code, scaffold an integration, and stand up a proof of concept in a week
But the nuance around an actual business workflow — the day one and beyond activities — those are dramatically harder than day zero ever was
Software engineering done well is craftsmanship.
There is more to it than generating code and turning a prototype into something a regulated enterprise can depend on. It means thinking about edge cases, failure modes, upgrade paths, observability, and long-term operability. It means deleting as much as adding. Simplifying interfaces. Collapsing concepts down to what actually matters
Inside my own teams, I see impressive first versions all the time.
That is not the hard part anymore.
The hard part is everything that comes after
We-Should-Just-Build-This-Ourse…
Faster Engineering Just Pushes Work Somewhere Else
There is a tradeoff that rarely makes it into the first ROI spreadsheet.
AI compresses build cycles. In regulated companies, that speed shows up downstream. More releases mean more validation, more SOP updates, more training, more compliance review, and more audit prep
Engineering gets cheaper.
Governance becomes the constraint
There is also a subtler version of this problem.
Agents make it easy to generate output at scale. More workflows. More automation. More code.
But in regulated environments, every new service or automation path increases surface area. More things to secure. More things to validate. More things to explain to auditors
Speed without discipline creates complexity faster.
For CTOs, that is an architectural concern.
For Regulatory leaders, that is an inspection risk.
Are You Trying to Be a Software Company?
This is the part of these conversations that most often gets skipped.
A MedTech company is not a software shop. Most are largely outsourced IT organizations, and there is nothing wrong with that. The core business is devices, science, R&D, manufacturing quality, clinical programs, and global commercialization
When internal teams talk about building major regulatory platforms, the question is not whether they can spin up a prototype.
It is whether they want to operate a full-time software company inside their enterprise
Building software at scale is a people problem. It is not a technology problem. The constraint is coordination, judgment, institutional knowledge, and sustained focus over years
The people problem does not get fixed by agents and AI.
Regulatory platforms are deeply vertical. They encode jurisdiction-specific rules, regulator expectations, submission templates, QMS integrations, inspection trails, and post-market obligations
That knowledge is earned slowly.
It lives in product decisions, data models, operating procedures, and support playbooks.
AI will reshape how these platforms evolve.
It does not remove the learning curve that created them
AI Agents and the Confidence Shift Inside MedTech IT
In some MedTech IT planning meetings, a new kind of confidence has started to show up.
Not everywhere. Not in every organization. But often enough that it is worth paying attention to.
It is subtle. Casual. The kind that appears when something new begins to feel inevitable
A VP of IT or a CIO sits in a planning meeting. Someone pulls up a demo. An AI agent drafts a regulatory summary, generates a workflow, and scaffolds an integration. It looks impressive. It is impressive
Then someone says it:
Why are we paying for a platform when we could build this ourselves?
I understand the impulse.
SaaS valuations are volatile. Boards are pressing on efficiency. Hiring is under scrutiny everywhere. AI arrives, and suddenly there is a clean story. Automate friction. Avoid headcount growth. Modernize everything
Some of that is real.
I am optimistic about AI. In the right hands, it is a genuine superpower
But hope, cost pressure, aggressive marketing, and very human psychology are colliding right now. That collision is shaping how executives talk about technology strategy
In regulated industries, that matters.
The Confirmation Bias Problem
When leaders already feel pressure to reduce costs or flatten organizations, they naturally gravitate toward stories that validate those instincts. Flashy demos and headlines about agents replacing departments reinforce the belief that a breakthrough must be right around the corner
Once that belief sets in, messy operational details get discounted. Risk gets deferred.
That does not make the technology fake.
It does explain why ambition so often outruns delivery reality
For CTOs and Regulatory leaders, this is the moment to slow the conversation down.
Because prototypes are not platforms.
What AI Actually Changes
Years ago, Harvard Business Review wrote about the “hidden data factory,” the idea that organizations accumulate thousands of small one-off efforts to clean data, reconcile systems, patch workflows, and keep operations moving. No single fix ever justifies a major initiative. In aggregate, it quietly costs millions
That concept maps directly to what AI is good at today.
Inside engineering organizations, we call this work toil.
The repetitive, manual, low-judgment effort that keeps systems running but should not consume the time of highly trained people. Environment setup. Data reconciliation. Migration scripts. Test generation. Documentation drafts. Classification lookups. Compliance artifacts
AI is excellent at eliminating toil. It removes friction, collapses queues, and gives teams back time
In regulated environments, that is meaningful.
But here is the distinction that matters:
Eliminating toil does not eliminate accountability
It does not remove the need for architecture, UX design, validation strategy, regulatory interpretation, or operational ownership.
What it does is allow smaller, more senior teams to focus on the work that actually differentiates platforms.
That is very different than from saying agents replace the platforms themselves
Why MedTech Regulatory Teams Are Delegating EUDAMED to IT
And Why That Creates Bigger Problems Over Time
As EUDAMED implementation accelerates and the UDI/Devices module becomes mandatory in May of 2026, many MedTech companies have made a seemingly practical decision. They hand EUDAMED compliance to IT.
At first glance, the logic feels sound. EUDAMED is a system. It requires integrations, data transmission, and technical connectivity. IT already owns those capabilities, so the project lands there.
But this handoff reveals a deeper misunderstanding of what EUDAMED actually represents. It is a tool that enables manufacturers to meet ongoing regulatory obligations that touch product data, submissions, post-market activities, and lifecycle management. EUDAMED also enables manufacturers’ ACTOR partners like Notified Bodies, Authorized Representatives, Importers, and Distributors to meet their obligations under those EU regulations. Treating it as an isolated, one-time IT project creates risks to EU regulatory compliance that grow and spread across partners over time. MDR/IVDR regulatory compliance cannot be established and maintained with a one-time technical integration.
The first problem with delegating EUDAMED to IT is what it signals internally. It frames the regulation as a single event rather than a continuous program.
EUDAMED is not just about getting data into a database. It requires ongoing updates tied to regulatory changes, product modifications, vigilance activities, certificates, and market status. Every change across the product lifecycle can trigger downstream updates in EUDAMED.
When EUDAMED is positioned as a one-time event, organizations underestimate the scope, effort, and ownership required to maintain compliance over time. That gap does not show up immediately. It appears months later when updates are missed; data falls out of sync, or responsibilities become unclear.
IT teams often take on EUDAMED with the expectation that once the pipes are built, the work is largely done. In reality, the opposite happens.
As regulatory data changes, IT becomes the default escalation point for updates they do not own and cannot validate. They are asked to manage regulatory timelines, interpret data requirements, and support continuous updates that fall outside their core mandate.
This creates friction on both sides. Regulatory teams feel blocked by technical dependencies. IT teams feel burdened by compliance work they were never meant to manage. Over time, updates slow down, workarounds emerge, and risk quietly increases.
The most damaging consequence of delegating EUDAMED to IT is architectural. When EUDAMED operates outside of a centralized Regulatory Information Management system, organizations lose the opportunity to reuse data and reduce burden across the business.
Most of the data required for EUDAMED already exists within product information management and resource planning systems. Product registrations, certificates, submissions, UDI, and post-market data are not new. They are part of the regulatory lifecycle. When EUDAMED is disconnected from RIM, teams are forced to duplicate work, reconcile inconsistencies, and manually manage updates across systems.
Instead of becoming a natural extension of regulatory operations, EUDAMED turns into another silo. One that increases workload rather than streamlining it.
Establishing and maintaining regulatory information in EUDAMED is a regulatory obligation, not a technical one. While IT plays a critical role in enablement and integration, there should be a strong partnership between regulatory and IT (or a third-party submitter), but IT shouldn’t own it completely.
When EUDAMED is managed as part of a centralized RIM approach, organizations gain consistency, traceability, and reuse. Regulatory teams can leverage existing data, control updates at the source, and reduce the ripple effects of change across departments. IT supports the infrastructure, but regulatory owns the process.
This shift also changes how organizations think about compliance. Instead of reacting to EUDAMED as a standalone requirement, they treat it as part of a broader regulatory operating model that supports long-term compliance and growth.
Delegating EUDAMED to IT is rarely a conscious strategy. It is usually a symptom of fragmented regulatory operations and unclear ownership.
As MedTech companies scale globally and regulatory expectations continue to evolve, these handoffs become harder to sustain. EUDAMED exposes the cost of treating regulatory compliance as a series of isolated projects rather than an ongoing operational discipline.
The companies that navigate EUDAMED successfully are not the ones with the most complex integrations. They are the ones that anchor EUDAMED within regulatory operations, supported by centralized RIM systems that establish data consistency and reduce duplication, improve visibility, and spread the burden across the organization in a controlled way.
Agentic AI and the Future of Regulatory Operations
Why Regulatory Operations Is Ready for Agentic AI
Regulatory operations teams are under increasing pressure. Global regulatory complexity is rising, data volumes continue to grow, and teams are expected to move faster, often without additional headcount. At the same time, employee turnover and fragmented systems make it harder to maintain continuity and institutional knowledge.
As outlined in the RIM & AI Maturity in MedTech Executive Guide, many organizations are still operating with scattered regulatory data, reactive processes, and manual workflows. These conditions increase compliance risk and slow growth.
This environment has created the conditions where a more advanced form of AI can deliver meaningful value. That is where agentic AI comes into play, not as a replacement for regulatory expertise, but as a way to strengthen how regulatory operations function day to day.
What Is Agentic AI and Why It Matters
Most AI used in regulatory environments today is assistive. It helps classify documents, extract text, or answer questions when prompted. Agentic AI goes further by operating within defined workflows and processes.
Agentic AI systems can monitor structured regulatory data continuously, identify upcoming risks or deadlines, recommend actions based on rules and historical context, and surface next steps within governed processes. Instead of responding to requests, agentic AI supports execution by working alongside regulatory teams inside their operational systems.
The distinction is important. In regulated environments, value does not come from generative output alone. It comes from intelligence that is embedded, auditable, and aligned with how regulatory work actually gets done.
Moving Regulatory Teams Off the Data Treadmill
The executive guide describes early-stage regulatory teams as being stuck on a back-office data treadmill. Highly skilled professionals spend a disproportionate amount of time searching for information, reconciling spreadsheets, and repeating manual tasks rather than applying their expertise strategically.
Agentic AI helps reduce this burden by continuously organizing and validating regulatory data, identifying missing metadata or inconsistencies early, and reducing reliance on individual memory or tribal knowledge. Over time, this improves not just efficiency, but operational resilience. Teams become less vulnerable to audits, turnover, and last-minute regulatory surprises.
Why Agentic AI Depends on Operational Maturity
One of the most important insights from the paper is that AI value scales with RIM maturity. Advanced AI capabilities are not effective without centralized regulatory information and standardized processes .
At higher maturity levels, AI can surface upcoming risks across markets and renewals, analyze submission history to recommend reusable content, and identify bottlenecks before they impact timelines. At this stage, agentic AI begins to function as an operational partner, helping teams anticipate issues rather than react to them.
This is also where many organizations encounter friction. Skipping foundational steps may create the appearance of progress, but it limits reliability and long-term impact. Agentic AI is only as effective as the data, governance, and workflows it operates within.
From Task Automation to Predictive Compliance
At the most mature stage of regulatory operations, AI becomes fully embedded in daily work. The guide describes this level as one where real-time monitoring, predictive analytics, and continuous improvement are standard practice .
In this environment, agentic AI supports predictive compliance by identifying emerging risks, highlighting resource constraints, and improving visibility across submissions and renewals. These insights allow teams to act earlier and with greater confidence.
The paper is clear on one point. AI enhances regulatory expertise, but it does not replace it. Human judgment remains essential for interpretation, decision-making, and accountability. The real value of agentic AI is that it frees regulatory professionals from low-value work so they can focus on the decisions that matter most .
Regulatory Operations as the Heart of Compliant Growth
The most significant impact of agentic AI is not automation alone. It is the elevation of regulatory operations from a reactive support function to the heart of compliant growth.
Organizations that invest in strong RIM foundations, data governance, and workflow integration are better positioned to apply AI in a way that is safe, scalable, and durable. When implemented thoughtfully, agentic AI helps regulatory operations keep pace with growth, reduce risk, and support faster, more confident decision-making across the business.
Rimsys Announces Rimsys AI to Eliminate Repetitive Tasks and Enhance Decision-Making for MedTech Regulatory Teams
Enterprise-grade AI built for MedTech: secure, explainable, and embedded directly into regulatory workflows
PITTSBURGH, PA - September 3, 2025 - Rimsys, the leading Regulatory Information Management (RIM) platform for the MedTech industry, today announced the launch of Rimsys AI, a suite of embedded artificial intelligence (AI) agents designed to streamline regulatory workflows, reduce friction, and empower global regulatory teams to work smarter and more efficiently.
Rimsys AI delivers secure, embedded AI agents purpose-built for the unique complexities of MedTech. While generic AI tools often operate as disconnected assistants, Rimsys AI is fully integrated into existing regulatory workflows, serving as a force multiplier for regulatory teams. Unlike competing offerings, each Rimsys AI agent operates within the Managed Context Protocol (MCP), accessing only customer-authorized data and workflows. This ensures that every output is explainable, secure, and regulatory-ready.
Key benefits include:
- Embedded intelligence within Rimsys workflows – Enables smarter authoring, review, and decision-making without leaving the platform.
- Confidence in outcomes – All AI-driven recommendations are traceable, context-aware, and aligned with industry best practices.
- Enterprise-grade security – Safeguards sensitive product and regulatory data with SOC 2 Type II and ISO 27001 certifications, ensuring customers’ proprietary data is protected and meets the highest compliance standards.
Early features include:
- AI-powered Submissions that automatically reuse content across global markets, reducing time to submission by up to 90%.
- Regulatory change assessments powered by AI agents that analyze regulatory changes across products, risk classes, markets, and industry laws.
- Real-time global monitoring that continuously tracks new and changing regulations and guidances, keeping teams ahead of compliance shifts.
“With Rimsys AI, we’ve taken a fundamentally different approach than generic AI integrations,” said Jeff Burk, Chief Technology Officer at Rimsys. “Our architecture embeds AI directly into the regulatory workflows our customers use every day, which makes the intelligence contextual, trustworthy, and secure. It’s not a disconnected assistant, but an integrated force that amplifies decision-making. We’ve designed Rimsys AI to evolve with the RIM Maturity Model, so as organizations progress in their digital journey, it continuously unlocks greater automation, higher efficiency, and deeper insights.”
Rimsys AI is built to grow with customers’ digital maturity. Guided by the RIM and AI Maturity Model (see RIM Adoption as a Maturity Model), Rimsys enables organizations to realize greater value as they advance along their digital transformation journey.
Available Q4 2025
Embedded Rimsys AI features will begin rolling out to select enterprise customers in Q4 2025, with general availability planned for early 2026. Live demonstrations will be available for customers and attendees at RAPS Convergence, October 7-9, 2025.
About Rimsys
Rimsys is the leading provider of AI-powered Regulatory Information Management (RIM) software purpose-built for MedTech manufacturers. The comprehensive platform digitizes and automates regulatory activities, helping MedTech regulatory affairs teams to efficiently achieve regulatory compliance and get products to market faster. Rimsys is designed around MedTech workflows and supports a full breadth of regulatory functions including registrations, submissions, UDI, EUDAMED compliance, essential principles, and standards management in a unified platform. Rimsys is trusted by half of the world’s top 12 MedTech companies to power their global regulatory operations. For more information, visit www.rimsys.io.
The Future of MedTech Compliance: How AI Is Transforming Regulatory Affairs
MedTech regulatory affairs teams are facing a turning point. Regulations are expanding in number and complexity, resources are limited, and manual processes cannot keep up. At the same time, artificial intelligence (AI) has become a serious topic of discussion in regulatory circles. Leaders are beginning to ask: How can AI help us manage change, reduce risk, and accelerate compliance efforts?
The answer is clear: AI is no longer just a buzzword. When combined with effective regulatory information management (RIM), it can be a powerful enabler of efficiency, accuracy, and strategic decision-making.
Why AI is Trending in Regulatory Affairs
The Surge of Regulatory Data
Regulatory teams must now track requirements from multiple global markets. Each regulator frequently updates its regulations, guidances, templates, and recognized standards, which creates large volumes of data to organize and analyze. AI can scan and classify this information, highlight changes, and prepare it for structured use within RIM systems.
Doing More with Limited Resources
Most teams are expected to deliver more without additional staff. High turnover makes continuity difficult, and according to the 2024 RAPS Global Workforce Report, the number of professionals “open to work” has grown in North America and Europe. AI offers relief by taking on repetitive tasks such as document formatting or data entry, allowing experts to focus on higher-value work.
Global Complexity and Diverging Standards
No two markets are exactly alike. AI can help by flagging differences, surfacing potential risks, and recommending reusable content drawn from a company’s submission history. Faster, more accurate submissions directly improve time-to-market and compliance outcomes.
The RIM & AI Adoption Maturity Model
Not every organization is ready to fully embrace AI. Success depends on RIM maturity: how structured and centralized your regulatory processes and data are. The RIM & AI Adoption Maturity Model provides a roadmap from basic to optimized states.
- Levels 0–2: Early Stage
- Data is siloed and processes are ad hoc. AI provides value in isolated ways, such as cleansing records or scanning for regulatory changes.
- Level 3: Proactive
- A RIM system centralizes information. AI begins to surface reminders, deadlines, and global impact assessments.
- Level 4: Well Managed
- Processes are standardized across the lifecycle. AI generates insights, monitors KPIs, and supports reuse of regulatory content.
- Level 5: Optimized
- AI is fully embedded, delivering predictive analytics, continuous monitoring, and smarter decision-making.
Practical Applications of AI Today
Today, regulatory teams see the greatest opportunities in:
- Regulatory submissions: Automatically detecting changes in templates and suggesting updates.
- Document classification: Using natural language processing to tag and organize regulatory documents.
- Regulatory intelligence: Monitoring health authority updates and highlighting what matters most.
- Impact assessments: Linking changes (e.g., regulations/standards/design) directly to the affected products and registrations and evaluate the potential impact.
- Content reuse: Recommending approved content to accelerate submissions.
How to Start Your AI Journey in Regulatory Affairs
Adopting AI is not about jumping to the most advanced capabilities overnight. Instead, consider these steps:
- Assess your RIM maturity. Where does your organization sit on the 0–5 scale? What foundational gaps (data centralization, process standardization) need to be addressed first?
- Identify quick wins. Focus on repetitive, rules-based tasks where AI can add value without major disruption.
- Implement governance. Establish policies for safe, compliant AI use, particularly around data privacy and model training.
- Pilot in phases. Start small, validate results, and expand AI use as confidence and maturity grow.
- Keep people at the center. AI should enhance the expertise of regulatory professionals, not replace it.
Building a Smarter Future for MedTech Compliance
AI is becoming a trending topic in regulatory affairs not just because it’s new, but because it directly addresses the challenges teams face: rising complexity, limited resources, and scattered data.
For organizations that take this approach, the benefits are clear: lower compliance risk, faster execution, and stronger competitive positioning. AI does not replace regulatory professionals. Instead, it enables them to spend less time on manual tasks and more time on strategic contributions that improve patient access to life-changing technologies.
In other words, AI isn’t about futuristic transformation. It’s about helping regulatory teams step off the “data treadmill” and reclaim their time for what matters most: bringing safe, life-changing medical technologies to patients faster.
