The life sciences industry has undergone a significant transformation over the past decade. R&D cycles have accelerated, and companies are integrating software and AI into nearly every product pipeline. And the barrier to entry is lower than ever, as reflected by the FDA’s approval of over 1,250 AI-enabled devices by the end of 2024.
This rapid pace of change enables more effective treatments for a wider range of conditions and patients. However, requirements for demonstrating safety and efficacy are increasing, while compliance practices have not evolved accordingly. Most life sciences companies manage quality and regulatory obligations much as they did twenty years ago, which risks creating bottlenecks for those developing life-saving products.
Recent data shows FDA warning letters per 100 inspections have increased by 43% since 2019, with medical device warning letters rising 96% in FY2024. Regulatory agencies are accelerating their efforts and, as they adopt AI, their assessment capabilities will continue to expand.
What’s wrong with the status quo?
For decades, compliance approaches have remained largely unchanged. Companies begin by developing SOPs, building internal processes, and documenting activities affecting product quality, safety, and efficacy. However, when regulatory audits approach, teams often scramble for months to compile the necessary documentation for audits, submissions, and certifications.
Product development, quality assurance, and regulatory teams use various tools to manage compliance. Some rely on spreadsheets, shared drives, and paper-based workflows that require ongoing manual coordination. Others use traditional QMS, PLM, or RIM tools, which are inherently reactive and often result in issues being addressed only immediately before or after audits.
Some teams are experimenting with general-purpose AI tools such as ChatGPT and Claude. However, without validation, integration with business context, or adherence to policy rules, these tools require human review of every output due to their non-deterministic nature.
And over time, the linear growth of costs with scale means companies either build bigger teams or constrain their regulatory velocity to their available bandwidth. All this creates delays in developing life-saving treatments and drives up the costs to do so.
All four approaches share a core challenge: significant effort is spent building and managing systems from scratch, without an efficient way to measure progress against regulatory requirements.
Why now?
Today, medical device companies must comply with FDA 21 CFR Part 820, ISO 13485, EU MDR, and MDSAP. Products with software components also face AI- and SaMD-specific requirements, such as ISO 42001 and IEC 62304. Managing compliance across these frameworks and markets slows product development and delays submissions and approvals.
And in Pharma, between 2025 and 2030, the industry will encounter a $300 billion-plus patent cliff, with over 70 blockbuster drugs (nearly 200 in total) losing exclusivity. This exposes about one-sixth of annual revenue to biosimilar and generic competition. The industry is responding with compressed R&D cycles, aggressive acquisitions, and parallel market-entry programs. Companies must now manage a broader regulatory scope under greater time pressure than ever before.
Flipping the model
There is a better approach: start by flipping the sequence entirely.
Instead of building from process to regulation, begin with the regulation itself (FDA, ISO, GxP, etc.) and work backward. AI enables us to take the unstructured regulatory standards into objective, measurable controls. Mapping these controls to business data provides clear insight into compliance status. With agentic capabilities, this visibility is continuous, enabling proactive identification and remediation of gaps before audits.
This model has proven effective in other industries, such as cybersecurity. For example, global banks use compliance automation to continuously monitor and proactively manage IT security controls, reducing audit times and improving threat response. We have applied this approach to compliance intelligence, enabling companies to automate compliance as they grow. This accelerates product development, streamlines compliance, and provides real-time risk visibility for informed decision making.
The process works as follows: (1) Leaders identify the core regulatory frameworks relevant to the organization; (2) Qualio assesses all evidence, including current processes, data, and documentation, against these requirements; and (3) provides a prioritized list of issues with remediation support. Teams then automate controls mapping and reporting, create cross-functional remediation task lists, and establish regular checkpoints to maintain audit readiness.
In practice, customers adopting compliance intelligence achieve audit-readiness for new frameworks in weeks instead of months and spend over 80 percent less time on audit-related tasks. In several cases, AI-driven gap analysis has identified issues missed by both internal teams and external auditors, helping companies avoid costly delays and significantly reduce regulatory risk.
As the pace of change accelerates, we’re excited to be leading the way with automating compliance in life sciences, and can’t wait to deploy these capabilities to more and more of our customers.