Why Are Life Science Companies So Poorly Prepared For RIM's Future?

By Steve Gens, managing partner, Gens & Associates Inc.

Regulatory information management (RIM) has been a focus of sustained investment across the life sciences industry for the better part of a decade. Most organizations have modernized their global RIM systems, improved data quality, and begun exploring AI and automation. Yet modernization and readiness are not the same thing, a distinction now set to become significant in the context of a raft of converging market changes. This is confirmed in the latest cycle of Gens & Associates’ long-standing World Class RIM benchmarking study.¹

The life sciences industry is now simultaneously contending with AI and advanced automation, cloud-based regulatory spaces (CBRS), structured data mandates, and significant workforce transformation. Each of these developments would be substantial individually; together, they are reshaping the way that regulatory information is created, managed, and exchanged at a pace and breadth the function has not previously encountered.

Against that backdrop, our RIM benchmark’s new Future Readiness Indicator (FRI) asks not how well organizations are performing today, but whether they are structurally equipped to sustain that performance through what comes next. The overwhelming majority, it seems, are not. Just one of the 59 study’s participating organizations qualifies as having “ready and leading” status when measured for future readiness. More than a fifth (21%) fall into an “at risk” category. The remaining 77% are not as acutely exposed but carry gaps that will need intentional investment to address.

The Technology Investment Trap

The reflex response to a readiness gap in a period of rapid technological change is to buy more technology. Our benchmark data, accumulated across more than a decade of research cycles, argues consistently against that instinct, however. Since 2014, there has been no correlation between top performers and any particular software vendor or system strategy. The differentiator has been, and remains, the organizational and process layer beneath the technology: how data is governed and owned, how processes are measured and improved, and how change is managed as an ongoing competency (versus a series of discrete projects).

The single organization to assume a “ready and leading” status in the FRI analysis illustrates what that layer looks like when it is fully developed. The company is a consistent strong performer across multiple research cycles and is differentiated by a combination of capabilities operating simultaneously: effective cross-functional collaboration, high process maturity, embedded change management as a core competency, and a fully implemented data governance model with explicit ownership of mission-critical data elements. KPI-driven continuous improvement is standard operating practice here, rather than a goal on a road map.

Data Accountability: A Narrow Yet Decisive Advantage

One of the standout findings of the current study is how rare — and how consequential — the practice of holding individuals and teams explicitly responsible for the accuracy and quality of data in their systems turns out to be. While this is an organizational strength in just four of the 59 organizations surveyed, the performance gap between those four and the others is striking. Those four organizations recorded an aggregate data quality confidence score of 93%, compared to 50% for everyone else. Aggregate efficiency across 15 core RIM capabilities was 93% for the leaders, compared with 70% for their peers. For specific authoritative sources — health authority commitment tracking data being one example — the differential widens to 100% high confidence versus 44%.

Almost two-thirds of the broader participant group report actively working toward stronger data accountability, which indicates that the principle is broadly understood. The obstacle is implementation. In regulatory operations, accountability has historically been organized around documents and dossiers, where ownership is well established. Extending it to individual data elements is newer territory, even though clinical operations and supply chain have functioned this way for years — a transition that IDMP structured data standards have done much to drive. Building data accountability at scale means embedding it at the functional, individual, and team levels simultaneously, with product teams owning the quality of regulatory data pertaining to their portfolio. The framework mirrors established practice in other data-intensive functions; the gap we are witnessing is in applying this consistently to regulatory.

Process Maturity As A Business Outcome Driver

Process maturity — measured formally for the first time in the latest cycle across nine core regulatory process areas using the Capability Maturity Model Integration (CMMI) framework² — emerges as one of the clearest available predictors of business outcomes. Top performers operate predominantly at Level 4 (measuring) or Level 5 (optimizing); the broader participant group largely sits at Level 3 (controlled) or Level 2 (repeatable, but inconsistently so).

The business impact of that gap is considerable and measurable. Top performers report operational throughput improvements at 80% versus 47% for peers; time-to-filing improvement in secondary markets at 70% vs. 26%; operating cost improvement at 90% vs. 39%; and user productivity gains at 90% vs. 50%. Higher maturity is associated with organizational capacity — the ability to absorb additional workload, integrate new technology, and navigate change without the friction that underdefined or inconsistently applied processes create. That capacity accumulates through sustained investment in process design, measurement, and improvement, which is why organizations that have done that work extract disproportionate value from each successive wave of new technology.

Adjusted AI Expectations

The AI situation emerging from the latest study is nuanced. Forty-seven percent of companies report pilots or implementations underway, and every large organization in the study claims significant AI investment. Of 131 benefit-realization responses tracked across all AI and advanced automation use cases, however, just six exceeded companies’ expectations. Consensus on realistic implementation timelines has moved out to 2027-28, which we interpret to be a recalibration of expectations rather than a distancing from the technology.

Today, AI is delivering measurable results in areas where processes are well defined, data is structured, and the scope of the task is bounded. In late-stage biopharmaceutical R&D, clinical document generation leads adoption, followed by AI-assisted translation, and, more recently, CMC content generation. The combination of AI-generated first drafts (e.g., of eCTD Module 3 [Quality] sections), AI-assisted quality review, and reduced translation cycles has genuine potential to compress the timeline from clinical study closure to dossier filing. Broader authoring transformation — at the level of dossier sections rather than individual documents — is a realistic medium-term prospect, with the tipping point projected in the 2028 timeframe.

Cloud-Based Regulatory Spaces And The Data Infrastructure Shift

Cloud-based regulatory spaces have gained significant momentum during the last benchmarking cycle. Forty-one percent of companies are now actively participating in a CBRS initiative, a further 47% plan to do so within a year, and 71% believe it will fundamentally change how the industry interacts with health authorities within five years. Early pilots have demonstrated the technical feasibility of a single dossier reviewed simultaneously by multiple regulators — a structural departure from the current one-to-one submission model with significant implications for how regulatory operations are designed.

Alongside this, data aggregation platforms (data lake, data fabric, etc.) are connecting regulatory systems with clinical, safety, quality, and commercial data for analytics and AI model training; all large organizations in this study now have that connectivity in place. As agentic AI capabilities mature, this infrastructure will likely reopen a strategic question many organizations thought settled: whether a single integrated regulatory platform or a well-connected best-of-breed architecture better serves the function’s evolving requirements.

Foundations Are The Defining Success Factor

Taken together, the latest findings point to an industry that broadly understands where it needs to go but has not yet made the organizational investments required to get there. The organizations most at risk aren’t necessarily those with the oldest systems or the most constrained technology budgets, but rather those that have accumulated capable, modern technology on top of immature processes and inconsistent data ownership — and have not yet recognized that combination as a structural vulnerability. A well-configured RIM platform cannot compensate for the absence of data accountability, nor can advanced AI tooling or increased tech investment fill in for a lack of process maturity. Over time, the organizations that consistently perform at the highest levels, and that the FRI now identifies as best-placed for what comes next, are those that have done the fuller foundational work.

References

1. Gens & Associates Inc, 2025 Operational Excellence and World Class RIM℠ Study. Available from mid-April 2026 at https://gens-associates.com/2026/04/03/2025-operational-excellence-and-world-class-rim-study-whitepaper/ .
2. Capability Maturity Model Integration (CMMI) Institute: www.cmmiinstitute.com.

About The Author:

Steve Gens is managing partner of Gens & Associates Inc., a life sciences benchmarking and advisory firm specializing in regulatory information management. He has over 30 years of business experience. His early career was spent at Waterford Crystal and Johnson & Johnson before he moved into consulting, managing global healthcare consulting practices for Booz Allen Hamilton and First Consulting Group. His expertise spans deep strategy formulation, organization development, industry benchmarking, and information management strategy.