The data beneath the disruption: why process maturity and data accountability will determine who is ready for what comes next

Regulatory information management (RIM) has been transformed over the past decade. Most organisations by now have modernised their global RIM systems, improved data quality and begun deploying AI and automation in targeted areas. According to our latest research, however, that progress may not be sufficient for what comes next. The industry is now contending simultaneously with AI and advanced automation, cloud-based regulatory spaces, structured data mandates and workforce transformation — each development substantial in its own right, while collectively reshaping the way that regulatory information is created, managed and exchanged.

Gens & Associates’ World Class RIM℠ benchmark, now in its 47th cycle, includes a Future Readiness Indicator (FRI) (1). Where traditionally the benchmark has focused on current operational status, the FRI measures whether companies are structurally equipped to sustain performance through a period of simultaneous, compounding change. The findings suggest most are not. Just one of the 59 organisations participating in the study achieves “ready and leading” status as per FRI analysis. More than a fifth (21%) fall into the “at risk” category. The remaining 77% occupy a “developing” band: not critically exposed, but with gaps requiring deliberate attention.

Strong performance today, it emerges, will not automatically translate into resilience tomorrow. The instinct to address the gap primarily through technology investment may be misplaced, too.

Why technology alone does not close the gap

Across every research cycle since 2014, the Gens & Associates benchmark has found no correlation between top performers and any particular software vendor or system strategy. The differentiator has consistently been the organisational and process layer that sits beneath the technology — how data is governed and owned, how processes are measured and improved, and how change is managed as an ongoing competency rather than a one-off project.

Our latest study reinforces that finding and extends it forwards. The organisation identified as achieving “ready and leading” status is a consistent strong performer across multiple research cycles, distinguished not by its technology choices but by a combination of capabilities operating simultaneously. These include effective cross-functional collaboration, high process maturity, effective workforce development, change management embedded as a core competency, and a fully implemented data governance model with explicit ownership of mission-critical data elements.

In other words, KPI-driven continuous improvement is standard practice, not aspiration at this company.

Data accountability: a narrow but decisive advantage

Among the most striking findings from this cycle is the rarity — and the measurable impact — of genuine end-user data accountability: the practice of holding individuals and teams explicitly responsible for the accuracy and quality of data in their systems. Of 59 organisations surveyed, only four had established this as an organisational strength.

The performance differential is substantial. Those four organisations recorded an aggregate data quality confidence score of 93%, against 50% for the remaining 55. Their aggregate efficiency across 15 core RIM capabilities was 93%, compared with 70% for their peers. For specific authoritative sources — health authority commitment tracking data, for instance — the gap widens further: 100% high-confidence versus 44%.

Almost two-thirds of the broader participant group are actively working toward stronger data accountability, which suggests the principle is understood. The difficulty lies in implementation. Accountability in regulatory operations has historically been organised around documents and dossiers, where ownership is well-established. Extending it to individual data elements is newer territory for regulatory teams, even though clinical operations and supply chain have operated this way for years. IDMP structured data standards have helped accelerate the shift from a document-oriented to a data-oriented regulatory function, and that shift is now working through most organisations at differing speeds.

Building accountability at scale means embedding it at the functional, individual and team levels simultaneously — with product teams owning the quality of data pertaining to their portfolio in the regulatory systems. The framework mirrors long-standing practice in other data-intensive functions; the gap is in applying it consistently to regulatory.

Process maturity as a performance multiplier

Process maturity, measured formally for the first time this research cycle across nine core regulatory process areas, emerges as one of the clearest predictors of business outcomes. Top performers predominantly operate at Levels 4 and 5 in the CMMI framework; 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. Top performers report operational throughput improvements at 80% versus 47% for peers; time-to-filing improvement in secondary markets at 70% versus 26%; operating cost improvement at 90% versus 39%; and user productivity and effectiveness at 90% versus 50%. What maturity at the upper levels actually provides is organisational capacity — the ability to absorb new workload, integrate new technology and manage change without the friction that poorly defined or inconsistently applied processes introduce. That capacity cannot be acquired quickly. It is built through sustained investment in process design, measurement and improvement, which is why organisations that have already done that work tend to extract disproportionate value from each successive generation of new technology.

AI: a realistic assessment

The data on AI from the current study is more nuanced than either optimistic or sceptical commentary tends to suggest. 47% of companies report pilots or implementations underway, and every large organisation in the study claims significant AI investment. Of 131 benefit-realisation responses tracked across all AI and advanced automation use cases, however, just two exceeded expectations. Broad consensus on realistic implementation timelines has shifted to 2027–2028 — a recalibration rather than a retreat.

Where AI is already delivering measurable results is in areas where processes are well-defined, data is structured and the task scope is bounded. In late-stage biopharmaceutical R&D, clinical document generation leads the field, followed by AI-assisted translation and, more recently, CMC content generation. The combination of AI-generated first drafts, AI-assisted quality review and reduced translation cycles has the potential to compress the timeline from clinical study closure to dossier filing substantially. Broader authoring transformation — covering dossier sections rather than individual documents — is a realistic medium-term horizon, with the tipping point projected in the 2028 timeframe.

Cloud-based regulatory spaces and data infrastructure

Two further developments are noteworthy. Cloud-based regulatory spaces (CBRS) have gained significant momentum: 41% of companies are already participating in a CBRS initiative now, a further 47% plan to do so within a year, and 71% believe it will fundamentally change how the industry works with health authorities within five years. Early pilots have demonstrated as technically feasible a single dossier reviewed simultaneously by multiple regulators — a structural shift from the current one-to-one submission model.

Alongside this, data aggregation platforms are connecting regulatory systems with clinical, safety, quality and commercial data for analytics and AI model training; all large organisations in this study now have that connectivity in place. As agentic AI matures, this infrastructure is likely to reopen a broader strategic question about whether a single integrated regulatory platform or a well-connected best-of-breed architecture better serves an organisation’s evolving needs.

What the data implies for those setting strategy now

The picture that emerges from the new study is of an industry that understands the direction of travel but where the gap between strategic intent and organisational readiness remains uncomfortably wide. Most organisations occupy, according to change management consultant William Bridges’ framing (2), a neutral zone: the uncertain space between an established way of working and an emerging one that has not yet fully taken shape.

Bridges’ observation was that the neutral zone is where the most important developmental work occurs, provided organisations invest in it rather than rushing through it. Those best placed for the period ahead treat data governance, process maturity and change management not as preparatory activities but as permanent operating disciplines — and are thereby better placed to extract value from each new technology development as it arrives.

Perhaps the more thought-provoking implication from the latest benchmark data is that the organisations most at risk are not necessarily those with the oldest systems or the smallest technology budgets. Rather they are those that have accumulated capable technology on top of immature processes and inconsistent data ownership — and may not yet recognise that as a vulnerability.

References and notes

1. Gens & Associates Inc, 2025 Operational Excellence and World Class RIMSM Study. Available from mid-April 2026 here https://gens-associates.com/2026/04/03/2025-operational-excellence-and-world-class-rim-study-whitepaper/
2. Bridges, W. (2004) Transitions: Making Sense of Life‘s Changes (revised 2nd edition). Da Capo Press, Cambridge MA.