Is Pharma Solving the Right Problem with its Data Transformation Efforts?

The Identification of Medicinal Products (IDMP), the International Organization for Standardization’s (ISO) specific standards for medicinal product identification, ought to have been a bigger deal by now (1, 2). The structured data standards promise to radically transform the way that life sciences companies—and regulators—manage, exchange, and use information, ultimately for the benefit of patients. Lamentably, those benefits have been delayed, lost in the process of getting the detail right. But is it too late?

IDMP has been on this industry’s agenda, in the European Union certainly, since around 2012. Yet repeated postponements, shifting timelines, and years of uncertainty around the actual specifications led to a loss of momentum. As a result, companies have failed to capitalize on the inherent benefits of having better, richer, and more reliable data in a much more re-usable format. Instead of investing early in modern data foundations, many organizations adopted a “wait-and-see” stance—doing the minimum to maintain compliance rather than building toward a broader data strategy.

“We are already too late. We should have started 10 or 15 years ago,” Peter Brandstetter, senior manager, Accenture, said, referencing the industry as a whole, during the panel discussion. Companies in other sectors, he argued, have across similar timelines built robust product lifecycle management (PLM) capabilities, unified core data assets, and adopted structured digital processes—far beyond regulatory needs. The life sciences industry, by contrast, has typically implemented only what has been necessary “to pass the next deadline”. In so doing, the industry has missed an important opportunity to modernize the way product data flows across manufacturing, supply chain, regulatory, quality, and safety.

The consequences of companies’ inertia are now surfacing. Projects have exceeded budgets, systems still don’t align, and internal teams are still at odds about sources of product “truth”. Frits Stulp, partner, Life Sciences, Implement Consulting Group, pointed to entire careers that have been derailed by hasty decisions, immature requirements, and runaway implementations—those that might that solved yesterday’s problems but have created new ones in the process (3).

And yet the industry is enthralled by the promise of artificial intelligence (AI) to transform the efficiency and output of essential every-day functions and processes, and even reframe the way that teams are doing these tasks. With still-fragile data foundations, companies that have not done the deep data work yet are now fired up by AI’s growing potential could run into problems.

What are the limitations of AI?

Whatever else it is capable of, AI can’t save an organization from poor-quality or inconsistent data (3). Although there is a popular belief that large language models (LLMs) and generative AI (GenAI) can “fill in the gaps”, infer missing structure, or make sense of patchy content, the reality is starker, certainly in a regulated environment.

“AI really requires good data,” Remco Munnik, owner and founder, Arcana Life Sciences Consulting, said in the panel discussion (3). “Without structure; without governance that provides meaning, AI struggles to make sense of information.”

Brandstetter agreed, noting that the current AI hype cycle is causing some companies to jump straight into experimentation without ensuring they have the foundations to support trustworthy output. This, he warned, “will lead to wrong results.” All of which could ultimately undermine their trust in AI.

The more effort companies put into improving their data and what can be done with them, the greater gains they can expect from their use of AI. There are no real short cuts.

And yet AI pilots are becoming increasingly commonplace, applied to improve safety signal detection, submission generation, labeling harmonization, and more. And yes, there have already been some promising results (3). But real and extended progress (e.g., beyond a single use case, or function) hinges on the quality and consistency of the underlying data.

Companies that have put off or skimped on IDMP, continuing to see it primarily as just another regulatory compliance burden; or those whose product data remains unstructured and locked in documents, remain fundamentally ill-equipped for the AI-enabled future they can now picture.

How can a company’s regulatory affairs department facilitate IDMP?

A company’s Regulatory Affairs department has the potential to become a “data powerhouse” for the organization, elevating its status beyond that of a compliance function. Traditionally, regulatory teams have been “admin heavy”, dealing in documents, variations, labeling updates, and lifecycle paperwork. Yet, as Stulp noted, the regulatory affairs department also holds some of the most valuable, regulator-validated product information in a life sciences company (3). If structured correctly, these data could serve as a strategic engine.

With clean, standardized data sets, AI could do more than generate templates or speed up submissions. It could help answer portfolio-level questions, reveal trends, support patent strategy, and reshape the way that organizations anticipate changes in global markets.

Munnik added that when AI is harnessed properly, it won’t replace regulatory specialists, but rather elevate them. He referred to one prototype scenario where structured product data allowed automated propagation of approved company core safety information (CCSI) changes downstream, through English and local labeling, patient leaflets, and multiple translations. Rather than making people redundant, this led to greater efficiency, consistency, and the elimination of expensive manual translation cycles (3).

This is the kind of value the industry has talked about for years, yet is only now beginning to unlock, as higher-quality, more structured and consistent data becomes a reality.

How do IDMP and AI impact the industry’s future?

Ultimately, IDMP is a critical enabler of automation, interoperability, and intelligence, if the relevant stakeholders approach it in the right way.

The panel noted that the European Medicines Agency’s (EMA’s) Product Management Service (PMS) is the “linchpin” of the shift toward structured regulatory data (4). Stulp pointed to its increasing maturity and its use in shortage management, electronic application forms, and future replacement of XEVMPD (the current Extended EudraVigilance Medicinal Product Dictionary) (3). EMA, Munnik said, has “done its homework”; in other words, the burden now sits with Marketing Authorization Holders (MAHs) to enrich, validate, and align their data.

From a higher viewpoint, IDMP creates a single language for product data, not just for EMA submissions but across internal functions and global markets. For AI, this consistency is essential. It is transformative for regulators too, while for patients it is the key to faster access to better-quality information.

Where companies embrace IDMP as a foundational data strategy, they will increase their opportunities to innovate. But where does that leave organizations that have fallen behind? How can they regain lost ground?

Where has the industry gone wrong?

A series of recurring themes has been seen across industry transformations that have contributed to a loss of momentum, or poor outcomes to date, including the following:

• Fragmented leadership
  • Successful organizations have cross-functional leadership: not regulatory alone, or IT alone, but rather enterprise-level alignment around data as an asset.
• Projects that have run away from their purpose
  • Programs often start well but eventually veer off, losing sight of their original goals until someone is brave enough to stop the clock, Crone noted.
• Companies chasing tools rather than outcomes
  • Front-runners view tools as experiments to pilot, test, adopt, or discard quickly; they don’t invest millions before proving value, Munnik said.
• Teams clinging to “waterfall planning” in an agile world
  • Incremental wins matter; so does transparency. Regulatory data journeys can’t be executed as monolithic, multi-year programs with no visible progress.
• Minimum compliance mindsets, which then backfire
  • Doing “just enough” in time for each respective IDMP deadline has left many organizations with an incomplete, inconsistent, or contradictory data estate. Now, attempts to introduce AI are exposing the cracks.
• Vendors are too often viewed as “black boxes”, rather than partners
  • As EMA’s interfaces go live companies should be working closely with their suppliers, Stulp said, to maximize readiness, transparency, and alignment on roadmap and capability. EMA’s PMS application programming interface (API) allows registered industry and network users to view and edit medicinal product data directly through their database systems.

All is not lost, though. MAHs now harness everything that EMA has done to ease their respective transitions, such as:

• Validating regulatory information management data against PMS
• Comparing the organization’s data granularity with EMA’s
• Fixing missing or mismatched product families
• Preparing for enrichment of manufacturing and packaging data
• Addressing any discrepancies that might undermine future automation.

In parallel, companies should align with their preferred vendors to ensure they will be able to exchange data with EMA PMS and support the required transparency; develop a long-term data vision that goes beyond Regulatory; and embrace small, value-driven steps that show visible progress (both to teams, and to senior stakeholders).

If companies begin this work now, they could still catch up, Brandstetter suggested. If they continue waiting for certainty, tools, or perfect requirements, on the other hand, they could be putting their futures at risk.

Giving glimpses of what that could have in store, Stulp referenced the rise of “trusted regulatory spaces”—shared cloud environments where regulators and industry can work collaboratively on data, review processes, and documents. Such facilities promise to accelerate approvals, reduce back-and-forth cycles, and dramatically improve the quality of information patients receive.

Brandstetter added that with fully data-driven submissions, the day would come when companies might submit a drug and receive immediate, even automated approval. Such a shift would compress timelines, reduce cost, and bring treatments to patients at unprecedented speed.

As for patients, superior structured data will lead to clearer, more reliable, and more accessible information, Crone said—something that will matter increasingly as AI becomes part of people’s everyday toolkit.

References

1. ISO. ISO 11615:2017 Health Informatics—Identification of Medicinal Products—Data Elements and Structures for the Unique Identification and Exchange of Regulated Medicinal Product Information (ISO, October 2017) https://www.iso.org/standard/70150.html
2. FDA. Identification of Medicinal Products (IDMP). FDA.gov, https://www.fda.gov/industry/fda-data-standards-advisory-board/identification-medicinal-products-idmp (accessed Dec. 15, 2025).
3. ArisGlobal. Powering Compliance, Data, and the AI-Ready Future. Life Science AI Exchange Podcast (October 2025). https://www.arisglobal.com/podcast/
4. EMA. Product Management Service–Implementation of International Organization for Standardization (ISO) Standards for the Identification of Medicinal Products (IDMP) in Europe. Oct. 15, 2025. https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/products-management-services-pms-implementation-international-organization-standardization-iso-standards-identification-medicinal-products-idmp-europe-chapter-1-registration-requirements_en.pdf

About the author

Ian Crone is VP Europe & APAC Regulatory Solutions at ArisGlobal.