Through the looking glass

2026 must be the year that pharma gets its data work back on track

Despite all their talk of more strategic exploitation of data over the last ten to 15 years, the life sciences remain significantly behind other industries in achieving this.

With AI now seen as an important means to transform workflow, the data transformation imperative is more critical than ever, says Ian Crone, ArisGlobal’s VP Europe & APAC Regulatory Solutions, reporting on a recent panel discussion on the topic.

In Europe, ISO’s IDMP standards for medicinal product identification have been on the agenda for life sciences for well over a decade now. Yet refining the detail has taken too long, deadlines have slipped, and as momentum has faltered companies have failed to capitalise on the inherent benefits of having better, richer and more reliable data in a more reusable format.

Might it therefore be too late to turn things around, especially given the industry’s appetite to harness AI to transform operational efficiency and more? Peter Brandstetter of Accenture believes so – unless something material changes.

“We should have started ten or 15 years ago,” he said of the industry as a whole, referencing the largely missed opportunity to modernise the way product data flows across manufacturing, supply chain, regulatory, quality and safety.

Crucially today, companies’ growing ambitions for AI are difficult to realise if the source data is fragmented, inconsistently structured and of unreliable quality. As Remco Munnik of Arcana Life Sciences Consulting put it, “Without structure – without governance that provides meaning – AI struggles to make sense of information.”

Peter warned that current hype around AI 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,” undermining 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 shortcuts.

And yet AI pilots are becoming increasingly commonplace, applied to improve safety signal detection, submission generation, labelling harmonisation and more. And yes, there have already been some promising results.

But proper progress (e.g. beyond a single use case or function) depends on what lies underneath. 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.

Regulatory Affairs departments could be doing more to lead the way, the panel suggested. As Implement’s Frits Stulp noted, this is a function that holds some of the most valuable, regulator-validated product information in a life sciences company.

If structured more optimally, that data could serve as a strategic engine. AI could then do more than generate templates or speed up submissions; it could help to answer portfolio-level questions, reveal trends, support patent strategy and reshape the way that organisations anticipate changes in global markets.

In so doing AI won’t replace regulatory specialists, but rather elevate them, Remco said. 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 labelling, patient leaflets and multiple translations.

Rather than making people redundant, this led to greater efficiency, consistency and the elimination of expensive manual translation cycles. Ultimately, IDMP ought to be a critical enabler of automation, interoperability and intelligence.

The panel noted that the European Medicines Agency’s Product Management Service (PMS) is the ‘linchpin’ of the shift towards structured regulatory data. Frits 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).

Ultimately, IDMP creates a single language for product data – not just for EMA submissions, but also 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. EMA, Remco said, has “done its homework”; in other words, the burden now sits with Marketing Authorisation Holders (MAHs) to enrich, validate and align their data.

Where companies embrace IDMP as a foundational data strategy, they will increase their opportunities to innovate. But what of those organisations that have fallen behind?
The panel pinpointed areas where companies have previously come unstuck, as a means to guide better onward action. These included:

• Fragmented leadership
Successful organisations 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
Programmes often start well but eventually veer off, losing sight of their original goals until someone is brave enough to stop the clock, Ian 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, Remco said

• Teams clinging to ‘waterfall planning’ in an agile world
Incremental wins matter; as does transparency. Regulatory data journeys can’t be executed as monolithic, multiyear programmes with no visible progress

• Minimum compliance mindsets that then backfire
Doing ‘just enough’ in time for each respective IDMP deadline has left many organisations 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 – Frits said – to maximise readiness, transparency and alignment on road map and capability.

To aid them in their next steps, MAHs should now look to harness everything that EMA has done to ease their particular transitions.

In parallel, companies should consult 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 demonstrate visible progress.

If companies begin this work now, they could still catch up, the panel agreed. There is a good deal to play for once companies find their stride.

This includes the opportunity to leverage emerging ‘trusted regulatory spaces’ (shared cloud environments where regulators and industry are able to work collaboratively on data, review processes and documents), with a view to accelerating approvals, reducing back-and-forth cycles and improving the quality of patient information.