
Munnik Revisits His 2026 Predictions: A Midyear Check-In on Regulatory Reform, Data, and AI in Pharma
Key Takeaways
- Regulatory Optimisation Group efforts are converging on a future-state lifecycle change process, shifting from document-based submissions toward structured database updates, with tangible success targeted for 2027.
- Economic and geopolitical pressures are driving short-term compliance prioritization, commonly deferring rotational programs and capability building despite persistent digital skills gaps.
Arcana Life Sciences' Remco Munnik revisits his 2026 predictions on EU regulatory reform, AI adoption, and data governance, and previews pharma's biggest challenges for H2 2026.
PharmTech recently caught up with Remco Munnik, founder and owner of Arcana Life Sciences, to follow up on predictions he made
When PharmTech last spoke with Munnik about 2026, he emphasized the need for investment in data-driven infrastructure to ensure traceability from the point of data creation to final registration. He also highlighted promising innovations, such as the work of the
Now that 2026 is half over, Munnik offers his thoughts on what predictions came to fruition, which didn't, and why, as well as what's in store for the second half of the year and into 2027.
PharmTech: In December, you noted that advancements from the Regulatory Optimisation Group, combined with the European New Pharmaceutical Legislation, were set to redefine how the industry maintains compliance and achieves speed to market. Has this redefinition occurred?
Munnik: The redefinition is ongoing and a work in progress. Especially with the ROG, we have made significant progress, including a face-to-face (F2F) workshop in June to outline a future state process. We are now in the process of finalizing detailed discussions on specific technology and process requirements. After the summer break, I expect to plan another F2F workshop to finalize these activities, which should set the ROG to achieve tangible success in 2027.
With regard to the full impact of the European Union's New Pharmaceutical Legislation (NPL), we need to be patient. Working in a highly regulated environment requires thoroughness. I expect that during the second half of 2026 we will get more information with regard to the impact of the NPL and the actual implementation acts.
You also noted a widening skills gap caused by the digital evolution, advocating for on-the-job rotations and a leadership mindset to bridge this gap. Have you seen such rotations and mindsets within pharma so far this year?
Many organizations still treat digital evolution as a technical upgrade rather than a leadership challenge. The companies moving fastest are those that frame transformation as capability building, not compliance, investing in cross-functional rotations, data literacy, and new operating models.
But there's another factor slowing progress: the economic reality. Global political uncertainty, pricing pressures, and constrained margins are forcing teams to prioritize short-term compliance deliverables over long-term capability development. When budgets tighten, rotations and skill-building are often the first to be postponed, even though they are exactly what is needed to close the gap.
In December, you identified fragmented legacy systems and limited data standardization as barriers to AI adoption, noting that successful AI adoption requires technical tools plus robust data governance and a significant culture shift within organizations. Have any of these occurred so far this year?
I do see progress, but it remains slow. Several large pharma organizations have now put data governance frameworks in place, especially those linked to identification of medicinal products (IDMP). There's a strong appetite from management to “solve everything with AI,” but the reality is unchanged: AI only works when the underlying data governance is solid. Without consistent standards, ownership, and quality controls, even the best tools can't deliver meaningful outcomes. Many companies are still working on the basics: establishing trusted data, using it consistently, and making decisions based on it.
The second half of the year should also be a real opportunity for the EU network (EMA, National Congress on Autonomous Systems, and industry) to make tangible progress with regard to post-market surveillance (PMS) qualification.
What's the most unexpected development in pharma manufacturing and development so far in 2026?
I'm genuinely excited by the level of support and momentum behind PMS and IDMP this year. Key stakeholders across the network have been far more aligned and vocal than in the past. That alignment is finally creating the conditions for real progress: clearer priorities, faster decision-making, and a shared understanding that structured data is no longer optional but foundational.
What do you see as the biggest challenge facing pharma manufacturing and development for the remainder of 2026?
The biggest challenge for the remainder of 2026 is digitalization with consistency. As more processes across manufacturing and development become data-driven, the pressure to maintain clean, aligned, and interoperable data increases dramatically. Digitalization only works when the underlying data is stable, governed, and consistent across regulatory, quality, and manufacturing systems. Today, many organizations still operate with fragmented and legacy systems, mismatched data definitions, and stand-alone processes. This creates friction, slows automation, and undermines trust.
The companies that will succeed are those that treat digitalization as a data consistency challenge, as opposed to a technology project, ensuring that what is submitted, manufactured, released, and reported is all driven from a reliable source.




