Christopher Cole

Beena Wood, Qinecsa, explains how AI could revolutionize pharmacovigilance, if data, trust, skills, and organizational barriers are addressed first.

Why Data, Trust, and Skills Are the Foundations of AI-Driven Pharmacoviginlance

 recently spoke with Beena Wood, chief product officer, Qinecsa Solutions, to get her perspective on trends that shaped pharmaceutical development and manufacturing in 2025 and where things are headed in 2026. In this part 2 of our three-part interview, Wood outlines a forward-looking vision for how AI could transform pharmacovigilance (PV), while candidly addressing the structural, regulatory, and organizational barriers that must be overcome first. She imagines a future in which AI-enabled surveillance systems can rapidly detect safety signals worldwide—across languages, geographies, and datasets—within hours rather than months. In Wood’s words, this would allow systems to “detect—not weeks, not months, but hours—the same reports across multiple languages,” cross-reference similar cases globally, and proactively alert manufacturers and regulators in near real time.

While Wood emphasizes that this vision is technically feasible, she stresses that today’s biggest obstacle is not algorithmic sophistication but weak data foundations. Fragmented, non-interoperable, and poorly validated datasets severely limit AI’s effectiveness in PV, she says. As she puts it, “No amount of AI sophistication will help” if the underlying data are not harmonized. She notes that leading organizations in 2025 are those investing in these “boring foundations” now, enabling faster and more scalable progress later.

Trust and explainability emerge as another critical theme, with Wood framing trust as a combination of transparency, auditability, accuracy, and risk-based evaluation. Black-box AI systems, she argues, will not gain traction in high-risk domains like safety monitoring, especially under such evolving regulatory frameworks as the EU AI Act. Clear guardrails, ethical frameworks, and responsible AI practices are essential for adoption.

Wood also highlights economic and organizational readiness as key barriers. PV teams must compete for investment by clearly articulating return on investment, while organizations need robust change management strategies to integrate AI effectively. She cautions that the real skills challenge is not simply technical proficiency but critical thinking—teaching professionals how to evaluate AI outputs, recognize limitations, and identify hallucinations. Ultimately, Wood sees AI not as a replacement for human expertise, but as an amplifier: tools that, when used responsibly, can make skilled professionals “super humans” rather than substitutes.

Editor's note: This transcript is a lightly edited rendering of the original audio/video content. It may contain errors, informal language, or omissions as spoken in the original recording.

So think of a patient in rural India and, say they are on this newly approved gene therapy, and they experienced an unusual reaction. And then think about the possibility of an intelligence surveillance system that, within hours, is able to detect—not weeks, not months, but hours—the same reports across multiple languages, is able to cross reference it with about 47 similar cases globally and then it identifies that there is a genetic marker correlation using real-world data analysis and then, simultaneously, alerts the manufacturers as well as the regulators and agentic AI coordinating already the proactive measures.

That's where I want PV to get to. And it is theoretically possible with AI capabilities, with proactive surveillances, but we don't have the foundations there that we need in order to make it real. And that's where the barriers that you're talking about come into play and things that I talked about earlier. So, for example, the data foundations, the data heterogeneity problem and the fragmentation problem. There is simply… the uncomfortable truth for us is that we are building AI systems on data foundation that aren’t either standardized or they're not interoperable and they're often not validated for AI use cases. And so, when you have this data scattered with incompatible formats and legacy systems and regulatory silos, and if you want to create that AI model without a harmonization, it's going to be extremely difficult.

No amount of AI sophistication will help. And so that's, I think, an uncomfortable truth, and that made us realize this year, in 2025. The good organizations are the ones who are starting to do those boring foundations so they can scale very quickly afterwards.

I think the other barrier is still the explainability and trust of these AI systems. So, when I look at the trust, I think of it as the explainability, meaning the transparency, the auditability, as well as the accuracy, and weighed by the risk. That's how I look at trust in an AI system.

All of these play a part in either magnifying it or diminishing the use cases for AI. So, if we still talk about AI as a black box, it's not going to work. It's super important to have those guardrails. What kind of guardrails, what kind of ethical framework, responsible AI are we bringing? How can it be trusted? So I think that's another important barrier that, as vendors, as well as consumers, and put that in context of EU AI Act and in a high-risk system—I know there are still conversations about what becomes high-risk systems within our domain—I think all of that becomes really important. And then the economic reality is actually a barrier. I can say, definitely, with my domain, it's about asking for initial investment for AI promising efficiency gains. We know there'll be efficiency gains, but how do we sit in around the R&D table where PV and safety can build those ROI and use cases, compete against other use cases, in order to get those investments to be able to do it? How do we build that ROI for the company?

I have to say the organization’s readiness is another one. I think not looking at it as a small change, but actually having an entire change management for AI and even modifying the way that we look at skillset. I mean, is it a job description or are we building in a set of skills we need that are transferable across the organization? So I think those are the things that are barriers and what needs to be overcome.

What concerns me about that is I'm not sure, as an industry, if we're solving for the right problem. When we are talking about skills or barriers, and we have the contradictory report about shadow AI and 90% are using AI anyway, whether it's enterprise approved or not, the real skills challenge over there is: How are we teaching people to critically evaluate AI outputs in the context of the work? For example, in context of safety, how are we teaching people to evaluate if AI is operating within competency or is it hallucinating confidently? How do we teach that? How do we teach when AI cannot answer a question? And so that's how I would frame that question.

And also I would see it as, not thinking about technical skills in isolation. AI skills sometimes is considered as a technical skill, so not thinking about that in isolation, but thinking it as an intersection of multiple capabilities and collaboration. So, for example, data scientists and technology folks, do they understand the PV domain? Do the PV domain folks understand technology? How do we bring that together as a collaboration to bridge those gaps? Are they change management capabilities so we can actually do get to those transformative successes?

I think human intellect is inherently beautiful. And I don't think it can truly ever be replaced, but what we can do is we can enable these AI tools to maybe make us super humans, is how I would think about it.

Videos

Beena Wood, Qinecsa, saw 2025 as an AI superposition in which pharmacovigilence needed better data foundations and regulation.

Realizing the Potential of Enterprise AI within the Global Pharmacovigilance Domain

 recently spoke with Beena Wood, chief product officer, Qinecsa Solutions, to get her perspective on trends that shaped pharmaceutical development and manufacturing in 2025 and where things are headed in 2026. In this part 1 of our three-part interview, Wood explores the complex landscape of pharmacovigilance (PV) in 2025, a year she characterizes as the "AI Superposition." Wood explains that the industry is currently navigating all stages of the 

 simultaneously, balancing breakthrough technological announcements against the disillusionment of failed pilots and the rise of a "shadow AI economy." Regarding this state of uncertainty, Wood states, "Unless we open the box of specific AI implementation within an organization, it's in superposition of either transformative success or an epic failure."

The discussion highlights a sharp divide between aspirational goals and implementation reality. While some organizations have achieved concrete successes, many in the PV domain are facing a significant "reality check." Wood points out that even leading pharmaceutical companies often lack the foundational data infrastructure necessary to support sophisticated AI systems. She emphasizes the high stakes involved in these advancements, noting, "very impactful use cases would be faster signal detection, earlier signal detection, improving the safety profile, and earlier patient protection."

Wood also addresses the perceived "skills gap," suggesting it may be misdiagnosed; the real issue often lies in the lack of maturity and context awareness in enterprise AI compared with consumer AI, she says. Furthermore, Wood highlights the rapid acceleration of the regulatory landscape, noting that 2025 saw pivotal new frameworks from the FDA and the EU. Ultimately, she argues that successful AI adoption requires moving beyond hype to focus on the critical prerequisites—specifically data foundations—to bridge the gap between promise and practice.


I am Beena Wood, and currently I'm chief product officer at Qinecsa Solutions, the software that brings pharmacovigilance [PV] into end-to-end, different products that ties the PV landscape together.

2025 was a year, it was all about the promise of AI and the implementation reality, and the gap in between, which that became impossible to ignore in 2025. Essentially, we experienced every phase of the Gartner Hype curve all at once, simultaneously. And what I mean by that is there were inflated expectations because of breakthrough announcements, what AI can do, and the pace at which AI was moving.

There was, of course, for a long time, there's no conversation about AI, especially from my domain, from pharmacovigilance domain. And then we were talking about cloud and SaaS, even as recently as five years ago. And then we jumped into, oh, there's AI ML use cases and NLP, and those dominated. And then the conversation very quickly shifted into now agentic AI and the possibilities, and then the inflated expectations with those breakthrough announcements of technology, and then, simultaneously living the trove of disillusionment because of failed pilots—investing money into AI, and I'll get to some of those failures and why, from my perspective, but also that disillusionment was happening. And then, simultaneously, there was a plateau of productivity of that hype curve, if we remember that, and that is from two factors: organizations getting this right, but it's also because we heard of shadow AI economy, in the MIT report, where people are plowing ahead, whether the enterprise is ready or not. They are using for their personal productivity.

So, I think that this is just an interesting confluence. Unless we open the box of specific AI implementation within an organization, it's in superposition of either transformative success or an epic failure. And so it is like, what 2025 made us, forced us, to do is open these boxes and confront what is actually working, and I still think we are in the early stages of that. I still think we are working in superposition of all of it as well as the hype curves. All these are hype curves. But what did we achieve, really in 2025, I would say, is some of AI moved to concrete use cases, from concept to concrete use cases for those who got the prerequisites right.

So, for example, clinical trial acceleration. I had been following this acquisition of Deep 6 AI, partly because one of my friends worked there, by Tempus and where they had showed the use cases of the clinical trials actually reducing by 50% the time, using real world data as well as the AI capability. So that is very interesting use cases to consider. And then, the other area of application, I think where it has been successful in this lifecycle is drug discovery and repurposing. Sanofi, for example, working on drug repurposing on their unpursued asset, or the asset that they have shelved, for rare diseases and then reducing that in silico model, the timeframe, because of AI.

And that kind of opens up… those are not just aspirational, but things that have been coming to life faster because of AI. Now, if I focus on PV itself, my area, there's a reality check. Theoretically, AI can, for example, very impactful use cases would be faster signal detection, earlier signal detection, improving the safety profile, and earlier patient protection. That exists; that enablement, the possibility, exists. But frankly, what I have experienced is most organizations, including leading pharma companies, haven't yet built the foundational layers that are necessary to make it successful.

So, for example, data foundation is an easy one. I think everybody will talk about it too, is that data infrastructure really doesn't exist yet to make these AI systems, those aspirational systems, come to life. I think that's an important thing that I experienced in 2025.

Another trend I saw was that perceived skill gap. I feel that problem of skillset is maybe partially misdiagnosed. And what I think is really look at why some of these consumer AIs are working really well. I would think it's because they are conversational, context aware, they are learning, or we can continue to help them learn and admit uncertainty if they're prompted, if they're built correctly. In enterprise AI, I think that maturity is lacking, or there's pockets of maturity but other areas that are lacking. I think that's another thing that I saw is that perception of skillset gap to reframing the problem correctly; instead of saying employees need to learn AI, is a real lesson “The teams are showing us what good AI looks like, and is that what we should expect from enterprise AI?” And I would say one more thing is regulatory. I think that's really important. I think that the evolution of regulatory guidances have accelerated. This year alone, we saw FDA, for example, coming up with their pivotal guidance on AI, their framework on risk assessments, and the CIOMS working group with their paper that just came out, which is pretty comprehensive. For example, FDA, establishing the CCRI, the Center for Real World Evidence Innovation, the EU Darwins, etc.

So, the regulators, I think, are starting to accelerate, or started to accelerate, this year, and it'll be really interesting to see how these trends take us into the next year.

Saharsh Part 3

 recently spoke with Saharsh Davuluri, Vice Chairman and Managing Director, Neuland Labs, to get his perspective on trends that shaped pharmaceutical development and manufacturing in 2025 and where things are headed in 2026. In this part 2 of our three-part interview, Davuluri explores the critical necessity of modernizing global manufacturing infrastructure to support the rapidly expanding peptide and API segments. He identifies a continuing trend in which peptides are becoming a vital modality in drug development, extending far beyond popular weight-loss analogs to encompass diverse therapeutic indications, such as cardiovascular health, dermatology, and central nervous system-related conditions. Davuluri notes that as these candidates move through the pipeline, the industry must diversify its manufacturing base. "There is a need, from a development point of view, to have destinations outside China and Europe for peptide manufacturing," he explains.

A central theme of the discussion is the urgent requirement for technological evolution within API manufacturing, which Davuluri argues has not kept pace with the times. He points out that the sector has been slow to adopt modern concepts—such as flow chemistry, electronic batch records, and completely automated manufacturing execution systems—which has led to the industry being characterized as "primitive" compared with other sectors. He envisions 2026 as a pivotal year for a "catch up" in infrastructure, especially as the world maintains a strong interest in small molecules and synthetic peptides rather than moving exclusively toward biologics.

To meet this future demand, Davuluri suggests that regions like India will be essential due to their capacity for large-scale manufacturing and their status as a geopolitically neutral option. Ultimately, he stresses that drug manufacturers must prioritize digital transformation to bridge the gap with other high-tech medical fields. "I would think that drug manufacturers need to think about their infrastructure, think about how they would like to invest in automation, modernization, and digitization, so that our sector can really catch up and be on par with what you would see in modern biologics or vaccine manufacturing today in the world," Davuluri concludes.

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We've talked a lot about peptides in the past. I see that as a continuing trend. There is a need, from a development point of view to have destinations outside China and Europe for peptide manufacturing. There is an increasing number of candidates from the peptide segment entering into drug development.

Yes, there've been a lot of GLP-1 analogs, but there are also a lot of amylin analogs. There are different kinds of peptides, not just for weight loss, but for different kinds of therapeutic indications: dermatology, cardiovascular, CNS, and therefore, we see that peptides will become a very important modality going forward.

And I think 2026 will be a year where we will see a stronger need for peptide scale up and commercial manufacturing, particularly from a region like India, which tends to be favorable for large-scale manufacturing like peptides and also at the same time is maybe geopolitical more neutral. I see a lot of investment potential for peptides in 2026.

I think API manufacturing, again, that's the perspective I speak from, has for a very long period, not modernized with the times… you know, concepts like flow chemistry, electronic batch records, completely automated MES systems, the adaptation of these concepts in API manufacturing has been very slow and has kind of put us together as in a category which you might even call as primitive; there hasn't been a lot of evolution in that area.

I think 2026 should be a year where a lot of catch up should happen because there is an increasing reemphasis on small molecules and synthetic peptides, and the world is not completely moving lock, stock, and barrel to biologics. And, therefore the need for having modern, synthetic, small molecule peptide manufacturing facilities is very important.

And, therefore, I would think that drug manufacturers need to think about their infrastructure, think about how they would like to invest in automation, modernization, and digitization, so that our sector can really catch up and be on par with what you would see in modern biologics or vaccine manufacturing today in the world.

Saharsh Davuluri, Neuland Labs, urges API and peptide automation by 2026 to match modern biologics standards.

The Critical Need for Digital Transformation in the Peptide and API Sectors

Mike Stenberg, LGM Pharma, explains how faster FDA approvals under CNPV shift bottlenecks to production, requiring onshoring and earlier supply chain risk.

Navigating Financial and Opportunity Risks in Accelerated Drug Launches

 recently spoke with Mike Stenberg, Vice President – Business Development, LGM Pharma, to get his perspective on trends that shaped pharmaceutical development and manufacturing in 2025 and where things are headed in 2026. In this part 3 of our three-part interview, Stenberg explores the evolving manufacturing paradigm in the pharmaceutical industry, driven by accelerated FDA approval timelines. Traditionally, companies have relied on a 10-to-12-month approval window to finalize launch plans; however, with approvals now potentially occurring in one to two months under programs like the FDA 

Commissioner’s National Priority Voucher

 (CNPV) pilot, the primary bottleneck has shifted from the FDA to the production floor, Stenberg notes. He emphasizes that to take advantage of CNPV, companies must first onshore their manufacturing to the United States.

Because the "padding" between submission and approval has largely vanished under CNPV, manufacturers must now integrate commercial-scale planning from the very beginning of a drug's development, Stenberg continues. This "day one" approach requires identifying and securing supply chains upfront, a process that demands significant time, capital, and "boots on the ground." Furthermore, formulation must now be conducted with an eye toward immediate scale-up, ensuring that equipment is ready to move a product from the bench to commercial volumes without the luxury of an interim adjustment period.

Stenberg highlights that activities previously reserved for the phase following submission—such as procuring excipients and packaging—must now occur much earlier to ensure a successful launch. This shift introduces substantial financial and opportunity risks. If a drug fails to receive approval, companies are left with a "huge hole" in their manufacturing schedules and a significant loss of expected revenue, compounded by the cost of materials already brought in-house.

Ultimately, while the industry benefits from faster patient access to drugs, Stenberg notes that the transition requires a difficult adjustment. The risk has moved earlier in the development process, forcing companies to commit resources and production capacity before a final regulatory decision is reached.



Step one in shifting your playbook is you have to be in the United States to take advantage of the voucher program. So, step one: onshore your manufacturing and move to the United States. But having done that, it's a bit of a dilemma. We have in our industry, bemoaned forever the fact of how long it takes to get a drug approved, and I think 10 to 12 months is a dramatic improvement. It used to be 18 months. But having said that, we also baked it into our processes and we baked it into our timelines. And we came to depend on that time. So now what's happened is you move the bottleneck associated with FDA approval, you move that bottleneck to the production floor.

And, so, approval's not the issue anymore. It's manufacturing the drug in commercial volumes. So that means you have to do everything earlier. In fact, you have to do it right from the beginning of the program. You have to identify and secure your supply chains right up front. That's not something you can do on the fly, by the way.

You have to have already started doing that kind of stuff because it takes boots on the ground, it takes time, it takes a lot of money to do those things. You also have to start formulating with an eye towards immediate scale up. So you have to have that entire equipment trained somewhere in your facility so that you can go from the bench all the way to commercial volumes.

It really affects virtually everything we do in the manufacturing world. You're having to do so much more of that from day one, from securing your supply chain to formulating more quickly, but formulating to scale up immediately. We used to have some time to do those kinds of things. That time is gone. We'll adjust of course, and it's a good thing overall, but I think the industry's gonna miss that timeline, that padding, that they had between submission and approval.

When you look at the supply chain, alone, and when you think about bringing in the materials for a commercial scale up, especially if it's gonna be a big drug as opposed to say an orphan drug, that means, yeah, you've secured your supply chain and you understand where you're gonna get your supply, but you have to bring that supply in early, right?

You have to bring in all the materials and all of excipients and all of the packaging required for immediate scale up and commercialization. That has always been—that planning for launch—has always been typically what occurred during that interim phase, “Hey, we've submitted, things look good. We have 10 to 12 months, if not longer, to prepare for the actual manufacturing and launch across the board of this particular product.”

So yeah, you have risk associated with the materials that you have to bring in. Industries like ours have opportunity risk. If we're planning to scale up, we started to carve out space in our schedule and on our machines to manufacture that product. That goes away, you have a huge hole in your schedule, and you have a huge hole in your potential and expected revenues as well.

So yeah, the risk is substantial and it moves the risk earlier on in the process.

Saharsh Part 2

 recently spoke with Saharsh Davuluri, Vice Chairman and Managing Director, Neuland Labs, to get his perspective on trends that shaped pharmaceutical development and manufacturing in 2025 and where things are headed in 2026. In this part 2 of our three-part interview, Davuluri explores a digital transformation within the API manufacturing sector. He observes that while the API industry has historically been slower to automate than other chemical sectors, a new paradigm is emerging through the adoption of AI-based tools and digital workflows. He notes Merck’s Synthia tool for route synthesis and equipping a new R&D campus with advanced parallel synthesis equipment to better mimic plant-scale production.

Davuluri’s ultimate vision is a fully paperless manufacturing environment driven by electronic batch records and a manufacturing execution system, which ensures complete traceability and product quality. Davuluri highlights the practical impact of these advancements for the workforce, stating: "This is real, and this is something that's going to make life a lot easier for process chemists who are looking at making scalable processes when it comes to manufacturing."

Central to this transformation is a shift attracting and retaining scientific talent through fostering a "knowledge hub" in which scientists collaborate on innovative processes for new chemical entities. Unlike traditional roles that may feel academic, scientists in such an atmosphere are encouraged to go into the plants to oversee scale-ups, allowing them to see their designs evolve into actual medicine for patients. This focus on high-level expertise marks a significant departure from traditional industry models. Davuluri explains, "We are living in a world where it's no longer about labor arbitrage; it's about knowledge arbitrage."

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We are starting to see that there is a clear trend in using digital tools and AI for process development and scale up, but especially when it comes to API process development, scale up and manufacturing. What we are seeing is adaptation of AI-based tools. I think there's a Merck tool called Synthia that Neuland’s been using now; helps us, you know, identify the most appropriate route of synthesis for making an API.

That's a tool that we've been using now for quite some time and, right now, Neuland's in the process of building a new R&D campus in Hyderabad. As part of our plan, we intend to equip all our labs with these parallel synthesis equipment called Mya 4 by this company called Radleys.

The idea would be to use Synthia as an AI tool for coming up with the right process and then use these automated synthesizers at lab scale, which actually do a fantastic job of mimicking plant scale manufacturing, and then use eLab notebooks to capture all the data from these modern lab equipments and essentially give us a fully prepared recipe with no human intervention, which is kind of locked and loaded to plug into a manufacturing execution system, or an MES, at plant scale. This is real, and this is something that's going to make life a lot easier for process chemists who are looking at making scalable processes. 

When it comes to manufacturing, I think there's a lot of interesting developments. You know, the API manufacturing sector has not automated or modernized as much as the other industries like chemical manufacturing has. It's time to kind of make some moves over there. So we are starting to see a better adoption of DCS systems in our manufacturing environment.

You know, our vision for Neuland would be to eventually move into a paperless environment where we would have a manufacturing execution system coming out of our ERP telling us what's the recipe, and then, you know, actually plug it in and get the entire manufacturing done through electronic batch records so we don't have any paper and there's complete traceability and the ability to kind of assure the quality of the product for the patients. I think some of these will take time, but I think there is a clear crystallization of how these tools can really help us start embracing modernization for API manufacturing.

There's always dearth of talent. And I think in light of these technological advancements, it's very important to figure out ways in which you can not only attract, but retain top talent, especially on the scientific side… try to create an ecosystem that nurtures scientific talent, try to create a knowledge hub where, you know, there's a lot of scientific learning other than just kind of, you know, the grind of delivering projects and getting stuff done. There's a lot of community-based scientific discussions, and we have a very interesting mix of scientists. Most of them are Indian, because we are based in India, but a lot of them are trained in different parts of the world, and therefore they approach science from a different perspective.

And we have a very open culture where we not only look at protecting IP, but within the framework of IP sensitivity, we also encourage this talent pool to exchange ideas on what might be an interesting way to crack a problem. A big part of what we do as a CDMO business, we spend a lot of time working with innovators, hand in hand, trying to develop novel, innovative processes for new chemical entities. And a big outcome of doing creative work over there is that our scientists get to put their names on patents, although these patents are attributable and owned by our customers, which is the, the pharmaceutical companies, the names of the authors are very frequently the scientists working at Neuland who have contributed to that IP. And that creates a very high level of attraction, especially in an industry, in an area, like India. There's a lot of generics business and, therefore, it creates an additional motivation.

The other thing I would say is, you know, there's a lot of emphasis on successful scale up and delivering product to the patient because Neuland is… you know, 90% of the work we do is commercial manufacturing, and therefore when a scientist wants to come and work for Neuland, they recognize that they are actually coming and working for a company that is likely to put a pill in the hands of a patient and not necessarily just doing work in the lab, which might seem academic at times.

So the ability for a scientist to go stand in the plant and oversee a scale up, that's something that does not happen in a lot of organizations. But we have started to encourage our scientists to go into the plants, oversee campaigns, learn from the engineering groups, learn from the manufacturing groups on how their process that they have designed is performing in the plant scale.

That is a unique exposure, which also stimulates a lot of scientists, and that's also something that helps us attract the right kind of talent. We are living in a world where it's no longer about labor arbitrage. it's about knowledge arbitrage. And we cannot operate in the old paradigm where… Neuland is located in India, which is a developing economy and therefore there are these labor arbitrage opportunities. I think that does not work anymore and therefore, you know, trying to get to a paradigm of knowledge arbitrage.

Saharsh Davuluri, Neuland Labs, discusses the move from labor to knowledge arbitrage for API manufacturers, using AI tools to empower scientists in process development.

Advancing Pharmaceutical Quality through Integrated Digital Systems and Collaborative Scientific Innovation

Remco Munnik, Arcana, details pharma's shift to data-first operations, AI adoption, and robust data stewardship.

The Evolution of Data-First Regulatory Operations

 recently spoke with Remco Munnik, founder and owner, Arcana Life Sciences, to get his perspective on trends that shaped pharmaceutical development and manufacturing in 2025 and where things are headed in 2026. Munnik, who has 25 years of experience in regulatory affairs, discusses the industry's "decisive, swift shift towards data-first regulatory operations.” Driven by initiatives from the European Medicines Agency, Munnik explains that the implementation of electronic application forms and product databases marks a turning point where "digitalization is no longer optional, but really a permanent fixture of regulatory practices."

A major focus of the discussion is the integration of AI. While the industry aims to use AI to automate document generation and enhance quality oversight, Munnik identifies significant barriers, including fragmented legacy systems and limited data standardization. He argues that successful AI adoption requires not just technical tools, but a robust data governance setup and a significant culture shift within organizations.

This digital evolution has also resulted in a widening skills gap. Munnik notes that the role of the pharmaceutical professional is changing; where they once focused on documentation, they must now act as data scientists capable of extracting insights from complex datasets. As Munnik describes the transition: "In the past, when you worked in pharma, you had to be a scientist, and the scientist then became an administrator to document everything." To bridge this gap, he advocates for on-the-job rotations and a leadership mindset that embraces inevitable change as a competitive advantage.

Looking toward 2026, Munnik emphasizes the need for investment in data-driven infrastructure to ensure traceability from the point of data creation to final registration. He highlights promising innovations, such as the work of the 

, which seeks to replace resource-intensive document submissions for lifecycle changes with direct database updates. These advancements, combined with 

upcoming European pharmaceutical legislation

, are set to redefine how the industry maintains compliance and achieves speed to market.


My name is Remco Munnik. I'm a consultant at Arcana. I started that company in July of this year. [I have] 25 years of experience in regulatory operations and regulatory affairs, a subject matter expert in electronic submissions.

In the regulatory operations space, I really saw a decisive, swift shift towards data-first regulatory operations. In Europe, the European Medicines Agency is really setting there the tone with the product database go live, which is also complemented by the rollout of processes that use data.

So, the electronic application form of shortage reporting. I think that really showed that digitalization is no longer optional, but really a permanent fixture of regulatory practices.

Across the industry, I think there's a strong desire to apply AI, whether to accelerate regulatory intelligence, automate document generation, or enhance quality oversight. And I think the challenge is not as to where to use AI but how to integrate it responsibly and effectively.

And I think the unresolved barriers to full [adoption] with AI, in practice, I see a lot of challenges still with data integrity and interoperability. So there's still fragmented legacy systems that are not connected, limited data standardization that hinders scalable AI and cross-departmental collaboration. I think that requires a robust data governance setup where roles and responsibilities are defined. And last but not least, I think it's also a talent and a culture shift. There's, from a talent point of view, people need AI skills. There might be an even digital maturity between departments, and change management is also really required to make sure that the AI can be scalable on an enterprise level.

I think there's a widening skills gap with digitalization. In the past, when you worked in pharma, you had to be a scientist, and the scientist then became an administrator to document everything. And I think what we see now is a shift that you need to be almost a data analyst or data scientist. So, still a scientist, but way more into data driven and really then extracting insights from complex datasets using statistics, machine learning, and data modeling.

I think here data governance and stewardship is key to bridge that gap, but I think best practices also from a selection, training on the job, learning on the job… so really embed the learning, on-the-job rotations, cross-functional team learning, I think that's very important. And last but not least, we also need a change perception within the leadership, because one thing that remains the same is change. So really embrace the change; it is inevitable, but try to leverage that as an advantage.

One of the most promising innovations that I've come across is the work that we have been doing at the Regulatory Optimisation Group. So this is a group that has been working with the European regulators to optimize life cycle management changes. So, traditionally, if somebody changes their name or address, you need to submit all kinds of documents, all kinds of submission to the regulators that needs to be assessed and processed. Now, this process is very time-consuming, resource-intensive, but also prone to inefficiencies. And what we've been now talking with the regulators [about] is also to submit this directly into a database. So, a name change, an address change, let's submit that directly into a database and everybody gets better because you have a cost efficiency, you have a quality improvement, everybody's informed at the same time, there's a speed to market, but also compliance, and I think that's really a win-win situation that we want to achieve.

In 2026, I still believe that companies need to invest quite a bit in data-driven infrastructure. So upgrade core systems, whether that's manufacturing execution systems or laboratory information systems, ERP platforms, how they are integrated with master data, but also regulatory data as part of IDMP. The linking between what is registered from a regulatory point of view and what is manufactured and marketed from a supply chain point of view will be a lot more important to capture. I think another investment is needed in governance [to] really have that clear traceability from the source where a data element is created until the submission and registration of the data element with the regulator there is important.

So, that's really important to define the data ownership across the functions, and I think last but not least, it's also the workforce enablement. Digital training needs to be standard, but also giving all the employees confidence with data standardization to really ensure that operators can interact and interpret fluently with standardized structured data.

I think one more thing in Europe that will be very important is the new European pharmaceutical legislation that will have quite an impact on the entire way of operating for pharma, but more to be shared in 2026 when the new pharmaceutical legislation draft is available.

Christopher Cole

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