Predicting the present

It’s common to start a new year with bold ambitions, but where is the industry really setting its sights for the year ahead, have organisations pitched their plans appropriately for this fast-changing market, and how much of the agenda is – and should be – AI-oriented?

Looking across late-stage pharma R&D, three dominant themes emerge for 2026. These reflect a maturing AI landscape, regulatory pressures driving digital transformation and fundamental shifts in how life sciences organisations must organise work and data.

These themes come together, or ought to, in the context of providing a better experience for patients.

After more than 18 months of experimentation, there is a growing consensus that 2026 will mark the transition from AI proofs of concept (PoCs) to enterprise-scale implementation.

“I am already fairly impressed with the wide scope of pilots for AI in life sciences,” comments Frits Stulp, a partner for life sciences at Implement Consulting Group, predicting that 2026 will see more of the results come to fruition.

“Our industry offers considerable scope for automation – not just of administrative tasks, but also in the area of research as well as regulatory submission preparations, where we are now seeing the first results,” he says. “We’re now able to address some of the historical concerns, to make approaches succeed where they have struggled before – for example, in structured content authoring.”

Jason Bryant, AI lead at ArisGlobal, agrees that 2026 will be a turning point for the technology’s uptake, as AI enters “its enterprise phase” – a recognisable milestone in the adoption curve.

“2026 will be a consolidation year,” he says. “The big swing of ‘agentic AI’ is behind us; that was the big theme of 2025. Success will now come from integration, governance and delivery.”

For Bryant, AI “orchestration” is the new differentiator for companies looking to truly drive transformation of the work their teams do.

“The challenge now moves beyond single models [trained software programs that learn patterns from large datasets to make predictions, decisions, or generate content for specific tasks], to connecting models, data and systems across domains and participants,” he explains.

This requires a control layer, he notes, “to coordinate generative AI (GenAI), agentic AI, and deterministic logic – as a means to reduce complexity and unify domains.”
So, what specific actions might that translate to for the year ahead?

For Bryant, the key is maintaining momentum. Specifically, he advocates thinking in terms of the overarching platform rather than discrete AI tools; and “an architecture that allows for agentic AI, connecting beyond the organisation’s walls.”

This will also help determine how organisations foster trust in AI’s output, and keep within regulatory comfort zones (there are numerous prerequisites in GxP, he notes).

For John Cogan, chief operating officer at Qinecsa Solutions, which specialises in pharmacovigilance optimisation, a big priority around AI must be to nail the technology’s return on investment.

“The real impact of AI, determining that use cases will bring actual ROI, is key now – so we can get away from the smoke and mirrors,” he says. “We have had 18 months of hype and PoCs; now it’s time to do the maths.”

One opportunity for AI to add next-level value in drug safety is in enabling predictive pharmacovigilance.

Lucinda Smith, ArisGlobal’s chief safety product officer, believes this will be a big theme in 2026, as well as the use of AI to support early signal detection through use of advanced analytics, AI and RDE (insights/evidence generated from real-world data using analytics or AI/machine learning), as well as integrating data from electronic health records, wearables etc.

“This will be a big step forward for PV and for patients – but will also take a significant effort to deliver,” she says.

Among the challenges that departments face is sourcing the right technology skills, building AI literacy and honing governance around AI.

“Typically, PV departments are very experienced in governance and oversight, but in the era of AI the way they achieve that has to evolve; increasingly team members will need a balance of both PV and AI skills.”

For Jean Redmond, chief operating officer at Biologit, which specialises in AI-powered literature monitoring platform for drug safety surveillance, regulators have a significant role to play in guiding pharma companies in their adaptation to using AI.

“2025 saw interesting draft guidance being released and initial AI programmes being adopted by regulators,” she notes. “I believe that in 2026 regulatory authorities will drive further frameworks, guidance and expectations for the compliant use of AI that will give organisations the confidence to move from pilot projects into production.”

The proliferation of point solutions (RIM, PLM, QMS, workflow tools) has created integration challenges for many pharma organisations.

In 2026, orchestrating work across systems, functions and data sources, rather than optimising individual tools, is likely to become not only a priority but also a means to sharpening a company’s competitive edge.

Megha Sinha, managing partner and CEO at Kamet Consulting Group, which advises on advanced product life cycle management spanning multiple functions, believes bringing down departmental divides will be a major theme over the next 12 months.

“In my part of the market, I think the dominant strategic theme for 2026 will be orchestration of work across functions and platforms.”

She explains, “Life sciences companies now have plenty of point solutions, but the real struggle is how work actually flows between them. The differentiator will be the ability to see the end-to-end work ‘graph’ (products, SKUs, markets, tasks, owners); apply consistent, codified business rules; and coordinate execution across regulatory, manufacturing, quality, supply and commercial.”

She points to orchestrated life-cycle change as an example.

“Instead of treating a rebrand, site transfer or marketing authorisation holder change as dozens of disconnected projects, organisations will need a single orchestration layer that sequences tasks, manages dependencies and continuously replans as constraints change.”

For Sinha, AI as a technology lever is not the primary theme.

“AI is critical, but its real value is as an engine within a work-orchestration fabric,” she clarifies, “not as yet another standalone tool or dashboard.”

Biologit’s Redmond echoes the point about overcoming departmental divides.

“My biggest hope for 2026 is that life sciences organisations embrace stronger cross-functional collaboration,” she says. “Too often, challenges in areas like safety, operations or technology are approached in silos, slowing progress and limiting impact. If teams across PV, regulatory, medical, data and engineering worked together from the start – sharing goals, data and ownership – problems would be solved sooner.”

Where companies move towards greater inter-department fluidity, there could be new opportunities to review how certain workloads are managed, according to Qinecsa’s Cogan.

He would like to see life sciences organisations in general, and in a PV context in particular, “stand back from their global end-to-end operating models, and do a full reanalysis on how their operations need to look beyond 2030,” he says. “That includes which processes and capabilities they should have (or bring back) in-house and which capabilities they will continue to rely on services partners for.”

For many companies, this is likely to present a significant organisational challenge however – one that extends beyond technology implementation.

Achieving true cross-functional orchestration will mean breaking down entrenched structural silos, which could prove more difficult than the technical integration itself.

Irrespective of the organisational considerations, the way companies handle data will have a significant bearing on interdepartmental fluidity and process agility.

It is here that regulatory operations greatest opportunity, and challenge, reemerges.

Although the roads to all of this are already well travelled, there are signs that structured data may now truly evolve from a compliance requirement to strategic infrastructure – driven by EMA’s Network Data Strategy; agreed specifications under IDMP and SPOR; and the reality that AI/automation capabilities depend on high-quality, interoperable data foundations.

Remco Munnik, founder at Arcana Life Sciences Consulting, explains: “The strategic priority in regulatory operations in 2026 will be the full-scale implementation of structured data across regulatory and interconnected functions.”

Equally important, and often underestimated, he says, is the EU Network’s evolving direction.

“PMS is set to become the central source of product data across both the entire product lifecycle and the full regulatory network,” he notes. “In practice, this means the scope will expand beyond authorised medicinal products to include investigational products under evaluation and for the entire EU Network.

“All NCAs will be required to align and map their product data to PMS, establishing a unified, interoperable foundation for regulatory operations across Europe. Industry, in turn, must not only map its data but also enrich it, ensuring completeness, accuracy and readiness for structured exchange across the product lifecycle.”

Renato Rjavec, senior director of regulatory product management at ArisGlobal, concurs.

“The biggest theme for regulatory affairs going forwards will be associated with the realisation that strategic investment in digitisation has become an inevitable prerequisite for successful regulatory operations,” he says. “The accelerated pace of evolving regulatory requirements in the area of electronic submissions; electronic exchange of product data; and electronic labelling will leave behind anyone who has not observed the trends.”

Remco agrees that AI is not a ‘tech-first’ opportunity or challenge.

“It’s a ‘people and process’ story – where the real bottlenecks are cultural, not computational,” he suggests. “The technical standards exist; organisational readiness and data quality remain the primary barriers.”

It is hoped that the life sciences industry will renew its commitment to patients in 2026, putting them at the centre of all of their strategic plans. Michelle Bridenbaker, chief operating officer at Unbiased Science, says: “In 2026, we need to further transform the way we engage with and reach our time-poor target audiences.”

“Whether it is for disease-state awareness or to ensure that patients use medications correctly, we are still struggling in the attention economy in Medical Affairs. We have made significant progress over the last five years, but there is still so much to be done within the sea of mis- and disinformation, the explosion of social media and, now, large language models,” Michelle adds.

She concludes: “We need to find ways to operate compliantly in the right social media channels, update our codes of practice and other legal/compliance barriers to define a robust framework.”

The Think Tank