Artificial intelligence, shifting healthcare professional behaviour and rising pressure on healthcare systems are converging to force structural change across life sciences in 2026.

For Christoph Bug, who leads global medical strategy at Veeva, the transformation is not simply about technology adoption. It is about redefining the role of medical affairs, rethinking how impact is measured and breaking long-standing silos between commercial, clinical and medical functions.

He describes 2026 as a turning point.

“I think 2026 will be a super interesting year. AI will introduce a new set of leadership challenges, particularly as it reshapes how people work and make decisions, but it will also bring a huge opportunity to simplify processes and operate with greater speed, efficiency, and impact,” he said.

From clinic to pharma to medical strategy

Christoph began his career in medicine before moving into the pharmaceutical industry. While he valued patient contact, he recognised that industry roles could influence far larger patient populations.

“When the front end of the organisation gets it right – how it engages, educates, and executes – the impact is felt by patients at scale,” he said.

After working across commercial, R&D and medical roles, he found medical affairs to be the most strategically underdeveloped yet potentially powerful function in pharma.

“There were many critical questions that went unanswered, like, who has the ultimate responsibility for medical care? How is impact measured?” he said.

That question of impact now sits at the centre of his strategic thinking.

AI shifts the balance of power

One of the most immediate pressures comes from changing healthcare professional behaviour. Christoph argues that AI tools are fundamentally altering how clinicians access and consume scientific information.

“People don’t have time to wait any longer. They have time limitations. They want the information as soon as they need it,” he said.

He notes that a large proportion of healthcare professionals in the US are already using AI tools in their daily work, changing the traditional model of pharma pushing information towards HCPs.

The shift from push to pull creates both risk and opportunity for medical affairs. If the function defines itself purely as a channel for data dissemination, it risks losing relevance.

“Data dissemination can’t be the core responsibility for medical affairs, because AI will do it,” he said.

Instead, medical teams must focus on interpretation, scientific alignment and ensuring that the right patients receive the right treatment. More science-driven engagement, he argues, will increasingly drive product adoption.

“Over the past decade, scientific evidence has played an increasingly central role in driving treatment adoption, a trend that will only continue to grow,” he said.

Beyond vanity metrics

A central theme of the discussion is measurement. Traditional metrics in medical affairs have often focused on activity volume: number of KOL interactions, publications shared, educational events delivered.

Christoph believes that approach is outdated.

“Being clear and explicit on what the actual outcome is and how companies define it is critical,” he said.

If medical affairs defines its role as aligning scientific belief systems among key stakeholders, then impact cannot be measured through activity metrics alone. Success should be reflected in shifts in perception, clinical confidence, and real-world behaviour. And if the ultimate ambition is to ensure the right patient receives the right therapy, measurement must move closer to patient-level outcomes and demonstratable treatment optimisation.

This shift becomes more urgent as AI automates routine work and as organisations demand clearer returns on investment.

Breaking silos across medical, R&D and commercial

Collaboration across functions remains a structural weakness in many life science organisations. Christoph argues that medical affairs plays a critical translational role between clinical development and commercial execution.

Medical teams interpret trial data, contextualise it for local markets and feed insights back into R&D. They also gather field insights that may influence trial design or recruitment strategies.

However, these benefits are often limited by fragmented systems and misaligned objectives.

“These functions have worked in silos,” he said.

On the commercial side, he highlights the risk of uncoordinated engagement with healthcare professionals, where multiple representatives from the same company approach a single stakeholder without alignment.

The solution, he argues, lies in shared objectives supported by unified data environments that enable a 360-degree view of the customer. That view allows organisations to assess engagement quality, collaboration patterns and potential gaps.

Digital transformation: leadership and mindset

Successful digital transformation depends less on tools and more on leadership commitment and organisational mindset.

“I would say there is clear commitment from senior level support,” he said, describing organisations that move faster.

Technology investment is also critical. Fragmented ecosystems stitched together by internal IT teams often slow progress. Unified platforms that enable data sharing across functions are more likely to support collaboration and efficiency.

But mindset may be the hardest element to shift. Employees must understand how AI and digital tools enhance their roles rather than threaten them.

“I think these are questions that need to be answered openly, upfront,” Christoph added, referring to concerns about job security.

When organisations align leadership, technology and mindset, they begin to see measurable improvements in customer experience and operational efficiency.

A moonshot for healthcare

Beyond pharma operations, Christoph sees AI as a structural lever for healthcare system sustainability.

“I think AI would enable us to be way more efficient than we have been in the past when treating patients, running clinical trials, finding the right patient,” he said.

With ageing populations, limited funding and thousands of rare diseases still lacking treatment, efficiency gains are not optional.

“We have systems that are not sustainably funded yet,” he said.

AI-enabled clinical trial recruitment, hybrid evidence generation models and more targeted scientific engagement could accelerate therapy development and delivery. Over time, AI may also influence how patients first interact with healthcare systems, potentially reshaping frontline care.

If life sciences companies could change one thing about how they use data, Christoph believes it would be closer scientific collaboration across academia, industry and regulators, supported by better coordination and trust.

“I see the many little things that need to be changed that, together, will drive the bigger and lasting impact,” he said.

For 2026 and beyond, the opportunity is not simply deploying AI tools, but redefining how medical affairs measures impact, how organisations collaborate internally and how data flows across the ecosystem. Those that adapt quickly may find themselves better positioned to deliver therapies faster, operate more efficiently and respond to mounting system pressures.