Steering generative AI into the pharma fast lane

Every Formula 1 Grand Prix has its strategic pit-stops where advantages are gained by resets and deploying fresh tyres with astonishing precision, agility and speed. For pharma, its AI progress appears to be slowed by a methodical procession of AI adoption through a series of chicanes.

Safety and privacy are valid reasons to ease back on the throttle, but expectant patients, healthcare professionals (HCPs) and key opinion leaders (KOLs) want to see digital and technological advances powering the rapid progress they
witness daily in other sectors.

Generative AI (GenAI) is already flourishing across drug discovery, clinical trials and healthcare system recalibration, but many fear its potential power is dissipating through lack of co-ordinated application across departments.

For launch excellence, where the cost of developing a drug has risen to $2.2bn in 2024 from $2.1bn the previous year, this is the equivalent of skidding out of a corner and watching the rev counter dwindle.

AI’s impact is accepted and its potential is revered, but industry is being challenged to move through the gears so that every aspect of business benefits, rather than admiring pilot schemes that are tested in isolation.

“From a corporate perspective, companies also need to move away from fragmented experimentation. Many organisations have piloted AI agents in isolated pockets, but launch excellence depends on connected execution. If commercial, medical, market access and clinical teams are working from separate systems and processes, AI will only accelerate the fragmentation,” said Manuel Möller, Vice President, Veeva Insights Strategy, Veeva Systems.

“GenAI needs a clear operating model: agreed definitions of what constitutes an insight; shared processes for prioritisation; clear accountability for action and a way to measure outcomes. The technology is important, but the governance and cross-functional discipline around it are what turn AI into business and patient impact.”

GenAI is a prolific source of critical insights, including game-changing patient feedback and predictive analysis, influential research and opportunities to engage across diverse stakeholders. All have the capacity to optimise clinical trials and supercharge R&D while also reducing costs and enhancing efficiency.

“AI has great powers, but one of the biggest barriers to AI is organisational rather than technical,” says Manuel. “Many companies still have fragmented systems, inconsistent insight capture, unclear ownership and limited cross-functional flow. Medical teams may collect valuable insights, but if there is no shared process for prioritising them or assigning accountability, they may not influence decisions in time.

“There is also a maturity gap. Biopharmas recognise that insights are strategically important, but many still rate their own ability to act on them as average. Technology has accelerated capture and analysis, but execution often has not kept pace. For GenAI to deliver value, companies need trusted data, connected systems, human oversight and a clear framework for turning insight into action.”

He adds, tellingly: “Without that, AI may simply make a broken process faster.”

Pharma is sitting on a well of intelligence and insights generated by its MSLs and medical teams during their scientific exchanges with physicians and KOLs, but much of it leaks away because traditional structures struggle to capture and utilise the data and evidence fast enough. But the use of agentic AI, where AI agents operate autonomously to find, evaluate and create strategies from masses of data, can liberate that knowledge.

A paper from McKinsey in October 2025, stated: ‘Agentic AI has the power to enable a reimagination of the entire life sciences value chain.1 The analysts also proclaimed: ‘Agentic AI is poised to boost the benefits from AI by changing its role from tool to coworker and catalysing an end-to-end reimagining of the life sciences value chain.2
Veeva Systems, leaders in cloud-based software for the life sciences industry, believes GenAI is ready to play a significant role across launch excellence.

“AI agents can support clinical development by helping teams understand barriers earlier and respond faster,” comments Manuel. “This is especially valuable as therapies become more targeted and patient populations become more specific. If companies can detect patterns in field intelligence earlier, they can identify potential recruitment barriers, evidence gaps and stakeholder concerns before they slow progress.

“AI agents can help by analysing large volumes of field interactions and identifying emerging medical themes in near real time. This allows teams to move beyond isolated insights and understand the patterns behind them.

“For launch teams, that means earlier visibility into what is working, what is creating friction and where scientific communications or evidence plans may need to adapt.
“The role of AI agents will grow as companies connect these insights more directly into launch planning, medical strategy, content development, evidence generation and stakeholder engagement. The real value is not just faster analysis, but better cross-functional action.”

‘AI can help biopharmas listen better, act faster and make more precise decisions when bringing medicines to market’

He sees GenAI, and AI agents, as the logical response to stakeholder expectations as 70% of physicians report using AI daily and 84% of those believe it makes them better at their jobs, according to its paper, The Role of Medical Affairs in Times of AI.3

“Importantly, KOLs are willing to contribute to the process. The issue is often whether their input is visibly acted upon,” adds Manuel. “When insights disappear into a system and do not appear to influence strategy, trust can erode. AI can help identify themes across thousands of interactions, but companies also need to close the loop and show how stakeholder input is informing medical strategy, evidence generation or scientific education.

“For patients, the link is indirect, but important. Better insight activation can help companies understand unmet needs, care pathway challenges, access barriers and education gaps earlier. That can influence how therapies are launched, how evidence is generated, and how biopharmas support clinical practice.

“Engagement should not be a one-way transaction. The future is a more continuous dialogue, where stakeholder input is captured, understood, acted on and fed back into the organisation.”

AI is likely to become more embedded and continuous, and organisations will be able to connect departments seamlessly. The value of medical affairs will become more apparent and will increasingly be seen as a strategic driver of launch excellence and patient impact, Manuel predicts.

“AI will not remove the complexity of bringing medicines to market, but it can help biopharmas listen better, act faster and make more precise decisions,” Manuel says. “In a world where most medicines never reach patients, that capability will become increasingly important.

“The biopharmas that benefit most will be those that combine AI with strong data foundations, connected processes and clear human accountability.”

References
1. https://www.mckinsey.com/featured-insights/week-in-charts/agentic-ai-advantage-for-pharma
2. https://www.mckinsey.com/industries/life-sciences/our-insights/reimagining-life-science-enterprises-with-agentic-ai
3. https://www.veeva.com/resources/the-role-of-medical-affairs-in-times-of-ai/