Artificial intelligence is becoming a major focus across pharmaceutical market access, health economics and outcomes research, and evidence generation. Organisations are exploring how AI can accelerate literature reviews, support evidence synthesis, and assist with the development of HTA submissions. This reflects growing artificial intelligence in pharma initiatives and practical pilots for AI in evidence generation.
Yet many companies are still determining where AI can deliver meaningful value, making ai adoption in pharma a measured, stepwise process.
During a recent discussion at the World EPA Congress, moderated by Thomas Gilboy, industry leaders shared practical perspectives on how organisations are approaching AI in market access today.
While the conversation covered a range of potential applications, one message emerged clearly. Organisations making real progress are not starting with the technology. They are starting with clearly defined problems and then evaluating whether AI can help solve them.
Key takeaways: AI in market access
Artificial intelligence is generating significant interest across pharmaceutical market access and evidence generation. However, successful AI adoption requires a focused and practical approach.
Key lessons emerging from industry discussions include:
- Organisations should start with clearly defined problems rather than adopting AI for its own sake
- Targeted use cases such as literature monitoring and evidence synthesis often deliver the most immediate value
- Human expertise remains essential when interpreting evidence and developing value narratives
- Many AI tools in pharma are still evolving and require careful evaluation through pilot projects
- Strong evidence management foundations help organisations maximise the value of AI technologies.
Start with the problem, not the technology
Artificial intelligence in pharma is often presented as a transformative solution across life sciences. However, adopting AI simply because it is available can create unnecessary complexity.
Before introducing new tools, organisations need to define the operational challenges they want to address. These challenges might include managing large volumes of published evidence, identifying relevant insights across multiple data sources, or improving the efficiency of evidence synthesis.
In some cases, the most effective solution may not involve AI at all. Automation, improved workflows, or stronger data management practices can sometimes deliver faster, more effective improvements.
Organisations that begin by clearly defining the problem are far more likely to identify situations where AI can genuinely support market access teams.
Focus on targeted use cases for AI in evidence generation
Rather than launching large scale AI transformation programmes, many organisations are exploring smaller and more focused applications.
For AI in pharma teams, evidence monitoring is one example. Market access teams frequently review large volumes of scientific literature to identify emerging data that could influence value dossiers or HTA submissions. AI tools can help scan publications and highlight potentially relevant findings more quickly.
However, the most successful initiatives focus on specific tasks within the evidence generation process. By breaking complex workflows into smaller components, organisations can identify where AI can improve efficiency without introducing unnecessary risk.
This approach allows teams to test AI capabilities, measure their impact, and expand successful use cases over time.
AI can support evidence review but human expertise remains essential
Despite rapid progress in artificial intelligence, human expertise remains central to market access strategy.
AI can assist with tasks such as summarising research findings, identifying inconsistencies in documents, or organising information across large datasets. In some cases, AI tools may highlight issues that human reviewers initially overlook.
However, developing the narrative behind an evidence dossier or HTA submission requires strategic interpretation. Market access teams must decide which data points are most relevant, how evidence should be positioned, and what message the overall evidence package should communicate.
These decisions depend on experience and contextual understanding. AI can support this work, but it cannot replace the expertise required to interpret evidence and communicate value effectively.
Many AI tools in pharma are still evolving
Another important theme emerging across the industry is the maturity of AI solutions.
Technology vendors often demonstrate impressive capabilities, but real-world performance can vary significantly. Some tools remain exploratory, while others require substantial validation before they can be integrated into existing workflows. As AI in pharma initiatives expand, careful validation remains essential.
For this reason, organisations are increasingly adopting a cautious approach. Pilot projects allow teams to evaluate AI tools in controlled environments and understand both their benefits and limitations.
This experimentation is essential for identifying where artificial intelligence can deliver meaningful improvements in evidence generation and market access processes.
Building a practical roadmap for AI in market access
While organisations are taking a measured approach, interest in AI across pharma market access continues to grow.
Many teams are developing structured roadmaps to guide their adoption of artificial intelligence. These roadmaps often include identifying priority use cases, testing technologies through pilot projects, and scaling successful initiatives once they demonstrate measurable value.
This approach allows organisations to explore AI while remaining focused on the practical challenges faced by market access and HEOR teams, and it supports thoughtful AI adoption in pharma.
This experimentation is essential for identifying where artificial intelligence can deliver meaningful improvements in evidence generation and market access processes.
The growing role of evidence management platforms
As access teams continue to explore AI, many are also rethinking how evidence is managed across the product lifecycle.
Market access and HEOR teams work with large volumes of clinical, economic, and real-world evidence. This information is often stored across multiple systems and internal repositories, making it difficult to identify relevant data when preparing value dossiers, publications, or HTA submissions.
Evidence management platforms help address this challenge by providing a structured environment where evidence can be organised, curated, and reused across market access activities.
When combined with AI capabilities, these platforms can support activities such as:
- Identifying relevant evidence across large datasets
- Supporting literature monitoring and evidence synthesis
- Highlighting potential inconsistencies across documents
- Accelerating the development of evidence packages for HTA submissions.
The effectiveness of AI in market access often depends on the quality and accessibility of the underlying evidence. Organisations that invest in stronger evidence management foundations are therefore better positioned to benefit from artificial intelligence.
Deep dive: AI adoption in pharma and market access
To explore this topic further, watch the on-demand webinar presented by Tom Gilboy.
In the session “AI Adoption: When It Helps, When It Hurts and How To Decide,” Tom explores how organisations can evaluate artificial intelligence. The webinar examines where AI can realistically support value, access & pricing teams and highlights common challenges organisations encounter when adopting new technologies.
