Generative AI in life sciences marketing: Use cases and roadmap for 2025

Generative artificial intelligence (Gen AI) is rapidly transforming life sciences, especially when it comes to marketing. What once was just a thought experiment is now transforming the life sciences industry, empowering organizations to cultivate stronger, more meaningful relationships with their customers. For organizations looking to stay ahead in the competitive industry, understanding and integrating Gen AI with the help of strategy marketing consulting is no longer optional; it is essential.

Key use cases for gen AI in life science marketing

• Accelerate concept creation: Gen AI agents are helping life science marketers accelerate the initial concept creation of a new campaign. They can now produce twice the number of quality concepts in half the time, significantly reducing the agency fees and iteration cycles.
  
  
• In-house content derivative generation: Once a campaign is finalized, Gen AI can help organizations optimize derivative content generation. While collaboration with agencies is still important for initial asset production, refining copy, imagery and channel specifications in-house can help reduce costs and accelerate time to market. Organizations are looking to scale gen AI, with expectations of 20% to 30% savings on agency spending.
  
  
• MLR process reengineering: The medical-legal review (MLR) process has long been a bottleneck in life science marketing. Some organizations are experimenting with MLR concept review in a tiered process, creating a fast track for low-risk derivative assets. To fully leverage the potential of Gen AI in marketing, organizations need to rethink the MLR process and identify ways to reengineer it to align with the new AI technologies.
  
  
• Integrating primary and secondary data for insight mining: Insight mining can be further enhanced by combining primary and secondary data. While a lot of progress so far has been made by experimenting with unstructured market research files using large language models, the real value of insight mining will be unlocked if we combine unstructured and structured data and put it in the hands of marketers.

The roadmap for 2025 life science marketing

• Leadership commitment to disruption: Organizations must take a more holistic approach to marketing workflows. Also, it is crucial to have leadership commitment to make this workflow more effective. This means investing not only in developing new technologies but also in areas like data strategies, compliance, training and change management.
  
  
• Rethinking the talent model: To fully leverage Gen AI, organizations should bring external experts with skills in content creation and other areas. This way they can reduce reliance on agencies and speed up adoption. At the same time, existing employees should be trained in areas like data-driven decision-making, prompt engineering and customer-focused marketing, ensuring they can effectively use AI tools for better results.
  
  
• Agentic workflow enablement: Instead of focusing on individual use cases, many organizations are aiming to scale multiple use cases like module extraction, derivative production and MLR review simultaneously. Through this, they aim to create a fully AI-enabled content supply chain.

As generative AI continues to reshape life sciences marketing, organizations must embrace this technology to remain competitive. By integrating AI into their workflows, an organization can not only enhance their marketing strategies but also foster deeper connections with their customers. 

Life science consulting firms can play a crucial role in guiding organizations through this transformation, offering insights and expertise to ensure effective implementation.