In the near future, it is assumed that generative AI will be the top driver of corporate priorities, surpassing digital innovation and cost concerns. However, to truly scale AI for value, companies must move beyond efficiency-focused experiments and drive fundamental transformations in their operations. While experimenting with various generative AI use cases is valuable, spreading investments too thin without linking them to proven digital programs can lead to stagnation. Although generative AI is a priority, companies cannot disregard traditional AI models that excel in pattern-based predictions. Read on to learn how generative AI can help unlock healthcare breakthroughs:
Generative AI in clinical development
Patient enrollment challenges affect nearly 85% of clinical trials, with complex protocols, inefficiencies and poor data management increasing costs and delays. When gen AI solutions are combined with existing AI programs, they can enhance trial design, optimize protocols and improve statistical measures. This leads to faster, more efficient trials with higher-quality data and better patient engagement.
Generative AI in commercial pharma
Integrating generative AI with existing AI systems can modernize healthcare provider (HCP) engagement by enhancing various aspects of the engagement process, including:
Content creation: Generative AI can produce personalized marketing materials tailored to individual HCP preferences and behaviors. This ensures more effective communication.
Insight generation: With Generative AI, HCP can process unstructured data to uncover deeper insights into needs and motivations.
Behavioral changes: Generative AI can simulate HCP reactions to different messages, allowing for the optimization of communication strategies before actual implementation.
By combining these capabilities, pharmaceutical companies can move beyond isolated productivity enhancements to fundamentally transform their HCP engagement strategies.
Generative AI in supply chain and manufacturing
Resilience, agility and sustainability are essential for building a robust and advanced supply chain. By combining generative AI with classical AI, companies can create real-time resilience agents that utilize the latest external data. This integration enhances supply chain adaptability and sustainability by enabling proactive identification of disruptions, efficient resource management and timely corrective actions. In all, this combination ensures long-term stability and provides a competitive edge in the future.
Life sciences solutions companies say that bringing AI solutions will help them move beyond productivity and efficiency into creating enduring business value.
