Anchoring Enterprise GenAI with Knowledge Graphs Jonathan Lowe (Pfizer), Stephen Chin (Neo4j)
AI Summary
Video Summary: Leadership and Generative AI in Life Sciences
- Introduction:
- Speakers: Stephen and Jonathan.
- Focus on leadership in technology and strategy within AI projects, particularly in Life Sciences.
- Industry Insights:
- Gartner’s prediction: 30% of generative AI projects will fail by 2025.
- Question posed about audience experience with failing AI projects.
- Challenges in Generative AI Implementation:
- Need for effective internal leadership and vision.
- Tensions with executives having unrealistic expectations and timelines for generative AI projects.
- Importance of a solid business case for technology transfer in BioPharma.
- The Case for Using Graph Technology:
- Jonathan discusses his experience with technology transfer, emphasizing the need for generative AI to assist with knowledge transfer in manufacturing as expertise declines.
- Leveraged graph databases to structurally manage medical and manufacturing data for enhanced searchability and performance.
- Strategy for Internal Buy-In:
- Importance of aligning with organizational goals at various levels.
- Steps for addressing concerns from different departments regarding adoption of new technologies.
- Navigating internal politics can determine project success or failure.
- Conclusion:
- The use of knowledge graphs and LLMs enhances contextual knowledge, leading to better operational outcomes.
- Emphasis on the life-saving implications of efficiently using generative AI in drug development.
Overall Takeaway:
- Generative AI presents significant challenges, but under effective leadership, it can transform operations in Life Sciences, ultimately benefiting society by making critical drugs more accessible.