AI platforms can outperform traditional scouting by analyzing curated internal databases alongside public data. This allows them to surface promising assets that were discontinued at an early stage by other companies and are no longer visible in standard industry databases, creating unique licensing opportunities.
Large biopharma companies have failed when attempting to use generalist large language models (LLMs) for deal scouting. These models lack the specialized focus and curated data required for the industry, leading to inaccurate results, disappointment, and ultimately, abandoned internal AI projects.
AI's adoption in business development will mirror its impact on IT: junior-level roles focused on data gathering will be automated away. The BD function will shift towards a lean team of senior experts who leverage AI-generated insights to focus on high-level strategy and decision-making.
The concern that AI will surface the same deals for everyone is unfounded. A competitive edge comes from using a complex infrastructure with multiple, specialized Large Language Models (LLMs) for data extraction, validation, and structuring. This sophistication ensures a differentiated output compared to simpler AI tools.