Why AI Alone Breaks Down in R&D
AI can generate answers in seconds, but in drug development, speed means little without completeness, accuracy and traceability.
Most AI tools rely on publicly available information that can be fragmented, inconsistent or difficult to verify. As a result, they often struggle to capture the full competitive landscape, particularly when it comes to emerging, preclinical and less visible assets.
In this report, you’ll learn:
- Why AI struggles with recall, completeness, consistency and provenance in complex scientific landscapes.
- Why preclinical assets remain a major blind spot for generic AI tools.
- How incomplete competitive intelligence can lead teams to underestimate competition and overestimate whitespace opportunities.
- Why trusted, repeatable and source-traceable intelligence is essential for portfolio, diligence and investment decisions
AI Found a Signal. Beacon Found the Landscape.
As part of an ADC safety benchmarking exercise, Beacon compared its curated intelligence against outputs from leading AI tools.
A single snapshot of the data reveals the scale of the gap:
| Metric | Beacon | Generic AI |
|---|---|---|
| Assets identified | 133 | 8-18 |
| Emerging & preclinical assets captured | 37 | 0 |
| Targets identified | 58 | 6-8 |
| Payloads identified | 46 | 7-9 |
Download the ADC Safety Intelligence Benchmark
Discover what leading AI tools missed, why it matters, and what becomes possible when your AI is built on trusted scientific data. See exactly how Beacon’s curated intelligence delivers more complete, accurate and defensible insights for drug development teams.
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