AI in life sciences is often discussed at a high level, but implementation depends on the specific use case, data context, and stage of the value chain.

This webinar, featuring Harini Gopalakrishnan (The HGFactor, Vespa.ai, Streamcare Health), breaks down how AI is applied across R&D and medical functions, with a focus on selecting the right approach for the problem at hand.

What The Slides Cover

This slide deck walks through practical AI use cases across the life sciences value chain, including:

• Discovery & Research:
  Grounding responses in published literature and internal data using retrieval-based approaches (RAG)
• Preclinical:
  Querying structured datasets (e.g., toxicology data) using deterministic methods like Text-to-SQL
• Clinical Development:
  Extracting and synthesizing information from protocols, CSRs, and study documents with traceability
• Regulatory:
  Combining retrieval with LLM-based summarization to support clear, auditable narratives
• Medical Affairs:
  Ensuring outputs are grounded in approved materials and compliant with published evidence

Most organizations don’t lack AI models—they lack systems that make those models usable.

In practice, AI initiatives often stall after prototyping. Not because the models underperform, but because they aren’t integrated into workflows, don’t support real decisions, or require too much effort to operationalize.

Closing that gap comes down to three things: usability, integration, and adoption.

Download the full infographic to use as a reference for evaluating and operationalizing AI analytics initiatives.