Pharma lab AI scales only when data architecture comes first

Pharma R&D leaders face a consequential investment decision: Which capabilities should they prioritize to accelerate drug discovery and scale AI?

Fragmented scientific data, disconnected lab systems and manual handoffs can delay decisions across the Design-Make-Test-Analyze (DMTA) cycle. Investing in advanced AI and automation before resolving these dependencies can compound technical debt and limit returns.

A connected lab data architecture creates the foundation for faster, more reliable scientific decisions. By sequencing investments in data governance, systems integration and workflow orchestration, pharma organizations can move toward closed-loop experimentation and improve the economics of R&D.

Explore how an architecture-first approach can help pharma R&D leaders:

• Prioritize investments. Identify the foundational capabilities AI and automation need to scale, from trusted scientific data to integrated systems and governed workflows.
• Accelerate scientific decisions. Reduce data reconciliation and manual handoffs across the DMTA cycle to shorten the time between experiments, analysis and the next scientific decision.
• Improve R&D economics. Enable automated experimentation, lower cost per insight and reduce avoidable errors through more reliable, connected workflows.

The white paper examines a seven-layer lab data architecture, the progression from connected data to AI-driven experimentation and a phased roadmap for building these capabilities.

Download the white paper