How top pharma companies are calculating ROI for AI in pharma R&D

This paper also includes contributions from Shuja Mohammed, director, business planning and operations, AstraZeneca,Ektaa Sharma, associate principal, ZS and Ankita Praharaj, principal, ZS.

Artificial intelligence (AI) has rapidly evolved from an experimental capability into a strategic imperative for pharmaceutical R&D. Leading organizations are making significant platform investments to accelerate discovery, improve clinical development and reduce costs. While these investments have already delivered tangible gains, such as faster target identification, higher trial recruitment rates and material cost savings, many R&D leaders still struggle to consistently articulate their true return on investment (ROI). Today, value is often described through isolated operational metrics or anecdotal success stories that fall short of rigorous, enterprisewide value quantification.

As AI moves beyond pilots, the need for a shared, standardized value language becomes critical.

To better understand how organizations are currently approaching AI value, the research team conducted in-depth interviews with R&D leaders and AI sponsors across the industry. These interviews revealed strong executive engagement and widespread enthusiasm for AI, alongside a shared frustration with ROI measurement.

Sponsors described cautious, phased adoption models, heavy reliance on pilots and “leading indicators” such as time saved or model accuracy, and a lack of clear baselines for attributing impact. Many acknowledged that current ROI estimates remain “finger-in-the-air,” underscoring the need for a more systematic, standardized approach to value articulation.

The research reinforces that AI delivers its full value only when embedded within a connected, data-first operating model.

To address these challenges, the paper proposes a multidimensional ROI framework tailored specifically for pharma R&D. The framework integrates scientific, operational and commercial metrics and ties them directly to strategic objectives.

Key metrics highlighted in the paper include productivity measures such as targets or candidates discovered, efficiency gains like cycle-time reductions and clinical outcomes including recruitment rate multipliers and diversity improvements. Other metrics include cost savings, commercial impact, model performance and user adoption.

Adopting a structured ROI framework offers several clear advantages:

• Enables a more transparent demonstration of bottom-line impact
• Supports more informed investment decisions
• Provides the evidence needed to scale successful pilots
• Aligns R&D value discussions with the language of corporate strategy

Together, these benefits allow organizations to move from anecdotal evidence to credible, repeatable value realization, turning AI from a promising technology into a durable enterprise value engine.

Read the report