Artificial intelligence (AI) has moved beyond experimental use and is now becoming an operational cornerstone across the pharmaceutical value chain, with companies increasingly deploying the technology in high-impact areas such as drug discovery, clinical trials, and medical writing, according to GlobalData. 

The intelligence and analytics firm notes that AI is reshaping R&D economics, accelerating innovation cycles, and influencing competitive dynamics across global biopharma markets.

Citing its State of the Biopharmaceutical Industry 2026 (Mid-Year Update), GlobalData reports that 34% of 157 surveyed pharmaceutical professionals have already implemented function-specific AI within their organizations, while a further 25% are in pilot or proof-of-concept stages, indicating accelerating but targeted adoption.

Gaffar Aga, Pharma Analyst at GlobalData, comments: “The findings reflect an industry that is embedding AI where it delivers measurable value rather than instituting a general company-wide rollout. Drug discovery and target identification lead as the highest-value use case, cited by 59% of respondents, followed by clinical trial design and recruitment at 45%, and then medical writing at 41%.”

In drug discovery, AI is enabling researchers to rapidly analyze large datasets, identify potential drug targets, and simulate safety and efficacy before laboratory testing, thereby reducing both time and cost. In clinical development, AI is being used to optimize patient recruitment and improve trial design by analyzing clinical records and matching participants more effectively.

While the role of AI in early-stage research continues to evolve, the report suggests that a growing number of AI-enabled therapies are expected to reach the market as technological capabilities improve and regulatory frameworks mature. 

AI-driven medical writing is also emerging as a key efficiency driver, helping streamline the preparation of clinical study reports and regulatory submissions, which directly impacts drug development timelines.

Aga added "Companies are not trying to transform everything at once; they are identifying the functions where AI already delivers a clear return on investment, with the ability to track impact and efficiencies."

The survey further highlights that 31% of respondents expect AI to improve R&D productivity by 11–20% over the next 12 months, while 30% anticipate gains of 5–10%. On cost savings, 38% expect reductions of 5–10%, and 22% foresee savings of 11–20%.