Pharma Companies Deploy AI in Core Operations for R&D Productivity Gains

Pharmaceutical AI is the targeted application of artificial intelligence technologies to high-impact functions in drug development and operational processes.

Executive Summary

Pharma companies in the biopharmaceutical sector have achieved a notable win by deploying AI in core operations. The GlobalData survey indicates that 34% of these companies have implemented AI in specific functions. This deployment focuses on areas such as drug discovery, clinical trials, and medical writing. The quantified outcome is a forecasted R&D productivity gain of 11 to 20% according to 31% of the respondents. This represents a shift from pilot stages to production use in key areas of the business.

Eli Lilly stands as one example of companies in this sector advancing AI use. The approach allows for measurable improvements in efficiency across the R&D pipeline. Companies are focusing on functions where AI provides clear value rather than attempting broad transformations.

The overall result is a move away from broad experiments to focused implementations that can be tracked for impact. This targeted strategy supports better resource allocation and outcome measurement.

Background and Context

The State of the Biopharmaceutical Industry 2026 Mid-Year Update from GlobalData provides the data for this analysis. The survey involved 157 pharmaceutical professionals. It was fielded from 20 March to 20 April 2026. Respondents were clients and prospects of GlobalData in the biopharmaceutical space. This timing allows for a current view of AI integration trends in the industry.

The context is one of gradual adoption across the sector. 25% of the respondents are still in the pilot or proof of concept phase. This means that adoption is building but not yet universal among all firms. The industry is taking a measured approach to AI integration in complex operational environments.

The background shows that pharma firms are selective in their AI strategies. They are not pursuing company wide transformations immediately. The emphasis is on identifying functions with potential for return on investment and efficiency gains.

This selective strategy enables better tracking of efficiencies and impact. It reflects a mature understanding of where AI can add value in the complex pharma environment. Firms like Eli Lilly can use this data as a benchmark for their own initiatives.

What's New in Detail

The new data from the mid-year update highlights the specific functions where AI is being deployed. Drug discovery and target identification is cited by 59% of respondents as the highest value use case. This leads the adoption statistics by a significant margin.

Clinical trial design and recruitment follows with 45% of respondents using AI in this area. Medical writing is third with 41% adoption. These figures show the concentration on early and mid stage development processes that can influence downstream results.

The detail reveals that AI is not spread evenly across all operations. The focus remains on high impact areas that can influence the overall R&D pipeline. This targeted approach is the key new insight from the survey results.

The percentages indicate a clear priority order in the industry. Discovery comes first because it can accelerate the identification of new drug candidates. The subsequent functions build on that foundation to improve trial success rates and documentation accuracy.

Technical Specifics

The technical application of AI in these pharma functions is function specific. This means AI tools are tailored to the needs of drug discovery rather than applied broadly across unrelated processes. The approach avoids the risks of large scale unproven implementations.

In drug discovery, AI assists in analyzing large datasets for target identification. For clinical trials, it may help in design optimization and patient recruitment strategies. Medical writing benefits from AI in generating and reviewing documents with greater speed and consistency.

The specifics show that the deployment is designed for measurable outcomes. Companies can track the efficiencies gained in each function separately. This modular approach supports the overall productivity forecasts reported in the survey.

Advanced computing resources from NVIDIA may support some of these AI implementations in pharma settings. The survey does not specify the underlying infrastructure but the outcomes depend on robust technical foundations for model training and inference.

Market and Stakeholder Implications

The market implications for the pharma sector are significant. With 34% already in deployment, the industry is seeing a tipping point in AI acceptance. Other companies may follow to remain competitive in drug development timelines.

Stakeholders including executives at firms like Eli Lilly can expect changes in R&D timelines and costs. The productivity gains of 11 to 20% could translate to faster time to market for new drugs and reduced development expenses.

The implications extend to workforce augmentation. AI can handle repetitive tasks in medical writing and trial design, freeing human experts for higher value work. This augmentation supports overall productivity without immediate large scale hiring changes.

Peer executives should note the selective nature of the adoption. This reduces the risk of failed large scale projects and allows for incremental gains that can be validated before expansion.

The sector as a whole may see accelerated innovation as more firms adopt similar strategies. This could lead to increased competition in drug development efficiency and better patient outcomes over time.

Expert Reactions

Gaffar Aga, Pharma Analyst at GlobalData, provided key insights into the survey findings. His comments highlight the strategic nature of the current AI integration in the biopharmaceutical industry.

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%.Gaffar Aga, Pharma Analyst at GlobalData

Aga further noted that 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.

This expert perspective underscores the pragmatic approach in the industry. The ability to track impact is a critical factor in the decision to deploy AI in production environments.

What's Next

Looking ahead, the forecast from 31% of respondents points to continued growth in AI driven productivity. The 11 to 20% gain is expected within the next 12 months across R&D functions.

Companies will likely expand their AI use to additional functions based on the success in the current leading areas. The move from pilots to production will accelerate for more firms as results are validated.

The next phase may involve deeper integration and possibly the use of advanced models from providers like NVIDIA to enhance capabilities in discovery and trial processes.

Executives should prepare for this by establishing metrics for success in their own AI initiatives. The survey data provides a benchmark for what is achievable in the sector.

The overall trajectory suggests that AI will become a standard tool in pharma R&D. The current data shows the foundation is being laid for broader impacts on development timelines and costs.

Stakeholders should monitor the results from the 34% of companies already deploying to learn best practices. This will inform their own strategies for AI adoption in the coming years.

AI Use Cases in Pharma by Adoption Rate

Use Case	Adoption Rate
Drug discovery and target identification	59%
Clinical trial design and recruitment	45%
Medical writing	41%

1. Assess current AI readiness in high impact functions such as drug discovery.
2. Initiate targeted pilots in areas with clear ROI potential.
3. Measure and track productivity metrics during deployment.
4. Scale successful implementations across the organization based on results.

Implementation Takeaways for Peers

Peer companies can draw direct lessons from the survey distribution of use cases. Starting with drug discovery offers the highest reported value according to the majority of respondents.

The ordered priorities provide a roadmap for phased rollout. Clinical trials and medical writing represent logical next steps after initial discovery successes.