by Neeraj Madan 
In 2019, developing a new pharmaceutical product typically took between 10 and 12 years and more than $2 billion in investment. High attrition rates in discovery, lengthy clinical trials, and complex manufacturing transfers slowed progress and limited patient access.
Five years later, the adoption of artificial intelligence (AI) has begun to alter this trajectory in measurable ways. Across discovery, development, and manufacturing, AI systems are compressing timelines, reducing costs, and improving reproducibility. These advances, however, must be considered alongside ongoing concerns around data protection, model interpretability, and regulatory acceptance.
READ NEXT: Smarter Science: Trends Shaping the Life Sciences Landscape in 2026
AI in Research & Development
One of the most visible areas of transformation has been early-stage research. Traditional drug discovery required three-to-six years of hypothesis-driven screening, structural biology, and preclinical validation.
Recent AI-driven methods, including protein structure prediction and compound–target interaction modeling, have reduced these cycles by a third, now taking around two to four years. Platforms such as AlphaFold have accelerated structural biology pipelines, enabling researchers to predict protein conformations in weeks rather than years.
A handful of breakthrough cases highlight the disruptive potential of AI:
- DSP-0038, an AI-designed drug from Exscientia, reached Phase I in roughly 12months.
- Rentosertib, from Insilico Medicine, advanced from concept to clinical trials in just 30 months.
Beyond discovery, AI-enabled trial design and patient stratification are reshaping clinical development. By analyzing electronic health records and genomic data, machine learning models improve cohort selection and reduce recruitment timelines by as much as 30–40 percent.
The result is greater efficiency in both trial initiation and downstream decision-making, with implications for cost savings and faster progression into late-stage development. AI-powered innovations are improving both speed and efficiency in clinical development.
For example, AI-driven trial site selection and patient enrollment can shorten asset development timelines by approximately 6 months, while generative AI tools reduce operational costs by as much as 50%, enabling better return on investment and faster decision-making. These efficiencies help drive the overall compression of discovery-to-launch cycles from 10–12 years historically to roughly 6–8 years in AI-enabled programs
AI in Manufacturing
Manufacturing, traditionally reactive and compliance-driven, is also shifting toward predictive intelligence. AI-enhanced Process Analytical Technology (PAT) now provides real-time monitoring of critical quality attributes, allowing for early detection of deviations. These systems have reduced batch failure rates from the historical range of 5–7% to under 2% in pilot implementations.
Digital twins are further extending this transformation by providing virtual models of production processes that simulate scale-up scenarios and accelerate technology transfers. Where transfers previously required 12–18 months, digital twin approaches have cut this timeline in half to 6–9 months, improving both speed and reproducibility.
In addition, natural language processing is increasingly applied to deviations and corrective and preventive action (CAPA) systems. This has helped with reducing cycle times and recurrence rates while strengthening regulatory readiness.
Figure 1. Validation of digital twins, comparing predicted vs. actual yield, demonstrating improved fidelity of AI-based simulation models. Illustrative example observed in industry validation practices digital twins undergo rigorous credibility assessment via verification, validation, and uncertainty quantification to ensure predictive reliability. Illustrative data adapted from industry case studies (ISPE, 2023)
Data Protection and Regulatory Perspectives
The adoption of AI across discovery and manufacturing raises critical questions around data protection and compliance. AI systems depend on sensitive datasets, including genomic sequencing, electronic health records, and patient registries.
Regulations such as GDPR and HIPAA require strict adherence to anonymization, informed consent, and access control. This includes layered data protection measures specific to AI-supported R&D, as shown below.
Both the FDA and EMA have issued position papers highlighting the need for lifecycle model validation, version control, and human oversight within Good Manufacturing Practice (GMP) contexts. These agencies caution against reliance on “black-box” algorithms, instead encouraging explainable and interpretable machine learning models that can be reviewed within regulatory submissions.
As a result, AI governance frameworks are emerging as essential components of pharmaceutical quality systems, linking technical performance to regulatory trust.
Time-to-Market Acceleration
The cumulative impact of AI across discovery, development, and manufacturing is a measurable reduction in time-to-market. Preclinical discovery cycles that once extended five or more years can now be compressed by 50–70%.
The time saved in the use cases mentioned earlier, combined with the improvements to compress discovery cycles, have helped create a reduction in the overall time from discovery to launch from 10-12 years historically to 6-8 years in AI-supported programs.
Risks and Limitations
Despite the promise, several challenges remain. Demand for AI scientists and data engineers in life sciences has tripled since 2020, yet talent pipelines remain underdeveloped, creating a skills gap that could limit adoption.
When looking at the AI talent demand versus supply in life sciences from 2020–2025, there are clearly widening workforce gaps despite increasing industry adoption. Economic pressures, including inflation and constrained venture capital funding, have slowed investment in biotech R&D. This has forced companies to focus on fewer high-confidence programs.
Figure 2. Indexed AI talent demand vs. supply in life sciences (2020–2025). Baseline (2020) = 100. Demand for AI specialists in pharma and biotech is projected to grow more than 3x relative to 2020.
Furthermore, validation of complex machine learning models under GMP conditions remains difficult, particularly when models lack interpretability. Regulatory authorities continue to stress the importance of human oversight and risk-based implementation.
These limitations underscore the need for balanced adoption strategies that weigh technical capability against organizational readiness and compliance.
Beyond technology adoption, the talent shortage is one of the greatest barriers to scaling AI in life sciences. Figure 2 shows indexed workforce size, where 2020 is set as a baseline value of 100. This means all future values are expressed relative to 2020 rather than absolute headcount.
By 2025, demand for AI specialists in pharma and biotech is projected to grow more than threefold compared to 2020, while the available supply of skilled professionals lags far behind. The widening gap illustrates why executives consistently cite talent as a top constraint on AI adoption and why investment in upskilling, partnerships, and workforce strategy is becoming essential.
Is It Time for Your Organization to Integrate AI?
AI is redefining pharmaceutical R&D and manufacturing by enabling shorter discovery cycles, more efficient clinical development, and predictive manufacturing systems. The shift from reactive compliance to proactive intelligence offers the potential for faster, safer, and more cost-effective therapies.
Yet the full realization of AI’s benefits depends on responsible integration. This includes ensuring data privacy, embedding governance frameworks, validating models to regulatory standards, and investing in the workforce of the future.
Companies that adopt such strategies will not only accelerate their pipelines but also secure a position of leadership in a rapidly evolving industry landscape. If your organization is ready to transform its processes, a partnership with a functional service provider like Insight Global can help.
Chandan Barhate, Ph.D., M.B.A., is Industry Principal for Life Sciences at Insight Global and Evergreen, our professional services division. Connect with him on LinkedIn.
Neeraj Madan is Solutions Director for Data & AI at Insight Global and Evergreen, our professional services division. Connect with him on LinkedIn.
by Julia Koslowsky 
by Celine Pham 