Artificial intelligence (AI) is transforming life sciences, from accelerating drug discovery to enabling personalized medicine and patient care. However, AI adoption isn’t an overnight revolution but a progressive journey, unfolding through three distinct stages:
Stage 1 – Gathering information
Stage 2 – Making predictions
Stage 3 – Innovating possibilities
Each stage represents a pinnacle shift in AI’s capabilities, autonomy, and impact on decision-making. For life sciences organizations, understanding these shifts is essential, not just for innovation, but to strategically plan and prepare their workforce.
Let’s explore how these stages are shaping the industry and what companies can do to keep up.
Stage 1 – Gathering information
Life sciences organizations generate massive datasets. Phase III trials alone produced an average of 3.6 million data points in 2021—triple the amount from a decade ago. AI now plays a crucial role in analyzing these datasets, uncovering patterns, and accelerating insights that were once buried in mountains of data.¹˒²
For example, drug development takes many years and can cost millions of dollars to take a drug from discovery to development. A part of the time and expense is spent on patient recruitment, which has historically been a time-consuming, labor-intensive bottleneck in clinical trials. But through AI-driven natural language processing (NLP), this data (i.e., electronic health records) can be scanned and potential candidates can be identified in a fraction of the time, reducing physician errors and recruitment delays and increasing trial efficiency.³˒⁴
Do your teams understand how to use and interpret AI-driven insights?
Stage 2 – Making predictions
Beyond data analysis, AI in life sciences now identifies patterns and makes predictions, impacting drug discovery, regulatory decision-making, and supply chain optimization.
Take demand forecasting in biopharma. AI can analyze market trends, historical sales data, and external factors to predict demand with greater accuracy, reducing the risks of overstocking or shortages. Similarly, AI algorithms can analyze chemical and biological data to predict which drug compounds have the highest potential efficacy—streamlining R&D efforts and reducing failure rates.⁵˒⁶
In personalized medicine, AI analyzes patient genomic data to tailor treatment plans. A 2018 study reported that an AI model predicting chemotherapy responses achieved over 80% accuracy—demonstrating AI’s potential to improve precision medicine and reduce adverse reactions.⁷
Are your teams equipped to trust and validate AI-driven predictions?
How will Regulatory Affairs and Compliance teams adapt to AI-assisted decision-making?
Stage 3 – Innovating possibilities
As AI reaches greater autonomy, it moves beyond assisting human decision-making to generating new scientific hypotheses and even diagnosing patients in real-time.
For example, FRONTEO’s AI-driven Drug Discovery AI Factory autonomously generates hypotheses for new drug candidates by analyzing over 30 million medical reports. Meanwhile, AI diagnostic tools like LumineticsCore provide rapid test results for identifying diabetic retinopathy—demonstrating AI’s potential to enhance, rather than replace, human expertise.⁸˒⁹
How will AI-driven automation reshape roles within your organization?
What upskilling programs are in place to ensure employees remain competitive in an AI-driven landscape?
Where we go from here: Strategic AI adoption
AI is not just a tool—it’s a fundamental shift in how life sciences organizations operate. And this shift isn’t a one-time event but an ongoing evolution. As AI progresses through the stages of gathering information, making predictions, and innovating possibilities, organizations must continuously adapt their strategies to keep pace. To successfully integrate AI, companies must take a strategic and workforce-focused approach²˒⁷:
Invest in AI literacy: Ensure employees understand how AI works, its capabilities, and its limitations.
Enhance data security: With AI’s ability to process vast amounts of data, strong cybersecurity and compliance measures are critical.
Develop cross-functional AI strategies: Regulatory Affairs, R&D, and Commercial teams must collaborate to set guidelines for AI implementation.
Assess workforce readiness: Evaluate infrastructure, technical expertise, and upskilling needs before full-scale AI adoption.
AI is here to stay. The question is, how will your organization continue to equip its workforce to embrace it? By taking proactive steps, life sciences companies can harness AI’s full potential—while ensuring their teams are ready for the future of innovation.
References
MacDonald G. Clinical trial data surge driving demand for management tech, Medidata says. FierceBiotech. Updated 2022. Accessed March 2025.
NSF. How Is Generative AI Driving Value in the Life Sciences Sector? NSF. Updated 2024. Accessed 3-9-25.
Ismail A, Al-Zoubi T, El Naqa I, Saeed H. The role of artificial intelligence in hastening time to recruitment in clinical trials. BJR Open. 2023.
Cai T, Cai F, Dahal KP, et al. Improving the efficiency of clinical trial recruitment using an ensemble machine learning to assist with eligibility screening. ACR Open Rheumatology. 2021;3(9).
Strategic Consortium of Intelligence Professionals. Pharmaceutical forecasting: Leveraging intelligence for accurate demand prediction. Updated 2024. Accessed March 2025.
Visan AI, Negut I. Integrating artificial intelligence for drug discovery in the context of revolutionizing drug delivery. Life (Basel). 2024;14(2).
Alowais SA, Alghamdi SS, Alsuhebany N, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Medical Education. 2023;22(689):1-15.
FRONTEO. Hypotheses Generative AI. FRONTEO.. Updated 2025. Accessed 3-10-25.
Matar K, Cakir Y, Ehlers JP. AI Advances for Diabetic Retinopathy. Ophthalmology Management. 2023;27.
