Ambidexterity in integration of data science tools in development of generics in big pharma

Abstract

This thesis investigates the strategic management of data science and Artificial Intelligence (AI) integration within pharmaceutical development of generics. It tackles the considerable digital transformation challenges encountered by an industry that consistently lags in technological maturity relative to other sectors.Subject and Main Issue: The pharmaceutical industry has very low digital maturity scores (2.8/5), while the banking (4.1/5) and technology (4.5/5) sectors have much higher scores. The main hurdles are strict regulators requirements and a competitive market. The leaders don't know enough about what data science and AI can do, which leads to strategic misalignment, unrealistic expectations, and a lack of investment in important technological infrastructure.Objective and Research Question: The primary aim is to formulate a comprehensive strategic framework for the effective implementation of data science and AI within generics development settings. The thesis examines the research question: How to integrate data science and AI tools in the heavily regulated and fast-paced environment of generics development in ‘Big Pharma’ in an effective way (focus on one particular development center)?Methodology: A mixed-methods approach was utilized, integrating quantitative surveys with qualitative interviews and statistical analysis. The study employed structured protocols for data collection, cross-tabulation analysis to examine response patterns, and correlational analysis to find principal adoption drivers among various stakeholder groups.Findings: Statistical analysis demonstrated robust positive correlations between adoption intention and expected benefits (r=0.776), openness to technology (r=0.740), familiarity with data science (r=0.699), and perceived relevance (r=0.696). The study found that hybrid delivery models that combine structured governance with iterative development cycles and leadership involvement and organizational culture are essential determinants of success.Conclusion and Future Prospects: The thesis suggests a three-phase strategic implementation framework that will take 36 months to complete. Phase 1 (months 1-12) focuses on building a strong foundation and running pilot projects with universities. Phase 2 (months 13-24) focuses on scaling and getting stakeholders involved. Phase 3 (months 25-36) focuses on setting up Centers of Excellence and rolling out the program to the whole development. This method uses academic collaboration to improve internal skills while staying compliant. It gives pharmaceutical companies a model they can use to close the digital transformation gap.