An empirical study to accelerate machine learning and artificial intelligence adoption in pharmaceutical manufacturing organizations

Abstract

The quantitative study investigated factors that influence the initiation, the convergence prospects, the highest return on investment sector, and the variables that can delay machine learning and artificial intelligence in the pharmaceutical industry. The study population constituted individuals from the US FDA pharmaceutical sites registry, representing all sectors of the industry. The study reports the trends and preferences identified by the industry executives who participated in the survey. The first hypothesis utilized Kruskal Walli confirming a statistically significant difference in the applicability of Rogers’s diffusion of innovation theory in the pharmaceutical industry. The second hypothesis test utilized Kendall’s τ identified that machine learning and artificial intelligence convergence is imminent. Spearman’s rank-order correlation test was used for the third hypothesis, providing insights into the high return-on-investment areas, namely, operations efficiency, product quality, supply chain integrity, market identification, and penetration, and engineering and maintenance sectors of the industry. The fourth hypothesis applied Spearman’s rank-order correlation test that confirmed that the five artificial intelligence (AI) implementation delay factors, namely, lack of strategy, finding talent, functional silos, management commitment, and behavioral change using the output can cause delays in machine learning and AI projects in the pharmaceutical industry.