Using big data to design clinical trials better and predict outcomes can make it commercially feasible to develop drugs for smaller patient populations. Pharmaceutical companies are looking to big data to reduce costs in research and development and manufacturing. With the explosion of health-related data in recent years, the market for artificial intelligence in drug development, valued at US$200 million in 2015, ballooned to US$700 million in 2018 and is predicted to appreciate more than US$5 billion in 2024, according to a report by big data analytics. Artificial Intelligence (AI) and big data have the potential to lower the cost and time of drug trials, to better determine patient outcomes with established drugs and to better design new drugs. Computer software and algorithms can provide better analytics before and during the manufacturing processes and stimulate insights to fuel better decisions in the pharmaceutical industry.