Artificial Intelligence in Pharmaceutical SMEs : Leveraging AI in the Business Processes

Sammanfattning

Artificial intelligence (AI) is rapidly evolving into one of the most impactful technologies of our time. Advancements in machine learning algorithms, rise in computational power, and increasing amounts of available data have enabled these systems to handle complex tasks like natural language processing and advanced analytics with remarkable speed and accuracy. Today, AI is considered a driving force behind the Fourth Industrial Revolution. Despite its potential, companies struggle to integrate artificial intelligence into their operations beyond the pilot stages (Enholm et al. 2021). AI imposes high requirements on data availability, IT infrastructure, and sufficient technical expertise to navigate this evolving landscape. Emerging regulatory frameworks for AI compliance add another layer of complexity, particularly in highly regulated sectors, such as the pharmaceutical industry. With its rigorous standards for product quality and patient safety, organizations within this industry face greater challenges in adopting artificial intelligence within their business processes. These barriers are even more pronounced in small and medium-sized enterprises (SMEs). Besides being burdened by general AI adoption challenges, SMEs face unique constraints stemming from inherent limitations in financial resources, underdeveloped IT infrastructures, and fragmented data structures. Despite these barriers, scholars believe adopting AI offers SMEs significant opportunities to transform their operations and gain a competitive edge (Schwaeke et al. 2024). SMEs constitute a significant share of the global economy and play a crucial role in job creation by contributing to more than half of global employment (Iyelolu et al. 2024). Given their substantial impact on the global economy, it is imperative to assist SMEs in overcoming barriers to AI adoption. This thesis investigates how artificial intelligence can enhance business processes within small and medium-sized enterprises (SMEs) in the pharmaceutical industry. Furthermore, the study explores barriers hindering AI adoption in pharmaceutical SMEs. To achieve this, a single case study methodology was employed in collaboration with Unimedic Pharma AB, a Swedish pharmaceutical SME. The thesis finds several critical barriers to AI adoption in pharmaceutical SMEs, including data structures, limited AI knowledge, lack of formal AI strategy, and regulatory requirements. A particular inhibitor is regulatory ambiguity, where evolving frameworks for using artificial intelligence make compliance requirements unclear and challenging to navigate. Despite these constraints, AI holds significant potential to enhance business processes within pharmaceutical SMEs. For instance, partial AI adoption strategies can be employed in highly regulated processes such as Product of the month (PM) process, leveraging AI as a recommendation-based system augmented by human oversights rather than fully automating the process. Furthermore, Intelligent Process Automation (IPA) and Machine Learning (ML) can be leveraged to expedite time-consuming activities and significantly improve insight generation in less-regulated processes such as Business Development (BD) and purchasing. As a result, the thesis provides a structured and detailed overview of AI inhibitors faced by pharmaceutical SMEs. It also presents technical examples of AI applications that can enhance Business Development, Product of the month (PM), and purchasing by leveraging cloud-based and on-premises solutions.