Artificial Intelligence and Program Management in the Pharmaceutical Industry: Streamlining Decision-Making, Clinical Trials, Regulatory Compliance, Commercialization, and Risk Management (Published)
The pharmaceutical sector is under increasing pressure to increase drug development, regulatory compliance, cost management and reduce risks without compromising patient safety and therapeutic efficacy. The complexity and size of the contemporary pharmaceutical operations have made traditional program management tools frequently fail to respond to such demands. Artificial Intelligence (AI) has been an enabler of transformation providing sophisticated tools to facilitate decision-making, make clinical trials more efficient, increase regulatory compliance, help in the commercialization strategies, and reinforce risk management. The following paper discusses the use of AI in the management of pharmaceutical programs and its possibilities to transform the key functions of the value chain. Tools AI-based decision support systems allow prioritizing the portfolio, allocating the resources, and planning the strategies based on the data. AI supports adaptive designs, expedited patient recruitment, data integrity and decentralized trial designs in clinical trials. To be in regulations, AI performs documentation automation, analyzes changing guidelines, and allows active pharmacovigilance. In the field of commercialization, AI offers predictive market data, tailored engagement approaches, and supply chain optimization. Moreover, risk analytics that are run with the help of AI enable the identification of operational, financial, and safety-related difficulties early. Although the advantages are significant in terms of efficiency and cost-saving to more accurate decision making the implementation of AI also has issues including the lack of privacy of information, ethical issues, biased algorithms, and regulatory ambiguities. Considering such opportunities and challenges, the study highlights the central role of AI in transforming the management of pharmaceutical programs and recommends a balanced framework that would be exploited by innovation and promote transparency, accountability, and trust.
Keywords: Artificial Intelligence, Commercialization, and risk management, clinical trials, pharmaceutical industry, program management, regulatory compliance, streamlining decision-making
Adopting Lessons Learned from Global Advanced Manufacturing Practices (Published)
Modern manufacturing experiences revolutionary changes through the integration of Internet of Things and Artificial Intelligence and large data analytics with additive manufacturing thus achieving enhanced productivity and automated systems. The research evaluates both benefits and challenges of modern manufacturing with additional focus on productivity improvements and data-based choices. Major implementation costs together with cybersecurity threats and system interoperability problems and required employee readjustment represent major implementation challenges. The solution to these problems demands purposeful funding and unified policy structures and must achieve alignment between industrial operators and academic institutions. New technological advances in quantum computing, 5G and edge computing systems enable the chance for considerable advancement. Excellent integration requires standardized cybersecurity methods that show resistance to attacks. Future investigations should concentrate on financial feasibility and staff expertise development and eco-friendly manufacturing approaches. Cooperation between policymakers and industries is essential for the formulation of regulatory guidelines. This research highlights the necessity of reconciling innovation with organizational preparedness, notwithstanding the restrictions of data availability and advancing technology. Effective adoption of Industry 4.0 can propel sustainable industrial transformation and enhance global competitiveness.
Keywords: Artificial Intelligence, Technology, computer security, cyber security, data science, internet of things
Applying Artificial Intelligence to the Digital Marketing: Opportunities and Challenges for the Marketer (Published)
The present work aims to explore the role and factors that influence the interaction between marketing and artificial intelligence, the developing role of the marketer in the digital age, and the effects of artificial intelligence on the marketing process. Through a comprehensive marketing analysis, the research highlights the emerging power that Artificial Intelligence is exerting in all the marketing and production phases. The article is divided into three phases: the first phase focuses on the transition from traditional to digital marketing, emphasizing how new technologies had a significant impact on the commercial scene. The focus transitioned to the operational frame of AI into marketing operations, recognizing the latter’s ability to add value throughout the modern consumer’s conversion funnel. Following that, the inquiry yielded exciting ideas for possible future developments. Finally, the presentation provided a complete overview of the transition of marketing to digital and the function of artificial intelligence in this context.
Keywords: Artificial Intelligence, Digital Marketing, consumer trend, digital ecosystem, marketer