The pharma industry is highly competitive, and companies are constantly pressured to maintain and grow market share. In this evolving, complex environment, challenges range from regulatory compliance to technological integration, with the industry caught between traditional and new methodologies.
With the global life sciences tools market expected to reach $118.5 billion by 2028, embracing intelligence-driven solutions is essential. These solutions enable organizations to improve quality operations, ensuring products are safe, effective and of the highest standard.
AI in particular can help companies provide precise, actionable insights, automating routine processes and improving decision-making capabilities of industry professionals. AI also can make a substantive difference to quality operations, driving innovation, efficiency and overall improvements to give companies a competitive advantage in end-to-end manufacturing, market access activities and financial performance.
The need for smart solutions in quality operation
Quality operations in the pharma industry need smart solutions for several compelling reasons. The industry generates a significant amount of data. Intelligent-driven solutions with AI capabilities can manage and analyze this data to extract meaningful insights that drive quality improvements.
For example, quality management procedures such as in-process inspections are critical in drug manufacturing. AI can help check dimensions in drug manufacturing, packaging and other aspects, validating that everything is within the defined tolerances, course correcting as necessary and/or flagging the need for human review and intervention. Timing is vital during such activities as the earlier such production deviations can be caught the smaller the potential impact in cost from product wastage or rework activities.
AI-powered insights: a new era in manufacturing
Management of quality activities extends throughout the product life cycle. Quality management permeates design, manufacturing and distribution activities, and it is crucial to post-market activities that continuously monitor product performance in the market. Quality Management Systems (QMS) are crucial for ensuring pharma companies develop and maintain product solutions that are safe, effective and adhere to global regulatory requirements and best practices.
AI is playing an ever-increasing role across healthcare QMS activities such as in data analytics, data mining and helping identify points of improvement. AI can also aid in navigating the extensive industry regulations and quality standards, identifying requirements and providing insights on how specific company products relate to global and local requirements. It also helps companies understand precedents, evaluate risks and support quality teams to make informed decisions based on historical data and trends.
For instance, AI plays a crucial role in streamlining processes in an environment where operational difficulties and regulatory complexity are prevalent, enabling connected workflows that maintain accuracy and compliance. A recent report indicates that AI in the global pharmaceutical market is anticipated to reach $1.8 billion by 2030, reflecting the industry’s robust inclination towards AI integration.
Impact and benefits of AI in quality operations
Artificial intelligence impacts quality operations in several ways. AI is not just about technology; in the pharma industry it is also about economics and especially patient outcomes. While AI can bring innovation and efficiencies, it must also be cost-effective for successful adaptation. The ultimate goal is to enhance patient safety and outcomes while managing costs effectively, ensuring that the innovations brought in by AI are accessible and beneficial to companies, patients and the health care system as a whole. Here are some of the benefits AI can deliver for quality operations:
1. Optimized data analytics and mining
AI can help analyze and mine vast amounts of data to identify improvement points and areas of focus in quality workflows. For example, an AI system could analyze historical data on a drug manufacturing process to identify patterns or anomalies in production. If a specific step in the process is consistently associated with a higher error rate, AI could flag areas for human professionals to investigate further. In this example, AI is responsible for the mining of data. AI could also be used to monitor high-volume structured and unstructured data in sources such as social media, websites and voice records from company call centers to identify potential issues in product quality and, where applicable, the need to report adverse events to local and global regulatory authorities.
2. Streamlined regulations and standards
The use of AI can enable manufacturers to understand and navigate the extensive global and local regulations and standards, making it easier to comply with the various requirements. An AI tool can be programmed to monitor change with the latest regulations and automatically cross-reference them with a company’s practices and product range to create an impact assessment and action plan for the review of a human ‘augmented’ worker.
In this example, AI can analyze information regarding regulations in different countries and process data in accordance with any nuanced requirements for a human review to verify global compliance. Additionally, AI can provide automated translation of documents, which is crucial as the pharma industry becomes increasingly globalized. It can also assist in drafting documents and preparing global regulatory submissions.
3. Enhanced decision-making support
AI provides insights and historical data that enhance decision-making processes, helping individuals make informed decisions at the correct times. For example, advanced analytics, including AI, can help executive teams make better decisions, potentially
4. Automated transactional processes
AI also brings in transactional automation, helping manage deviations and manufacturing errors. It can pull in relevant data quickly, saving time that would otherwise be spent manually searching through records. This automation enhances operational efficiency and improves the quality of investigations and responses to deviations.
5. Timely corrective actions
AI helps determine the right time to intervene in production processes, preventing costly errors and improving overall quality. By monitoring real-time data from the manufacturing process, AI can alert operators to deviations from expected results. This allows them to take immediate corrective action before significant problems arise.
AI can also be used to optimize Corrective Action and Preventive Action (CAPA) processes by looking at precedent actions and providing suggestions to a human ‘augmented’ worker for remediation of the existing CAPA. These processes are essential for identifying inconsistencies and problems within product manufacturing, finding the cause and rectifying them to prevent recurrence.
6. Improved investigations and response
Manufacturing companies can improve the quality of their investigations and responses to deviations by employing AI to provide better data and insights. This leads to more effective problem-solving. For instance, AI can help determine the root causes of product and process defects and suggest remedial actions based on precedent information thereby speeding up the investigation process which leads to faster, more effective responses to quality issues.
7. Holistic international oversight
Intelligence-driven solutions help companies manage the increasing global complexity of the industry, driving profitability while retaining a highly skilled workforce. A life sciences company could deploy AI to help coordinate and control operations across multiple countries through the identification of best practices and measurement of variation at specific sites/ regions. Thus, it can help ensure each location complies with local regulations and quality standards in an optimized way.
Targeted insights improve decision-making and efficiency across various stages of quality operations. For example, AI can analyze customer feedback and online reviews to provide focused intelligence about product performance. This then leads to insights into areas of product development and, where necessary, the need to file adverse events with local and global regulatory authorities.
8. Strategic cost management
AI can help manage company spend effectively, enabling innovations that are technologically advanced, economically viable and beneficial to the field. Research by Accenture shows AI applications can generate up to $150 billion in annual savings for a typical pharmaceutical company.
9. Increased patient safety and outcomes
By focusing on enhancing patient safety and outcomes, AI supports the technologies and processes in place to prioritize patient well-being and effective, efficient global healthcare delivery. AI could analyze patient and product data to identify potential risks or side effects associated with a medication, leading to safer products and better patient outcomes. Naturally, any such analysis would need to be considerate of global requirements for patient data.
10. Greater adaptability and flexibility
AI allows adaptability and flexibility in managing various aspects of quality operations, supporting continuous improvement and innovation. For example, AI could help a company adapt its manufacturing processes to regulatory changes or market demands in a conscious way that brings transparency to the global impact of such changes/ remediations (e.g., by identifying the impact on global submissions and the need for variations to be submitted) and optimizes the timeline of any such remediation.
11. Proactive problem identification
AI helps proactively identify potential issues and focus areas, enabling timely responses and preventing bigger problems. For instance, the system could predict potential manufacturing issues, equipment failures or supply shortages, allowing the company to take proactive measures to prevent disruptions before production outages occur.
12. Elevated employee engagement
By automating transactional activities, AI optimizes employees’ time, allowing them to focus on more strategic and science-focused activities and therefore improving overall job satisfaction and productivity. As the AI ‘augmented’ workers have more time allocated to their professionally trained activities, there could be an increase in employee engagement and an accompanying increase in companies being able to retain their top talent.
Each of these benefits enhances the overall quality of operations in the pharmaceutical industry, driving innovation, efficiency and improved outcomes.
Augmenting human capabilities for a competitive edge
In the future of AI in quality operations, there is a focus on managing increasing global complexity and driving profitability while retaining a highly skilled workforce. AI is not about replacing humans but rather augmenting their capabilities, enabling them to manage complexity with greater transparency and precision and to make more informed decisions. AI helps control the vast and diverse data, provide targeted insights to the ‘augmented’ worker who can then act with improved decision-making across various stages of pharmaceutical operations.
Industry leaders must embrace the integration of AI to realize a future where quality is maintained and continuously enhanced. By utilizing AI's capabilities, organizations can look forward to a future with steady growth, enhanced healthcare solutions and a sharp increase in operational quality and efficiency. Ultimately, the role of AI in health care will support companies in the provision of safe and effective product solutions in global markets.
