The Influence of Digital Transformation and AI in the Future of Life Sciences
Digital transformation within healthcare, especially the life sciences sector, is alive and well and currently driven by the ongoing expansion of artificial intelligence, inclusive of generative AI, whether in healthcare products themselves or within organizations’ Quality Management Systems. According to a recent McKinsey survey, only 5% of more than 100 healthcare and medical technology leaders who oversee generative AI implementation have realized the competitive value it brings. Further, 45% of respondents said they were either exploring the use of generative AI or were early in the process of deploying it, and indicated they were at varying steps of scaling their AI efforts.
In 2025, life sciences organizations are anticipated to spend a collective total of more than $10 million on generative AI, a from 2024 estimates. These increased investments target transforming care, simplifying administrative tasks, improving clinical productivity and the overall enablement of technology. Companies can leverage AI solutions to provide realistic results and solutions in patient safety and commercial performance within a company’s QMS and regulatory information management systems.
Laying the groundwork: The path to seamless AI adoption
The path to successful AI adoption is built on numerous prerequisites, including the development of the quality data that is pivotal to implementation and the simultaneous creation of a robust company data literacy program.
Data literacy programs are a critical step in ensuring data is collected, stored, read and understood in a way that drives efficient, conscious and transparent decision-making for key organizational activities. These programs also help companies manage risk and bring clarity to how data management activities support broader organizational goals. A report by the World Economic Forum further explores the significance of refined infrastructure within the healthcare industry and explains that without it, the industry risks falling behind in technological advancements.
Organizations must ensure their data literacy programs are designed with an approach that connects data streams across all divisions and products. By focusing on clear, tangible outputs of QMS and RIM systems that are driven by global regulations and standards, a company can potentially simplify processes and replace digital fragmentation with procedural cohesiveness, improved data quality and easier QMS/RIM scalability. Through this approach, logic-based AI-enhanced tools can be introduced to drive greater efficacy within QMS processes, enabling quality assurance and regulatory affairs professionals to dedicate a greater portion of their time to patient safety, product quality and strategic market access activities.
Maximizing AI impact through digital transformation
To mitigate rising costs, improve efficiency and validate accurate results from advanced technological sources, there must be a push for digital transformation of data sources to drive the production of high-quality data. The majority of today’s systems, including QMS, RIM, product lifecycle management and enterprise resource planning, all of which can yield benefits from successful AI deployment to drive benefits in targeted QMS use cases, depend on quality data in their procedural executions.
By expanding the layers of integration, data can flow better across differing departments, simplifying existing inter-organization complexities stemming from the use of distinct QMS, RIM, PLM and ERP systems. With this newfound collaboration, models reliant on comprehensive datasets can be further advanced. For example, using AI-driven predictive analytics can improve resource allocation and demand forecasting while automated quality control measures handle compliance and regulatory standards during product manufacturing and global distribution activities.
Company QMS and RIM processes enabled with pragmatic AI solutions can usher in the opportunities for enhanced scalability and flexibility of company processes, allowing healthcare organizations to better address evolving global demands while ensuring compliance with mandatory global regulations.
With a unified, comprehensive approach, organizations can better support product quality improvements and data-driven decision-making, streamline workflows and effectively position themselves for success in an ever-evolving, AI-enabled QMS/RIM landscape.
Technological troubles: The challenges of generative AI
As generative AI and other AI-based technologies continue to advance, there are some limitations to their use in healthcare QMS and RIM solutions. The challenges lie in ensuring that solutions operate within the bounds of global regulations (e.g., U.S. 21 CFR, ISO 13485, EU AI Act) while also being cost-effective enough to entice a company to replace its manual processes and existing technology pool.
Even with its abilities to augment human knowledge and abilities, generative AI is not a one-stop shop for all QMS/RIM processes. Prior to deployment, a company needs to consider its current digital ecosystem. Think of this: an organization with multiple document management solutions — some of which may be paper-based — has constraints that need to be addressed by data and process harmonization efforts. Process review, alongside data literacy, is a key precursor to deploying digital QMS/RIM solutions.
Another challenge with generative AI is its potential production of incorrect responses, or “hallucinations.” Within a clinical context, a hallucination reference that is cited as part of a global product registration/submission could have significant ramifications on a company’s reputation with a regulator, as it is in effect a false/fictitious reference. The resultant increase in a regulator’s scrutiny and lengthened approval timelines could delay market access. This potential underscores the critical role of a professional, or “human in the loop,” to be part of the global AI-enabled product registration process. Similar examples could be cited across other QMS activities where a human in the loop is needed as a risk mitigation measure to fully unlock generative AI’s potential and ensure that human expertise is delivered and enhanced alongside pragmatic AI use cases.
Another major concern lies in the need to ensure overall safety and security of confidential information for both patients and companies. In an age where cyber threats lurk around every click of the mouse, developing robust privacy measures is crucial in conjunction with implementing AI for QMS, RIM and broader company activities.
The past: Hinderance to digital transformation
Even as new benefits stemming from AI are discovered each day, ongoing deployment, integration and scaling challenges remain.
Some of these challenges have origins as far back as the dot-com and Y2K eras, while others are younger, such as the need to upskill a workforce’s AI literacy. Two significant challenges that continue to plague the industry’s digital transformation are the continued use of legacy and disparate digital systems and the reliance on paper-based processes. This is especially prevalent for QA/RA professionals working within organizations that have disparate digital systems due to mergers and acquisitions, or within small- to medium-sized enterprises that may lack the capital needed to invest in digital infrastructure improvements.
The future: Infrastructure that paves the way for AI success
The successful integration of pragmatic AI-based solutions into digital QMS/RIM technologies relies on overcoming the hurdles of legacy QMS approaches. Siloed data, fragmented digital systems and paper-based processes cripple operational efficiencies and reduce the efficiencies and benefits of deploying AI-enabled solutions. However, tackling these challenges directly can’t be underestimated. Automating burdensome administrative workflows within pragmatic AI use cases while gaining data-driven insights allows QA/RA professionals to focus on strategic, scientific and value-added activities. By leveraging AI augmentation, they can elevate product and process quality through enhanced data-driven decision-making, resulting in significant improvements to product quality, patient safety and commercial performance.
Companies investing in digital literacy programs in conjunction with ongoing digital transformation efforts with the goal of enhancing data quality across the digital ecosystem can lay the foundation necessary to drive enhanced value with AI-enabled QMS/RIM solutions. Today’s healthcare industry is evolving, which highlights the essential need to adapt and adopt new technologies. Organizations that fully commit to digital transformation, prioritize robust data literacy and focus on pragmatic use cases for AI will unlock AI’s true potential. This positions their companies for continued success in delivering safe and effective healthcare solutions to global markets.
Photo: nevarpp, Getty Images
As the senior director of Product and Strategy at IQVIA, Mike King ensures that healthcare solutions meet the demands of complex and diverse global regulations. He oversees IQVIA’s comprehensive solutions, including the award-winning SmartSolve® eQMS and RIM Smart, which streamline quality and regulatory compliance processes.
With 20 years of commercial experience, Mike focuses on optimizing business workflows through intelligence-driven simplification and automation across quality, regulatory and safety functions. He is passionate about improving patient outcomes and is an expert in AI applications within the quality and regulatory space. Mike leverages his extensive knowledge and skills to develop innovative solutions that advance the quality agenda in healthcare. Mike is dedicated to empowering regulatory and quality professionals, helping them recognize their direct impact on patient safety and organizational performance. His goal is to enable these professionals to enhance patient outcomes and drive commercial success.
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