The impact of digital transformation and artificial intelligence on the future of life sciences

Digital transformation in healthcare, especially in the life sciences field, is still alive, good, and is currently driven by the continuous expansion of artificial intelligence, including generating AI, both in the healthcare product itself and in the organization’s quality management system. According to a recent McKinsey survey, only 5% of the more than 100 healthcare and medical technology leaders overseeing the implementation of AI are aware of the competitive value it brings. Additionally, 45% of respondents said they were either exploring the use of generative AI or were in use early on deploying it, and said they were taking different steps to scale up AI efforts.

In 2025, life science organizations are expected to spend more than $10 million in generating AI, an estimate for 2024. These increased investment targets are transformational care, simplifying administrative tasks, improving clinical productivity and overall support for technology. Companies can leverage AI solutions to deliver realistic results and solutions in their company’s QMS and regulatory information management systems.

Laying the foundation: The path to seamless AI

The pathway to successful adoption of AI is based on numerous prerequisites, including the development of quality data that is critical to the implementation and the creation of a strong corporate data literacy program.

Data Literacy Programs are a key step in ensuring data collects, stores, reads, and understands data to drive efficient, conscious and transparent decision-making in key organizational activities. These programs can also help companies manage risk and enable how data management activities support a broader organizational goal. A report from the World Economic Forum further explores the importance of refined infrastructure in the healthcare industry and explains that without it, the industry’s risks lag behind technological advances.

Organizations must ensure that their data literacy plans are designed, which can connect data streams across all departments and products. By focusing on the clear, tangible output of QMS and RIM systems powered by global regulations and standards, companies can potentially simplify processes and replace digital fragmentation with procedural cohesion, improved data quality, and easier QMS/RIM scalability. With this approach, logic-based AI enhancement tools can be introduced to improve greater efficacy in the QMS process, allowing quality assurance and regulatory affairs professionals to spend most of their time on patient safety, product quality and strategic market access activities.

Maximize AI impact through digital conversion

To reduce cost increase, improve efficiency and verify accurate results from sources of advanced technology, digital conversion of data sources must be promoted to drive the production of high-quality data. Most systems today, including QMS, RIM, product lifecycle management, and enterprise resource planning, all of which can bring benefits from successful AI deployments, benefiting from target QMS use cases, depending on the quality data in their program execution.

By extending the integration layer, data can flow better between different departments, simplifying existing inter-organizational complexity due to the use of different QMS, RIM, PLM and ERP systems. Through this newly discovered collaboration, models that rely on synthetic data sets can be further proposed. For example, using AI-driven predictive analytics can improve resource allocation and demand forecasting, while automated quality control measures address compliance and regulatory standards during product manufacturing and global distribution activities.

Pragmatic AI solutions enable corporate QMS and RIM processes, which can bring opportunities to improve the scalability and flexibility of company processes, allowing healthcare organizations to better meet evolving global needs while ensuring compliance with mandatory global regulations.

With a unified, holistic approach, organizations can better support product quality improvement and data-driven decision-making, simplify workflows, and effectively position themselves as success in the evolving AI-NI-SPAING QMS/RIM landscape.

Technical Trouble: Challenges of Generating AI

As generative AI and other AI-based technologies continue to evolve, there are some limitations in their use in healthcare QM and RIM solutions. The challenge is to ensure that solutions operate within the scope of global regulations (e.g., US 21 CFR, ISO 13485, EU AI ACT) while also being cost-effective enough to attract companies to replace their manual processes and existing technology pools.

Even with the ability to enhance human knowledge and abilities, the generated AI is not a one-stop shop for all QMS/RIM processes. Before deployment, companies need to consider their current digital ecosystem. Think about it: The limitations of an organization with multiple document management solutions, some of which may be paper-based, need to be addressed through data and process coordination. Process review and data literacy are key pioneers in deploying digital QMS/RIM solutions.

Another challenge in generating AI is its potential to produce incorrect reactions or “illusions”. In a clinical context, hallucinatory references cited as part of global product registration/submission may have a great impact on the company’s reputation in the regulators as it is actually a false/virtual reference. The increase in reviews by regulators and extended approval schedules may delay market access. This potential emphasizes the key role of professionals or “humans in the cycle” and becomes part of the global AI-ai-ai-ai-aa-Suption product registration process. Similar examples can be cite in other QMS activities where people in the loop are needed as risk reduction measures to completely unlock the potential for generating AI and ensure that human expertise is provided and enhanced with pragmatic AI use cases.

Another major problem is the need to ensure the overall security and security of confidential information for patients and companies. In an age where cyber threats lurk around every mouse click, it is crucial to develop powerful privacy measures in conjunction with AI that implements QMS, RIM and wider corporate activities.

Past: Obstacles to Digital Transformation

Even if new benefits from AI are discovered every day, the ongoing deployment, integration and scaling challenges remain.

Some of these challenges originated from the Internet and Y2K era, while others were young, such as the need to improve the AI ​​literacy of the workforce. Two major challenges that continue to plague the industry’s digital transformation are the old version and different digital systems and the reliance on paper-based processes. This is especially common for quality assurance/RA professionals working in organizations with different digital systems due to mergers and acquisitions, or in small and medium-sized enterprises, which may lack the capital needed to invest in digital infrastructure improvements.

The future: the infrastructure that paves the way for AI success

Successfully integrated AI-based solutions into digital QMS/RIM technology relies on overcoming obstacles to legacy QMS approaches. Isolated data, fragmented digital systems, and paper-based processes undermine operational efficiency and reduce the efficiency and benefits of deploying AI-enabled solutions. However, these challenges cannot be directly underestimated. Automate heavy administrative workflows in pragmatic AI use cases while gaining data-driven insights enable QA/RA professionals to focus on strategic, scientific and value-added activities. By leveraging AI scaling, they can improve product and process quality by enhancing data-driven decision-making, thereby significantly improving product quality, patient safety, and commercial performance.

Companies investing in digital literacy initiatives along with ongoing digital transformation efforts, aiming to improve the data quality across the digital ecosystem, can lay the necessary foundation for enhancing value through AI-na-abled QMS/RIM solutions. The healthcare industry is growing today, highlighting the necessary needs to adapt and adopt new technologies. Organizations that are fully committed to digital transformation, prioritizing strong data literacy and focusing on pragmatic use cases of AI will unlock the true potential of AI. This enables their companies to continue to succeed in providing safe and effective medical solutions to the global market.

Photo: Nevarpp, Getty Images

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