Empowering the pharma workforce

Nov. 2, 2023
How exploring the synergy between AI and human expertise can help build a well-trained workforce

The pharmaceutical manufacturing industry, known for its stringent adherence to precision, quality and compliance, is amidst a profound transformation. As the industry advances with groundbreaking drug discoveries and innovative technologies, the responsibility to ensure a well-trained workforce grows heavier. With the U.S. witnessing a surge in training costs, passing the $100 billion mark in 2022, it's evident that all industries, including pharma manufacturing, are keen on reinforcing their workforce's expertise.

However, despite the enormous investments, the logistics and costs involved in learning and development (L&D) programs in specialized industries such as pharma manufacturing often make them vulnerable to budgetary cuts. These cutbacks, compounded with challenges like mass retirements, further deepen the skills gap.

Enter generative AI (GenAI). Poised to herald a new era in computing, GenAI is not just about automating tasks; it's about expanding human potential.

The GenAI revolution in pharma

The beauty of GenAI lies in its collaborative nature. Unlike previous iterations of AI, which predominantly focused on automating routine tasks, GenAI thrives on collaboration. It assists humans in areas where manual effort is resource-intensive, time-consuming or predisposed to error.

One such area in pharma manufacturing is the training and transfer of intricate knowledge. Given the sector's nature, the stakes are high; a minor oversight can significantly affect drug quality, compliance and safety. Although thorough, conventional training modules may not always be adaptive or personalized, leaving gaps in the learning process.

AI is becoming a game-changer in this constantly changing landscape. Imagine experts on the manufacturing floor possessing years, if not decades, of experience. Using everyday devices such as smartphones and tablets, they can effortlessly document their processes and procedures. But this is not about merely recording a video. Advanced algorithms work behind the scenes to process this raw footage, extracting critical insights and converting them into step-by-step, easy-to-understand visual guides.

Their adaptability highlights the dynamism of these tools. The global nature of many industries, pharma manufacturing included, means that workforces often span multiple countries, cultures, and languages. Traditional training resources, tethered to a singular language, pose obvious barriers. But these AI-driven tools possess a remarkable ability: rapid linguistic translation. A video tutorial created in English can swiftly translate to cater to speakers of any other language, ensuring that every individual, regardless of their linguistic background, receives the same quality of training.

Pharma manufacturing challenges

The pharma industry, while at the forefront of medical innovation, grapples with unique training challenges, particularly in the manufacturing domain:

  • Complexity of processes: Manufacturing drugs isn't merely about mixing compounds. It's a sophisticated process involving numerous stages with unique variables and intricacies. Mastery of these processes necessitates extensive training and experience.
  • Regulatory oversight: Given the direct implications on public health, the pharma industry is one of the most heavily regulated sectors globally. Staying compliant requires a deep understanding of domestic and international regulations, often a moving target given the evolving nature of these norms.
  • High stakes: An error in many industries might result in financial loss. In pharma manufacturing, the stakes are exponentially higher. A mistake can jeopardize patient safety, leading to severe health repercussions and, in extreme cases, loss of life.

The promise of AI

Against this backdrop, the role of AI in training becomes not just beneficial but critical. AI training platforms are increasingly beneficial in the pharmaceutical industry, leading to several innovative applications:

  • Continuous learning: The pace at which new information and research emerge in the pharmaceutical sector is breakneck. AI-integrated platforms allow organizations to rapidly update video tutorials and training materials in response to this evolving knowledge, ensuring the workforce stays updated with the latest information.

  • Interactive knowledge repositories: Beyond just static video tutorials or written materials, AI allows the creation of interactive knowledge hubs. Workers can delve into specific segments and engage with dynamic visualizations. These repositories aren't just for passive consumption; they encourage active engagement, allowing workers to understand specific tasks or procedures before implementing them in real-life scenarios, thereby reducing errors and bolstering confidence

  • Collaborative work environments: The merging of subject matter experts creating content and learners' feedback leads to a symbiotic learning environment. This dynamic interaction ensures continual refinement of training materials to meet real-world needs, bridging the gap between expertise and application, and fostering a holistic, responsive training ecosystem.

  • Redefining roles: As AI handles more routine tasks, human roles will evolve. Workers will focus more on strategy, innovation, and functions that require a deeper understanding of context, empathy, or nuanced judgment.

  • Global standardization: With AI-driven platforms offering training and standardization across geographies, we can expect a more standardized approach to pharmaceutical manufacturing, irrespective of location. This can lead to more consistent product quality globally.

Human-AI synergy: A win-win for pharma

  • Individualized learning: The pharma industry often grapples with ensuring that each individual comprehends complex procedures and protocols. Traditional training modules, though comprehensive, tend to follow a 'one-size-fits-all' approach. However, every learner is unique, possessing a distinct pace and style of assimilating information.

    AI-driven platforms, by their very nature, are adept at accommodating many learning style preferences. They cater to each individual's needs, ensuring the learning experience is informative but also engaging and resonant.
  • Accessibility: Learning in the modern age shouldn't be confined to a specific time or place. The true essence of training, especially in a dynamic field like pharma manufacturing, lies in its continuity and accessibility. With AI-driven modules, employees aren't restricted to fixed training schedules. They can access knowledge whenever needed, be it during an intricate process on the manufacturing floor or a quick recap before a critical operation. This continuous accessibility ensures that knowledge is always at one's fingertips, reinforcing best practices and reducing errors.

  • Cultural and linguistic inclusivity: The global nature of the pharmaceutical industry means that its workforce is incredibly diverse. This diversity brings along a plethora of languages and cultural nuances. Traditional training materials, often limited in linguistic scope, can inadvertently alienate segments of the workforce. AI, with its rapid translation capabilities, bridges this gap. By swiftly converting training materials into multiple languages, AI ensures that everyone feels included regardless of their linguistic background. This enhances knowledge uptake and fosters a more inclusive work environment.

  • Real-time feedback: Immediate feedback is instrumental in the learning process. It helps to promptly rectify errors and avoid incorrect practices from being implemented. In traditional training scenarios, feedback might be delayed or, in some cases, overlooked. AI-driven platforms, however, aid in providing quick feedback. 

  • Knowledge retention: The collective expertise within a pharmaceutical manufacturing organization is its most valuable asset. As veterans retire or transition to different roles, there's a palpable risk of this vast reservoir of knowledge evaporating. AI platforms offer a solution to this impending challenge. By allowing experts to seamlessly document their processes, procedures, and insights, these platforms ensure that the wealth of expertise is preserved for posterity. New employees can then tap into this documented wisdom, ensuring that decades of experience aren't lost but are passed down, enriching the next generation of professionals.

As the lines between human expertise and machine intelligence blur, the pharmaceutical industry stands on the brink of a renaissance. While challenges are manifold, the potential of AI to transform these challenges into opportunities is immense. The next decade promises a pharmaceutical industry that's not just efficient and compliant but also more adaptable and innovative, thanks to the symbiotic relationship between its workforce and AI.

About the Author

Sam Zheng | CEO and Co-Founder, DeepHow

Sam Zheng, CEO and Co-Founder of DeepHow, spearheads a rapidly evolving startup, backed by esteemed investors. DeepHow revolutionizes skilled workforce training with an innovative, AI-powered, video-centric knowledge capturing and transfer platform.

Prior to DeepHow, Sam dedicated over a decade to Siemens, driving digital innovation across various industries. His noteworthy projects, such as the Cloud Digital Inspection Jacket, have significantly improved technical knowledge sharing, efficiency, and user experience, earning his team the prestigious Siemens Innovation Award.

Simultaneously, Sam serves as an Adjunct Professor of Psychology at Tsinghua University and holds a Ph.D. in Engineering Psychology and a Master’s in Statistics from the University of Illinois at Urbana-Champaign.