Revolutionizing Engineering in the Biopharmaceutical Industry with Generative AI
Unlocking the next frontier in value creation by leveraging generative AI’s inherent modality creation capabilities.
In recent years, the world of generative AI has seen a remarkable transformation, with its democratization through groundbreaking applications like ChatGPT, DALL-E, and Stable Diffusion. As if that wasn’t impressive enough, the growing power of computing, mirroring the wider trend of AI’s integration into various businesses, has ushered in a new era of possibilities for the biopharmaceutical industry. But how exactly is this technological convergence reshaping the field, and what's in it for engineering?
In the biopharmaceutical sector, major players are rapidly embracing generative AI applications throughout the value chain. From the early stages of drug discovery, where novel protein structures are being designed based on specific properties, to the automation of clinical study report data entry, processing, and regulatory submissions, to the creation of standard operating procedures and steering of shop floor operations – the benefits are striking. These applications lead to:
Productivity increases of up to 50%
Cost reductions of up to 30% (both in terms of internal FTE hours and external contractor spending)
Quality improvements of up to 30% in accuracy, completeness, and compliance with regulatory standards.
When we turn our focus to engineering, we find that large language models (LLMs) play a pivotal role, showcasing core capabilities in text generation, summarization, extraction, translation, and search. LLMs, a subset of generative AI, are trained on vast amounts of textual data, often tailored for specific natural language tasks.
In the daily routines of engineering department employees, whether they are working on global projects or local initiatives, common pain points surface. One significant challenge is the continuous need to create similar documents with subtle nuances, a task that can be both time-consuming and prone to errors. This issue is particularly pronounced given the repetitiveness of certain activities performed by highly skilled professionals within the department. Another prevalent concern is the lack of consistency and compliance with global guidelines. Achieving uniformity in documentation and ensuring adherence to established standards is essential for maintaining quality and facilitating effective collaboration. Without a streamlined approach, variations in practices may emerge, leading to confusion and potential complications, especially in projects with international scope. Furthermore, employees often grapple with the absence of tools designed to identify existing relevant reference documents. This gap in resources can hinder productivity and result in the unnecessary duplication of efforts. A comprehensive system that enables easy retrieval of pertinent information would not only save time but also contribute to the overall efficiency of the engineering workflow.
The application of generative AI presents abundant opportunities throughout the entire project lifecycle, from early feasibility studies to commissioning and qualification, all the way to operation, maintenance, and decommissioning. Many organizations have only scratched the surface of what's possible, and the time to innovate is now.
We can identify several avenues for significant economic value potential of Generative AI applications within this context of a biopharmaceutical manufacturer:
- Reducing the time taken to create 20 to 100 first draft documents annually per engineer by up to 80%.
- Enabling highly skilled engineering profiles to shift towards more value-added activities.
- Strengthening an organization's competitiveness through improved accuracy, document completeness, compliance with regulations, and document effectiveness.
In a world where innovation is essential for progress, generative AI is proving to be a game-changer for the biopharmaceutical industry. By unlocking the full potential of AI technologies, companies are poised to improve efficiency, reduce costs, and enhance the overall quality of their work. With these advancements, the sky is the limit, and the possibilities for engineering teams in the biopharmaceutical industry are boundless.
