The 2024 Biopharma Technology Landscape report by Benchling, a leading R&D cloud software company, reveals the transformative impact of artificial intelligence (AI) and automation on biopharma research and development (R&D). The report highlights how large biopharma companies are leveraging these advanced technologies to optimize efficiency and accelerate innovation, setting a new standard in the industry. As large pharmaceutical companies push the boundaries of what’s possible in drug discovery and development, AI and automation are proving to be indispensable tools.
The Rise of AI and ML in Large Biopharma Companies
A substantial majority of large biopharma companies have integrated AI and machine learning (ML) into their R&D processes. The report indicates that 67% of large companies, defined as those with 1,000 or more employees, are utilizing AI and ML. This adoption rate is significantly higher compared to only 23% of small companies, underscoring a major shift in how major pharmaceutical companies are leveraging these technologies to speed up drug discovery and enhance their competitive edge. AI is seen as a critical tool to shorten the time to market for new treatments.
According to the report, 75% of large companies expect AI and ML to significantly accelerate their development processes within the next 12 to 24 months. This optimism is largely driven by AI’s capability to automate the analysis of vast datasets, identify novel therapeutic targets, and optimize the design and execution of clinical trials. Strategic initiatives at the highest organizational levels are supporting the adoption of these technologies. The study found that 50% of large companies have already implemented programs dedicated to AI and ML. However, smaller companies are slower to adopt these technologies, with only 28% having similar programs. This difference highlights the more tentative approach smaller biopharma companies take towards integrating AI and ML.
Automation and Scientific Data Management
Automation and scientific data management platforms are playing a crucial role in the digital transformation of laboratories, particularly among large biopharma companies. The report notes that these companies are adopting robotics and automation technologies at twice the rate of smaller organizations. However, the study points out several persistent challenges, including limited connectivity to lab instruments and the slow adoption of cloud solutions. For instance, only 23% of small businesses and 17% of large businesses have adopted SaaS-based scientific software, which hampers the full exploitation of AI.
Additionally, only 37% of small businesses report that more than 60% of their lab instruments have automated data capture. This technological divide between large and small companies is evident, with large, well-structured laboratories making significant investments in AI and automation, while smaller entities tend to adopt a more cautious approach, often limited to pilot projects. Despite the clear trend towards the digitalization and AI transformation of biopharma R&D, the industry faces challenges related to technology integration, data quality, and cloud infrastructure adoption. The study outlines the readiness levels of companies in adopting AI, showing that only 25% of large companies and 9% of small companies have achieved a foundational level of AI readiness, which involves key elements such as talent acquisition, data structuring, and automation.
Challenges in Technology Integration and Cloud Adoption
These figures drop to 14% and 3%, respectively, for an advanced level of AI readiness. Generative AI usage is reported at 73% among large biopharma companies, compared to 26% among smaller companies, indicating a more robust and widespread implementation in larger entities. One of the major insights from the report is the fragmentation and complexity of IT systems in the biopharma sector. The report highlights that 54% of the scientific software used by large companies is custom-developed, which complicates the integration of new technologies. Additionally, 36% of IT managers in these large companies manage more than 100 laboratory instruments, necessitating increased interoperability between systems.
This fragmentation is seen as a significant barrier to SaaS adoption, with 68% of large enterprises citing system fragmentation, data security, and regulatory requirements as holding back SaaS adoption. Laboratory technology and connectivity pose additional challenges. Only 41% of large companies believe they have good integration between their wet and dry labs, which is identified as a major obstacle to achieving AI readiness. Furthermore, 78% of small businesses consider the high cost of investing in robotics and automation as a significant barrier. On a positive note, 62% of large companies and 54% of small companies cite improved productivity as the main driver for adopting R&D data platforms.
Drivers for Adopting R&D Data Platforms
The 2024 Biopharma Technology Landscape report from Benchling, a premier R&D cloud software company, illustrates the groundbreaking influence of artificial intelligence (AI) and automation on biopharmaceutical research and development (R&D). This detailed analysis emphasizes the crucial role that AI and automation play in revolutionizing large biopharma companies’ operations, enhancing efficiency, and speeding up innovation. By integrating these cutting-edge technologies, these companies are not only optimizing their processes but also setting new industry benchmarks. The report underscores the indispensability of AI and automation in pushing the limits of drug discovery and development. As pharmaceutical giants continue to explore what’s achievable in drug R&D, the adoption of these advanced tools is proving to be vital. The findings indicate a new era in biopharma, where technology is driving unprecedented advancements and transforming the way medications are discovered and brought to market, ultimately benefiting global health outcomes.
