Today, the pharma sector, like many others, appears to be on the cusp of a transformation thanks to the influence of automation on drug discovery careers. Companies ranging from Insilico Medicine to Big Pharma firms like Janssen and Sanofi are putting AI at the heart of their operations. Many are increasingly recruiting workers from the tech sector.
Complex dynamics are at play amidst this shift. The integration of automation in the pharmaceutical industry is not simply a story of job loss and displacement. While 2023 will go down as notable year for layoffs in the sector, AI also promises to redefine existing jobs as create new ones as well.
An exploration of the influence of automation on drug discovery careers
To shed light on the varying susceptibility to automation of different job roles within the pharmaceutical industry, we created the stacked bar chart below that examines thirteen roles in the industry, each evaluated on fourteen different metrics that contribute to their risk of automation.
Evaluating the susceptibility of pharmaceutical job roles to automation. Each role was scored for factors associated with automation potential. Scores reflect low, medium or high susceptibility. Image courtesy of Drug Discovery & Development.
What does automation mean for different roles in drug discovery careers?
The factors range from the level of information processing and documentation required to the potential for AI integration into the role. Each role is scored on these factors, and the total score determines its ultimate exposure of automation. Higher scores don’t necessarily equate to a higher risk of job loss, but higher scores do indicate an elevated possibility that the role could evolve or be displaced.
- Drug Manufacturing Workers: High risk of automation as a result of the repetitive nature of tasks, high level of information processing, and documentation involved. The experience and expertise of such workers can be hard for robots to completely emulate.
- Lab Technicians: Medium risk of automation as a result of the repetitive tasks and high levels of information processing. These professionas’ work also involves complex decision-making and data analysis which are less prone to automation.
- Quality Assurance/Control Analysts: Medium risk as a result of repetitive tasks, high levels of information processing and the need for regulatory compliance. Such professionals require high levels of education, training and expertise, which are factors that are less susceptible to automation.
- Data Scientists: Relatively low risk of automation. This role requires high levels of complex decision-making, creativity, strategy, data analysis, education and training. While the data analysis component of data scientists’ work is more susceptible to automation, this trend could result in job evolution rather than displacement. As automated tools can handle routine tasks, data scientists can focus on more complex, strategic issues.
- Research Scientists: Relatively low risk of automation. This role centers around high levels of complex decision-making, creativity, strategy, data analysis, collaboration, education and training.
- Clinical Trial Coordinators: Low risk of automation as a result of high levels of complex decision-making, collaboration, regulatory compliance, education and training. But the information processing and documentation part of clinical trial coordinators’ work could be automated.
- Regulatory Affairs Specialists: Low risk of automation as a result of to high levels of complex decision-making, creativity, strategy, collaboration, regulatory compliance, education and training.
- Clinical Research Associate (CRA): Low risk of automation as a result of to high levels of complex decision-making, collaboration, regulatory compliance, education and training. The information processing and documentation part of their work, however, will likely be increasingly automated.
- Biostatistician: Low risk of automation as biostatisticians’ work requires substantial levels of complex decision-making, creativity, strategy and expertise.
- Pharmacovigilance Specialist: Low risk of automation given the high levels of complex decision-making, training and experience required.
- Medical Science Liaison (MSL): Low risk given the high degree of creativity, strategy, training, expertise and human interaction involved.
- Regulatory Affairs Manager: Low risk given the high levels of complex decision-making and creativity required.
- Medical Information Specialist: Low risk of automation given the nature of complex decision-making and experience required. The information processing and documentation portion of their work could be automated.
Matrix breakdown
Here’s a closer look at the factors and the weights used in the bar graph:
| Job Role | Ultimate Risk of Automation | Information Processing and Documentation (10%) | Repetitive Tasks (5%) | Routine Data Analysis (2.5%) | Advanced Data Analysis (2.5%) | Complex Decision-Making (10%) | Creativity and Strategy (5%) | Education and Training (10%) | Collaboration and Communication (10%) | Regulatory Compliance (5%) | Experience and Expertise (15%) | Job Evolution Potential (10%) | Resilience to External Shocks (15%) | Economic Impact (20%) | Potential for AI Integration (20%) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Drug Manufacturing Workers | High | High | High | Medium | Low | Low | Low | Medium | Medium | High | Medium | Low | Medium | High | Low |
| Lab Technicians | Medium | High | High | High | Medium | Medium | Low | Medium | Medium | High | Medium | Medium | Medium | High | Medium |
| Quality Assurance/Control Analysts | Medium | High | High | High | Medium | Medium | High | High | High | High | High | Medium | Medium | High | Medium |
| Data Scientists | Low | High | Low | Medium | High | High | High | High | Medium | Low | High | High | High | High | High |
| Research Scientists | Low | High | Medium | Medium | High | High | High | High | High | Medium | High | High | High | Medium | High |
| Clinical Trial Coordinators | Low | High | Medium | High | Medium | Medium | High | High | High | High | High | High | High | High | Medium |
| Regulatory Affairs Specialists | Low | High | Medium | High | Medium | Medium | High | High | High | High | High | High | High | High | Medium |
| Clinical Research Associate (CRA) | Low | High | Medium | High | Medium | Medium | High | High | High | High | |||||
| Clinical Research Associate (CRA) | Low | High | Medium | High | Medium | Medium | High | High | High | High | High | High | Medium | Medium | |
| Biostatistician | Low | High | Low | High | High | High | High | Medium | Medium | High | High | High | High | High | |
| Pharmacovigilance Specialist | Low | High | Medium | High | High | High | High | High | High | High | High | High | High | Medium | |
| Medical Science Liaison (MSL) | Low | Medium | Low | High | High | Medium | High | High | Medium | High | High | High | Medium | Low | |
| Regulatory Affairs Manager | Low | High | Medium | High | High | Medium | High | High | High | High | High | High | High | Medium | |
| Medical Information Specialist | Low | High | Medium | High | Medium | High | High | High | Medium | High | High | High | High | Medium |
Automation influence on drug discovery careers: The human-AI synergy
Historically, technology has tended to fuel job evolution rather than job extinction. In that vein, McKinsey foresees a growing need for technological literacy and human skills such as creativity, collaboration and communication. Professionals are not necessarily immune to automation. Earlier this year, Goldman Sachs in their report “The Potentially Large Effects of Artificial Intelligence on Economic Growth” highlighted professions like law, engineering, data analysis and administrative work as most susceptible to automation from generative AI.
In pharma, data-heavy jobs like data science are prone to transformation from automation, but also have potential for AI augmentation. But ultimately, the future of AI-based automation is not set in stone. AI replacing humans across industries is not inevitable, nor is it necessarily beneficial. It thus becomes crucial for workers in industries with high automation potential, including pharma, to take an active role in shaping how AI impacts their work. Employees should participate in redefining their roles and determining how AI tools can best augment human skills rather than replace them.
Thoughtful, strategic implementation of automation and AI can help biopharma shape the workplace of the future rather than be shaped by it. The industry may have lagged in technology adoption thus far, but the opportunity is ripe to tap AI in empowering ways that benefit both workers and companies.
A note on the methodology
In creating our automation risk assessment methodology, we considered 14 unique factors, each contributing to a composite score reflecting an occupation’s automation risk. Some of these elements (like repetitive tasks) have a direct relationship to the risk score, while others (like creativity and decision-making complexity) are inversely related. For instance, having a job with a high degree of repetition invites a higher degree of risk than one with considerable variety. Similarly, a job with high decision-making complexity is less prone to automation.
Some criteria such as regulatory are more complex to evaluate. While some aspects such monitoring and reporting can be automated as a result of their repetitive and data-intensive nature, other aspects such as interpretation and application of complex regulations tend to require human expertise. Given the latter, we concluded that positions with a regulatory focus tend to be more resistant to automation.
Here is the ranking criteria with a note which factors were inverted:
- Information Processing and Documentation (10%)
- Repetitive Tasks (5%)
- Routine Data Analysis (2.5%)
- Advanced Data Analysis (2.5%): Inverted. Higher scores mean lower risk. Jobs that require advanced data analysis skills, such as those of data scientists who define data workflows, are less likely to be automated. Thus, higher scores in this category signal lower automation risk.
- Complex Decision-Making (10%): Inverted.
- Creativity and Strategy (5%): Inverted.
- Education and Training (10%): Inverted.
- Collaboration and Communication (10%): Inverted.
- Regulatory Compliance (5%)
- Experience and Expertise (15%): Inverted.
- Job Evolution Potential (10%): Inverted.
- Resilience to External Shocks (15%): Inverted.
- Economic Impact (20%):
- Potential for AI Integration (20%): No inversion needed.