Future of work Transformation

Reimagining life sciences roles with AI and automation 

Technological acceleration is revolutionizing jobs and fundamentally changing the life sciences landscape, from risk management to research and development.

In response to a complex and evolving business environment, employers in life sciences are now exploring artificial intelligence (AI) and automation to reach new heights of efficiency, productivity and agility. 

The advent of large language models (LLMs) has vastly expanded AI’s capabilities. Generative AI (Gen AI) can analyze large datasets and create content in response to a user’s prompts. Agentic AI can learn, reason and act on its own to achieve goals — without the need for human input. 

The evolving roles of life science professionals

To realize the full potential of these technologies, life sciences organizations can engage in work design. Work design is the process of deconstructing jobs, redeploying tasks to the optimal mix of talent and tech, and creating new ways of working. AI and work design will have a significant impact on life sciences jobs, including:

  • Discovery biology roles

    For scientists and other discovery biology roles, data underscores nearly every task in the laboratory. AI can help analyze vast omics datasets for patterns and other findings that aren’t immediately apparent to human researchers. Gen AI is even predicting molecular interactions to design new compounds. By leveraging AI for these capabilities, scientists can shift their focus from data crunching and trial-and-error to hypothesis generation, experimental design and innovation. 

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    Clinical trial managers

    Clinical trial managers (CTMs) often juggle a range of logistical tasks, such as candidate selection and trial design, alongside more high-value work like decision-making and stakeholder engagement.  Generative AI can identify trial candidates with greater speed and precision than humans alone — it can also simulate different trial designs and predict possible outcomes. By automating the tactical work, AI frees up clinical trial managers for complex and relational tasks. 

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    Medical science liaisons

    Like other life sciences roles, medical science liaisons (MSLs) must stay informed about the latest developments in science and technology, as well as medical ethics, regulatory compliance and industry trends. MSLs have the added responsibility of connecting life sciences organizations to the broader medical community. Their core job responsibilities include building relationships with healthcare providers, and applying scientific expertise to provide insights and guidance. 

All life sciences roles handle a range of tactical and administrative tasks that technology can help support. The question is: how will AI continue to transform their work and more broadly shape the future of healthcare and innovation? For these three roles specifically, AI and automation can support: 
  • Subject matter expertise
    The life sciences workforce needs to stay current with new developments in the field. AI and automation can help curate and summarize updates from scientific journals and other sources. Gen AI can even turn these insights into podcasts for on-the-go listening. 
  • Data analysis
    Life sciences professionals often need to review and interpret scientific data, but this task becomes exponentially harder with large and disjointed datasets. AI and automation can help manage and clean data so it’s easy to work with, while also performing advanced analytics at high speed and scale. 
  • Communications
    Gen AI helps convey complex scientific information in a clear and effective way, weaving research findings into engaging stories in different formats, contexts and languages. AI can also critique presentations and research reports, and even role play to help employees prepare for important meetings. 
  • Administrative work
    AI agents can serve as virtual assistants to independently handle a range of tasks, from recruiting trial participants to booking travel arrangements for an upcoming conference. This frees up time and mental space for life sciences workers to engage in higher value activities, like building relationships.
As life sciences job duties change, so will the skills required to stay effective and employable. Strong professional judgment and interpersonal skills will remain paramount. But instead of working with their hands — to write emails, design presentations or scan research databases — the future life sciences workforce will need to emphasize strategic thinking and problem-solving. 

Consider this scenario:

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    An MSL is preparing for a meeting with a group of oncologists. Where in the past they would have spent hours researching the latest data on a new cancer therapy, today the MSL can leverage AI to analyze datasets and extract clinical information in a matter of minutes.

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    This allows the MSL to spend more time anticipating the needs and concerns of these oncologists, enabling them to foster more meaningful discussions that can potentially lead to improved patient outcomes. 
AI is a powerful tool, but it’s up to humans to use it effectively. This requires discovery biology roles, CTMs and MSLs to develop new skills related to AI use — such as data literacy, resource management, prompting and output validation. This ongoing evolution signifies a shift in the skill set required to succeed in the field, encouraging professionals to embrace continuous learning and adaptability.

Augmenting life sciences operations with AI

The impact of AI and automation has broader implications for life sciences experts and organizations. Leading employers in the field aren’t simply digitizing legacy processes, they’re engaged in a digital transformation — adapting their talent strategies, operating models, and corporate cultures to become increasingly human-centric and tech-enabled. 

Digital transformation sets the stage for an AI-augmented operating system, the future state of digital-first organizations. It leverages AI and automation for exponential gains in efficiency, productivity and innovation. It also maximizes employee engagement and well-being by addressing the myriad risks and opportunities that arise from these technologies.  

From an organizational perspective, the integration of AI into workflows, systems and processes can lead to significant efficiencies and cost savings. By streamlining routine tasks like data entry while augmenting relational tasks like storytelling, life sciences employers can allocate resources more effectively and focus on strategic initiatives that drive growth and innovation. 

Navigating the risks of AI within life sciences

As life sciences organizations embrace the rise of AI and automation, these technologies also pose a number of risks to people and businesses, some of which include: 
Despite the promise of AI and automation, life sciences professionals might hesitate to adopt them. Investments in these technologies are raising expectations around productivity, and Mercer data shows an increased workload could lead to feelings of stress or potential burnout. Change management strategies, including training and support for the people impacted by AI, could encourage the life sciences workforce to fully embrace these tools. 

Life sciences fields such as pharmaceuticals, biotechnology and medical devices are highly regulated. Companies in these spaces are subject to strict rules for how they manage sensitive patient data, intellectual property (IP), product information and advertisements. Connecting AI to these areas may present several risks: 

  • Data breaches leading to compromised patient privacy 
  • Harmful misinformation due to model errors, also known as hallucinations 
  • Non-compliant or off-label promotions generated by AI 
  • IP infringement if AI ingests or reproduces protected content 
Given AI’s potential for bias and errors, there’s tremendous risk in giving it too much control over clinical data, communications and decisions. Life sciences organizations have a moral obligation to establish and follow guidelines for the ethical use of AI. This includes using high-quality data to minimize the risk of bias, as well as keeping humans in the loop to validate AI’s performance and outputs. 
As AI and automation transform the future of work, life sciences employers and the workforce have exciting opportunities to unlock efficiency, productivity and agility. This requires a full-scale digital transformation and a careful, collaborative effort between individuals, organizations and technology. Employees will play a critical role in leading the charge, upskilling to leverage AI effectively while maintaining the highest standards of scientific integrity. 

AI in the future of life sciences work

As life sciences organizations look to integrate AI into their operations, it’s important to approach this transformation thoughtfully. Here are five ways to do so more effectively: 
  1. Start where AI can add the most value
    Identify tasks or roles with the biggest opportunity for improvement through AI, whether it’s saving time, reducing errors or boosting performance.
  2. Clarify how work and skills will change
    Understand which tasks will be automated, augmented or newly created — and what that means for the skills people will need going forward. 
  3. Assess the impact on jobs and people 
    Consider how these changes affect current roles, job structures and the people in them, including training needs or potential job redesign.
  4. Manage risk and measure progress
    Update performance metrics as needed and proactively assess risks, working with HR, risk and compliance to address any concerns early.
  5. Adopt AI gradually and collaboratively
    Start small, targeting individual roles before scaling up. Engage employees in the process to build understanding, trust and success. 

The integration of AI and automation in life sciences roles is not just a technological shift; it represents a fundamental transformation in how these professionals connect across the organization, engage with the broader medical community and drive business results. By embracing these advancements carefully and collaboratively, organizations can enhance efficiency and innovation while ensuring that the human touch remains at the heart of healthcare.

Whether you’re interested in life sciences work design or launching a digital transformation, contact a Mercer consultant to get started.

About the author(s)
Andrew Dickson

Life Sciences Industry Leader

Ravin Jesuthasan

Senior Partner, Global Transformation Services Leader

Adriana O’Kain

AI Strategy, Innovation and Solutions Leader, Mercer

Jaymin Kim

Managing Director, Emerging Technologies

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