The AI Balancing Act: Use Cases & Watch-Outs for Life Sciences Leaders

By Nick Kovacs, PhD, Talent Consultant at Mix Talent

Artificial Intelligence. By now, everyone is aware of its massive leaps forward in the past year, and many have tinkered with the generative models on the market – including ChatGPT, Gemini, and Copilot – witnessing first-hand just how incredible they have become.

Ask ChatGPT, for example, to write a funny sonnet in the voice of William Shakespeare about life sciences recruiting and see what you get. (I’ve pasted the results to the very end of this post – pretty impressive!)

And yet, in the midst of all of this excitement and buzz, there are many serious questions about what AI will eventually be able to do and what it can do right now, including in life sciences recruiting and talent management.

As a behavioral scientist at Mix with a PhD in Industrial-Organizational (I-O) Psychology, I have studied machine learning and artificial intelligence for years. Many of the issues I focus on as an I-O psychologist – including productivity, employee selection, and well-being in the workplace – are already being affected by AI in significant ways. As such, my hope is to help life sciences organizations navigate the tricky questions brought on by AI, which is why I have served on an AI Panel (“How AI Changes Work and Workspaces”), partnered with startups to deliver human-AI interventions at work, and continue to keep a pulse on both the regulations and tools used to integrate AI into the workplace.

Through all of this research and education, it’s clear to me that the premise is not that AI will replace recruiters, HR leaders, or talent acquisition specialists. Instead – just like the latest ATS or behavioral assessment – AI will be a valuable tool for each of these functions in years to come. At least, that’s the hope. There are also plenty of ways it can result in significant ethical, legal, and business risks.

The issue, then, is how do you make responsible choices around AI to ensure you are using it to enhance efficiencies while not putting yourself or your organization at risk?

Taking an AI Pulse of the Life Sciences Industry

Before I delve into this question, let’s first take a quick look at some first-party data Mix has gathered over the past few months about AI in the life sciences. Based on a recent LinkedIn poll and our 2024 Trends Report survey of 88 life science leaders, we see perspectives across the spectrum, from highly optimistic to strongly opposed.

Is your organization actively using AI?

  • 31% Yes
  • 69% No

Do you think that automation and machine learning add or detract from the human connection that’s vital to recruiting?

  • 29% Add to it
  • 29% Detract from it
  • 43% Uncertain

How do you feel about the use of AI in life sciences recruiting?

  • 9% Very Positive
  • 38% Cautiously optimistic
  • 32% Somewhat worried
  • 22% strongly opposed

Do you feel your organization is using AI to the fullest extent it should?

  • 0% Yes
  • 69% No
  • 31% Uncertain

Clearly, there is a lot of hesitancy around AI, although, as we see in the question about using AI to its fullest extent, there is also the sense that its opportunities cannot be ignored.

This is a sentiment that we at Mix Talent share, and our goal here is to help life sciences leaders navigate how to balance the pros and cons of AI to make intelligent – yet risk-averse – decisions about how to incorporate it into their talent efforts.

As such, we’ve developed two lists. One is focused on the use cases of AI in recruiting today and the other is focused on watch-outs that are critical to understand for the safe and effective use of AI. Let’s dig in.

Use Cases for AI in the Life Sciences

Let’s start with how AI is already being used in the life sciences. A good way to approach your understanding of AI is to recognize that there are different types, some of which are safer to use than others.

Generative AI

Generative AI involves using models to create new content or data that mimics the style, characteristics, or patterns of its training data (the data the model is built upon).

This type of AI, which is the model ChatGPT and other chatbots are built on, is perfect for recruiting and HR tasks as it can replace and reduce the time spent on administrative work, leaving more time to focus on the human component of our work.

Examples of things generative AI can help accomplish today:

Writing or Optimizing Text Documents

  • Job descriptions
  • Job postings
  • Personalized messages to candidates
  • Marketing messages

Summarizing or Organizing Material

  • Notes from conversations with candidates/hiring managers
  • Candidate summaries for hiring managers

Candidate Searching Terms

  • Boolean strings for optimal sourcing
  • Alternative names for relevant job titles or skills

These types of AI models are popping up every day and being integrated into many of the tools recruiters and HR professionals use on a daily basis – for example, take the recent and ongoing updates of LinkedIn Recruiter.

Predictive AI

Predictive AI – as the name suggests – involves leveraging models to analyze patterns and relationships within data to predict future outcomes.

Though this type of AI can be beneficial for predicting strong candidates, heavy caution will be needed when using such models due to the biases that are often inherent with predictive AI. Examples here include:

Sourcing candidates

  • Predicting where to find the best candidates for specific roles
  • Predicting which candidates are most likely to have relevant skills for a role
  • Predicting which job experiences may lead to success for a role

Predicting timelines

  • Predict time to fill based on past experience of similar roles
  • Predict when best to engage and re-engage candidates
  • Predict retention timelines based on historical data

Reducing Bias

  • Review past hiring decisions to detect bias and develop ways to reduce it in the future

The ceiling may be high with predictive AI use cases, but so are the risks.

Think of predictive AI in terms of fully-autonomous vehicles: the technology is close, but the ethical, technological, and regulatory hurdles are significant. Some argue that fully self-driving vehicles will never arrive because of AI’s inability to account for all possible situations – such as a bag on a bicyclist’s handlebars that confused one vehicle’s AI systems and led to a very tragic death.

While hiring decisions aren’t typically life-or-death matters, they are critical decisions that have significant effects on individuals’ lives. So the future of predictive AI use in recruiting may look more like the equivalents of lane assist, auto-lane switching, and variable speed features in modern vehicles: helpful advancements, but far from a replacement for the humans doing the real work.

Watch-Outs for AI in the Life Sciences

Clearly, there are a variety of “watch outs” to consider for effective, ethical, and compliant use of artificial intelligence in life sciences organizations.

Here are eight of the top ones to consider:

  1. Bias, bias, and bias – AI can be the most predictive tools we’ve ever made, but it is ripe with bias. AI works by understanding the patterns inherent in training data and leveraging them to predict outcomes, so if any bias exists in that training data, it will impact the model – and it’s not great when that happens (see Amazon).
  2. Is your data secure? – Data security is huge, particularly when working within HR and recruiting. Given the large amounts of data required for AI to be successful, understanding what data must be given for a tool to work, how secure that data is, and even how ownership of the data will be decided are important things to consider before bringing in an external tool.
  3. ROI – It can be easy to be enamored by AI and begin incorporating it into your organization so you can say “I use AI.” However, with the resources needed for internal training and fine-tuning models based on bias, accuracy, and compliance, there may be massive overhead – meaning other tools or processes may be more effective as a solution given the overall cost of AI.
  4. Do you have “good data”? – For AI to function correctly and do what you want it to do, your data must be accurate and relevant to the models that are using it. As the saying goes with AI – “garbage in, garbage out!”
  5. Data upkeep – AI is often trained on historic data to produce results; however, especially in a volatile, ever-changing world like life sciences, updates will be required. Understanding when you need to update models, and ensuring the updates do not increase bias or decrease accuracy, is always important with AI.
  6. AI Accuracy – Though AI is generally accurate, with any tool there is still risk. Considerations for checking AI accuracy and procedures for what happens if that AI is incorrect will be important. At the end of the day, who will be on the hook if something is wrong or noncompliant?
  7. Training – Though AI can seem intuitive, there is clearly a lot more that goes into it. If it’s brought into an organization, ensuring employees are properly trained will ensure fewer mishaps with AI use.
  8. Candidate experience – Though AI can bring efficiencies, they may come at the cost of the human component within our work, something that may be just as important (if not more so) than being efficient. Put another way, have you ever enjoyed being on the phone trying to cancel your cable or internet and thought to yourself “Gee, I love this automated chatbot!” Doubtful!

Conclusion Points to Consider

Ultimately, deciding how to explore and implement AI within your organization is dependent on the answer to several questions – the same questions you would ask when deciding to incorporate any new tool into the organization. At the top of that list: Is it worth the investment, and is it compliant?

At Mix, we’re in the cautiously optimistic camp when it comes to AI’s use in life sciences recruiting. We don’t believe in reckless adoption of it, but we also don’t believe in burying our heads in the sand. The potential for AI to help us all become more efficient and effective in our work is undeniable, and it’s our jobs as life sciences talent leaders to safely guide the conversation around AI – and the implementation of it – forward.

Right now, this means we should engage in light adoption of some generative AI applications while exploring and carefully experimenting with predictive applications. Using AI to support administrative work will be safest while providing more time for the human component so essential to our work.

There’s still much to discuss as we consider the use of AI and its applications within life sciences HR and recruiting. It’s not just about machine learning, it’s about human understanding. If you’d like to continue the conversation, reach out to me anytime at – I’d love to hear from you!

ChatGPT’s Funny Sonnet About Life Sciences Recruiting (Unedited)

Upon the stage of life, a curious dance,
Where life sciences and recruiting prance.
In fields of genes and cells, they seek to hire,
A troupe of minds to kindle science’s fire.

The recruiter, like Puck, with merry jest,
In LinkedIn’s forest, seeks the very best.
With potions brewed in algorithms rare,
They conjure CVs from the digital air.

“Oh, life sciences, thou wondrous realm,
Where scientists in lab coats overwhelm.
Recruiting fairies flit from post to post,
In search of minds that science loves the most.

In this grand comedy of work and play,
Life sciences recruiting has its day.
So, let us laugh and toast with mirth and cheer,
For science and recruiting, hold them dear!”

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Nicholas Kovacs, PhD

Talent Consultant

Nick is an industrial-organizational psychologist by training and a talent consultant at Mix Talent. His expertise in research includes artificial intelligence, resilience, occupational health, analytics and selection, and his expertise in practice includes building and administering selection and development assessments, consulting on best practices regarding interviewing, compliance and other selection-related processes, and conducting analyses using Excel, R programming, and machine learning. Nick began his career working within healthcare research at the National Institutes of Health, and he has since shifted to a career providing support to Fortune 100 and start-ups alike within the life science/biotech industry.

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