Leveraging AI to Beat the R&D Talent Shortage: a Lifeline for the Life Sciences Industry

Leveraging AI to Beat the R&D Talent Shortage: a Lifeline for the Life Sciences Industry

Explosive growth, high demand and limited supply: these are challenging times for the life sciences industry. The talent crunch has hit the sector hard, and it could not have come at a worse time. In the midst of unprecedented growth in innovation and research, pharmaceutical and medical research companies urgently need to scale operations without scaling costs. And the traditional method of throwing more people at the problem is simply no longer an option in a tight labor market, with recruitment pipelines already running dry.

The challenge is twofold: how do companies get around R&D and data talent shortages? Perhaps even more urgently, how can they optimize business processes in order to empower their existing talent to spend less time on manual searches and more time making decisions and innovating?

In this article, we’ll outline the various ways in which AI and machine learning solve both problems at once, allowing companies to muscle in on the opportunities that rapid, ongoing growth provides.

Talent shortages in the life sciences industry: water, water, everywhere – but not a drop to drink.

In the wake of world historic levels of biopharma innovation during and after the COVID-19 pandemic, the industry has been left in a kind of paradox: growth and investment are up, but confidence is tepid. There is serious concern about whether this can be sustained, given the available crop of STEM talent in many developed countries. In the US, there are roughly twice as many job postings for life sciences positions as there were before the pandemic. Unsurprisingly, executives have been sounding the alarm: according to Randstad, 33% have identified talent scarcity as a major pain point, with 45% ramping up hiring to protect their businesses against staff attrition. 

The life sciences enter the global race for digital skills 

Part of the problem stems from the fact that the industry is so hungry for data and engineering skills – skills that are already in high demand across many other industries. And the urgency of this need is compounded by the skills gap that has been noted by the Association of the British Pharmaceutical Industry (ABPI): 43% of respondents in a recent survey said that digital literacy was a concern. These findings suggest that life science professionals are not currently receiving adequate training in digital and data skills, making it all the more urgent for companies to hire outside traditional boundaries. 

Bridging the gap and empowering people: the promise of AI

Advances in AI, machine learning and Natural Language Processing (NLP) are providing a much-needed way forward. One of the areas in which it’s making the biggest impact is in revolutionizing the way researchers work. In place of labor-intensive manual search, platforms like Similari offer smooth, automated processes that are more accurate and more scalable. 

By leveraging machine learning algorithms, researchers are able to sift through large volumes of data much faster than they could before, identifying patterns that would have otherwise been invisible. They can also avoid costly mistakes in clinical trial design by having unfettered, instantaneous access to results of relevant trials from around the world.

Faster, better drug discovery through enhanced technical data monitoring

AI also has the potential to revolutionize drug discovery and development. Data-driven algorithms and machine learning techniques make it easier to identify new drug candidates more quickly and accurately than ever before. Going even further, AI can be used to optimize existing drugs for improved efficacy and safety profiles. 

For example, AI can analyze large datasets at a much faster rate than humans can, making it easier for researchers to identify promising compounds. It can also help reduce costs associated with drug discovery by automating mundane tasks such as data entry and analysis. Even better, AI systems can learn from each iteration of the drug discovery process, enabling them to refine their strategies over time.  

AI in life sciences: unlocking human potential

But the benefits of AI go beyond simply reducing the need to grow R&D headcounts. The shift to AI-enhanced monitoring and research isn’t even about replacing people. Rather, it’s about augmenting human capital, and empowering human researchers to do their jobs better. And because AI-based systems can be “always on” in ways that humans cannot (and should not) be, they allow researchers to keep pace with a rapidly evolving environment.

Amidst dangerously high staff turnover levels, and the looming threat of burnout dogging the industry at large, it makes strategic sense to equip teams with tools that make their lives easier and their workflows more efficient. Companies who do so will enjoy higher talent retention rates, a priority in the current climate.

Similari: best-in-class software for R&D teams

In the world of life sciences research, AI has gone from nice-to-have to essential in a fairly short space of time. At Similari, we’ve brought together leading expertise in AI, machine learning and NLP to create the solution that the industry needs. With Similari’s always-on, intelligent surveillance of technical data, your teams can stay in the know at all times, while offloading up to 80% of the time they’re currently spending on research.  

To learn more about how Similari is changing the game businesses around the world, schedule a demo. Our team is ready to show you what lean, AI-enhanced research could do for your business. 

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