AI is increasingly present across the life sciences sector, reshaping the future of human health from drug discovery and diagnostics to commercial operations. It’s also becoming part of everyday recruitment practice, particularly in IT, data, and digital roles, where demand is high, and talent pools are limited. As the tools evolve, they’re impacting how employers source, assess, and match candidates, to make hiring faster, smarter, and more predictable. However, their growing influence raises questions around effectiveness, fairness, and appropriate use, and many organisations, as well as jobseekers, are asking, is AI a friend or a foe in the search for specialist talent?
The use of AI in sourcing and screening
AI-driven systems can sift through vast numbers of online profiles, research publications, technical forums, and talent pools in seconds to highlight individuals whose skills match specific life sciences IT requirements. For roles such as bioinformatics engineers, data scientists or software engineers, which require niche combinations of scientific knowledge and digital expertise, these tools can help employers to identify candidates who are not actively applying for roles but who have the right technical background.
At the screening stage, CV-parsing and skills matching tools can extract experience, qualifications, and technical keywords, benchmarking them against job descriptions to produce shortlists. This helps recruiters to process applications more effectively and maintain consistency, especially in fast-moving or high-volume projects.
Used in the proper context, these systems can reduce manual effort and give recruiters more time to liaise directly with candidates and hiring managers. The insights they provide can support, rather than replace, human judgment, and recruiters still need to verify technical experience, skills, motivations, and cultural fit to determine whether a candidate is suitable for a role.
Benefits for employers and candidates
For employers, AI offers several practical benefits:
Efficiency and speed – in a sector where talent competition is intense, sourcing candidates quickly matters. Automated sourcing and first-round screening can speed up the initial stages of the process and reduce the administrative workload.
More consistent shortlisting – when appropriately trained, AI tools can apply the same criteria to every applicant, minimising human subjectivity and creating a more consistent screening process. This is particularly useful in technical hiring where experience and skills can be assessed against explicit criteria.
Improved reach in niche markets – tools that can analyse patterns between skills, technologies, and project experience are able to highlight candidates with rare or emerging capabilities and reveal connections or overlaps that may not be immediately obvious.
For candidates, AI-driven processes can also be beneficial:
More relevant opportunities – automated sourcing helps recruiters to identify roles that are aligned with a candidate’s skills, experience and background, even if they haven’t applied for a role directly.
Faster responses – screening tools can speed up the early stages of the recruitment process and reduce long, frustrating waiting times.
Risks and potential pitfalls
Despite its promise, AI also introduces new risks to the hiring process. Employers and candidates need to be aware of the technical and ethical considerations:
- Potential bias – AI learns from historical hiring data, and if that data reflects past inequalities, the model will replicate them. This is especially relevant in STEM hiring, where disparities in gender and ethnic representation remain common. A poorly trained model can inadvertently filter out our qualified candidates, limiting diversity and disadvantaging both them and their potential employer.
- Lack of transparency – many commercial tools provide little insight into how matches are generated and decisions are made. In an industry where auditability and compliance are paramount, this can create uncertainty. In addition, candidates may not be aware of how and when AI has been used during their assessment.
- Misuse or misunderstanding – over-reliance on automated scoring can also lead to strong candidates being rejected without meaningful human input, meaning that the tool, which is designed to support sourcing, may be improperly screening candidates out
- Over-filtering – similarly, keyword-driven systems can disadvantage candidates with non-traditional career paths and academic backgrounds or those who use non-standard terminology. This can lead to missed talent, particularly in niche technical fields where CVs rarely convey the full spectrum of personal motivations, career shifts, or nuanced technical achievements.
Employers and candidates benefit most when AI is used as a support system, combined with sector expertise, within a transparent recruitment process, rather than as a replacement for specialist insight.
Ethics and good practice
Here at nufuture, we believe that AI should be used responsibly, and we’ve adopted a few guiding principles:
- Apparent human oversight – we use AI to inform decisions, not replace our expert judgment. To achieve this, we conduct a human review of every candidate on an AI-generated shortlist and maintain the final decision-making authority.
- Transparency – we always inform our clients and candidates when AI is used within the hiring process, what data is being used and whether the tools we use have been independently audited for fairness
- Regular monitoring – we regularly review and update the algorithms we use to ensure that there’s no drift, bias, or unintended consequences
- Careful alignment – we establish precise alignment between the AI tool’s criteria and the job requirements, rather than relying on generic keyword lists.
Friend? Foe? Or simply a helpful tool?
At nufuture we regard AI as a set of utilitarian applications that can enhance the speed and quality of life sciences recruitment, if it’s used correctly. When applied thoughtfully and responsibly, it equips recruiters to work more efficiently and identify suitable talent, enables more efficient and consistent screening that benefits employers seeking specialist skills, and supports candidates looking for roles that genuinely match their experience.
However, it does not replace specialist knowledge, structured assessment, or human judgment, the most difficult trait for artificial intelligence to replicate. It lacks context for motivation, capability, or potential. In a sector built on precision, ethics, and trust, perhaps its most effective use is when it’s blended with expert perception, understanding, and empathy, so that it supports the hiring process rather than distorts it.
In a crowded life sciences recruitment market, we pride ourselves on building human connections rather than relying solely on technology. We’re comfortable with using AI as a powerful assistant, but we always ensure that honest conversations, real insight, and authentic relationships remain at the heart of what we do.
Connect with nufuture for more information about how we can support your life sciences recruitment strategy that’s augmented by AI but defined by people.