AI is transforming life sciences, but talent shortages and training challenges are slowing adoption. As demand grows for professionals who understand both AI and pharma, organisations are turning to freelancers to bridge the gap, bringing agility, fresh thinking and expertise to help teams learn, adapt and scale responsibly in a tightly regulated space.
Artificial Intelligence is already embedded in every part of the life sciences sector - from day-to-day tools that support internal teams to longer-term strategies around product development, regulatory planning and patient outcomes. But while the technology is moving quickly, the industry’s ability to adopt and apply it is often struggling to keep pace.
Organisations are facing a surge in demand for AI roles, such as engineers, data scientists, strategists and product specialists. Many of these are entirely new roles, while others are traditional jobs that now require AI fluency layered on top. The pressure to find people who can both understand pharmaceutical processes and apply AI technologies in the right way is growing. But the talent pool is thin, and recruiting the right mix of technical and domain expertise has become a challenge for almost every company operating in this space.
One of the biggest hurdles is that AI isn’t a single tool or platform. It’s a broad category of technologies that require continuous experimentation and hands-on learning. This makes conventional training methods difficult. You can’t simply run a workshop and expect people to be ready. AI skills are developed by building, testing, exploring and understanding how these tools behave in different business and regulatory environments. In life sciences, especially, the margin for error is small, and understanding the “why” behind an AI output is just as important as the outcome itself.
This tension is visible in the regulatory terrain. In the US, the FDA has already cleared over 500 AI-enabled medical devices last year, with radiology and cardiology leading the way. For example, Eko’s AI-powered stethoscope can detect heart murmurs and low ejection fraction during routine exams, enhancing early diagnosis without changing clinical workflows. Empatica’s wearable seizure-detection device, Embrace, is another: trained on physiological data, it alerts patients and caregivers to possible epileptic episodes in real time. These tools show how AI can support human decision-making without removing it, which is a key distinction when it comes to regulations.
At the same time, health authorities may still prefer models built in Excel because they can be easily traced and audited, unlike AI-generated outputs that may lack transparency. Until there’s a shared understanding between companies and regulators about how AI results should be explained and validated, adoption will remain uneven. And the absence of necessary skills is making the adoption that much harder.
In this environment, freelancers are increasingly being used to bridge the gap. They bring in-depth experience from across companies and industries, helping internal teams make sense of new tools, prioritise the right use cases and accelerate testing without needing to hire full-time staff with hard-to-find skills. But they’re not just gap-fillers. When deployed well, freelancers can act as translators between business and technical teams, catalysts for change and educators who leave behind stronger internal capabilities.
That last point matters. Many organisations still treat freelance support as a quick fix, but the most effective freelance partnerships are those where knowledge is transferred and confidence is built. A contractor who delivers a working prototype and trains the team to run with it leaves more impact than one who delivers in isolation. There’s a growing expectation that short-term expertise should lead to long-term capability.
Freelancers are also better equipped than most organisations to stay on top of new tech tools. They see what works and what doesn’t across different client environments, and they’re able to bring fresh thinking into processes that may otherwise be resistant to change. That objectivity is often what’s needed to challenge assumptions, rethink workflows or identify alternative applications that may have been overlooked internally.
Still, organisations need to think carefully about how they bring AI tools and expertise into the business. Choosing the right product can be as challenging as building the business case for using it. Procurement needs to be involved early, not just to assess cost and value, but to ensure flexibility and scalability. A tool that only one supplier can deliver may create complications down the line if the business needs to renegotiate or expand. As AI capabilities grow, so do the expectations on return, and decisions made today will affect the organisation’s ability to respond tomorrow.
As with most industries, introducing AI into life sciences involves careful re-evaluation of how people work, how decisions are made, and how knowledge is shared. That takes time, with the right combination of internal momentum and external support.
If your organisation is looking for freelance support to scale AI skills or explore new tools in the life sciences space, Malt can help. Our network connects you with experienced professionals who bring both technical depth and industry knowledge, ready to contribute from day one.
