One of the biggest questions facing the medtech industry today is how generative artificial intelligence (AI) will redefine the future of work. From clinical studies to product design to software engineering, just about every aspect of device development, manufacturing, and marketing will be transformed by the proliferation of the technology. No doubt its impact will help OEMs put advanced life-saving products into the hands of clinicians and patients more quickly and cost effectively.

This is already happening, as the integration of ChatGPT, Gemini, Copilot, and many other generative and agentic AI tools ramp up in medtech operations around the world. A Deloitte survey of sector leaders found the technology was already delivering value across functions, with 42% reporting benefits in product development and 35% in IT and cybersecurity.¹ Forbes reported AI can shorten the device design process, sometimes from years to months.²

There’s no doubt device makers will reap even greater benefits from AI, but how should they optimize collaboration between people and machines?

The Age of Work Pixelation

A big, disruptive innovation like generative AI requires organizations to be equally bold in their strategies to fully harness its potential. First and foremost, this means redefining work as we know it. Device makers increasingly use generative AI to perform discrete tasks such as analyzing large volumes of clinical data, predicting production equipment failure,³ and taking on iterative product designs at speeds beyond what humans can achieve. This is only the beginning. Agentic AI further promises to elevate what’s possible with technology. It will eventually work together with people to help them make informed decisions based on predefined parameters, improving accuracy and adaptability.

These advancements will have an enormous impact on the workforce, but how can organizations adapt to such a seismic shift in new ways of working? Our research and work with enterprise clients show employers are adopting innovative work models in which jobs are deconstructed into “pixels” of tasks, enabling dynamic reconfiguration to changing needs and priorities. As AI continues to reshape the way work gets done, by whom, and when, employers are rethinking how outcomes are achieved. This approach will also expand the possibilities for how people think and learn as well as lead to optimal resourcing and enhanced agility.

Indeed, agility is a top priority for many device companies. The Randstad Enterprise 2025 Talent Trends research, which surveyed 1,060 talent and C-suite leaders around the world, found that among those in life sciences, a majority (51%) say creating a more fluid and flexible workforce is a top priority, while an additional 42% say it’s happening already at their organization. About half (48%) are prioritizing investments in data and market intelligence so they can better understand how to create a fluid and flexible workforce.⁴ 

Following are several examples of how AI is helping medtech businesses achieve more fluidity.

Software Development 

With medical devices becoming more connected and intelligent, software development is critical to device innovation. Generative AI can potentially cut coding development time, and there are many examples of this happening outside the medtech sector. For instance, last year, Google CEO Sundar Pichai revealed more than 25% of new code generated by the company is done with AI, with humans overseeing and managing these projects.⁵ Github also reported 92% of U.S.-based developers are using AI coding tools.⁶

Such a paradigm shift requires medtech and other OEMs to redefine a programmer’s role. By separating the tasks that people currently perform and allowing them to focus on activities that require human skills, such as problem-solving and empathy, employers optimize the output of human-AI collaborations. Separating work into activities executed by AI and those performed by people necessitates a thorough assessment of the work that needs to be completed, but the potential benefits can be breathtaking.

Clinical Trials 

With AI’s ability to evaluate large datasets quickly and effectively, device makers are poised to accelerate innovation in the future. Historically, one of the most time-consuming tasks is management of patient data, both in premarket development and post-market surveillance. The deployment of AI will not only benefit OEMs but also the contract research organizations they rely on. Integration of data from multiple sites, recognizing bias in study enrollment, and protocol generation are just some of the functions technology can perform quickly and effectively. Furthermore, the extraction and organization of data from electronic health records and medical claims can also benefit from AI.

As companies deploy these tools, the responsibilities of clinical trial specialists must evolve. They will most likely focus on tasks heavily dependent on human judgment, such as site monitoring, facilitating communications between investigators and sponsors, enrollment and screening, and managing the consent process. Some of the work will be a collaborative effort with generative AI, such as interpretation of results. Work involving data collection and analysis can be potentially shifted mostly to AI agents, accelerating studies and ultimately development times. 

Additional Applications and Responsibilities

Many other functions are likely to transform in the future, including validation and testing, regulatory submissions, curriculum creation, and individualized training, to name a few. AI’s growing presence in medtech workflows allows organizations to amplify the strengths and creativity people can bring to their organizations while reducing the overhead and delays associated with tactical activities better performed by generative and agentic AI.

Conclusion

As with any effort to redesign workflows in the medtech industry, OEMs must do so in a compliant and transparent way. The rise of technology supporting workers has always carried risks, but the stakes are higher in the deployment of AI in device development due to the broad impact it will have across numerous functions. Many companies are still training their workforces to make the best use of technology and are creating frameworks in support of this ambition. Undoubtedly, government agencies around the world will likely undertake additional rulemaking that will affect the use of AI in the workplace. 

To ensure they proceed in a compliant manner, device makers must be vigilant about the shifting regulatory landscape in their efforts to deconstruct jobs into tasks. Guardrails must be installed to ensure the work and results created by generative and agentic AI are always reviewed by human decision makers with an eye on both ethics and accuracy. While there are certainly many tasks that don’t fall under regulatory scrutiny, it’s always best to adhere to a well-validated set of guiding AI principles when redefining the roles and responsibilities of people and machines.

References

1. tinyurl.com/mpo250471
2. tinyurl.com/mpo250472
3. tinyurl.com/mpo250473
4. tinyurl.com/mpo250391
5. tinyurl.com/mpo250475
6. tinyurl.com/mpo250476

MORE FROM THIS AUTHOR: 3 Talent Trends Medtech Employers Need to Focus on in 2025

Elke de Loecker is EVP, global head of enterprise sales for Randstad Enterprise, driving growth for EMEA HQ-based organizations and supporting their talent lifecycles. With more than 20 years of experience, she specializes in workforce transformation, solution design, and talent strategy, with a strong focus on life sciences, technology, and energy.