AI Can Help Preserve Expertise for the Next Generation of HTM Professionals

Experienced healthcare technology management (HTM) professionals carry years—often decades—of knowledge that isn’t documented in a service manual. It’s the intuition developed after troubleshooting thousands of medical devices, recognizing subtle patterns in equipment behavior, and knowing which solution is most likely to work based on experience.

About a third of HTM professionals are older than 55. As many of these seasoned technicians approach retirement, the HTM industry faces a challenge that extends beyond replacing headcount. It must also preserve the expertise that has been accumulated over entire careers.

Healthcare organizations are already navigating an aging workforce, a limited pipeline of new biomedical technicians, and increasingly sophisticated medical technologies. Recruiting more talent remains essential, but organizations must also find ways to transfer knowledge more effectively, accelerate technician development, and maintain equipment reliability. When implemented thoughtfully, artificial intelligence can help accomplish these goals. 

AI in the workforce will never replace the judgment, problem-solving ability, or relationships that define clinical engineering. It can, however, make valuable knowledge more accessible, helping experienced technicians extend their impact while enabling the next generation to learn faster and work more confidently.

Turning decades of experience into an organizational asset

One of the greatest risks facing HTM isn’t simply that experienced technicians are retiring. It’s that decades of practical knowledge retire with them.

Over the course of a career, technicians develop insights that rarely exist in documentation alone. They remember recurring equipment issues, understand how different device models behave over time, and discover troubleshooting techniques that come only through hands-on experience. Historically, much of that knowledge has been shared informally through mentorship or years of working alongside experienced colleagues.

AI creates an opportunity to preserve more of that expertise.

By organizing repair histories, troubleshooting approaches, service documentation, and historical maintenance data, AI can help transform institutional knowledge into a searchable resource that technicians can access when they need it. Rather than relying solely on memory or waiting for an experienced colleague to become available, technicians can quickly surface relevant recommendations and guidance based on years of accumulated organizational experience.

Technology alone, however, is only part of the equation. Effective AI depends on high-quality, real-world service data and organizations with the expertise to transform that information into meaningful insights. Without reliable data, AI cannot deliver reliable guidance.

Helping new technicians learn faster

Preserving knowledge matters because today’s new technicians are entering a profession where medical equipment continues to become more connected, software-driven, and technologically advanced.

While hands-on experience will always remain the foundation of clinical engineering, AI can help shorten the learning curve.

If a technician encounters an unfamiliar device issue, AI can provide faster access to relevant service information, historical repair data, and recommended troubleshooting steps. Instead of spending valuable time searching through documentation or waiting for assistance, they can begin solving problems more efficiently while continuing to build their own expertise.

Surveys have shown that many young people already use AI-powered tools in their everyday lives to learn new skills and answer questions. Incorporating similar capabilities into HTM workflows aligns with how many newer technicians naturally approach learning while allowing experienced professionals to dedicate more time to coaching, mentoring, and developing critical thinking skills.

Giving experienced technicians more time for higher-value work

Incorporating AI in the workforce also helps address another reality facing healthcare organizations: experienced technicians are increasingly being asked to do more.

Administrative responsibilities, documentation, and searching for technical information consume valuable time that could otherwise be spent supporting clinicians and maintaining critical medical equipment.

AI has the potential to streamline many of these routine activities by helping technicians locate information faster, surface relevant documentation, and simplify troubleshooting workflows. These efficiency gains become increasingly valuable as retirements reduce available staffing across the industry.

Strengthening the future talent pipeline

Knowledge preservation is only one part of preparing HTM for the future. The profession must also continue attracting talented individuals into the field.

Today’s students often gravitate toward careers involving software, automation, and artificial intelligence. That presents an opportunity for clinical engineering to showcase itself as a profession where advanced technologies directly improve patient care.

Modern HTM professionals do far more than maintain equipment. They work with connected medical technologies, cybersecurity, predictive analytics, and increasingly sophisticated digital tools that help ensure devices remain safe, reliable, and available when patients need them. AI reinforces that evolution.

Combined with apprenticeships, technical education programs, and other workforce development initiatives, AI can help position HTM as an innovative career path for individuals who want to work with cutting-edge technology while making a tangible difference in healthcare.

Building trust in AI

Despite its potential, AI adoption will only succeed if technicians trust the technology. Research has shown most Americans don’t fully trust AI yet. 

Healthcare is a high-stakes environment where equipment performance directly affects patient care. AI-generated recommendations should never replace professional judgment, and organizations must establish clear governance to ensure humans remain accountable for every clinical engineering decision.

Leaders also have a responsibility to communicate openly about how AI will be used, provide training, validate recommendations, and invite technician feedback throughout implementation.

Trust develops over time through consistent performance and responsible deployment—not through promises about the technology itself.

Keeping people at the center

Clinical engineering has always been a people-centered profession built on collaboration, experience, and sound judgment.

Technicians don’t simply repair medical equipment. They partner with clinicians, solve complex problems, adapt to unexpected situations, and make decisions that require critical thinking and professional expertise. Those qualities cannot be replicated by AI.

Instead, AI should serve as another tool that strengthens the workforce by making knowledge easier to access, reducing routine burdens, and helping technicians develop faster throughout their careers.

As healthcare organizations prepare for significant workforce changes over the coming decade, preserving expertise will become just as important as recruiting new talent. Organizations that use AI to capture institutional knowledge, empower technicians, and strengthen mentorship will be better positioned to build a resilient HTM workforce capable of supporting safe, reliable patient care for years to come.