Employees invest their time when learning is accessible and translates into opportunities for career growth.
By Michelle Westfort, InStride
Ensuring your teams can work confidently alongside AI requires upskilling that’s strategic, practical and tied to real job requirements.[Photo by uwimages/AdobeStock]
Consider a diagnostics manufacturer integrating AI into its imaging platform. Product teams that once focused on refining hardware performance are now shaping the data that powers this platform and validating how algorithms perform.
AI didn’t replace these employees. It changed what their job looked like.
That shift captures the real opportunity ahead. The FDA is approving 43% more AI algorithms for medical devices year-over-year. By 2030, the U.S. medtech market is projected to reach $955 billion, and the AI-in-healthcare market is projected to reach nearly $188 billion.
Ultimately, the biggest barrier to AI success in medtech isn’t the technology. It’s workforce readiness. The solution? Ensure that your existing teams can work confidently alongside AI. That requires upskilling that’s strategic, practical and tied to real job requirements.
Why AI skills matter and how to get started
When tech leaps ahead of people, the consequences show up as delayed product launches, compliance issues that increase the risk of recalls and audits, and rework that erodes margins. For example, quality engineers flag AI outputs but struggle to document why in ways that satisfy regulators. Design teams extend validation timelines because they’re still learning to override algorithmic recommendations based on clinical context.
Building an AI-ready workforce starts with understanding where your team stands today and assessing how people actually interact with AI tools in their daily work. Observe how teams use AI tools during validation reviews or ask engineers to explain their decision-making when they override an algorithm. A quality engineer who recognizes when an AI quality check flags false positives demonstrates readiness. So does a regulatory specialist who can articulate why an AI recommendation doesn’t meet FDA standards.
From these observations, create a framework for identifying skill gaps across three key areas: data fluency (understanding how data shapes AI decisions), technical capability (knowing when and how to use AI tools) and analytical judgment (evaluating when AI recommendations make sense in context).
Once you understand your team’s baseline capabilities and where the gaps are, the next step is embedding learning into daily operations. AI upskilling works best when it combines structured learning with real-world application. Skills develop faster when employees can apply what they’re learning immediately. A design engineer, for instance, might take a course on AI-driven simulation and use those tools on an active project the same week. Clinical specialists build data fluency faster when coursework connects directly to work they’re already doing.
Future-proofing through culture and career mobility
Upskilling delivers lasting results when it’s treated as a business strategy. Companies need to prepare their people for what’s ahead, and employees need to see how developing AI skills connects to career progression and greater responsibility.
When learning is accessible and translates into opportunities for career growth, people invest their time. We worked on Medtronic’s debt-free education program to make it a model for how skills-based initiatives can remove financial and degree barriers that often exclude experienced but credential-limited employees. The company covered 100% of tuition upfront for career-focused education, from individual courses to full degree programs. The business impact was clear. Over 3,000 employees enrolled, 20% were promoted within a year, and the company saved $13 million in turnover costs.
What can leaders learn from that success? Here are three lessons worth carrying forward when thinking about how to AI-upskill the workforce:
Align upskilling to business-critical needs
Prioritize AI literacy in areas like quality systems, design controls, and post-market surveillance where noncompliance creates the highest risk or could result in costly audits or market delays.
Keep in mind that AI upskilling can and should look different at different levels, so tailor education formats that match your business goals, whether individual courses, cohort-based programs, or custom pathways.
Design upskilling around real workflows
Involve employees in creating AI workflows for their roles. When functional experts design prompts and processes, they become co-creators rather than competitors. This approach is especially important when employees worry AI threatens their jobs. Acknowledging them as experts who shape the technology shifts the narrative from replacement to enhancement.
Showcase success stories to make payoffs visible and build momentum. Let’s say a design engineer attended an AI-upskilling program and used that newly acquired knowledge to cut prototype iteration time by 30%. Share that win in team meetings or internal newsletters. It’s equally important to recognize experimentation and adaptability, even when outcomes aren’t perfect. This builds a culture where trying new approaches is encouraged.
Connect skills to career advancement
Align AI skill development to career paths and promotions. Employees should see how building these competencies opens doors to new roles and responsibilities. Companies have to build career-connected learning pathways that train people for advanced manufacturing, QA/QC, regulatory, and service roles.
Human + machine = sustainable innovation
Companies that approach AI upskilling this way build workforces that evolve as fast as the technology itself. The payoff shows up in faster product launches, smoother regulatory audits, and stronger collaboration between engineering and operations.
That’s how your organization stays competitive as the industry continues to make breakthroughs in new technologies.
InStride Chief Product Officer Michelle Westfort [Photo courtesy of InStride]
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The opinions expressed in this blog post are the author’s only and do not necessarily reflect those of Medical Design & Outsourcing or its employees.
