“AI won’t take your job, but someone who understands AI just might.”

That was one of many key takeaways from a June 17 event at Inver Hills Community College, where Western Governors University (WGU) teamed up with local healthcare and technology leaders to spotlight career opportunities emerging at the intersection of AI, analytics and patient care.

Learners, professionals and curious community members gathered for the first official event of WGU’s Minnesota HealthTech Impactors Network, a new initiative aimed at building stronger bridges between workforce demand, upskilling opportunities and real-world impact across the state’s healthcare and technology sectors.

The evening featured a career resource fair, free professional headshots, a panel of industry leaders and the chance to win an iPad or free enrollment in WGU’s new AI Skills Fundamentals certificate program.

Equipping Minnesotans for the Jobs of Tomorrow

As emerging technologies transform how care is delivered, roles across nursing, cybersecurity, product development and data science are rapidly evolving. The AI Career Panel featured leading experts and offered more than career advice; they delivered real talk on how AI is shaping their industries and what it means for job seekers.

The panel was moderated by Paul Bingham, senior VP and executive dean at WGU’s School of Technology, and featured:

• Nicole Proviano, director of clinical informatics & analytics, Ecumen

• Diana Mancino, simulation faculty, Inver Hills Community College

• Alex Melton, VP of engineering, Invene

• Jessa Gegax, information security testing analyst, Surescripts

Their message was clear: while AI is a powerful tool, the future of healthcare will always need human insight, empathy and ethical leadership.

AM: I think the first time I worked with AI was back in college. For my senior capstone project, we built an autonomous race car. While writing the software for it, we kind of accidentally created a machine learning model. 

One thing you have to understand about machine learning is that it’s really just a bunch of numbers and parameters, and you're trying to map an input data stream to a specific output. If you don’t know how to write the exact algorithm to get there, you let the computer figure it out by adjusting the numbers itself. 

In our case, the application was more like signal processing. It was pretty basic, but that project led to a big realization for me: so much of the world is based on probability and statistics. And if you can model something that way, you can make really accurate predictions. 

Since then, it's been validating to see the rise of large language models and generative AI. At their core, that's what they're doing. They're great at statistical modeling. They’re just rolling the dice, really. And it’s kind of wild to think how much of the world comes down to chance.

WGU: What would you say are some of the misconceptions about AI? 

AM: This might be a bit of a hot take, but I don't think AI can actually invent anything new. What I mean is that every output from a machine learning model or AI is just a reflection of the data it was trained on. 

A good example of this comes from the life sciences. When you look at a protein model, you're often seeing a static snapshot of that protein at one moment in time. But proteins move, shift, and bounce around. They're dynamic. If you train a model using only a still image, it has no understanding of that movement. And that limitation shows up in other areas, too. 

That's where hallucinations come from. The model is making its best guess based on the patterns it knows, but it doesn't really understand anything. It's not capable of producing something truly original. 

People think AI is thinking, but it's not. There's no actual thought happening. What looks like thinking is often just the model reflecting your own thoughts back at you. You're the one steering it, interpreting it and giving meaning to what it produces.

WGU: What steps can aspiring healthcare and tech professionals take to prepare for future AI-enabled careers that are still evolving?

AM: AI literacy is where I would start. The important thing to understand is that there are a lot of job seekers, so your approach really depends on the path you're pursuing. If you're in healthcare and aiming for an executive track—say, becoming a chief nursing officer or chief medical officer—then having a baseline understanding of the technology being implemented in health systems is essential. 

I’m often surprised by how many people making decisions to purchase multi-million-dollar equipment, licenses, or tools don’t actually understand how large language models work. Whether its AI today, quantum computing tomorrow, or something else down the line, having even a slightly deeper understanding than your peers can give you a real advantage. 

It’s about building understanding. Don’t fear it, and don’t dismiss it as something only for tech experts. Try to build an intuition for how these systems work. That alone can take you pretty far.

WGU: As someone who’s made hiring decisions, what does the ideal resume from a health/tech professional look like? 

AM: Finding ways to stand out means showing something that the place you're applying to will actually find interesting. And that doesn't necessarily mean writing a fancy cover letter. What really matters right now is having dual competencies. 

I see a flood of resumes filled with technical skills like C#, ASP.NET, Python, and so on. But the ones that really catch my attention are the candidates who also show at least a basic understanding of clinical workflows or healthcare-specific knowledge like HIPAA compliance. 

That doesn't mean your resume needs to show you’ve been both an engineer and a nurse. But if you’ve done a side project using AI for ICD-10 coding or anything that demonstrates your ability to apply technical skills in a specific domain, it makes a huge difference. It’s about showing you can operate within a vertical, not just that you know the tools.

At the end of the day, companies make hiring decisions based on who brings the most value to the organization. And more often than not, that value is tied to money. If you bring a mix of experience that offers real strategic insight, that’s going to be seen as high-value. 

For example, if you’re an engineer writing code that nurses will use, and you show you understand—even a little—what their day-to-day looks like, you’re already ahead. That kind of awareness makes you better at the job. And if nothing else, it helps you stand out to the hiring manager.

WGU: What’s one key insight you want everyone who attended this event tonight to go home with? 

AM: Don't get discouraged. There's a lot of talk about AI taking everyone's jobs, but if you understand how it actually works, you'll see that it's not capable of replacing roles in the way people fear. It's not going to fully replace clinicians or nurses. 

What AI is more likely to take over are the repetitive, tedious tasks, even in specialized fields. For example, with computer vision used to detect pathogens in images, the AI isn't making the clinical decision. It's simply flagging something that the doctor might have otherwise missed. 

The future of AI is about assistance. It’s changing the way jobs function, not eliminating them. We’re still going to need people doing the work. If you’re trying to break into the job market, one of the best things you can do is build deep expertise in something complex.

I also think the jobs most at risk of automation are the ones that show up most frequently in AI training data. Web development is a good example. It’s highly represented in training sets because there’s such a large volume of publicly available code and projects. That makes it easier to automate parts of those jobs. 

On the other hand, something like embedded systems development using Rust for medical devices is much harder to automate. So, choose something challenging, something meaningful, and align it with your interests. 

Ask yourself where your passion lies. Is it on the technical side or in the domain itself? You need to be strong in at least one of those areas. If you're energized by building things and love the technical aspect, then find a domain with strong market potential and dive in.