Walk into a hospital, a warehouse, a cornfield, or even a Walmart—and odds are you’ll find artificial intelligence at work. Not the kind generating images or writing poems, but something far more practical: embedded, applied AI making physical systems smarter, faster, and more precise.
Your Amazon package? Handled by a self-driving forklift at your local distribution center. Your groceries? Those fruits end veggies were harvested by autonomous tractors. Even your iPhone was made with AI-enabled robots and machines.
It’s easy to miss this hidden layer of intelligence. Product engineers and software developers have worked hard to embed the artificial intelligence into these technologies so seamlessly that we hardly notice them. That’s the point.
All of this embedded intelligence means that careers in technical fields are changing. Sensors, edge computing and machine learning capabilities are now standard on equipment and processes in almost every industry. Of course, not all students will become software developers. But even operators, technicians and programmers will need to at least understand the underlying technologies and how AI enables them.
Here’s how some of the world’s most iconic companies are using applied AI in ways that are already shaping the workforce—and the next generation of skilled professionals.
HEALTHCAREIf you’ve ever had an MRI, you know the drill: lie still in a giant, humming machine while it slowly collects images of your insides—sometimes for 45 minutes or more. It’s not fun. And for hospitals, it’s not efficient.
The old way relied heavily on the skill of the technician and the cooperation of the patient. How the machine was calibrated, how long the scan took, and even the image quality could vary depending on who was operating it. Patients with movement issues or anxiety often needed to be rescanned, adding delays and costs.
GE Healthcare is changing that with a new generation of AI-powered tools built directly into their MRI systems. When we talk about embedded AI, these aren’t add-ons—they’re baked into the machine’s core functionality.
Take Sonic DL, a deep learning-based technique that dramatically speeds up scans—up to 12 times faster in some cases. Suddenly, that 45-minute scan can take just a few minutes, without losing any of the critical detail doctors rely on.
Then there’s AIR Recon DL, which sharpens images and cuts scan time in half. It reduces the noise and blur that sometimes make MRIs hard to interpret, helping radiologists catch problems sooner and with more confidence.
Finally, AIRx brings consistency to the process by using AI to automatically detect anatomy and prescribes MRI slices. That means every patient in every facility gets the same high-quality imaging experience.
The result? Faster scans. Clearer images. Fewer do-overs. And a better experience for both patients and healthcare providers. But it doesn’t take away the job of the MRI technician – it just makes their work easier. It doesn’t take away from radiologist’s work, either. It just enables them to read higher-quality scans with a better chance of not missing any critical details.
Source: GrayMatter Robotics
In manufacturing, sanding is one of those jobs that’s repetitive, messy, and physically exhausting. It takes a steady hand, good muscle endurance, and serious attention to detail—especially when you’re working with oddly shaped parts or custom orders. It’s not the kind of job you want a human doing by hand eight hours a day, five days a week.
For years, manufacturers tried using traditional robots to do the job, but this kind of finishing work was nearly impossible to automate. Robots could follow a programmed path, but they couldn’t adjust on the fly. The program only worked for one part design. New part? You’re back to square one. And if a part wasn’t perfectly aligned or had small variations, the robot didn’t know what to do. Humans still had to step in and finish the job.
That’s exactly the problem GrayMatter Robotics solved with artificial intelligence.
Their system, called Scan&Sand, uses an array of advanced sensors and artificial intelligence to automate the sanding process. Cameras create a 3D map of each part, then the AI software writes a program based on that part’s design and the finishing specifications. Then, when the robot starts the sanding process, force and torque sensors track how the material responds during sanding, and the robot can even change its program in real-time!
These are AI-enabled “digital workers,” CEO Ariyan Kabir told The TechEd Podcast.
Instead of spending hours sanding, workers are now learning to operate and oversee these robotic systems. Thanks to the AI in the software, operators don’t need a background in coding or robotics to do it. A technician who’s spent years sanding parts can now be trained in under an hour to run the system—placing parts, initiating scans, monitoring quality, and making adjustments through an intuitive interface.
The work is cleaner, safer, and more engaging. It’s also often better paid. And it reflects a bigger trend happening across manufacturing: AI isn’t replacing skilled workers—it’s re-skilling them for the next generation of technology-enabled roles.
Source: Walmart
When you walk into a Walmart to buy..well, anything…the organization and inventory is designed to look effortless. You expect to find the groceries you need, even on a busy day. You take for granted that the pharmacy will have extra cold and flu meds during those winter months. You expect aisles to be packed with the appropriate seasonal décor—all in the right place, at the right time.
But behind the scenes, the scale is staggering. Walmart serves over 230 million customers each week. Managing inventory across thousands of stores, distribution centers, and delivery networks isn’t just a logistics challenge—it’s a data problem. And that’s where artificial intelligence is quietly transforming retail.
Walmart’s AI- and machine learning-driven inventory system uses a massive library of historical data to predict future trends so that the shelves always have what consumers need, when they need it. The system analyzes past sales, online searches, product views, weather forecasts, macroeconomic trends, even local events.
That predictive power helps Walmart position products across the country with surgical precision—making sure pool toys don’t end up in Michigan in December, and heavy coats don’t clog shelves in Florida. It can even recognize anomalies, like a one-time snowstorm, and exclude those events from future planning to keep forecasts accurate.
And it’s not just about supply. Walmart’s AI also optimizes delivery routes, adjusts to changing buying habits by zip code, and integrates insights from online and in-store purchases to make sure shelves stay stocked—and customers stay happy.
Roles in supply chain technology, business intelligence, data analysis, and IT systems are all part of this ecosystem. And with the right technical skills, today’s students could be the ones making that all happen tomorrow.
Source: Waymo
The next time you’re in Phoenix, open your Uber app and see if it offers you a driverless car. If it does? Say yes.
In Phoenix, Austin, San Fransisco and a growing number of U.S. cities, Waymo’s autonomous vehicles are quietly becoming part of the daily traffic flow. They stop at red lights, yield to pedestrians, and navigate construction zones—without anyone touching the wheel.
What seems like Sci-Fi is actually here, and it’s all due to applied AI. Each vehicle begins its journey by referencing an ultra-detailed map of its surroundings—built by scanning every curb, traffic signal, lane line, and sidewalk. As it drives, the car continuously compares this map to live data streaming in from onboard lidar, radar, and high-resolution cameras.
The system identifies nearby vehicles, cyclists, and pedestrians and predicts how they’re likely to move. It knows that a person on the curb holding a leash is different from one looking at their phone in the crosswalk. It can adjust speed, change lanes, and take evasive action in a fraction of a second, based on those predictions. These decisions are informed by over 20 million miles of real-world driving and billions of simulated scenarios. Every experience adds to the system’s ability to handle unpredictable situations safely and efficiently
Behind the scenes, these systems rely on an entire workforce of skilled professionals. Sensor calibration, real-time systems monitoring, data labeling, and vehicle maintenance all play a role in keeping AV fleets functional and safe.
Students in technical programs—from automotive to robotics—are stepping into a world where AI is built into the machines they’ll be servicing. The job isn’t about programming a car to drive. It’s about understanding how systems interact, how to test and troubleshoot complex components, and how to keep intelligent machines performing the way they should.
As vehicles grow more autonomous, the technical work to support them shifts. So should automotive programs.
Source: John Deere
Not long ago, farming meant long hours in the cab of a tractor—driving straight lines through a field for hours, sometimes days, to get the job done. Tillage, spraying, hauling—each task depended on skilled labor behind the wheel, and timing was everything.
Today, the equipment still rolls through the fields—but the operator doesn’t always have to.
John Deere’s newest machines are equipped with a second-generation autonomy kit that combines computer vision, lidar, and AI to navigate complex environments on their own. The system uses 16 cameras arranged for 360-degree visibility and calculates depth and distance in real time, even at faster speeds.
“We’re extending our technology stack to enable more machines to operate safely and autonomously in unique and complex environments,” said Jahmy Hindman, Deere’s CTO. “This will not only benefit our customers, but all of us who rely on them for the food, fuel, fiber, and infrastructure we depend on.”
These autonomous systems can do a whole range of applications. Orchard machines that navigate dense canopies while spraying crops, dump trucks that move raw material around quarries, mowers that do commercial landscaping autonomously.
All of it is managed through an app—where users can monitor machine health, view live video, adjust settings, and get alerts if anything goes wrong.
Autonomy doesn’t mean fewer people. It means different skills.
Instead of driving a tractor, operators are learning to manage and monitor intelligent systems. They don’t need robotics degrees to run these machines—just the right technical training. And for students entering precision ag, ag-tech or heavy equipment programs, that opens the door to safer, more specialized, and higher-paying roles than the ones they might have expected.
If you didn’t catch it by now, there’s a pattern across all these examples:
You’ll see the same pattern repeat itself in nearly every industry. Examples abound beyond what we’ve shared already.
Here’s what it means for technical education:
Students don’t need to become data scientists to work with AI. But they do need to understand that AI is becoming a standard feature in the equipment and systems they’ll be using—whether it’s in manufacturing, agriculture, healthcare, logistics, or beyond.
Technical programs can no longer afford to separate “learning the tools” from “understanding the technology.” Those are now the same thing.
To prepare students for the workforce they’re entering—not the one we used to know—technical education needs to evolve. That means rethinking curriculum, updating lab equipment, and embedding AI literacy into hands-on training.
The next generation of skilled workers will still turn wrenches, drive tractors, and run machines. But they’ll be doing it with systems that think. And it’s our job to teach them how to work with that intelligence.
