Vision Pro gets all the press for watching movies in virtual environments. Quest gets attention for gaming. But the most interesting spatial computing deployments I’m seeing aren’t consumer applications at all—they’re industrial.

Factories using AR for assembly guidance. Maintenance crews using mixed reality for equipment repair. Training programs replacing classroom time with immersive simulations. These applications aren’t sexy, but they’re generating real ROI in ways consumer spatial computing hasn’t yet.

Let’s talk about where spatial computing is actually working in industrial contexts, because it’s a very different story from the consumer narrative.

What Makes Industrial Spatial Computing Different

Consumer spatial computing needs to be delightful, intuitive, and compelling enough that people choose to use it for entertainment. Industrial applications have different requirements:

Utility over experience. It doesn’t need to be fun. It needs to save time, reduce errors, or enable capabilities that weren’t possible before. If a technician can fix equipment 30% faster with AR guidance, nobody cares if the experience is magical.

Clear ROI. Consumer apps monetise through subscriptions or ads. Industrial apps justify themselves through productivity gains, error reduction, or training cost savings. The business case is straightforward: does this pay for itself?

Harsh environments. Industrial headsets need to work in factories, warehouses, and outdoor environments. They need to handle dust, temperature extremes, and rough handling. That drives different hardware requirements than living room VR.

Hands-free operation. Many industrial workers need their hands free to do actual work. Voice control and gaze-based interaction aren’t nice-to-haves—they’re requirements.

Assembly and Manufacturing Guidance

This is probably the most mature industrial spatial computing use case.

Workers wearing AR glasses see step-by-step assembly instructions overlaid on the actual parts they’re working with. Visual guides show exactly where components go, what orientation they should be in, and how to connect them.

Boeing has been using AR for wiring harness assembly for years. Workers see virtual guides showing where to route wires through complex aircraft structures. Error rates dropped by about 90% compared to paper instructions, according to their internal metrics.

Automotive manufacturers are deploying similar systems. BMW uses AR glasses on assembly lines to show workers the correct torque specifications and fastener positions for different vehicle configurations. When the assembly order changes (different options on different vehicles), the AR instructions adapt automatically.

The value proposition is clear: faster assembly, fewer errors, less training time for new workers, and the ability to handle product variation without drowning in paper manuals.

Smaller manufacturers are starting to deploy this too, not just giants like Boeing. The hardware costs have come down enough that it makes sense for mid-sized operations producing complex products with variation.

Maintenance and Repair

The technician walks up to a broken machine. They put on AR glasses. The system recognises the equipment, overlays diagnostic information, and guides them through troubleshooting.

This is huge for maintenance operations, especially for complex equipment where expertise is scarce.

An energy company in Australia is using AR for turbine maintenance. Technicians in the field see virtual overlays showing where sensors should be placed, what readings to expect, and step-by-step repair procedures. They can also pull up remote experts who see what the technician sees and provide real-time guidance.

The Australian Defence Force has experimented with AR for vehicle and equipment maintenance, particularly useful when field technicians encounter unfamiliar equipment variants.

The ROI comes from reduced downtime (fixing things faster), better first-time fix rates (getting it right without multiple trips), and enabling less experienced technicians to handle complex repairs with expert remote support.

Remote Expert Assistance

Speaking of remote experts, this might be the killer app for industrial AR.

A technician in a remote location encounters a problem they can’t solve. Instead of waiting for an expert to fly out, they call them via AR. The expert sees exactly what the technician sees, can annotate the view with virtual markup, and guide them through the fix in real time.

This is valuable in industries with geographically dispersed operations—mining, oil and gas, utilities, agriculture.

A mining company I know deployed this for equipment maintenance across remote sites. They reduced expert travel by about 60% in the first year. The cost of AR headsets was covered within months just from reduced airfares and accommodation.

The technology isn’t even that exotic—it’s essentially video calling with spatial awareness and annotation tools. But the impact on operational efficiency is significant.

Training and Simulation

Traditional industrial training is expensive. You need physical equipment, dedicated training facilities, instructor time, and you’re still training in relatively low-risk environments that don’t fully replicate real conditions.

Spatial computing enables high-fidelity simulations for a fraction of the cost.

Dangerous procedures can be practised safely. Expensive equipment can be simulated without risk of damage. Trainees can make mistakes and learn from them without real-world consequences.

Walmart has trained over a million employees using VR simulations for scenarios like Black Friday crowd management and active shooter situations. They can expose workers to high-stress scenarios that would be impossible or unethical to create in real training.

Surgical training is using VR extensively. Surgeons can practise procedures on virtual patients, get feedback on technique, and build muscle memory before operating on real humans. Some programs report significant improvements in trainee performance compared to traditional methods.

Industrial training—welding, equipment operation, hazardous material handling—is following similar paths. The ability to train at scale, repeatedly, without consuming physical resources or creating safety risks is transformative.

Design and Prototyping

Engineers and designers using spatial computing to visualise and interact with 3D models at full scale before anything physical is built.

Automotive design teams are walking around virtual cars, evaluating proportions and sight lines at 1:1 scale. They can spot design issues that weren’t obvious on screen or in scale models.

Architects and construction firms are using AR to visualise buildings on actual sites before construction. Stakeholders can see what the finished building will look like in context, which improves design decisions and client communication.

Factory layout planning is another application. Instead of moving physical equipment around to optimise a production line, planners can use AR to test different configurations virtually, identifying ergonomic issues and workflow bottlenecks before committing to a layout.

This reduces expensive physical prototyping iterations. If you can catch design problems in virtual space, you save the cost of building, testing, and scrapping physical prototypes.

Quality Control and Inspection

Using spatial computing to compare physical objects against digital specifications, detecting deviations that would be hard to spot manually.

AR overlays can show whether a physical part matches the CAD model. Variations beyond tolerance get highlighted automatically. This is useful for precision manufacturing where dimensional accuracy is critical.

Building inspections are using AR to compare as-built conditions against plans. Inspectors can see where walls, pipes, or equipment don’t match design specifications, making deviation tracking much faster than manual measurement.

The Hardware Reality

The hardware for industrial spatial computing is less elegant than consumer devices but more practical.

HoloLens has been the dominant platform for many industrial deployments. It’s expensive ($3,500+), but it’s rugged, hands-free, and Microsoft has built decent enterprise support around it. HoloLens 2 added better field of view and hand tracking, making it more usable.

RealWear HMT devices are popular in harsh environments. They’re not fancy—they’re essentially a small display on a hard hat with voice control—but they work in environments where fragile consumer headsets would die.

Magic Leap struggled in consumer markets but has pivoted to enterprise and is seeing traction in industrial and medical applications.

Varjo makes high-end headsets with exceptional visual fidelity for applications like pilot training and design work where resolution matters.

And increasingly, tablets are being used for AR applications that don’t require heads-up displays. Holding up a tablet to see AR overlays is less immersive but more practical for many use cases.

The ROI That Actually Matters

Industrial spatial computing works because the ROI is measurable and often compelling.

Reduced training time and costs. If you can cut training time by 30% while improving competency, that’s money saved and productivity gained.

Lower error rates. Assembly and maintenance errors are expensive. If AR guidance prevents mistakes, the payback is direct.

Less expert travel. Remote assistance drastically cuts travel costs and response times. This alone often justifies the investment.

Faster task completion. If workers complete tasks faster with AR guidance, that’s productivity improvement that flows to the bottom line.

Better safety outcomes. If simulation training reduces workplace injuries or safety incidents, the benefits are both financial and human.

These aren’t abstract benefits. They’re trackable metrics that CFOs care about.

The Barriers Still Remaining

It’s not all smooth sailing. Industrial spatial computing has real challenges:

Hardware cost and durability. Devices are expensive and still relatively fragile. Some industrial environments are too harsh for current hardware.

Content creation is expensive. Building AR applications and training simulations requires specialised skills and significant time. The development cost can be prohibitive for smaller companies.

Integration with existing systems. AR applications often need to integrate with CAD systems, work order management, training databases, etc. That integration work is complex and costly.

User acceptance. Some workers are resistant to wearing headsets or changing familiar workflows. Change management is real.

Battery life and ergonomics. Wearing a headset for 8-hour shifts isn’t comfortable with current hardware. Weight, heat, and battery life are ongoing issues.

Where This Is Heading

Over the next 3-5 years, I expect industrial spatial computing adoption to accelerate while consumer adoption stays niche.

Hardware will get lighter, cheaper, and more durable. Battery life will improve. Developer tools will get better, reducing content creation costs.

AI integration will make these systems smarter—automatic diagnostics, adaptive training, predictive maintenance guidance. Spatial computing combined with AI is more powerful than either alone.

And as the first-mover companies demonstrate clear ROI, others will follow. We’re past the experimental phase. Industrial spatial computing is becoming standard practice in specific domains.

The consumer metaverse might still be years away. But the industrial metaverse is already here, it’s just not evenly distributed yet.

And frankly, it’s more interesting than gaming. These are applications that change how real work gets done, improve safety, and generate measurable business value.

That’s the spatial computing story worth paying attention to. Not the hype about virtual concerts and digital real estate, but the boring, practical applications making factories, maintenance operations, and training programs measurably better.