Spatial computing

Today’s ways of working demand deep expertise in narrow skill sets. Being informed about projects often requires significant specialized training and understanding of context, which can burden workers and keep information siloed.

This has historically been true especially for any workflow involving a physical component. Specialized tasks demanded narrow training in a variety of unique systems, which made it hard to work across disciplines.

One example is computer-aided design (CAD) software. An experienced designer or engineer can view a CAD file and glean much information about the project. But those outside of the design and engineering realm—whether they’re in marketing, finance, supply chain, project management, or any other role that needs to be up to speed on the details of the work—will likely struggle to understand the file, which keeps essential technical details buried. 

Spatial computing is one approach that can aid this type of collaboration. As discussed in Tech Trends 2024, spatial computing offers new ways to contextualize business data, engage customers and workers, and interact with digital systems.

It more seamlessly blends the physical and digital, creating an immersive technology ecosystem for humans to more naturally interact with the world.

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For example, a visual interaction layer that pulls together contextual data from business software can allow supply chain workers to identify parts that need to be ordered and enable marketers to grasp a product's overall aesthetics to help them build campaigns. Employees across the organization can make meaning of and, in turn, make decisions with detailed information about a project in ways anyone can understand.

If eye-catching virtual reality (VR) headsets are the first thing that come to mind when you think about spatial computing, you’re not alone. But spatial computing is about more than providing a visual experience via a pair of goggles.

It also involves blending standard business sensor data with the Internet of Things, drone, light detection and ranging (LIDAR), image, video, and other three-dimensional data types to create digital representations of business operations that mirror the real world.

These models can be rendered across a range of interaction media, whether a traditional two-dimensional screen, lightweight augmented reality glasses, or full-on immersive VR environments.

Spatial computing senses real-world, physical components; uses bridging technology to connect physical and digital inputs; and overlays digital outputs onto a blended interface (figure 1).

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Spatial computing’s current applications are as diverse as they are transformative. Real-time simulations have emerged as the technology’s primary use case. Looking ahead, advancements will continue to drive new and exciting use cases, reshaping industries such as health care, manufacturing, logistics, and entertainment - which is why the market is projected to grow at a rate of 18.2% between 2022 and 2033.

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The journey from the present to the future of human-computer interaction promises to fundamentally alter how we perceive and interact with the digital and physical worlds.

At its heart, spatial computing brings the digital world closer to lived reality. Many business processes have a physical component, particularly in asset-heavy industries, but, too often, information about those processes is abstracted, and the essence (and insight) is lost.

Businesses can learn much about their operations from well-organized, structured business data, but adding physical data can help them understand those operations more deeply.

That’s where spatial computing comes in. “This idea of being served the right information at the right time with the right view is the promise of spatial computing,” says David Randle, global head of go-to-market for spatial computing at Amazon Web Services (AWS). “We believe spatial computing enables more natural understanding and awareness of physical and virtual worlds.”

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One of the primary applications unlocked by spatial computing is advanced simulations. Think digital twins, but rather than virtual representations that monitor physical assets, these simulations allow organizations to test different scenarios to see how various conditions will impact their operations.

Imagine a manufacturing company where designers, engineers, and supply chain teams can seamlessly work from a single 3D model to craft, build, and procure all the parts they need; doctors who can view true-to-life simulations of their patients’ bodies through augmented reality displays; or an oil and gas company that can layer detailed engineering models on top of 2D maps. The possibilities are as vast as our physical world is varied.