My take: Through data, spatial computing is set to shift how we interact with and simulate our world

By David Randle, worldwide head of go-to-market for spatial computing at Amazon Web Services 

Information technology is on the verge of a revolution, driven by spatial data (three-dimensional, location, time, and behavioral data). Harnessing this data will transform how we understand and simulate the real world.

Spatial computing is the technology that enables this more natural understanding and awareness of physical and virtual worlds. It involves blending real-time operational data (from the Internet of Things, asset performance databases, and monitoring applications) and standard business data (documents, images, and videos) with three-dimensional data (computer-aided design, 3D meshes, light detection and ranging, and point clouds) to create digital representations that mirror the real world. The promise of spatial computing is the ability to serve super-contextualized information—the right information at the right time with the right view, through a natural, humanized, three-dimensional medium.

To date, spatial computing has largely been conflated with virtual reality and augmented reality (AR). These interaction layers will play important roles in use cases, but the value of spatial computing doesn’t need to be bound by the current state of wearables and headsets. Businesses that are interested in delving into spatial data don’t need to wait for AR and virtual reality to mature.  

The truth is, there’s much more value in the data itself than in the consumption medium. Spatial data is often the most faithful representation of real-world operations. Consider sensor data created by heavy industry and manufacturing equipment. There’s a direct link between the data produced by that equipment and its actual, real-world functioning. The challenge is how to develop meaningful insights from it. Because this is a relatively new field, there are few standards around spatial data. Much of it sits in silos; some of it is in the cloud, but most is not. At most enterprises, this data landscape is extremely scattered and not well managed. Our customers’ first problem is managing their spatial data.

Another challenge is that spatial computing involves a broad technology set, including game technologies, sensors, databases, and interaction devices. Businesses don’t know where to begin because there are so many entry points. There’s general confusion around how to stitch together the right solution that will scale for the right use case and show a strong return on investment. Lots of companies want to do it all, but there’s still a lot of experimentation needed to put together the right stack.

We are starting to see businesses solve these challenges, though, supercharged by generative AI. There’s always been a need for a better understanding of spatial data, and generative AI is a game-changing component. It allows users to interact with various data types in natural language. It also enables AR experiences with greater contextual awareness. The prospect of a device you can wear that has both a natural understanding of and agentic influence on your world is super-exciting.

Now that some enterprises have improved in managing and understanding their spatial data, cutting-edge use cases are starting to follow. For example, heavy industries like manufacturing and mining are using spatial data to develop digital twins of machinery to monitor maintenance needs and simulate operations under various conditions, to power 3D virtual facility dashboards and augmented worker scenarios. This capability allows them to enhance safety for workers and better use available resources.

Immersive commerce is one of the biggest areas where spatial computing is having an impact today. Auto manufacturers are now allowing shoppers to configure cars on their websites and see high-fidelity mock-ups of their customizations, even in the context of their driveway. Retail businesses are developing virtual 3D stores where people can “walk” around and replicate the feeling of shopping off the rack. Early adopters are reporting increased transaction rates for high-value products sold this way.

Once you unpack spatial computing, it becomes obvious that there are implications for any organization that provides a product or a service to customers—essentially any business. That’s why we believe it’s only a matter of time before you see wider adoption. Certain industries have more shorter-term implications for the technology, but we’re constantly surprised by the use cases coming out of various sectors.

Most people don’t like to admit that they need to invest in areas that seem like plumbing, and that’s what managing spatial data seems like. But by investing in and managing spatial data, and connecting it to an end-use case, companies can drive innovative new ways of running their businesses and growing revenue. The enterprises that will differentiate themselves will be those that understand the importance of managing this valuable trove of data, often representing a one-to-one replica of their physical infrastructure. Once they understand that, the sky is the limit on the type of groundbreaking use cases they’ll unleash.