The Challenges Apple Face to Make Spatial Computing Mainstream

Spatial computing has been in the air lately, partly due to Apple’s foray into the industry with its $3,500 Vision Pro spatial computing set.

But beyond Apple’s Vision Pro, the spatial computing landscape has moved beyond just the mixed reality mantra over the past few years, bridging the gap between artificial intelligence (AI), the Internet of Things (IoT), and digital twins.

The technology has many potential applications and benefits at the enterprise level, such as enhancing decision-making, training, collaboration, customer engagement, data analytics, and safety. These use cases are predicted to contribute tremendously to the technology’s adoption over the next decade.

Apple is going to have to work hard to make spatial computing mainstream, and many other companies are working towards that goal, too. If they succeed, analysts suggest the space will be worth $280 billion by 2028.

However, there are many challenges in cost, availability of hardware and software, the integration and interoperability of the solutions, user adoption and experience, and more.

In this article, we will look at these challenges in detail and discuss the future — can augmented reality finally go mainstream?

Table of Contents Table of Contents

1. Key Takeaways
2. 5 Key Challenges of Implementing Spatial Computing in the Enterprise
3. The Future of Spatial Computing in the Enterprise
4. The Bottom Line

Show Full Guide

Table of Contents

1. Key Takeaways
2. 5 Key Challenges of Implementing Spatial Computing in the Enterprise
3. The Future of Spatial Computing in the Enterprise
Show Full Guide
4. The Bottom Line

5 Key Challenges of Implementing Spatial Computing in the Enterprise

5. Cost and Availability of Hardware and Software

Spatial computing’s core is rooted in the capability of these devices to create virtual objects within a three-dimensional space through real-time 3D rendering.

Which currently leads to expensive, high-performing devices, not just for the end user but also for the developers who create the apps.

According to Horacio Torrendell, CEO and Founder at Treeview, achieving this level of 3D rendering requires the devices to refresh all visible objects at a very high rate of up to 60 times per second, demanding significant computing power. As the industry inches towards more compact and lightweight devices, fitting in the required computing power becomes a big challenge.

And while Apple, for instance, provides a developer kit for businesses that want to develop their own apps for use within the VisionOS, there is still a lengthy learning curve. Along with a high-spec Mac (macOS Monterey or a more recent version), developers also need the latest release of Xcode and the Vision Pro developer kit.

In other words, software development for spatial computing is complex and requires specialized skills and expertise, which might not be readily available in the market.

4. Integration and Interoperability of Software

Spatial computing solutions often involve multiple technologies, platforms, and systems, which pose challenges for integration and interoperability. Research on the integrability and interoperability of spatial data alludes that the spatial datasets, apart from being multi-sourced and heterogeneous, will continue to grow in volume.

This condition causes more fragmentation in how organizational data are sourced as well as raises the issue of compatibility between spatial computing solutions and existing business systems, data sources, and workflows,

Let’s imagine a situation where a doctor wants to have a meeting with a patient using a spatial computing headset; there is a high probability that this headset needs to integrate with existing electronic medical records, medical devices, and other systems to provide accurate and timely information to doctors and patients.

3. The Adoption Hurdles

Spatial computing is a relatively new and unfamiliar technology for many businesses, which might affect its adoption and satisfaction. This is coupled with the fact that new technologies often grapple with transitioning from early enthusiasts to widespread acceptance. This is not just a challenge of finding the right product for the market but also a marketing and adoption hurdle.

In a recent post, Deloitte’s NExT team highlighted this as one of the obstacles that providers of spatial computing software and hardware are currently facing. This challenge is also evident at the enterprise level.

Given the intricacies involved in understanding and implementing these technologies, there may be hesitation in adoption, as businesses may be unsure of the benefits and return on investment.

2. Disruption to Business Operations

Although the term spatial computing is already everywhere, it’s hard to say how many businesses are ready to face the disruptive effect it heralds. Like every technology aiming to go mainstream, spatial computing use cases have every tendency to revamp already established business workflows. And in many cases, as we’ve seen in the past, many businesses are almost always not ready or lack the means to cope with such change.

For instance, retailers may find themselves transitioning from the customary 2D images to creating intricate 3D models of their products for augmented reality (AR) displays — or risk falling behind. This shift is not just significant but also indicative of the transformative potential of spatial computing.

Moreover, the existing IT infrastructure may require substantial upgrades or even complete overhauls. This could entail significant investments in robust systems and high-speed connectivity.

1. Privacy and Security Concerns

Spatial computing systems, like augmented reality and virtual reality headsets, collect a massive amount of data about the user and their environment.

This includes personal information such as biometric data, like facial scans and fingerprints, and behavioral patterns. While these devices require this data to provide an immersive, realistic experience, it also raises serious privacy and security concerns.

Nils Pihl, CEO of Auki Labs, told Techopedia that:

“The spatial computing industry is creating a massive surveillance apparatus that will be able to see the world through our eyes.”

For example, companies like Apple utilize biometric data like fingerprints and facial recognition as part of their human-centered approach to Vision Pro development.

However, biometric data is highly sensitive personally identifiable information (PII). There are questions about how these companies collect and store this data and how it is secured against security breaches or privacy violations.

The Future of Spatial Computing in the Enterprise

The global market for spatial computing is on a significant upswing, indicating its growing demand and transformative potential across various sectors. A recent report estimates the spatial computing market will be worth $280 billion by 2028.

Vlad Panov, Global Head of Web3 at Publicis Sapient, concurs with the above prediction, stating that most people will adopt the technology in a few years.

However, he insists that the devices need to be less bulky and that enterprises need more robust data security and privacy measures to ensure the ethical use of spatial computing sets.

“There is no doubt in my mind that this technology will become widely used by most people.

“Still, for many customers, that means headsets need to become less bulky and less expensive and have lower health concerns (e.g., eye strain and motion sickness). At the same time, for enterprises, that also means better guardrails around data security and privacy; headsets collect a lot of potentially sensitive data about users.”

Panov also argues that the industry’s future looks brighter now as more enterprises will look toward how they can deliver more immersive experiences to their customers in the comfort of their homes.

The Bottom Line

While the potential benefits of spatial computing at the enterprise level are vast, the journey toward widespread adoption is marked by some challenges.

The high costs and technical complexities associated with hardware and software, integration issues, and the learning curve for new technologies pose substantial obstacles.

Available market reports show the industry is on an upward trend. But the question is: will we let the industry thrive before coming up with some standards around the development and use of spatial computers, or wait for it to blow away our minds as ChatGPT did before putting together some industry standards?