Spatial Computing Crosses the Chasm
I’ve just returned from Augmented Enterprise Summit, an annual independent enterprise extended reality (XR) conference that nearly doubled in size this year to accommodate the rapidly growing number of people dipping their toes into the world of immersive tech. Over the course of three days, my team spoke with over 100 people who are actively experimenting with augmented and virtual reality (AR/VR) to solve critical workforce and operational challenges. Over and over, in 1:1 conversations, on panels, in case studies, over meals and coffee, and just walking around, I heard the same thing: XR is officially here, it’s just gotten stuck trying to cross the chasm.
First, the good stuff: When I say XR is officially here, I mean it is being used all around us. The most salient lesson of my MBA comes to mind: in order to find product market fit, look for a problem your technology can uniquely address that is so profound that people are spending time and money pulling together wildly sub-optimal solutions. Anyone who spends time in manufacturing, oil & gas, high tech, and other industrial sectors will likely tell you the same thing – current knowledge management, safety training, and first wave support tools are inconsistent, sub-optimal, and constantly being solved through point solutions. Deloitte’s Unlimited Reality practice has identified the industrial metaverse as the most promising use case, doubling down on investments around end-to-end industrial solutions including enterprise spatial data lakes, augmented worker solutions integrated with enterprise systems, and digital twin simulations for operational efficiency.
In particular, I spend a lot of my time thinking about the Future of Work as it pertains to spatial computing. And while there are incredibly exciting opportunities across the talent lifecycle—VR job simulations for talent acquisition, immersive onboarding experiences and persistent environments, collaboration in 3D spaces, etc.—the conversation that clients often want to have is around VR training and AR for knowledge augmentation in the flow of work. And right now, that’s where many companies are ready to experiment, because that tends to be where the capital investment is manageable, the degree of disruption to work processes is typically limited, and the ROI is easy to calculate (e.g., reduction in safety incidents, reduction in travel costs, etc.).
But, here’s the hard stuff: It’s a tale as old as time. Every technology innovation struggles to move from early adopters into the mainstream. It’s a product-market fit challenge, a marketing challenge, and an adoption challenge rolled into one. This is, of course, the challenge that XR hardware and software providers are facing, but it is easily applicable to enterprise scale as well. XR (and especially AR) is a complex technology that will likely take time to reach maturity and mass adoption. In some cases, engineers are trying to replicate real-world physics in virtual spaces. So how should companies navigate the next few years?
1) Recognize that your early adopters and early majority do not have the same needs. Again, this is mostly a challenge that XR hardware providers are facing, but it’s also a critical challenge for enterprise leaders thinking about moving from pilot projects to scaled deployments. A pilot can be designed to solve a very niche use case with very specific boundaries and KPIs. Scaling the technology means solving multiple problems, investing in a large fleet of novel hardware, and considering mobile device management and enterprise system integration. Take the time to understand your entire stakeholder landscape and ensure that your plan for scale addresses critical needs across stakeholder groups, offers a clear path to ROI (whether quantitative or qualitative), and is flexible enough to adjust as the technology landscape adjusts.
2) Tech doesn’t solve problems on its own. Another lesson I learned having been in and around the startup ecosystem for some time—and especially watching the explosion in Generative AI interest when it became more accessible through the chat construct— is that cool tech is cool, but it means nothing if you are not considering the whole solution. What is your technology ecosystem around XR? What kinds of training and support systems have you put around it? What new policies and standards are required to reduce risk? And most importantly, is it being used to solve actual problems that end users have identified?
3) Embed change management across your program. All of this leads to my #1 reflection, for which I will pound the metaphorical table endlessly: scaling XR is fundamentally an adoption problem. Think about the challenge here. If you’re implementing a new enterprise system, it usually requires months or years of change management support, even though often no new hardware is being introduced. In the case of XR, you’re introducing new hardware, new software, new processes, and new systems. It’s a wholesale change—a digital transformation 2.0. These considerations must be baked into your solution from day one.
We have a long journey ahead of us—although who knows, maybe upcoming XR releases will blow us all out of the water and the hype cycle appears to be on the upswing. I left AES with a profound belief in the metaverse, or spatial computing, or immersive, or whatever you want to call it. And as a rather embarrassingly self-proclaimed Luddite, take that as a huge sign of faith.
- Dany Rifkin, Deloitte Consulting | Portfolio Manager, Augmented Workforce Experience