Generative AI Will Scale Much Faster than XR, AR, VR

“Metaverse” in particular, but also augmented reality, virtual reality or extended reality, have been eclipsed by the fever over generative artificial intelligence and large language models in 2023. 

Though interest will return eventually, generative AI will scale commercially much faster.  

 

There are good reasons why generative AI will get commercial traction faster than AR, VR or XR: cost, ease of use and scalability. 

Broadly speaking, the cost to create a commercial use case, at scale, is far easier with generative AI. 

Generative AI is software-based, and can be used with virtually any existing application, to add content creation; support or code-writing tasks to any existing app. That means the time to deploy and cost to deploy--while far from insignificant--can rely on existing app use cases and deployed instances. 

Any form of “Metaverse,” AR, VR or XR apps require new specialized hardware, generally are not “mobility enabled” and also require creation of new apps and ecosystems. That takes time and money. 

So generative AI is easier to create and deploy and easier to use. It requires no new hardware; no new behavioral changes; no new applications. It simply adds features to what already exists. 

Since generative AI is essentially a “bolt on” for existing use cases and apps, it can scale quickly.

Will 70% of AI Projects Fail? Probably

According to a study conducted by Wakefield Research and sponsored by Pure Storage, 90 percent of IT buyers stated that the pressure of their digital transformation agenda led them to buy technology their infrastructure could not support. 

That problem is likely to be reflected in a mass rush to deploy artificial intelligence as well.

Also, more than 62 percent of information technology buyers reported they “feel pressured all the time or often to make decisions on purchasing technology based on current needs without fully exploring the consequences of these decisions in the longer term.”

Perhaps that is another good example of why 74 percent of digital transformation efforts fail and why AI, metaverse and AR projects are likely to fail as well. 

One study shows the difficulty of successfully shifting from development to commercial deployment of Kubernetes, for example. The survey by D2iQ found 42 percent of Kubernetes applications that work in a pre-production phase were actually deployed commercially. 

General business transformation or information technology change projects likewise tend to have success in the 30-percent range. 

By common rule of thumb, as much as 70 percent failure rates are common for IT efforts. That same ratio also tends to hold for organizational change efforts. The rule of thumb is that 70 percent of organizational change programs fail, in part or completely.  

Historically, most big information technology projects fail in some major way, failing to produce expected cost savings or revenue enhancements or even expected process improvements. 

Some would argue the digital transformation failure rate is the same. “74 percent of cloud-related transformations fail to capture expected savings or business value, ” say McKinsey consultants  Matthias Kässer, Wolf Richter, Gundbert Scherf, and Christoph Schrey. 

Of the $1.3 trillion that was spent on digital transformation--using digital technologies to create new or modify existing business processes--in 2018, it is estimated that $900 billion went to waste, say Ed Lam, Li & Fung CFO, Kirk Girard is former Director of Planning and Development in Santa Clara County and Vernon Irvin Lumen Technologies president of Government, Education, and Mid & Small Business. 

BCG research, for example, suggests that 70 percent of digital transformations fall short  of their objectives. 

From 2003 to 2012, only 6.4 percent of federal IT projects with $10 million or more in labor costs were successful, according to a study by Standish, noted by Brookings.

So perhaps Kubernetes applications succeed at a higher rate than for other IT projects: about four out of 10, where bigger projects succeed about three times out of 10.