Things in their own buckets are easy to talk about and easy to track. But the tech industry has gotten to the point where these convenient buckets actually get in the way of technology decision making. For instance, CIOs all will tell you “yes” if you ask them if they are planning on using, say, 5G or IoT. But at its heart, those big buckets aren’t helpful beyond slide-ware.

The specifics of what and how plus the business case are far more important. This doesn’t necessarily mean speeds and feeds, or management issues, it means drilling down through the overarching layer of generalization, most commonly found on hyped/bucketized technologies. Specific and important characteristics and subclasses get routinely obscured by bucketizations.

A good example of this is IoT. IoT is constantly treated as a thing, or a single category of technology. Is IoT true and a real category. Yes, sort of. It’s a top level bucketization of other technologies, and while it can be effectively used in very broad and very high level conversations, its use beyond them turns into a liability. The hype would tell us your company has to get in on the wave of “IoT.” Broadly true, but it misses the point. IoT devices are almost all heavily verticalized.

IoT devices for heating and cooling systems versus IoT devices for security are wildly different. Are they both IoT? Sure, at a uselessly high level. If an enterprise wanted to do strategic planning about how it may take advantage of IoT devices, it needs to drill down to its business or process, or premises, just to name a few.

A proliferation of buckets

Other hot technology topics that have been bucketized into a single term include metaverse, AI, edge computing, and cloud, just to name a few. The current shrieking and hand-waving over large language model chat AI paints all AI with the same brush, making assumptions that all AI suffers the same flaws and advantages. There are multiple ways to do AI. Even worse, the term AI itself has become all-encompassing to include machine learning (ML) as well. If a product uses ML, more than likely the marketing department has renamed that feature “AI” to get the bounce from AI’s current moment in the sun.

While that distinction in some ways is pedantic, it illustrates how the all-encompassing bucket terminology blurs distinctions and makes enterprise decision making considerably more difficult, to say nothing about how it effects tech-clueless government policymakers.

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By GlobalData

The use of broad, overarching bucket terminology is hardly new. However, some vendors use this high-level terminology to describe existing equipment or features, even when there may be significant caveats. Press, industry analysts, and pundits are all guilty of using these terms when it’s not appropriate. Enterprise CIOs and evaluators need to push vendors or service providers to move on to specifics on how they solve business and IT problems. The more grandiose a given claim is around a high level technology bucket, the more likely that the specifics are less impressive. Good products and services stand out on their own.