Predicting the future is a risky business…
“Nuclear-powered vacuum cleaners will probably be a reality within ten years.”-Alex Lewyt, president of Lewyt vacuum company, 1955
…but that’s what a purveyor of technology does day-in and day-out. To our knowledge, Mr. Lewyt never prototyped his nuclear-powered vacuum cleaner. And we’re all better for that.
This quote, and countless others, emphasize that the risk that is inherent in a revolutionary new product is just one of the many things that technology entrepreneurs constantly evaluate.
Whether a truly new product takes root and blossoms is a function or two basic constraints – relevance and timing. Relevance is the easier of the two to determine. The standard customer discovery processes and beta deployments can significantly risk-reduce the potential relevance and required feature set of a new offering. The road to failure is littered with the bones of great products that were simply ill-timed. Technology services and products are extremely susceptible to timing risk.
Hype or necessity?
In the 1990s, several theoretical models were developed to attempt to predict if and when new internet-based services would be adopted. Several of those models blew up along with the bubble-burst. However, one has persisted through the years, and illustrates a conceptual model of how sustainable adoption can occur, given time. This is what the generic Gartner Hype Model looks like, just in case you haven’t seen it a hundred times already:
If you follow Eye-bot’s blog or have been to our site, you know that we focus on providing the very best, multi-dimensional (3D, 4D, moving to 5D), multi-system (photogrammetric, LIDAR) data sets. These can be collected by a drone; data that is verified for use in the many software applications that deliver components of, or a fully functional digital twin for purposes of remotely monitoring and managing large capital assets.
As industry insiders, we have observed the evolution and maturation of these software applications for the past six years. And, we chose to target the most fundamental driver to adoption – the data. But even great data couldn’t drive adoption until those software applications ‘caught up’ to be easy to use and provide a sufficiently rich user experience that delivers on the promise. This evolution has occurred in a manner that tracks pretty consistently along the Gartner curve:
We are now seeing strong indicators that what has been a somewhat nascent digital twin market is beginning its march along the curve towards productivity. It is delivering calculable ROI; that is, moving from a ‘cost’ to a critical component of delivering demonstrable value.