- In 2015, the term "Big Data" was king, used widely by executives speaking on corporate earnings calls, according to research by CB Insights, which analyzed 10 years of earnings calls transcripts from more than 6,000 public companies for mentions of "AI" and "Big Data"
- At its peak in 2015, "Big Data" was mentioned more than 300 times during earnings calls and over a five year period received at least 200 mentions annually, according to the report. However, the hype around the term has started to dissipate, succeeded by executives mentioning different variations of "artificial intelligence," including "machine learning," "neural network," "deep learning," "natural language processing" and "computer vision."
- Use of "AI" has skyrocketed since mid-2016 by companies across sectors, from technology vendors to organizations such as Proctor & Gamble, with it's AI-infused "Olay Skin Advisor," and media company Tronc, which has touted AI solutions streamlining advertising operations. In Q3 2017, "AI" and "artificial intelligence" were mentioned 791 times on earnings calls, a 25% increase year-over-year.
"Hype, hype, hype." You can practically hear the masses chanting.
In recent years, with the rapid adoption of the cloud and the increase in "as a Service" models, companies across sectors have looked to implement more advanced solutions.
Data used to be considered the new oil, but companies were confronted with an inability to adequately analyze it all. For some, the promise of AI became the answer. Instead of relying on humans to sift through enormous data sets, organizations could turn to AI to derive insight and make sense of an overabundance of information.
Attendees of any tech conference can go to an expo floor and be inundated by advertisers promising to solve all technology business problems with AI solutions. The hype promised that AI will change industries, whether that's healthcare or transportation or retail.
But the hype cycle of AI is an example of expectation vs. reality. AI is sure to reshape the technology landscape, but organizations need to consider if the powerful solution is necessary.
A "give me" attitude toward advanced tech will find executives with technology solutions that are too complex for their environment. Some technology executives will race toward AI, while the more cautious will look toward vendors injecting AI into "as a Service" solutions. From there, with well illustrated POCs, companies can decide whether AI-based tools are really necessary for a given project.