What’s New in A.I. (Artificial Intelligence) from the 2022 Gartner Hype Cycle

What’s New in A.I. (Artificial Intelligence) from the 2022 Gartner Hype Cycle

AI continues to be a hot topic of discussion across business, government and society, making the task of identifying AI innovations with real business utility challenging. Data and analytics leaders can use this research to strategically plan their AI initiatives for success.

What You Need to Know
The use and adoption of AI within enterprises are pervasive as we move into the stage of “everyday AI” where AI techniques are used to add intelligence to previously static business applications, devices and productivity tools. This Hype Cycle focuses largely on hype surrounding new AI technology and techniques that have varying levels of commoditization, and operationalization of those techniques to create systems that go beyond everyday AI. It also addresses the impact of these systems on people and processes within and outside an enterprise context.
Data and analytics (D&A) leaders must leverage this research to prepare their AI strategy for the future and utilize technologies that offer high impact in the present.

The Hype Cycle
The AI market moves at a fast pace, with innovations reaching a trigger state for inclusion every year and movement along the curve remaining fluid for established innovations. The most crowded part of the cycle is toward the Peak of Inflated Expectations. This is because innovations are hyped as breakthroughs in the traditional AI bottlenecks of data acquisition, hardware acceleration, sustainable operationalization and multidomain applicability of AI models. The hype often originates from R&D labs of large tech companies, is subsequently amplified by the press, and either fades or multiplies as enterprises seek paths for adoption. Even greater levels of funding will ensure that the AI field will continue to attract high levels of research and development (R&D) and equity investments.
AI innovation and hype are being generated on multiple fronts. Academia and R&D labs are advancing state-of-the-art accuracy in model