A lot has been written about artificial intelligence (AI) and machine learning (ML) over the past few years. But for all of the promises and predictions about what’s coming next, so far, AI has remained in the early-adopter stage. The technology remains something only a select few people know, understand and are able to use and benefit from. But that may be about to change as AI becomes both more mainstream and more commercialized, allowing the democratization of the technology to extend to everyone. In fact, according to a recent Gartner report, manual data tasks could be reduced by as much as 45% by 2022 thanks to machine learning.
The reason for the rapid adoption is simple. Your company’s data has gotten too large, complex, diversified and too decentralized for mere humans to manage alone. Not only do they need to embrace scalable cloud solutions to manage it all, but soon they’ll also need to embrace AI and ML to automate processing and analysis. As datasets grow in size, the speed of analysis only grows in importance, meaning AI will soon be a vital component to getting the job done in a timely manner.
Another factor in the democratization of AI and ML will be the transition from open source to commercial applications. Up until now, open source solutions have dominated that AI and ML landscape, but it’s expected that 75% of new end-user solutions are likely to be built with commercial solutions rather than open source alternatives by 2022. Open source solutions often provide a lot of initial innovation, but they fall short in many key areas. That’s why there are many players already hard at work building connectors into existing open source AI systems in order to provide the features, security and scalability that enterprises need in order to deploy AI systems more broadly throughout their organization. Once these key features have been addressed, enterprises will finally be able to fully realize the benefits of AI and ML systems and deploy them reliably at scale.
As with most new technologies, commercialization also brings with it new tools and user interfaces to make it more approachable and usable for everyday users. This means choosing and deploying AI and ML models will no longer be just for data scientists and IT professionals, instead, everyone in your company will have the self-service tools they want to get the insights they need from that growing mountain of data your company generates every day.
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