Artificial intelligence (AI), machine learning (ML), and deep learning (DL) technologies are expected to permeate day-to-day business as well as customer activity. Industries such as healthcare (advanced diagnosis and treatment), transportation (advanced driver assistance systems and autonomous vehicles), and life sciences (rare disease treatment research) are some of the early adopters of AI. The goal for any organization adopting AI/ML/DL is to deliver meaningful insights and predictions that can significantly improve products, processes, or services across industries and use cases.
Today, as AI becomes mainstream, many organizations find themselves in the initial proof-of-concept (POC) stage; only a few are in full production. IDC’s 2019 Artificial Intelligence Global Adoption Trends and Strategies Survey found that 18% of organizations had AI models in production, 16% were in the POC stage, and 15% were experimenting with AI.
To live in a digitized world where AI drives business processes, customer engagements, and autonomous infrastructure and where consumers’ lives are hyper-personalized in nearly every aspect of behavior will require creating and storing more data than ever before. Data is now an invaluable asset for AI/ML/DL use cases. Unstructured data — such as videos, images, and satellite imagery across verticals such as transportation, mining, healthcare, life sciences and research, and government — is the key driver of data growth in AI initiatives. According to IDC’s Worldwide File- and Object-Based Storage Forecast, 2018–2022, some 881EB of capacity will be shipped in support of unstructured (file and object) data by 2022.
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