Most companies are struggling to develop working artificial intelligence strategies, according to a new survey by cloud services provider Rackspace Technology.
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The survey, which includes 1,870 organizations in a variety of industries, including manufacturing, finance, retail, government, and healthcare, shows that only 20 percent of companies have mature AI/machine learning initiatives. The rest are still trying to figure out how to make it work.
There’s no questioning the promises of machine learning in nearly every sector. Lower costs, improved precision, better customer experience, and new features are some of the benefits of applying machine learning models to real-world applications. But machine learning is not a magic wand. And as many organizations and companies are learning, before you can apply the power of machine learning to your business and operations, you must overcome several barriers.
Three key challenges companies face when integrating AI technologies into their operations are in the areas of skills, data, and strategy, and Rackspace’s survey paints a clear picture of why most machine learning strategies fail.
Machine learning is about data
Machine learning models live on compute resources and data. Thanks to a variety of cloud computing platforms, access to the hardware needed to train and run AI models has become much more accessible and affordable. […]
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