Most companies are struggling to develop working artificial intelligence strategies, according to a new survey by cloud services provider Rackspace Technology. 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. 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.

Most companies are struggling to develop working artificial intelligence strategies, according to a new survey by cloud services provider Rackspace Technology. 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. 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.

Data, technology, and people are at hand to make artificial intelligence and machine learning available to all commerce companies. To be certain, artificial intelligence and its sub-field, machine learning, have gone through cycles of inflated expectations followed by disappointments. For example, in the 1950s and 1960s, the United States government funded research for the machine translation of languages. The hope was that Russian-language documents could be instantly translated to English. But by 1966, a report from the Automatic Language Processing Advisory Committee, a government team of seven scientists, essentially killed machine translation research in the U.S. for about a decade.