Enterprise machine learning and AI: Use cases and challenges
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It's easy to find examples of how machine learning, and AI in general, is disrupting industries. The automobile industry uses AI for smart car technology, oncologists for early cancer detection and marketers to develop personalized advertising. Banks incorporate AI capabilities to determine loan risk and detect fraud, and enterprise machine learning has become a valuable malware detection tool.
The point is AI is now an essential element of the digital economy, and its IT influence is only expected to grow. IDC predicted global AI spending will reach $57.6 billion by the year 2021, with investments from retail, banking and healthcare among the wide range of industries leading the charge.
But CIOs looking to take advantage of enterprise machine learning and AI often have a thicket of data processing, technical and personnel questions to wade through first. The company will have to gather the huge amount of high-quality data that machine learning and AI rely on to develop algorithms. Employees must be trained for their role in incorporating AI and machine learning processes. And, of course, IT executives will face big purchasing decisions as vendors look to capitalize. The modern CIO will have a major voice in all of these decisions, but what is working for CIOs when deploying enterprise machine learning and AI, and what is holding them back?
We'll answer these questions and more in this SearchCIO handbook, where we examine real-world AI success stories that can help guide your AI strategy. From constructing AI infrastructure to building an AI team to implementing machine learning projects, the advice will go a long way toward taking advantage of AI and machine learning business disruption and avoiding any pitfalls along the way.