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Cognitive System Users will Reach One Million by 2022

Large enterprises have an asset that many SMBs do not. They are in possession of massive quantities of historical and current data. In addition, the business case for employing cognitive systems to analyze that data is more compelling, when compared with smaller companies.

ABI Research predicts the number of businesses adopting artificial intelligence (AI) technologies worldwide will grow significantly, up from 7,000 this year to nearly 900,000 in 2022 -- that's a CAGR of 162 percent.

AI is now making significant strides in cloud processing, storage capacity, and machine learning algorithms to enable cognitive systems and robotics to surpass people in performing some key tasks in manufacturing and other industries.

AI and Machine Learning Market Development

Increasingly, businesses are applying these emerging technological advancements to deliver various forms of automation and innovation that will eventually equal or exceed human capabilities.

"Even though nearly one million businesses will adopt AI by 2022, it will not be a great fit for every company," says Jeff Orr, research director at ABI Research. "Many businesses will have to adapt their corporate governance policies to deal with the lack of a guaranteed outcome when implementing machine learning."

While most enterprises start using machine learning to analyze their existing business data for insights, the technologies have far-reaching application in specific industries -- ranging from reduction of false positives in fraud detection, to powering conversational interfaces for chatbots and virtual assistants.

While some of the world's largest and innovative enterprises -- such as American Express, Coca Cola, Netflix, PayPal, and Uber -- already deploy projects powered by machine learning, ABI Research finds that not all organizations will likely benefit from these cognitive system technologies.

According to the ABI assessment, progressive organizations that are comfortable with uncertainty in outcomes and measuring changes in key performance indicators (KPIs) will find the most to gain from enacting machine learning projects.

On the other hand, more traditional companies that focus only on ROI timetables will find emerging technologies -- including machine learning, cybersecurity, and IoT -- to be somewhat frustrating to implement and difficult to measure.

Outlook for Machine Learning Apps

Several SaaS solutions are available for machine learning and businesses looking to experiment will have many vendors to choose from in the near future.

Best practices include starting off with a pilot project, and requesting case studies about enterprises that have already gone through their first operational deployment.

"It is the companies that choose to ignore AI entirely that will quickly find themselves at a competitive disadvantage," concludes Orr.

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