GIVEN all the talk about how much potential artificial intelligence (AI) has and the exciting use cases vendors and companies have created together, it’s easy to start putting it on a pedestal and misunderstanding its power.

SwissCognitiveGartner, who speaks to several SME leaders and CXOs, feels that there is a lack of clarity on what AI really is and what it can do — and this causes them to expect more than they should from the technology.

“With AI technology making its way into the organization, it is crucial that business and IT leaders fully understand how AI can create value for their business and where its limitations lie,” explained Gartner Research VP Alexander Linden.

Yet, companies continue to spend more on AI products, projects, and solutions — and experts and consultants fear that, as a result, ROIs won’t be met.

According to IDC, global spending on cognitive and AI systems is forecasted to continue its trajectory of robust growth as businesses invest in projects that utilize cognitive/AI software capabilities.

IDC’s guide suggests spending on cognitive and AI systems will reach US$77.6 billion in 2022, more than three times the US$24.0 billion forecast for 2018. The compound annual growth rate (CAGR) for the 2017-2022 forecast period will be 37.3 percent.

There are no comments on ROIs as yet, as projects are still in their initial stages.

“AI technologies can only deliver value if they are part of the organization’s strategy and used in the right way.”

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To help business leaders fight misconceptions about AI, Gartner’s Linden sheds light on five of the most common myths (and sheds light on the truths):

# 1 | AI works in the same way as the human brain does

AI is basically a software program that lives in the computer and solves problems in a way that it has been taught. It’s quicker than the human brain but lacks the creativity that the human brain possesses.

“Some forms of machine learning (ML) – a category of AI – may have been inspired by the human brain, but they are not equivalent.”

“Image recognition technology, for example, is more accurate than most humans, but is of no use when it comes to solving a math problem. The rule with AI today is that it solves one task exceedingly well, but if the conditions of the task change only a bit, it fails.”[…]

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