Steer Clear of the Hype: 5 AI Myths

Focus on the most relevant facts and myths about AI to best inform your decision making.

Hype isn’t always a bad thing. Within limits, it fosters attention, investment and innovation. A little bit of hype can build excitement about potential, while too much leads to false hopes and misguided planning assumptions.

“Right now, the myths surrounding artificial intelligence (AI) are rampant,” says Alexander Linden, research vice president at Gartner. “Wisely for now, most organizations’ commitments are tentative and more oriented toward experimenting and learning, rather than trying to transform their enterprise or industry as fast as they can.”

Enterprise architecture and technology innovation leaders must walk a fine line between embracing and overplaying AI technologies’ role in delivering business value for digital business.

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“Leaders shouldn’t trust any of the myths and hype around AI. Instead, they must become centers of expertise if they are going to educate senior business executives on the real benefits — and shortcomings — of AI,” says Linden.

Don’t be fooled into believing the technologies are more capable than they really are. Investing under deceptive pretenses can lead to unsatisfactory results and, worst case, career failures.

Currently, plenty of myths surround AI. Here are five of the top misconceptions:

Myth 1: Buy an AI to solve your problems
Reality: There is no such thing as “an AI.” Enterprises don’t need an “AI.” They need business results in which AI technologies may play a role.

“AI is a collection of technologies that can be used in applications, systems and solutions to add specific functional capabilities. Organizations should select best-fit, best-of-breed AI technologies to meet targeted business needs,” Linden says.

Myth 2: Everyone needs an AI strategy or a chief AI officer
Reality: AI is a loose collection of many technologies, and although they will become pervasive and increase in capabilities for the foreseeable future, focus instead on business results that these emerging technologies can enhance. AI will affect all C-level roles.

Myth 3: Artificial intelligence is real
Reality: “AI” has become a generalized marketing term, often with little substance or value. Very useful, specific functions have been created (such as speech and image recognition, game playing, fraud and failure prediction), but no general intelligence is in sight. The concept of “intelligence” is an overrated generalization that leads to imprecise thinking. Be specific. Look for specific functional capabilities that drive a desired business result.

Myth 4: AI technologies define their own goals
Reality: People define goals; technologies execute them. Technologies (whether AI or not) do not have their own goals that they seek to achieve. Machines execute the programs they have been fed, whether the programs consist of code, data or both. In AI, goal-seeking is an illusion programmed in by people.

Myth 5: AI has human characteristics
Reality: AI developers use advanced analytics, special algorithms and large bodies of data to deceive people into believing that their product learns on its own and understands, thinks and empathizes with the user. Management (and others) will continue to unconsciously anthropomorphize technologies. Don’t be fooled into believing the technologies are more capable than they really are. Investing under deceptive pretenses can lead to unsatisfactory results and, worst case, career failures.

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Gartner clients can find out more by reading “Hype Hurts: Steering Clear of Dangerous AI Myths" by Alexander Linden. This research is part of the Gartner Trend Insight Report “Applying Artificial Intelligence to Drive Business Transformation.” This collection of research shows the way for organizations to begin, right now, to make sense of the AI opportunity for digital business and to take practical steps to realize it.

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