What is artificial intelligence? Well, these days, the answer depends on who you ask. For some, it means computers have finally achieved, just like us, general intelligence; what Ray Kurzweil would call the “singularity“. For others, it’s merely a conglomeration of existing tools; it’s machine learning, natural language processing, deep learning and so on.

But, with AI technology making its way into the real world of business, it is crucial that business and IT leaders fully understand how AI can create value for their business and where its limitations lie.

As Alexander Linden, research vice president at Gartner, said: “AI technologies can only deliver value if they are part of the organisation’s strategy and used in the right way.”

As such, Gartner has identified five common myths and misconceptions about AI.

Myth #1: AI works in the same way the human brain does

For Gartner, we need to hold our horses here, AI, in its current state, consists of a host of software tools designed to solve problems. While AI might seem smart, it’s not yet similar of equivalent to human intelligence.

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

Myth #2: intelligent machines learn on their own

We’ve all seen various headlines about AI being independent, be it Facebook’s AI system developing its own language, or DeepMind’s AlphaGo Zero which supposedly mastered an ancient Chinese board game from scratch, and with no human help beyond being told the rules.

But, for Gartner, this is all a bit misleading, it says human intervention is always required to develop AI-based machines or systems. The involvement may come from experienced human data scientists who are executing tasks such as framing the problem, preparing the data, determining appropriate datasets, and – most importantly- constantly improving the software to enable the integration of new knowledge and data into the next learning cycle.

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Myth #3: AI can be free of bias

All AI technology requires input from humans, therefore, like humans, Gartner thinks AI will also have an intrinsic bias in one way or the other.

“Today, there is no way to completely banish bias, however, we have to try to reduce it to a minimum,” said Linden. “In addition to technological solutions, such as diverse datasets, it is also crucial to ensure diversity in the teams working with the AI and have team members review each other’s work. This simple process can significantly reduce selection and confirmation bias.”

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Myth #4: AI will only replace repetitive jobs that don’t require advanced degrees

There’s no doubt that AI-based technologies have taken over many mundane tasks, but according to Gartner, they also augment complex ones.

As an example, Gartner pointed to the use of imaging AI in Healthcare. A chest X-ray application based on AI can detect diseases faster than radiologists. While in financial services, roboadvisors are being used for wealth management or fraud detection.

With the advancement of AI in the workplace, business and IT leaders should adjust job profiles and capacity planning as well as offer retraining options for existing staff.

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Myth #5: not every business needs an AI strategy

We all need to be thinking about how AI will impact our lives and businesses, warned Gartner. Failing to investigate how AI can be applied in an organisation could be the same as giving up the next phase of automation, which ultimately could place organisations at a competitive disadvantage.

“Even if the current strategy is ‘no AI’, this should be a conscious decision based on research and consideration. And – as every other strategy- it should be periodically revisited and changed according to the organisation’s needs. AI might be needed sooner than expected,”  Linden concluded.