6 AI Myths Debunked

To generate value, leaders must fully understand how AI works and where its limitations lie.

“Artificial intelligence (AI)I will automate everything and put people out of work.” “AI is a science-fiction technology.” “Robots will take over the world.”

The hype around AI has produced many myths, in mainstream media, in board meetings and across organizations. Some worry about an “almighty” AI that will take over the world, and some think that AI is nothing more than a buzzword. The truth is somewhere in the middle.

“Throughout the COVID-19 crisis, the majority of organizations have been maintaining or even increasing their investments in artificial intelligence,” says Saniye Alaybeyi, Senior Director Analyst, Gartner. “However, only half of these projects ever make it to production.” 

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AI is not only augmenting mundane jobs. IT leaders need to create value for AI by establishing business benefits such as cost reductions and operations improvement by providing practical applications of the technology. 

Gartner has identified six common myths and misconceptions about AI.

Myth No. 1: AI Is an unnecessary luxury in times of the COVID-19 pandemic

AI is emerging as an important enabler of cost optimization and business continuity during the COVID-19 crisis. Contrary to the misconception that AI is an unnecessary luxury when enterprises are struggling with cash flows and uncertain economic conditions, AI is generating revenue. It is improving customer interactions, analyzing data more quickly, generating early warnings about upcoming disruptions and automating decision making. 

Myth No. 2: AI and machine learning (ML) are the same and interchangeable

Machine learning is a subset of artificial intelligence. ML requires a well-thought-out training and data acquisition strategy. AI, on the other hand, is an umbrella term for a broad set of computer engineering techniques, ranging from ML and rule-based systems to optimization techniques and natural language processing (NLP). 

Myth No. 3: Intelligent machines learn on their own

A finished ML product gives the impression that it is able to learn on its own. However, experienced human data scientists frame the problem, prepare the data, determine appropriate datasets, remove potential bias in the training data and, most importantly, continually update the software to enable the integration of new knowledge and data into the next learning cycle.

Myth No. 4: AI can be 100% objective

Every AI technology is based on data, rules and other kinds of input from human experts. Because all humans are intrinsically biased in one way or another, so is the AI. Systems that are frequently retrained — for example, using new data from social media — are even more vulnerable to unwanted bias or intentional malevolent influences.

Even if your current AI strategy is “no AI,” this should be a conscious decision based on research and consideration.

“At the moment, there is no way to completely banish bias; however, we have to try our best to reduce it to a minimum,” says Alexander Linden, VP Analyst, Gartner. “In addition to technological solutions, such as diverse datasets, it is crucial to also 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.”

Myth No. 5: AI will only replace mundane jobs

AI enables businesses to make more accurate decisions via predictions, classifications and clustering. These abilities have enabled AI-based solutions to reach deep into work environments, not only replacing mundane tasks, but also augmenting those that are more complex.

Take, for example, the use of imaging AI in healthcare. A chest X-ray application based on AI can detect diseases faster than can radiologists. In the financial and insurance industry, roboadvisors are being used for wealth management and fraud detection. These capabilities don’t eliminate human involvement in those tasks but will eventually limit it to observing and dealing with unusual cases. Adjust job profiles and capacity planning and offer retraining options for existing staff.

Myth No. 6: My business does not need an AI strategy

Every organization should consider the potential impact of AI on its strategy and investigate how this technology can be applied to the organization’s business problems. In many ways, AI exploitation is the same as forgoing the next phase of automation, and could place enterprises at a competitive disadvantage.

“Even if AI is not an immediate fix to a problem, businesses should revisit the decision to not implement AI periodically,” says Alaybeyi. “Organizations need to find appropriate use cases that leverage AI’s power to augment human work, decisions and interactions, as well as other functional innovation opportunities.” 

In the next four years, 69% of what a manager currently does will be automated. In such a disruptive environment, enterprises need a reality check on how best they can integrate AI into their strategy and be ready for forthcoming disruptions.

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This article has been updated from the February 14, 2019 original to reflect new events, conditions and research.

Recommended Gartner client* reading: Debunking Myths and Misconceptions About Artificial Intelligence, 2021 by Saniye Alaybeyi, et al.

 

*Note: Some documents may not be available to all Gartner clients.

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