Skip to main content

Gartner Debunks Five Artificial Intelligence Misconceptions

IT and business leaders are often confused about what artificial intelligence (AI) can do for their organizations and are challenged by several AI misconceptions. Gartner, Inc. said IT and business leaders developing AI projects must separate reality from myths to devise their future strategies.

“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,” said Alexander Linden, Research VP at Gartner. “AI technologies can only deliver value if they are part of the organization’s strategy and used in the right way.”

Gartner has identified five common myths and misconceptions about AI.

Myth No.1: AI Works in the Same Way the Human Brain Does

AI is a computer engineering discipline. In its current state, it consists of software tools aimed at solving problems. While some forms of AI might give the impression of being clever, it would be unrealistic to think that current AI is similar or 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 explained. “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 No. 2: Intelligent Machines Learn on Their Own

Human intervention is required to develop an AI-based machine or system. The involvement may come from experienced human data scientists who are executing tasks such as framing the problem, preparing the data, determining appropriate datasets, removing potential bias in the training data (see myth No. 3) and — most importantly — continually updating the software to enable the integration of new knowledge and data into the next learning cycle.

Myth No. 3: AI Can Be Free of Bias

Every AI technology is based on data, rules and other kinds of input from human experts. Similar to humans, AI is also intrinsically biased 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,” Linden said. “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.”

Myth No. 4: AI Will Only Replace Repetitive Jobs That Don’t Require Advanced Degrees

AI enables businesses to make more accurate decisions via predictions, classifications and clustering. These abilities have allowed AI-based solutions to replace mundane tasks, but also augment remaining complex tasks.

In the financial and insurance industry, roboadvisors are being used for wealth management or fraud detection. Those capabilities don’t eliminate human involvement in those tasks but will rather have humans deal with unusual cases. 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.

Myth No. 5: Not Every Business Needs 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, avoiding AI exploitation is the same as giving up the next phase of automation, which ultimately could place organizations 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 organization’s needs. AI might be needed sooner than expected,” Linden concluded.

Gartner clients can read more in “Debunking Myths and Misconceptions About Artificial Intelligence”. More information on how to define an AI strategy can be found on the Gartner AI Insight Hub.

Hot Topics

The Latest

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...

Gartner Debunks Five Artificial Intelligence Misconceptions

IT and business leaders are often confused about what artificial intelligence (AI) can do for their organizations and are challenged by several AI misconceptions. Gartner, Inc. said IT and business leaders developing AI projects must separate reality from myths to devise their future strategies.

“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,” said Alexander Linden, Research VP at Gartner. “AI technologies can only deliver value if they are part of the organization’s strategy and used in the right way.”

Gartner has identified five common myths and misconceptions about AI.

Myth No.1: AI Works in the Same Way the Human Brain Does

AI is a computer engineering discipline. In its current state, it consists of software tools aimed at solving problems. While some forms of AI might give the impression of being clever, it would be unrealistic to think that current AI is similar or 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 explained. “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 No. 2: Intelligent Machines Learn on Their Own

Human intervention is required to develop an AI-based machine or system. The involvement may come from experienced human data scientists who are executing tasks such as framing the problem, preparing the data, determining appropriate datasets, removing potential bias in the training data (see myth No. 3) and — most importantly — continually updating the software to enable the integration of new knowledge and data into the next learning cycle.

Myth No. 3: AI Can Be Free of Bias

Every AI technology is based on data, rules and other kinds of input from human experts. Similar to humans, AI is also intrinsically biased 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,” Linden said. “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.”

Myth No. 4: AI Will Only Replace Repetitive Jobs That Don’t Require Advanced Degrees

AI enables businesses to make more accurate decisions via predictions, classifications and clustering. These abilities have allowed AI-based solutions to replace mundane tasks, but also augment remaining complex tasks.

In the financial and insurance industry, roboadvisors are being used for wealth management or fraud detection. Those capabilities don’t eliminate human involvement in those tasks but will rather have humans deal with unusual cases. 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.

Myth No. 5: Not Every Business Needs 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, avoiding AI exploitation is the same as giving up the next phase of automation, which ultimately could place organizations 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 organization’s needs. AI might be needed sooner than expected,” Linden concluded.

Gartner clients can read more in “Debunking Myths and Misconceptions About Artificial Intelligence”. More information on how to define an AI strategy can be found on the Gartner AI Insight Hub.

Hot Topics

The Latest

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...