IT and business leaders often have several misconceptions as to what artificial intelligence (AI) can do to their organisations-- leading Gartner to point out how they can separate reality from myths in order to devise future strategies.
“With AI technology making its way into the organisation, it is crucial that business and IT leaders fully understand how AI can create value for their business and where its limitations lie,” the analyst says. “AI technologies can only deliver value if they are part of the organization’s strategy and used in the right way.”
Gartner lists 5 common myths and misconceptions about AI, as listed below.
- AI Works in the Same Way the Human Brain Does: AI is a computer engineering discipline, and consists of software tools designed to solve problems. Some AI might give the impression of being clever, but it is in no way equivalent to human intelligence, even if some forms of machine learning (ML) are inspired by the human brain. Either way, AI currently remains able to solve one task exceedingly well, but fails if the conditions of said task change even slightly.
- Intelligent Machines Learn on Their Own: Humans need to intervene in order to develop an AI-based machine or system. Human data scientists need to execute certain tasks, such as framing the problem, preparing the data, determining appropriate datasets, removing potential bias in the training data, and continually updating the software to enable the integration of new knowledge and data in the next learning cycle.
- AI Can Be Free of Bias: AI is based on data, rules and other knds of input from human experts. And like humans, AI carries intrinsic biases in one way or another. Of course, one can try to reduce the bias to a minimum by using diverse dataset and ensuring the teams working with the AI are diverse, and have team members review each other's work. Such a process can reduce selection and confirmation bias.
- 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. Such abilities allowed AI-based solutions to replace mundane tasks, but also augment remaining complex tasks. For example, an AI-based chest X-ray application detect diseases faster than a radiologist, while roboadvisors manage wealth or detect fraud in the financial and insurance industry. Such capabilities do not eliminate human involvements in those tasks, but bring a need for humans to deal with unusual cases.
- Not Every Business Needs an AI Strategy: Every organisation should consider the impact of AI on strategy, Gartner insists. In many ways, avoiding AI exploitation is the same as giving up the next phase of automation, leading to a competitive disadvantage. In case an organisation decides on "no AI," such a decision should be based ono research and consideration, and needs to be periodically revisited and changed according to organisation needs.
