Why AI Projects Fails?

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It is difficult to determine an exact number or percentage of AI projects that fail, as it depends on various factors such as project complexity, data quality, team expertise, and stakeholder buy-in.

However, some studies and reports suggest that the failure rate of AI projects can be relatively high.

For example, a survey conducted by Gartner in 2019 found that only 53% of AI projects had moved beyond the pilot phase to become fully operational, while the rest had either failed or stalled.

Another report by McKinsey & Company found that 70% of AI projects do not meet their objectives.

These statistics suggest that AI project failure rates can be significant, and emphasize the importance of proper planning, execution, and evaluation to increase the chances of success.

Why ai projects fail by gestaltit.com
Why ai projects fail – image source

AI projects failure rate

The failure rate of AI projects can vary depending on several factors, such as the complexity of the project, the quality of data and algorithms used, the expertise of the team working on the project, and the level of investment made in the project.

Some studies suggest that the failure rate of AI projects is relatively high.

For example, a survey conducted by Gartner in 2020 found that 54% of AI projects fail to deliver the expected business value.

Another survey conducted by McKinsey in 2019 found that only 8% of companies report having deployed AI at scale, and among those that do, only 16% report achieving significant financial benefits.

However, it’s important to note that not all failures are equal.

Some AI projects may fail to deliver the expected results due to technical issues, while others may fail because of organizational or cultural barriers.

Also, failure can be a valuable learning experience that can help organizations improve their AI capabilities and avoid similar mistakes in the future.

Source: Dilbert (https://dilbert.com)

How many AI projects fails

It is difficult to determine an exact number or percentage of AI projects that fail, as it depends on various factors such as project complexity, data quality, team expertise, and stakeholder buy-in.

However, some studies and reports suggest that the failure rate of AI projects can be relatively high.

For example, a survey conducted by Gartner in 2019 found that only 53% of AI projects had moved beyond the pilot phase to become fully operational, while the rest had either failed or stalled.

Another report by McKinsey & Company found that 70% of AI projects do not meet their objectives.

These statistics suggest that AI project failure rates can be significant, and emphasize the importance of proper planning, execution, and evaluation to increase the chances of success.

Gartner report

Gartner, a research and advisory firm, conducted a survey in 2018 to find out how many CIOs are planning to use Artificial Intelligence (AI).

The results showed that only 4% of CIOs have implemented AI, while 46% are planning to do so.

Despite high interest in AI, most organizations are facing challenges implementing it.

Gartner has identified four lessons from early AI projects that can help organizations succeed in AI implementation.

  • 1ST organizations should aim low at first and start with small AI projects.  
  • 2nd AI should be used to augment people, not replace them.
  • 3rd organizations should plan for knowledge transfer to enable in-house capabilities in AI.
  • 4th organizations should choose transparent AI solutions that provide insights into reasoning when the system is not effective.