Doomed to fail? Most AI projects flop within 12 months, wasting billions of dollars
New research shows that the vast majority (80%) of AI-based projects fail. That’s twice the normal failure rate for non-AI technology proposals.
A study of the Rand Corporation Only 14% of organizations felt fully prepared to implement AI, while 84% of business leaders said they are confident the technology will have a significant impact on their organization.
The number one reason for project failure was a lack of understanding and communication between stakeholders and technical staff about the intent and purpose of the project. This means that managers often do not give teams the time and resources they need – ensuring that leaders and technical teams have the same goals is essential.
Magpie syndrome
Another problem with new projects was the lack of the necessary data to train the AI model sufficiently. There was too little investment in the infrastructure to support data governance and model deployment. As a result, AI projects took longer and were less effective.
This is reminiscent of earlier research by Lenovowhich raised concerns about the computing power and data resources needed to train models.
Another challenge new projects often faced was an over-enthusiasm to use the latest, shiny new technology rather than focusing on solving real problems for users. Experimenting with new technologies helps to fuel development, but too often they are used for the sake of use, rather than when they are the best fit. Researchers explain that successful projects do not get distracted by chasing the latest developments in AI, but focus on the problem at hand.
Finally, and perhaps not surprisingly, the report found a tendency to overestimate the capabilities of AI itself. While investment has increased by a factor of 18 since 2013, it is not a be-all and end-all for automating all tasks and the technology still has significant limitations. Understanding the capabilities of the models is crucial to success.
With so much pressure to deploy AI across industries, businesses must realize that AI is an investment like any other, one that carries serious risks if not fully understood or managed properly.