New research has claimed the vast majority (80%) of AI-based projects fail, double the normal failure rate for non-AI tech proposals.

A study by the Rand Corporation found only 14% of organizations felt fully ready to adopt AI, despite 84% of business leaders reporting they believe the technology will have a significant impact on their organization.

The top reason for project failure was identified a lack of understanding and communication between stakeholders and technical staff about the intent and purpose of the project. This means managers often don’t allow teams the time and resources needed – ensuring leaders and tech teams both have the same goals is key.

Magpie syndrome

Not having the necessary data to sufficiently train their AI model was another issue for new projects – an under investment into the infrastructure to support data governance and model deployment means AI projects take longer and weren’t as effective.

This echoes earlier research by Lenovo, which revealed concerns over the computational power and data resources required to train models.

Another difficulty that new projects often faced was an over-eagerness to utilize the latest shiny new technology instead of focusing on solving real problems for users. Experimenting with new technologies helps to drive development, but too often these are used for the sake of using, rather than when they are the best fit. Researchers explain that successful projects don’t get distracted in chasing the latest advances in AI, but focus on the problem to be solved.

Finally, and perhaps unsurprisingly, the report found a tendency to overestimate the abilities of AI itself. Although investment has increased 18-fold since 2013, it is not a fix-all in automating all tasks, and the technology still comes with significant limitations. Understanding the capabilities of the models is crucial to success.

With such massive pressure to use AI across a variety of industries, businesses should keep in mind that AI is an investment like any other, and comes with serious risks if not fully understood or properly managed.

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