By some estimates, more than 80 percent of AI projects fail—twice the rate of failure for information technology projects that do not involve AI.

Thus, understanding how to translate AI’s enormous potential into concrete results remains an urgent challenge. 

In this blog post, we document lessons learned based on the findings of RAND research report1 so that you can avoid these failures and mitigate risks in your planning.

Key takeaways

The five leading root causes of the failure of AI projects:

1. Industry stakeholders often misunderstand—or miscommunicate—what problem needs to be solved using AI. Trained AI models are often deployed with the wrong metrics in mind or without aligning them to the business's workflow and context.

2. Many AI projects fail because the organization lacks the necessary data to adequately train an effective AI model.

3. AI projects fail because the organization focuses more on using the latest and greatest technology than on solving real problems for its intended users.

4. Organizations might not have adequate infrastructure to manage their data and deploy completed AI models, which increases the likelihood of project failure.

5. AI projects fail because the technology is applied to problems that are too difficult for AI to solve. AI is not a magic wand that can make any challenging problem disappear; in some cases, even the most advanced AI models cannot automate away a difficult task.

Recommendations

To overcome these issues, leaders should consider these five principles for success in AI projects:

1. Ensure that technical staff understand the project purpose and domain context: Misunderstandings and miscommunications about the intent and purpose of the project are the most common reasons for AI project failure. Ensuring effective interactions between the technologists and the business experts can be the difference between success and failure for an AI project.

2. Choose enduring problems: AI projects require time and patience to complete. Before they begin any AI project, leaders should be prepared to commit each product team to solving a specific problem for at least a year. If an AI project is not worth such a long-term commitment, it most likely is not worth committing to at all.

3. Focus on the problem, not the technology: Successful projects are laser-focused on the problem to be solved, not the technology used to solve it. Chasing the latest and greatest advances in AI for their own sake is one of the most frequent pathways to failure.

4. Invest in infrastructure: Up-front investments in infrastructure to support data governance and model deployment can substantially reduce the time required to complete AI projects and can increase the volume of high-quality data available to train effective AI models.

5. Understand AI’s limitations: Despite all the hype around AI as a technology, AI still has technical limitations that cannot always be overcome. When considering a potential AI project, leaders need to include technical experts to assess the project’s feasibility.

P.S. To avoid the first issue—stakeholders misunderstanding or miscommunicating what problem needs to be solved using AI—we've created a free problem statement tool for AI projects. If you'd like to use the free problem statement tool, please contact us using the form below.

1) Source: RAND Research Report

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