Why Artificial Intelligence (AI) pilot projects fail: 4 reasons

The artificial intelligence (AI) industry is continually evolving, with new solutions being created and deployed every day. Gartner predicts that 75 percent of organizations will have operational AI by 2024. However, Gartner’s research shows that only 53 percent of AI projects make it from prototype to production. What is holding new AI pilot projects back from hitting production?

Successful AI projects are all around us, but there is no single best way to create and deploy an AI product with all of these developments. There are, however, four reasons businesses might be missing the mark when it comes to their AI solution.

1. Not enough data

AI is consistently learning and growing from its results and algorithms to provide better, more efficient, and more accurate outcomes in the future. For AI projects to learn, they need an abundance of information. The more data AI can ingest, the higher the accuracy of its output. Yet a common issue is a lack of sufficient data sets for developing AI solutions.

AI needs to ingest enough data to identify patterns within the dataset. A lack of data can impact predictions and output. Providing AI with substantial training data sets can combat this issue and help limit the risk of biases.

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