ThinkTWENTY20

AI has been a major focus of organizations over the past couple of years. However, According to a recent IDC survey, only about 30 percent of companies reported a 90 percent success rate for AI projects. Most companies reported failure rates of 10 to 49 percent, while some even said that more than half of their AI projects failed.

A report released by McKinsey last fall indicated that two of the biggest challenges facing AI projects involve data. In fact, data issues are among the chief reasons why AI projects fall short of expectations.

There are several reasons for this. For AI, the data must be used to train their machine learning algorithms. Properly labelled data is required for this purpose, otherwise it must be labelled manually. Another issue is not having the right data for the project, whether because of lack of availability, or bias, or simply a lack of structure in the data.

A recent PricewaterhouseCoopers survey showed that more than half of companies don't have a formal process for assessing AI data for bias.

And according to a recent Deloitte Consulting survey, 62 percent of companies still rely on spreadsheets, and only 18 percent have taken advantage of unstructured data such as that available from social media, because of the extra work required to add structure.

The lesson is that before launching AI projects, a good deal of effort must be spent on data gathering, cleansing, and evaluation.

For a good summary of these issues, check out this link.