Rackspace has published the results of a study of the state of AI / ML projects in companies. We share the most exciting findings
Companies are at various stages of adopting artificial intelligence and machine learning. Most companies are in their very early stages:
Companies often have data problems when projects go out of pilot status. The data is available to try out an idea, but finding the right amount of valuable information becomes challenging when the project goes into productive use.
In 44% of cases, pilot projects in artificial intelligence and machine learning were successful, 22% of projects are now in the testing phase. And 34% of projects failed or were rejected at the idea stage. Here are the main reasons for failure:
Companies use different KPIs to assess the effectiveness of AI / ML implementation. For most, the main criterion is money – an increase in profits or revenue. There are other criteria, such as improving the quality of processes or the speed of finding insights, but these indicators are more difficult to quantify. Here are the main KPIs that companies focus on:
Companies have different approaches to the development of AI / ML projects. Some rely on their strength; others rely entirely on the help of partners:
Experienced providers will help you develop strategy, implement results and support you in the process of using AI / ML. The key to achieving the desired results is the joint efforts of companies and providers.
First of all, companies strive to use AI and machine learning in IT and operations:
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