Companies Lack Internal Resources To implement AI / ML successfully

Companies Lack Internal Resources To implement AI / ML successfully

Rackspace has published the results of a study of the state of AI / ML projects in companies. We share the most exciting findings

Only 17% Of Companies Fully Use AI / ML

Companies are at various stages of adopting artificial intelligence and machine learning. Most companies are in their very early stages:

  • 51% – study what artificial intelligence and machine learning are, try to implement them;
  • 31% are moving from a pilot project to a productive operation;
  • 17% – AI / ML is already an ineffective operation; these companies can effectively scale their solutions.

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.

AI / ML Implementation Fails Due To A Lack Of Data And Internal Resources

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:

  • 34% – insufficient data quality;
  • 34% – lack of experience in the company;
  • 31% – lack of data ready for production;
  • 31% is a poorly thought out strategy.

The Leading Indicator Of Efficiency Is Money

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:

  • 52% – profit,
  • 51% – revenue growth,
  • 46% – data analysis,
  • 46% – customer satisfaction

More Than Half Of Companies Partner With AI / ML Providers

Companies have different approaches to the development of AI / ML projects. Some rely on their strength; others rely entirely on the help of partners:

  • 41% rely entirely on partners;
  • 38% use only their power;
  • 21% combine their strengths and the support 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.

AI / ML Is Primarily Implemented In IT

First of all, companies strive to use AI and machine learning in IT and operations:

  • 43% – IT,
  • 33% – operating activities,
  • 32% – customer service,
  • 32% – finance.

Also Read: Fast, Ultra-Fast Drives In The Cloud: How To Get The Most Out Of Cloud Storage

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