Partner Content Articles and Reports

There’s more to successful AI adoption than picking the right technology. Business leaders should be aware of the technical requirements of the initiative they’re undertaking, and few of those requirements are as important as data.

One highly sought after engineering role at major tech companies today is the natural language processing, or NLP, engineer.

This week on AI in Industry, we are talking about the ethical consequences of AI in business. If a system were to train itself to act in unethical or legally reprehensible ways, it could take actions such as filtering or making decisions about people in regards to race or gender.

When contemplating a new venture into AI or machine learning, companies need to take on a number of important considerations that relate to talent, existing data, and limitations. One way executives can judge how successful or appropriate and AI project would be for their company is to examine use cases of businesses that have previously done something similar.

Rather than coming up with completely new processes or products that involve deep learning, companies say they are using this AI technology to expand on functions or tasks that already existed at their organization, according to a new report published by O’Reilly.

For large eCommerce, media and travel companies, running huge campaigns requires substantial effort and human oversight. Translating and laying out ads for dozens of languages is a challenge that one day machines may master — but they’ll do so with the help of constant human guiding and adjustments.

AI could play a major role in the way European insurers do business going forward. It may provide insurers new methods for collecting customer data that offer valuable insights insurers can use to attract and maintain customers.

Understanding customer feedback gets harder and harder at greater scale and with a greater variety of channels through which customers can provide feedback. Businesses that seek to better grasp the sentiments of their customers might have to sift through thousands of messages in order to get a feel for what customers are saying about their products or services.