This article is sponsored by IBM. It is a new concept in which creators can apply passion and creativity to make money, instead of simply relying on likes and views. It focuses on bringing more life and meaning to the traditional media landscape in a way that empowers creative people worldwide to bring out the best in themselves, entirely driven by their passion. According to EMarketer, the creator economy is defined as follow: "We define creators as people or entities that develop original content for digital properties, and who consider creating that content to be either their full-time or parti-time career or livelihood. Of course, there is some overlap between many of these groups; for instance, celebrities can also be creators and vice versa. What's more, few successful influencers today are purely sales-oriented, and many of them are also creators, developing digital or, at times, physical products."

While it isn't the only buying motive – it is among the most powerful. Vendors across industries from financial services to pharmaceuticals will tout many different kinds of benefits on their homepages, but at Emerj, we have noticed that risk reduction is often most important to the actual enterprise buyers we speak to (and to the AI vendor salespeople who are getting deals done). There are three main reasons that fear rules as an AI adoption motive – some of which are counterintuitive – and we explore them in-depth in this article. We also explore what enterprise AI buyers and smart artificial intelligence vendors should do about this reality of AI adoption. But first – why is fear driving AI adoption in the enterprise?

The tools, announced at Amazon's annual cloud event in Las Vegas, aim to help the company's cloud customers incorporate functionality such as natural language processing, but without long waits often associated with AI-related projects. Amazon says it is making this possible by integrating machine learning into the two new stand-alone services. "There's no machine-learning expertise required for either of these services. They're just plug and play. You don't have to get into all the weeds and get the training data and label the data and all those sorts of things," said Matt Wood, vice president for artificial intelligence services at Amazon Web Services.

The insurance industry is responsible for a multitude of sensitive financial data concerning both its customer base and staff. Any breach to an insurance company's CRM or other claims database could compromise the personal data of multiple people at once, which puts the company at risk as well. However, there are always new cybersecurity innovations, and this includes AI and machine learning-based solutions. In this article, we explain how artificial intelligence applications can help insurance companies protect their networks and databases from cyberattacks. We cover how predictive analytics and anomaly detection can be instrumented within a large enterprise network's security system and how it can counteract these threats.

Chatbots are one of the most talked-about uses of natural language processing (NLP) software in business. Some of the most common application areas for chatbots include customer service, healthcare, and financial advisory. We compiled a list of 20 chatbot applications currently being used across multiple industries. We explain what the chatbot software vendors offer their client companies and how that translates to customer satisfaction. This also includes internal chatbots made to help customer-facing employees work with their clientele. We begin our list of enterprise chatbot solutions with Ada Health's app for personal medical guidance. Ada Health offers an AI-powered telemedicine service in the form of a chatbot app called, "Ada – Your Health Guide." The app helps users or patients identify their symptoms by asking for details about how they are feeling and where they are feeling pain.

There are several companies claiming to offer AI solutions to consumer packaged goods (CPG) companies. AI solutions for business problems in the CPG industry appear to be less legitimate than we first thought. All of the companies discussed in this report employ relatively credentialed people in their C-suites, but their AI experience is generally lacking compared to other sectors we've covered (in terms of AI-related talent density, and experience actually using AI). The companies we examine in this report are older firms, who, unlike some of their startup competition, have no founding team members or C-level leadership with a strong background in AI. Many of the firms featured in this article, however, have hired experts in AI to run their AI practices and build AI-related products and services, but others have not hired any such experts to back up their claims of AI use. Fractal Analytics employs a Head of Artificial Intelligence with a PhD in Computational Neuroscience and Machine Learning that he earned from Caltech in 2007.