Artificial Intelligence (AI), a computer’s ability to replicate the human thought process and solve problems, is hard at work in today’s news media. And for those not already leveraging AI, the time is now. In the new report, Artificial Intelligence: News Media’s Next Urgent Investment, Martha L. Stone, CEO of the World Newsmedia Network, in association with the INMA, explores how AI is being applied in today’s news industry.

Stone explains that AI has three main forms of behavior: natural language processing; predictive analytics; and machine learning/neural networks. Publishers can apply all three forms to address a wide range of news challenges.

Natural Language Processing

The first form describes the way in which computers understand the natural language process (NLP). It allows for automatic creation of articles (“robo-journalism”). Both the Associated Press and the BBC use Wordsmith, an NLP automated database, to create huge volumes of stories within seconds.

Natural language processing enables speech recognition and is used in devices like Apple’s Siri, Amazon’s Alexa, or Google’s Home. This type of AI process allows CNN, The New York Times, The Washington Post, the Chicago Tribune, Quartz, the Huffington Post, and others to offer “flash” new briefings. Users signal these audio devices to inform them of the day’s news.

Media companies use sentiment analysis, a subset of NLP, to identify opinions in social media and blogs. The sentiment analysis sorts through comments about people, brands, etc. by analyzing both positive and negative words used in online discussions.

NLP also powers the recommendation engines used by many news publishers. Story recommendations help increase both traffic and user engagement. Chat apps and bots can also be used to drive traffic. However, they are especially good at repetitive tasks such as answering specific questions and offering data alerts. A successful example is The Washington Post’s Facebook Messenger feed bot. Users ask the bot questions and responds to overall news inquiries by suggesting links to other relatable news stories.

Predictive Analysis

The second form of AI, predictive analytics, allows analysts to predict trends and behaviors based on a subset of data. Predictive models are often used to target advertising, subscription, or membership offerings. The analytics identify consumer patterns and project the potential the outcome. The Financial Times uses predictive analytics to correlate revenue to content usage and conversion rate to engagement.

Schibsted, one of the biggest news media publishers in Europe, uses predicted analytics to identity the gender of their users. Using predictive analytics, Schibsted’s accuracy of gender prediction grew from 15% – 20% to 100%. Demographic assignments are extremely important in serving personalize content and advertising. Likewise, The Weather Channel uses weather trends to help predict the optimal time for advertising. A cold front or snow storm approaching is a perfect time to advertise hot breakfast foods or batteries.

Machine Learning

The third form of AI is machine learning, which is essentially computers learning to make decisions. Computers identify patterns and apply new logic based on the results. Algorithms allow publishers to make predictions on data, including consumer usage patterns and personal preferences. The New York Times uses machine learning to help identify content for readers. Pinterest uses machine learning to identify relevant user-generated content for their users.

Personalization is a great way to utilize machine learning. These practices include recommendations of text and video, location-specific content, segment-based personalization (identifying users or specific products or a demographic, etc.) and newsletter recommendations. Of course, there are concerns that personalization bubbles leave little room for new content discovery.

Artificial Intelligence offers support and efficiency in real-time using sentiment and machine based audience analytics. It presents the news media with a way to connect with consumers and provide relevancy. Importantly, the use AI technologies has direct and positive impact on revenue and customer engagement.