To ease the burden that is associated with content production, AI in content production has been deployed to augment writers’ work and to help monitor and measure post engagement.
Content production helps connect brands with customers, governments with citizens and organizations with their supporters. But, while important, content production can also be a labor- and time-intensive task. Producing articles, infographics, videos and a variety of other content requires a significant amount of work from writers and editors. With organizations producing and managing content daily, many have begun to turn to various technologies to help. One of the more capable of these technologies is AI. The use of artificial intelligence content writing systems can help create, manage and analyze content across the production lifecycle and ease the burden that organizations feel.
Natural language and content production
One of the strengths of modern AI technology is its ability to understand, process and even generate human-readable content. One aspect of natural language processing is natural language generation (NLG), which uses machine learning to enable computers to transform structured data into natural language. These systems take data from a variety of sources, including databases and quantitative sources, and turn it into narrative text. NLG-powered content generation systems are used to produce items such as reports, social media text, chatbot conversations, and custom content for web or mobile applications. Advanced NLG systems can then take the text that has been generated and process it through content design systems that can give a human-like personality to the content.
Use cases and voice content creation
With NLG systems, large quantities of content can be generated in a short amount of time, thus helping an organization to save time while still reaching its content goals. Artificial intelligence content writing also enables brands to keep a consistent message and tone even if different departments produce content. Content production outlets such as the Associated Press use AI to generate thousands of quarterly earnings reports and sports articles. Yahoo! Sports uses AI to produce weekly fantasy football reports, while the Orlando Magic basketball team uses AI to personalize in-app and email messaging to its fans. Organizations are also turning to voice-based applications to reach their audience. The major voice assistant companies, including Amazon, Apple, Google and Microsoft, are introducing NLG-powered offerings that enable companies to deliver original content to users without having to spend time crafting those messages.
Through the power of AI-enabled content production, organizations can integrate voice assistants with their back-end NLG platform to provide the voice assistant with subject matter expertise and industry-specific knowledge. The traditional writing process takes time and needs to be edited and refined before being published. Because computers can work far faster than their human counterparts, artificial intelligence content writing systems can create thousands of pieces in a relatively short amount of time. But humans aren’t fully removed from this process. Rather than publishing the AI-generated pieces directly to the audience, they are instead sent to human editors for moderation, filtering and further editing.
Intelligent monitoring tools
Creating content is just one part of the content production process. Monitoring and measuring the content and how readers engage with it is equally important. Intelligent monitoring tools help companies track a variety of different metrics to make sure content companies produce effective and attractive content. Social networks such as Facebook, Twitter, Instagram and Pinterest want to keep users on their site. To do this, they must provide content to keep them engaged. To assist with this, these companies are increasingly employing AI systems to show relevant and personalized content to each user. By continuously monitoring users, these platforms can improve their machine learning models, provide real-time feedback to advertisers and tweak their messaging for improved performance.
Additionally, AI is getting better at translating content into different languages. This is particularly important for multinational companies who want to reuse content but don’t have writers that are fluent in multiple languages. AI can accurately translate content, thus saving time, resources and money. Humans may still be the original content creator, but the AI-enabled system can take that work and replicate it across multiple languages and with culturally relevant content. This enables content developers to increase their reach. Organizations are looking for ways to gain a competitive advantage over their competition while also delivering meaningful content and value to customers. Over the last few years, great strides have been made in using computers for content production and we see this trend continuing for years to come.