Source: jou.ufl.edu
Artificial intelligence (AI) has been touted by many as the transformational technology of the digital age that one day will power everything. It is being used in practically every industry sector, and the media industry is no exception.
To understand how it is being used and potential challenges that might emerge, University of Florida College of Journalism and Communications Telecommunication Professor Sylvia Chan-Olmsted conducted a thorough review of how AI is being implemented within the media sector.
According to Chan-Olmsted, “As media are now interwoven into consumers’ daily lives and technology bundles, media companies must deliver engaging individual experiences to every consumer in context, in the moment, and all the time. Consequently, human resource commitment is significant in this new reality and the solution is logically the adoption of cognitive technologies.” AI can be used to alleviate the volume of work and make the interaction of media, content, audiences, and operations faster and better. However, the complexity of producing appealing content based on both creativity and data, and integrating AI into the existing media ecosystems, might be challenging.
Chan-Olmsted narrowed the field of how AI is being used to eight distinct categories: audience content recommendations, audience engagement, augmented audience experience, message optimization, content management, content creation, audience insights, and operational automation. These applications can deliver significant strategic values. For instance, AI-enhanced content recommendations/discovery can increase audience satisfaction in an on-demand content environment; AI-assisted real-time, in-context engagement can nurture better relationships; AI-powered audience experience and messaging can improve user experiences.
Among the uses of AI in media cited by the researcher:
- Netflix has pioneered the application of algorithms in many of its audience decisions. Streaming services like Neftlix and Spotify use AI to create recommendation playlists and improve content management and customer experience.
- iHeartMedia uses Super Hi-Fi AI to offer customized song transitions and adjust volume discrepancies.
- The Washington Post’s Knowledge Map applies cognitive technologies to correlate massive and complex data and gives readers an easy way of catching up on ongoing stories quickly and seamlessly with relevant background and additional info when readers request it.
- Reuter’s NewsTracer tracks down breaking news, so reporters are not tied down to grunt work. It uses the power of cognitive computing and machine language to extract insights from the stream of social media.
- BBC’s Juicer, a news aggregation and content extraction API system, takes articles from the BBC and other news sites and algorithmically parses/tags them to make them searchable and useful for trend analysis.
- Voyc, a voice-scanning AI system is designed to identify a questionable statement almost in real-time to fact-check live news. It works by transcribing live audio and crosschecking the statement against a fact database from verified sources and accredited fact-checking organizations.
- From a different perspective, marketers have used AI algorithms to create customer personas based on massive data sets, including geo-specific events, onsite interactions, referral source, psycho-graphic factors, purchase behaviors, and past communication, in designing media campaigns for different customer segments.
Chan-Olmsted said, “The analysis of AI applications in media thus far reveals some implied challenges. There are issues of balancing human intelligence/experience and artificial intelligence, as well as the management of AI’s evolutionary capacity with the future and social good in mind.”
Other challenges include understanding the right approach of using AI-applications to interact with audiences; and developing the competency and insights necessary to integrate AI into the existing systems and processes.
By evaluating how AI is currently being implemented in the media industry, Chan-Olmsted paints a broad picture to demonstrate just how much room remains for growth. Future studies can focus on a more micro level evaluating media company case studies and specific technological or operational aspects of AI within the media industry.