AI is making its presence felt across various sectors, and the television news industry is no exception. In India, media companies have embraced this trend, with numerous houses taking the initiative. As far back as early 2016, several prominent news providers like the Associated Press, Forbes, ProPublica, and the Los Angeles Times have also incorporated AI into their operations.  

In April 2023, an Indian media group unveiled its first full-time Artificial Intelligence (AI) news anchor — a bot named Sana. 

AI has played a pivotal role in bringing about significant changes across diverse industries, acting as a force for disruption and transformation. The advancements achieved in fields like Generative AI and Robotics have positioned us on the cusp of a new disruptive wave. This wave is particularly focused on an industry recognized for its inherent uncertainties and contentiousness—the broadcasting and media sector. 

To better comprehend these advancements and their potential, it’s essential to reflect on the progression thus far. A key focal point is the intersection of Automated Journalism and the evolving capabilities of next-generation Generative AI.  

This convergence raises questions about the opportunities and challenges ahead. Can this synergy uplift the broadcasting and media industry to new heights? 

During the early 2010s, the concept of automated journalism, often referred to as algorithmic journalism or robot journalism, took its initial steps. As news reporting consistently evolves, the dynamic technological landscape continually reshapes the methods through which information is both broadcasted and consumed. 

“An automated software or an inexperienced reporter can never substitute a skilled journalist,” as expressed in a 2017 Nieman Reports piece authored by Nicola Bruno. 

Nevertheless, in 2020, Microsoft took a significant step by replacing 27 contract journalists with AI-powered robotic journalism. 

In September 2020, The Guardian took a noteworthy stride by publishing an article entirely generated by the neural network GPT-3. However, it’s worth noting that the human editor manually selected the fragments that were eventually published.  

Enterprises specializing in data science and AI, like Automated Insights, Narrative Science, United Robots, and Monok, play a pivotal role in creating and offering these algorithms to various news platforms. 

Initially, the initial applications primarily centered around stories rooted in statistical data and numerical values. 

The realm of automated journalism finds prominence in four principal domains of utilization: automated content creation, data mining, news distribution, and content refinement. 

Use cases of generative AI in journalism

Generative AI has already started delivering its potential to revolutionize the way content is produced, curated, and disseminated, transforming it into a futuristic robotic media house. Many industries and users across are already using the Generative AI for many cases:

  • Content Generation: Generative AI can create a wide range of content, including articles, videos, images, music, and more. It can generate high-quality and diverse content quickly, eliminating the need for human authors and creators. This enables the media house to produce a vast amount of content on a scale that was previously unimaginable. 
  • Personalization: Generative AI can analyze user preferences, behaviors, and historical data to personalize content. This ensures that the content delivered to everyone aligns with their interests, leading to higher engagement and customer satisfaction. 
  • Multimedia Fusion: Generative AI can blend different forms of media to create unique and captivating experiences. For instance, it could generate a video based on a written article, add dynamic visuals to a podcast, or create a custom music soundtrack for an article. 
  • Real-time News Generation: Generative AI can scan vast amounts of data from various sources to create real-time news updates. It can quickly summarize complex information and generate news articles, allowing the media house to be the first to report on breaking events. 
  • Language Translation: Generative AI can instantly translate content into multiple languages, enabling the media house to reach a global audience without the need for human translators. 
  • Content Enhancement: Generative AI can analyze and enhance existing content by suggesting improvements, generating relevant visuals, or adding informative charts and graphs. This ensures that content is informative, engaging, and visually appealing. 
  • Content Curation: Generative AI can curate content from across the web based on specific topics or themes. It can gather information from various sources and compile it into comprehensive reports or articles. 
  • Interactive Experiences: Generative AI can create interactive content that allows users to engage with the material in novel ways. This might include interactive simulations, quizzes, polls, and more. 
  • Automated Social Media Engagement: Generative AI can manage social media accounts by creating and scheduling posts, responding to comments, and engaging with followers. This maintains an active online presence and enhances audience interaction. 
  • Quality Control: Generative AI can ensure content quality by detecting and correcting errors in grammar, spelling, and factual accuracy. This is crucial for maintaining the media house’s credibility. 
  • AI Anchors and Presenters: Generative AI could create lifelike AI anchors or presenters for video content. These virtual presenters could deliver news, interviews, and other content, enhancing the visual appeal of the media house’s offerings. 
  • Dynamic Advertising: Generative AI can create dynamic advertisements tailored to individual users, making advertising more relevant and effective. 

Presently, AI has reached a stage where it can comprehend content, enhance its quality, and craft narratives in diverse styles. Additionally, AI is equipped to present this content in various formats like blogs, videos, articles, and more. 

AI’s initial stages have transitioned into a more advanced phase, akin to preparing for sprints in a race. 

The concept of AI hosts assuming control over broadcasts is captivating, encompassing promising advantages alongside associated challenges. Here’s a comprehensive outlook on the possibilities and factors to be considered: 

Potential benefits of Generative AI in journalism

  • 24/7 Availability: AI hosts can be available around the clock, eliminating the need for breaks, vacations, or shifts. This ensures continuous broadcasting and accessibility for global audiences. 
  • Consistency: AI hosts can deliver content with consistent tone, mannerisms, and pacing, reducing the variability that might come with human hosts. 
  • Language Diversity: AI hosts can easily present content in multiple languages, enabling broadcasters to reach a wider and more diverse audience without the need for human translators. 
  • Reduced Costs: Once developed, AI hosts could be more cost-effective compared to human hosts who require salaries, benefits, and other resources. 
  • Quick Adaptation: AI hosts can rapidly adapt to changes in information, ensuring that the latest news and updates are presented accurately and promptly. 
  • Customization and Personalization: AI hosts could be tailored to individual viewer preferences, providing a personalized viewing experience.

Considerations and Challenges

  • Emotional Depth: AI hosts might struggle to convey genuine emotions and connect with audiences on an emotional level, which is often a critical aspect of broadcasting. 
  • Unpredictable Situations: AI hosts might struggle to handle unexpected or highly dynamic situations that require quick thinking and improvisation. 
  • Cultural Sensitivity: AI hosts might inadvertently offend or misunderstand cultural nuances, leading to PR issues or misunderstandings. 
  • Creativity and Authenticity: Human hosts often bring creativity, spontaneity, and authenticity to broadcasts. AI hosts might lack these qualities, leading to potentially bland content. 
  • Ethical Concerns: Deepfake technology could raise ethical issues if AI hosts are used to manipulate or present false information convincingly. 
  • Human Connection: Humans tend to connect more readily with other humans. AI hosts might not be able to create the same level of rapport and connection. 
  • Acceptance by Audience: It remains to be seen if audiences will be receptive to AI hosts and whether they’ll find them engaging and trustworthy. 
  • Technical Limitations: AI hosts could face technical glitches, malfunctions, or limitations in understanding nuanced topics. 
  • Loss of Jobs: Widespread adoption of AI hosts could lead to job losses in the broadcasting and entertainment industry.
  • Intellectual Property: Issues related to copyright and ownership could arise when AI generates content based on existing works. 

Hybrid approach

A potential middle ground could involve a hybrid approach, where AI hosts are used in conjunction with human hosts. This could capitalize on the strengths of both AI and humans – AI hosts for continuous coverage and routine content, while human hosts handle complex, emotional, or dynamic situations. 

In conclusion, Generative AI has the potential to reshape the media landscape by becoming a robotic media house capable of producing vast amounts of personalized, engaging, and relevant content. However, it’s crucial to address ethical concerns, maintain authenticity, and find the right balance between automation and human creativity.