Media can overcoming generative AI challenges to develop new use cases in content

By Justin Eisenband

FTI Consulting

Washington, DC, USA


Over the last six months, the topic of Artificial Intelligence — and specifically generative AI — has been the subject of great debate amongst news and other media publishers.

While publishers have been utilising generative AI for years to produce more commoditised, data-driven content like sports game previews and financial reporting news, several notable publishers — including CNET, BuzzFeed, and Sports Illustrated — have reported new forays into using generative AI to both author and develop more sophisticated content.

As with any new technology, or adaptation thereof, using generative AI to create original content has had challenges. There have been reports of material (yet obvious) errors and repetitive, inauthentic content in some of the early attempts.

However, generative AI will continue to improve. More importantly, those who leverage it will continue to learn lessons on how to best employ the technology.

Generative AI in content generation

Generative AI tech can now create original auditory, visual, and written content using machine learning and algorithms, with capabilities that now span a variety of media with increasing sophistication. Those media types can range from simple text development to images and video as well as audio.

While generative AI cannot suitably replace editorial and journalistic functions in their entirety, there are numerous use cases across content production workflows that can help improve efficiency and allow for greater time investment in creative development, investigative research, and simply more output.

Limitations and risks

As generative AI has become more prevalent, concerns and objections to its use in journalism have arisen, particularly those concerns centered around generative AI being considered as a replacement to original and thoughtful journalism. For publishers considering expanding utilisation of generative AI, it is critical to understand the limitations and risks for employing these technologies from both a technical and business perspective.

Publishers can mitigate these limitations and risks with proper context and judgement. This would require focusing on “surgical” applications of generative AI into workflows to improve efficiency and the employee experience.

For example, to avoid erosion of audience trust, publishers should clearly convey in bylines when an article is partially or fully AI-generated and should clearly publish public guidelines around usage of AI in content publishing. Additionally, publishers need to develop clear policies and workflow procedures around AI usage to avoid legal and copyright vulnerability and mitigate risk of potential errors and biases in reporting that may arise from generative AI-created content.

Generative AI opportunities

While most of the focus in the news about generative AI has been around original content development, publishers should be considering how to employ the technology across multiple functions both in newsrooms as well as in audience development and content distribution.

Generative AI can be used in content adaptation to support versioning for different platforms. For example, once an article is written, edited, and finalised for publication, versions can be automatically generated to be published across platforms like truncated summaries for social media and newsletter distribution, translated versions for other languages, and even audio formats for either podcast distribution channels or accessibility needs.

Another area where generative AI can be helpful is in content processing and finishing. Editing, including functions like recolouring and sound adjustments can be time-consuming and costly activities. However, generative AI can create recommendations and also reduce time replicating edits from one frame or image across an entire video or set of images.

Generative AI can also help with content localisation beyond translation including swapping out text and images, or providing dubbing on published videos where previously it only made sense to subtitle or translate in certain languages.

In audience development and marketing, generative AI can support areas by embedding metadata and optimising content for search and social discovery. While many publishers already leverage personalisation in content marketing, generative AI advances can help create more tailored audience experiences and promote content in a way that is more likely to drive engagement.

Generative AI growth likely to continue

It is clear that while publishers have been using some forms of generative AI for some time now, new developments and technical capabilities are likely to accelerate publisher usage in content generation, content processing and adaptation, and marketing and distribution.

For publishers planning on expanding use cases, it is critical to develop clear plans and communications as well as processes and guardrails to ensure ethical, legal, and efficient usage of generative AI-based technologies.

About Justin Eisenband

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