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5 min read
Laurence Mifsud

The shift to personalised content distribution and competitive engagement metrics are changing how people access content and information.


Accelerated by digital-first services and social media, many readers now expect relevant content to come to them on their favourite channels and platforms. This has fragmented the landscape, placing audiences at risk of exposure to misinformation spread by illegitimate channels manipulating digital algorithms to their advantage.


However, with the right data strategy and AI tools, publishers can create, repurpose and syndicate accurate journalism and quality content in the right place at the right time.


In the information services space, audiences increasingly demand fast, intuitive access to archived assets and search engines that optimise results based on their recognised intentions. Another use case well served by a unified data strategy and AI.


In this blog, we’ll explore three ways AI can help you attract and retain readers, drive recurring engagement and optimise operational efficiency – responsibly.


A note on responsible AI use


Before we dive in, it’s important to understand that AI requires careful usage.


To ensure AI tools and algorithms only produce accurate and trustworthy results, it’s important to remember the concept of ‘garbage in, garbage out’. This means feeding AI with flawed, biased or poor-quality data will produce results of comparable quality.


Plus, AI only augments human intelligence – it doesn’t replace it. That’s why AI must be monitored and managed by qualified personnel to ensure its output complies with legal and brand expectations.


Three ways AI can help drive meaningful industry change


Whether you’re already experimenting with AI or exploring its possibilities for the first time, these three use cases can help you build a compelling business case for AI-led content creation and user experiences.


Automate basic content creation


Generative AI tools like ChatGPT promise the ability to create compelling original content based on predefined rules and boundaries.


By combining natural language processing (NLP) and natural language generation (NLG), publishers can automate routine content creation to save writers time that can be better spent on more value-adding tasks. It also helps minimise the risk of human error while improving process efficiency, allowing you to create repeatable content tailored to your stylistic and editorial needs.


You can also use generative AI to translate and personalise existing content to reach new audiences through channel syndication.


Common examples of AI-generated content include:


  • News and article summaries
  • Basic marketing materials
  • Finance reports and research enquiries
  • Product and content descriptions
  • Recommended headlines


Asset classification and search optimisation


Information services providers can use AI to simplify content management and optimise archived asset tagging for improved searchability and monetisation.


Large language models (LLM) and visual question answering (VQA) tools can rapidly assess articles, photos, videos and other content types to identify relevant keywords for meta-tagging. This includes words related to themes, stories, scenarios, imagery and actions found or performed in the content.


For example, the AI may recommend that an image of a vintage Ford includes tags related to its location – say, Blackpool seafront. This makes it easier for a researcher to get the exact, albeit niche, image they need. Plus, VQA can answer open questions about your tagged content to help serve up more accurate results faster.


As for monetisation, democratising access to archived content may help repurpose it for research and commercial use, maximising its value while diversifying your business.


Hyper-personalise content at scale


Data-driven experiences normalised by the likes of Netflix, Spotify and social media have made personalised content delivery a competitive necessity.


For publishers, this means serving audiences relevant content – in the right place, at the right time – based on their engagement histories and channel preferences. While it’s true that many users still prefer to find content independently, countless others prefer to have it reach them through a social media feed, newsreel, email update or website headline.


That’s where AI comes in. Machine learning (ML) algorithms can analyse data from consenting users to predict and recommend the content most likely to resonate with an audience segment, including the best time and place to share it. ML tools can even preview the most relevant parts to a defined audience to help pique user interest and automate routine editorial tasks.


This is just a snapshot of AI’s true potential


This blog offers just a short preview of the advice, challenges and high-impact use cases explored in our e-book on how publishers and information service providers can seize the AI opportunity responsibly.


To learn more, read the full e-book here.


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