5 Artificial Intelligence Trends Emerging in Publishing
Learn how large language models (LLMs) are revolutionizing the publishing and media industry
Business leaders are feeling almost the entire spectrum of human emotions when it comes to using artificial intelligence (AI) to drive their businesses forward. Many media executives have told us that it is hard not to be curious, cautious, and overwhelmed by all the possibilities that AI offers. In particular, large language models (LLMs) are opening up new possibilities as organizations seek to optimize the teams and tools they already have to extract value from their exploding volumes of text-based data.
Before we explore the five emerging AI use cases that are beginning to reshape the future of publishing, let's clear up a common misconception. AI isn't here to replace the jobs of publishers and journalists; it's here to revolutionize the way they work, empowering them and streamlining their processes. In publishing, LLMs can improve efficiency, enhance the evaluation of new content, enable collaborative writing, and usher in a new age of media publishing.
Trend 1: New workflows for a new technology
In the publishing world, AI has led to a shift in roles, with data scientists increasingly moving out of silos to become more active members of product teams and product management decisions. This shift is driven by the need to efficiently harness the power of AI across the entire IT stack behind these publishing organizations. By bringing data science expertise together with product management, publishers can unlock the potential of AI without losing the human touch that makes content unique and engaging.
Trend 2: Content evaluation
One of the biggest challenges in publishing is managing the vast amounts of text data in the form of documents, archives, research, and notes. The role of AI here is not to replace editorial decision makers, but to assist them.
Consider this: a 2018 study found that the publishing industry lost more than 15 million hours of productivity just evaluating manuscripts that were ultimately rejected. Using an LLM filter to weed out unpublishable manuscripts would dramatically reduce this time. Employees would be free to focus on high-priority projects that really matter – such as identifying and advancing quality content.
Trend 3: Collaborative writing
Collaboration has always been at the heart of media publishing. With AI-powered tools, publishers can continue to foster collaborative writing between human writers and AI systems. Currently, writers are using LLMs to help with outlines, and LLM-powered tools have been deployed to assist some editors. These hybrid teams create content more efficiently, increasing the speed and quality of production. It's about increasing productivity, not replacing the pen with the machine.
Trend 4: Text summarization
Publishers are home to vast amounts of text – but in many cases, a distilled version of a longer piece of content is what is needed. Writers, editors, and marketing professionals can use automated summarization to produce social media posts, product descriptions, email campaigns, blurbs, and other short formats. LLMs are able to produce summaries of any tone and length in a fraction of a second.
Again, the goal is not to replace humans but rather to provide them with powerful tools to increase their efficiency.
Trend 5: Advanced research capabilities
Research is an essential step in the ideation process for any writer. It helps them understand the theoretical underpinnings of their topic, identify potential content, and dive deep into specific aspects. But in an environment of growing data silos and low-quality content, sifting through massive amounts of data can be exhausting.
By integrating LLM-driven features with traditional research methods, publishers can give their writers a powerful tool to find relevant content faster and more efficiently. They can even implement an interactive research tool on top of their organization's own database to provide an interactive and highly intuitive, natural language-based approach to research.
How deepset Cloud can help
Deciding whether to add LLMs to your organization's IT stack can be difficult. But it doesn't have to be. deepset Cloud, our LLM platform for AI teams allows you to experiment with different setups and collect feedback on them. This helps you quickly understand if your LLM-powered project can add real value to your business use case. What’s more, once you’ve decided on an application, you can move to production with the click of a button – because deepset Cloud also handles infrastructure scaling and data management for you.
Consider the case of Manz, a legal publisher that built an AI-powered recommendation system to speed up research workflows for lawyers and reduce operational costs. In the course of their project with deepset Cloud, Manz began to explore generative AI beyond their initial use case.
By experimenting with the technology and engaging users early on, they uncovered potential for new applications powered by their data. Manz's case shows that if you're a text-based business and open to innovation, LLMs can significantly improve efficiency and empower teams to deliver more value.
Learn more about AI in publishing
Want to learn more? Join our free webinar on the business applications of large language models in publishing and media. Use the link to register your seat, and share it with anyone in your organization who is interested in applying generative AI to the content production process, but is unsure where to start.