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Use Cases for AI in Publishing

Use cases for AI in Publishing

The publishing industry has been changing rapidly in the last two decades. The widespread adoption of personal computers, the growth of Adobe suite of tools and other publishing software brought about a positive change in publishing. Costs reduced, books got printed faster. With outsourcing of jobs becoming a major source of saving costs for publishers in Europe and America, digital files were sent to printing firms in countries like China and India to get books market-ready. The next significant change in the publishing industry happened with Amazon’s Kindle Direct Publishing or KDP, which enabled authors to self-publish digital books by their own with basic computing knowledge. In the last couple of years, we are seeing the rise of Generative AI or GenAI and Large Language Models or LLMs, which are transforming modern publishing extensively.

Let’s look at some interesting use cases for deploying AI in publishing:

Peer Reviews for Scientific Journals and Texts

Scientific journals, texts, and research papers, make up a major chunk of publishing globally. From medicine to agriculture, and rocket science to cyber-security, the volume of research literature that is created every day is staggering. AI tools help in peer reviews and checking for plagiarism ensuring that the final output that is printed is error-free and not copied from some other source without proper attribution.

Alt Text for Images in Digital Books

Accessibility is a major concern for publishers. Several countries have strict laws that require websites and digital book publishers to comply with accessibility standards to ensure that people or learners with disabilities are able to access content without any problems. AI-powered tools and websites that generate alt-text for images, make life much easier for publishers.

Data-Driven Publishing – Meeting the Demands of Buyers

Predictive analytics and data-modeling software now help publishers predict the demands of readers and buyers. Advanced machine learning algorithms that power such software, study the buying trends of readers and examine the sales numbers of books and authors to give publishers an idea of what genre of books and type of content is selling more. This then allows publishers to invest in authors writing similar genres of content.

The success of JK Rowling’s Harry Potter series spawned several imitations in magical fantasy for children and teenagers. This is just one example. Today, publishers make every decision with solid data as evidence to justify their actions. Pricing a book lower in developing countries with slightly inferior quality of paper, limiting hardbound editions to few regions, handing over translation and syndication rights to local publishers, these are all decisions that are backed by data. AI and predictive analytics have truly made publishing more focused and driven on profits.

Automating Editing, Proofreading, and Accuracy-Checks

Earlier we spoke about peer reviews and plagiarism checks. Today, AI-powered software ranging from Grammarly to Revisely and Copyscape to Hemingway, offer a high degree of professional excellence in English language editing. Similar solutions are being developed for other languages as well. The amount of pressure that a human copy editor has to undergo is significantly reduced. The initial draft can be reviewed by AI-powered software and then the human editor can validate the AI-corrected draft. This leads to significant savings in time and costs for publishers.

AI vs AI? An Interesting Scenario Ahead

Sometime last year, a popular self-published author on Amazon, shared in a podcast episode his tips to create fantasy fiction using AI. He claimed that he had written, published, and sold multiple books in quick time with the help of AI. This presents an unusual conundrum for publishers. Are buyers and readers so foolish that they are not able to discern the type of content being generated? Or is there a herd mentality to buy and read books that peers seem to have bought? On Kindle with pricing sometimes being less than 2 dollars, it may not seem a big thing, but the authors with the knack of using AI effectively are making a tidy profit.

Traditional and self-publishing companies will have to use AI-tools to check if the content submitted for publication has been heavily influenced by AI and if the content has been rehashed from some other source. In the near future organizations in the content and publishing industry would need to invest heavily in quality assurance to check and validate that information published is unique, genuine, and not plagiarized from other sources. Perhaps, the differentiator would be that “This content is 100% written by a human. No AI tools used to create this content.”

Creating Summaries from an Existing Library of Content

As bite-sized learning becomes the norm in corporate and academic learning, the world of research and publishing is also gravitating towards abridged texts or summaries of longer works and research papers. Semantic tagging helps publishers find the right content and enables them to create summaries with appropriate summary generators. By engineering relevant prompts, the summary generator creates an abridged text that meets specific requirements. This can then be printed as a physical book or made into a digitized eBook. This is an interesting use case and has a lot of takers in the scientific and medical research community.

In addition to publishing, content management systems or learning repositories are also using such AI tools to index, locate, and retrieve content to provide summaries to specific questions and provide contextual answers.

Content Creation and Images

The use of GenAI-powered tools for content and images is something that brings a unique set of legal ramifications. Recently, The New York Times, sued OpenAI and Microsoft for using their archives to train their LLMs. This is just the tip of the iceberg. Graphic designers and artists whose work has been taken from their public portfolios without due permission or payment are also against the use of AI. The next few years will see an increased awareness about the copyrights and ownership rights of content created using AI.

We are at the cusp of an interesting time in modern publishing industry. The growth of eBooks and eReaders has not significantly dampened the sales of paperbacks. There is a definite demand for good quality printed books and publishers will look to use AI effectively to deliver books at affordable rates to buyers and increase their revenues.

NINJA is an integrated suite of AI-powered tools that are designed specifically for the publishing and learning industry. From interactive content to accessible learning and adaptive learning paths, we ensure that all your publishing requirements are met with care and empathy. We help publishers get their books to the market faster and offer end-to-end pre-press and post publication support. Write to us at to learn more and experience the power of AI-driven intelligent publishing services.



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