8 Ways Artificial Intelligence Takes Publishing to the Next Level

Publishers are the gatekeepers of modern literature. As technology advances, both traditional publishing houses and self-publishing authors benefit from technology-enabled tools and analytics which were previously not available.

There is already a wide variety of tools for all stages of publishing. These do not only help automate daunting processes, but also give power to new independent voices by making self-publishing more accessible. From automated formatting to royalty management software, here are eight ways that AI helps take publishing to the next level.

1. Automated Text Analysis

Automated text analysis has multiple benefits in the publishing realm, ranging from editorial optimization to copyright enforcement and plagiarism detection. Publishing has always been rife with plagiarism, even in cases of large publishers, such as Grand Central Publishing.

Automated text analysis can detect plagiarized passages without human input, generating errors which can be brought to the author’s attention in advance of publication. It can also be used to monitor copyright infringements on third-party publishing platforms. Programs such as Grammarly help reduce editorial workload and increase overall editorial error detection rates by allowing writers and editors to self-check and self-edit their work.

Grammarly lets writers self-check and self-edits their writings.

2. Global Demographic Analysis

When it comes to success in publishing, reach, and sufficient visibility are among the most significant factors in a book’s success, second to author reputation for pre-existing authors. Demographic analysis allows publishers and self-published authors alike to determine the most efficient markets for targeted and blanket advertisements.

Global market analysis is available in both Google-style dashboards as well as mores privatized, Al-driven options, providing high-quality data for publishing firms who can afford to pay-to-play.[ES1] Artificial Intelligence data extraction APIs are among the above-mentioned options that give the ability to extract data from relevant websites. Publishers or self-publishing authors can use the extracted data from websites like Goodreads or Amazon to understand key demographics of readers who might be interested in their books based on their ratings and wishlists.

Google Dataset Search lets users find different datasets across the web through keyword search.

3. Contracts, Rights, and Royalty Management

Contracts, content rights, and royalty management have all produced headaches for traditional publishing staff but are vital facets of publisher success. As mentioned above, AI-driven systems can use text analysis to monitor copyright infringement on third-party publishing platforms, such as Smashwords, Amazon Books, and others.

Royalty management systems (RMSs) can link up with platform APIs to determine sales numbers and automatically calculate the royalties which need to be divvied up between the various parts of the publishing company and the author(s).

Contract management can be engaged for publishing companies who need to identify clauses from specific contracts quickly-often to determine royalty information or in case of a dispute. This process can be made easier through contract management systems, allowing publishers to automatically search the document like one would search Google to see if specific clauses are present in the contract.

Through AI, the software can be trained to recognize patterns and thus effectively extract data, such as clauses, dates and their contexts, parties, etc. This will make it more efficient for the publishing house to manage its contracts and extract necessary data. Furthermore, the software can be trained to recognize various contract types and adjust their behavior accordingly.

4. Auto Text Tagging

Text tagging is a historically tedious process. Books and blurbs must be tagged accordingly; otherwise, customers cannot find them. Al-driven text analysis allows our machine counterparts to scan and generate tags for any length of text automatically. As sentiment analysis and machine learning continue to advance, text tagging can be expedited even further and with greater tagging accuracy. Publishing powerhouse lxxus has developed a system just for this purpose.

5. Automated Formatting

Formatting books is no easy task, especially in the case of non-traditional submissions, such as written scripts with specific font features for insertion within the text. Automated formatting into both e-reader and traditional formats have become much more comfortable with tools such as Scrivener and Amazon e-publishing interface.

Scrivener helps writers to concentrate on the writing itself by offering automated formatting features.

Reducing time spent on menial, yet absolutely vital tasks, such as formatting will provide meaningful room for growth within traditional publishing houses. Not only can publishing houses reduce dependence on humans who make mistakes for functions that offer ample room for error, but they can also focus on higher quality content, editors will have more time to spend on content edits as opposed to simpler tasks, such as formatting and grammatical editing.

6. Predictive Analytics

Predictive analytics take data usage a step beyond demographics. Al-enabled analytics can identify and adapt to consumer search trends and predict the ‘next big thing’ with more accuracy than traditional market research methods. Predictive analytics not only impact the content of stories, but also the marketability of covers and blurb targeting.

Increased overall marketability also comes in the form of optimizing story length according to market expectations, reading level for specific stories, and it can even influence the number of books per series.

7. Content Personalization

Content personalization applies to both e-reader stories and marketing campaigns. Statistics show that personalized marketing campaigns have substantially higher engagement and return on investment.

According to McKinsey and Co. “Personalization can reduce acquisition costs by as much as 50 percent, lift revenues by 5 to 15 percent, and increase the efficiency of marketing spend by 10 to 30 percent.”

Content personalization is not as simple as using an HTML template to insert a user’s name in each email or SMS. Personalization is transitioning to customized content curation within newsletters and audience segmentation.

8. Content Translation

Content localization helps make the content accessible to a much wider audience, and while translation tools have been around for a while now they are far from perfect to be used in publishing just yet. Fortunately, thanks to the use of AI, there has been significant progress in creating professional translation automation tools.

Tools like Unbabel are aiming to use machine learning to make translation tools to create authentic texts. Even with faulty translation, these tools can still be used by translators to create the first draft and get a publishable version through human input.

Unbabel is an AI-powered translation platform that aims to provide human-quality translations.

With proper development, AI translation tools can help accelerate the process of translation. Book translation process might take up anything between three months and a full year. Meanwhile, translation tools can do it in significantly less time.

Regardless of the state of operation which a publishing company is at, technology in the modern era has benefits for everyone. The most significant benefits of the use of technology in publishing are the authors’ new ability to publish their works effectively, while also enabling publishing houses of all sizes to become more efficient in their processes.

At the same time, these technologies can help empower writers who want to publish their work independently, which will help bring new voices to the world of literature. From efficiency to assisting new writers in getting their voices heard, AI has a lot to offer to the publishing industry.

By Max Kalmykov
VP, Media & Entertainment at DataArt