Generative AI technology can support book publishing

It is a momentous and momentous time in the publishing industry. The promise of generative AI is being realized in new and remarkable ways, offering the potential to fundamentally change our workflows, business models, and the products we offer readers. Nevertheless, the technology is still evolving, and the way forward is still being paved. As someone on the front lines of the technology revolution in publishing, I’ve become a firm believer in the promise of generative AI. Its applications are vast and diverse, while its potential to disrupt the industry is immense.

In my work, I have seen for myself how generative AI can be utilized for many areas of use. It is not a theoretical tool limited to academic research; it is an operational tool and is here now. By highlighting the promises and pitfalls of generative AI from my perspective, I hope to foster a deeper understanding of this powerful technology and its potential to revolutionize our industry.

For me, generative AI has been critical for tasks that previously required hours of effort. It has helped me create engaging and targeted marketing copy in a fraction of the time than before, allowing me to manually customize and repeat messages in ways that would be nearly impossible without AI assistance. Furthermore, AI has proven remarkably adept at generating book metadata, streamlining a process that can be tedious yet critical to book discovery and sales.

Other applications of AI I have tried have included deciphering long strings of customer service emails, initial analysis of content and supply chain supplier contracts, extracting rights and royalty terms in contracts, cleaning up extracted text to create e-books, identifying competitive titles and identify potential DEI issues in manuscripts. For most of these applications, what was once an hour-long task prone to human error can now be completed much faster and more accurately.

While these applications of AI have been invaluable, they are not without challenges. Harnessing the power of AI in publishing, it turns out, isn’t as simple as plug and play. It requires thought, effort and understanding of the technology, its application and the industry. The prompts to perform tasks often require significant iterations, and the results need careful human review and editing.

For me, one of the most exciting applications of AI has been extracting contract terms. Generative AI, equipped with a pattern recognition capability, can sift through dense legalese, identifying and extracting key terms with impressive accuracy. When researching royalty agreements, terms, and types of rights granted, each element is often buried in a thicket of legal jargon that can be time-consuming to decipher. Generative AI can be trained to identify these specific terms, significantly reducing time spent on contract review to populate royalty or title management systems.

A typical challenge for production editors everywhere is extracting text from documents in formats like PDF or, even worse, from scans of printed pages. The extraction process often results in dirty copy with incorrect character encoding, misplaced line breaks or missing sections. The standard process often uses third-party providers to take additional steps to clean up the text and make it suitable for further use. I have used generative AI to replace the whole process. The application can even highlight the corrected elements for a quick review.

Incorporating AI is not just about improving the operational aspects of publishing. It is equally valuable for data analysis. Using OpenAI’s Code Inspector, I’ve dug deep into the wealth of market and logistics data publishing generates daily. A critical aspect of educational publishing, especially during high season, is the analysis of delivery times. By feeding logistics data into the AI ​​model, I discovered trends and identified bottlenecks affecting delivery times. The AI ​​model skillfully handled large amounts of data and provided insights that could have taken humans days or weeks to arrive at. It was still important to know what to look for and to create the right visualizations to show the problems, but the basic figure only took a few minutes. Watching the tool try different approaches, reach dead ends, and try something else until a suitable result was produced was breathtaking.

Powerful but not infallible

These examples highlight an essential truth about generative AI’s role in publishing: its power is immense, but it is not infallible. AI tools can make remarkable achievements, but their output must be treated with discernment and care.

Take the example of finding competitive titles. This seems like an easy way to use generative AI, but it still requires a good understanding of the industry and its data. In an email exchange with Thad McIlroy, a frequent contributor to Publishers Weekly and a longtime colleague, he noted, “I think we’re saying AI is going to be good at finding comps without understanding what that means. The traditional approach to finding comps is superficial, almost to the point of being useless. What do we want from a comp? It crosses recommendation engines. We want to identify the best books that match the stylistic/content profile of the manuscript we plan to publish. That’s a tall order… and bypasses the almost insurmountable challenge of bringing in copyrighted titles in a comp database.”

That is absolutely correct. By processing huge data, AI can generate lists of potential comp titles with only a phrase or two as input. In my case it generated a list of reasonable sounding comps… that didn’t actually exist! To be fair, the developers behind AI systems, such as OpenAI, the company behind ChatGPT, acknowledge this caveat. They have added caveats to the AI ​​output, noting that generated titles are illustrative of what to look for rather than a definitive list of existing books.

Even with AI’s ability to analyze data and generate insights, the onus is on the user to ask the right questions and to know what to look for in the answers, underscoring the ongoing importance of human involvement in the application of AI. While the AI ​​provided the tools, I had to direct its focus and interpret the results.

While this may initially seem like a limitation, it can also be a strength. It reinforces the role of AI as an enabler rather than a substitute for human activity. It helps us become more efficient and informed, enabling us to handle tasks at a scale and speed that would not otherwise be possible. Still, that doesn’t diminish the value of industry knowledge and human judgment; it highlights the importance of these elements to harness AI’s full potential.

Truly scalable business applications strive for predictability, consistency, and accuracy—you don’t want your financial systems to invent the data your business operates on. Although generative AI has not yet achieved this level of accuracy, developers continue to work to eliminate the uncertainty associated with the factual and formatting accuracy of the responses returned by AI. Their goal is to remove much of the routine busywork, thereby allowing human creativity and judgment to shine through.

OpenAI continues to release features to help with this. For example, its developers recently introduced a feature to make the data returned from API calls more systematic and predictable. But there is still a long way to go.

Early examples

There are many promising applications of generative AI underway in publishing. For example, PanOpen Education has incorporated AI into its courseware platform. The AI ​​acts as a tutor, helping students, helping them with misunderstandings and allowing class time to be used for deeper discussions. As the president of PanOpen, Brian Jacobs, aptly puts it, “Generative AI is helping to realize the long-held dream of person-centered learning, to finally break with a factory model of education. In that sense, we see such tools as empowering teachers and students in ways that would be unthinkable without them. And far from replacing teacher creativity, AI can be an extraordinary enabler of it in new forms.”

Similarly, Gutenberg Technology uses AI to improve the accessibility of content created with its authoring tools. Gutenberg uses AI for accessibility remediation (a problem for all publishers), standard customization, and test object generation (educational publishers). The president of Gutenberg Technology, Gjergj Demiraj, says: “Our incorporation of AI is about precision and consistency, which brings significant benefits to authors and publishers. It helps us ensure that publishers’ content complies with standards and is accessible to all, without restricting the creative vision of their author.”

These examples highlight how companies are making progress in marrying AI with human creativity and judgment to provide a more efficient, accurate and innovative platform. There are many other possible applications of AI in publishing, including title development, sales, marketing and, of course, operational and financial functions.

As we stand on the cusp of this transformative journey, staying informed and engaged is critical. Let’s not shy away from the opportunities that generative AI offers, but instead lean into the learning curve. Experiment with AI tools, involve them in your projects and explore their potential. Engage in discussions about the ethical use of AI, its limitations and its promises. Most importantly, consider how we can shape this technology to serve our industry, our readers, and our shared future. AI’s role in publishing is not a matter of if but of when and how. It is up to us to ensure that the “how” is consistent with our highest aspirations and ideals.

Ken Brooks is the founder of the consulting firm Treadwell Media Group and is a founding member of Publishing Technology Partners. He has served as chief content officer at Wiley and COO at Macmillan Learning.