Can AI Tools Help the Book Publishing Industry Evolve

As many authors and literary professionals know, the book publishing industry is notoriously slow to change. For example, industry-standard platforms like QueryTracker and Publisher's Marketplace are behind in accessibility best practices and modern UI/UX principles over the years. Sadly, this is true for most tools within the industry.

Not only that, but query submissions still lead to saturated agent inboxes to this very day, which takes time from their busy schedules. Moreover, though diverse book buzz is plentiful in recent years, pinning acquisition and distribution due to lack of data is a challenge. Finally, without a surgical approach to omnichannel attribution and operations, publishing's digital-traditional marketing efforts can often look like tech's most fractured silos.

As a sci-fi author and tech-nerd working with many of the same tools publishers use, I wonder if there’s a better way to go about this entire bookish business.

What if intentional AI tools could offer a better path for the publishing industry?

For literary agents, AI might just be the ticket to inbox zero. An AI-powered layer could quickly sort through submissions based on genre, word count, and sentiment analysis. This would be a dream come true for overworked literary agents. Of course, diversity must be top of mind to prevent repeating . Inclusion of best practices, DEI, and AI ethicist consults—as well as GIGO-cleaning for publishing data—would be non-negotiable.

Artificial intelligence may even help demystify much of the querying process for authors as well. For example, submission labels might be possible to cross-reference with an agent's client acquisitions using data from QueryTracker, had they had an API (they do not). This would not only save time but could increase the chance of finding perfect-fit agents. This is especially important for marginalized authors so that our unique work finds the right talent to support it.

Not only that, but if AI was trained on successful queries, it could perhaps give tooltips to authors, not unlike Gmail’s response suggestions. Considering how many query letter examples and submission rubrics exist, it can be challenging for authors to know if their query is up to snuff. Less guesswork means more time saved and happier inboxes for everyone.

In terms of book discoverability, AI could help surface diverse books that struggle for space. For example, Amazon uses machine learning to power its recommendations engine. However, Amazon’s algorithms are often biased and lack accountability, which is a big flaw for the eCommerce giant. A better tool can and should be built. We have the technology.

What about book acquisition data? Employing a refined AI tool may lead to automating easily searchable databases that all can employ to make invaluable decisions. Decisions like marketing next steps, agents to query, what work to acquire, what work has been acquired, and more. Everyone benefits from data accessibility, especially marginalized authors. When you can see the landscape all at once and filter on a dime, then it’s easy to know what inclusion work remains to be done.

Let’s talk marketing. If you’ve ever had to track metrics from traditional to digital—over an omnichannel spread—above Influencers, through op-eds, and over social media, you know true marketing agony. AI can help publishers, bookstores, and authors alike with that hurdle by centralizing the process and layering it with a user-friendly dashboard. This is the pipe-dream of book marketers the world over. Should you crack this, consider yourself my personal hero.

Finally, being able to get a vantage on operations is paramount for any industry, publishing included. It’s a sad state of affairs when platforms like Ingram—an industry standard for bookstores, libraries, authors, and publishers alike—lack APIs. As for small presses, indies, and anyone using Amazon, ops fails to provide necessary referral data.

There are many more examples of poor insight visibility than just these. Effectively, everywhere you turn in publishing’s digital sea, you notice just how far behind it all is. That’s the critical challenge: Overcoming legacy tools.

What are the downsides of a publishing industry AI push?

Sadly, many authors worry about AI replacing writers, which seems very unlikely. In fact, AI can help with the drudgery of editing so that writers can focus on their craft. For example, Grammarly's artificial intelligence proofreading tool can help identify errors in real time as you write. To further panacea author anxiety: AI cannot create real artwork, which is what books are. It cannot write novels that move readers, characters to fall in love with or prose that strikes our hearts. Our craft is safe, I promise. There exists no world where a robot who never lived can create literature.

As per publishing professionals, there seems to be little reason not to pursue streamlined processes. A neater inbox, better marketing data, higher quality queries, a bird's eye on the industry? All of that sounds demonstratively positive.

What negatives exist for AI technology?

Here’s where hope for a publishing revolution fades.

For what ails AI, it always boils down to GIGO. Just like with , using publishing’s historic data without accounting for biases and retooling for inclusion is a critical failure. Quite simply, any AI tool leveraged without data-cleaning both calcifies and replicates systemic issues. For this reason alone, artificial intelligence can’t fix publishing’s problems right now, though I wish it could.

Is it possible for AI and publishing to join hands and walk together towards a bright literary future? Not at the moment, but that would truly be a novel concept, indeed. I hope someday I can see that happen. At the very least, it’d be nice if legacy tools made publishing data more accessible for all so that better tools can be built.

---

K. Leigh is an ex-freelancer, full-time author, and . Check out their , connect on , or if you’d like to work together. 🌈 🏳️‍⚧️