Top 10 ways AI is transforming book publishing
- why we should Shimmer, not shake
Nadim Sadek, Founder & CEO of Shimmr AI, which produces automated advertising to sell books, shares how AI is transforming book publishing.
There has been a cycle, throughout millennia, of publishing initially being sceptical, then experimenting, iterating, improving, and finally embracing new technologies. There were significant doubts about moving from clay tablets to papyrus, from printing presses to desktop publishing, from bound books to ebooks. The same will happen with AI, though the pace of experimentation then implementation will be exponentially swifter than ever before.
It seems to me that where AI can bring things never before available to publishing, because they were unfeasible, uneconomical or the industry didn’t have the relevant skill-sets, it should be whole-heartedly embraced. Where AI augments efforts, especially relieving tedious, repetitive, predictable tasks, I suggest there should be little hesitation in adopting it. When AI impinges on human creativity, origination and judgment, circumspect evaluation of its adoption should take place before it’s implemented. In any of these cases, publishing houses need to bring in AI-skilled staff to oversee and guide the AI tools and models being employed.
With AI, publishers can become more efficient, give better care to authors and include far more diversity. Authors can be ‘companioned’ by tools that inspire and assist original thinking, as well as employing marketing tools to help their reach and discoverability. Readers can be far better matched with psychologically fulfilling titles, better identified to suit their needs.
Here are 10 things that can be considered good for Book Publishing.
1. Gen Z and AI could together ignite a publishing renaissance
Seismic change may hit publishing as Gen Z’s rise coincides with AI’s ascent. Visionary young publishers and authors, amplified by tech’s exponential efficiency gains, will spur original works interrogating how society lives and is governed. Assumptions will be probed, ideas will pour forth. Long-held beliefs will face questioning from iconoclastic writers. Two AI disruptions accelerate this wave: vastly increased productivity that compels re-evaluating work’s purpose, and democratised instant wisdom. Gen Z’s fluidity with real-time information heralds an era emphasising conceptual dexterity over rote knowledge. This generational change and computational revolution may stimulate a modern renaissance of literature and publishing.
2. Greater exposure to more cultures and authors
AI can fluently translate in great volumes and at great speed. It may not perfectly capture the original work, but it does a stunningly good job, most of the time – and will only get better at it. This automation localises works into countless languages, exponentially expanding access to literature, worldwide. If you live in India, and you’ve never read a ‘Mongo-noire’ you might find yourself delightedly exposed to a new author from Ulaanbaatar. It may be imperfect as a translation, but getting something 90% good into millions more minds than previously possible seems worth it. It may even have positive effects on peaceful co-existence, as better understanding of people tends to make us live better with them.
3. Automated book descriptions
Large language models figure out stylistic patterns from sample texts with remarkable accuracy. This enables them to generate marketing copy tuned to a book’s unique voice, being faithful to the author and bringing their work to life for people to discover with greater ease and relevance. Particularly when back-list titles have not received attention for some time, AI can generate materials that give the book a new lease of life. Without this, the burden on a human resource would mean that title was never again given the push it needs to entice audiences to read it.
4. Streamlined workflows
AI can make rote tasks easier to get through, and often with greater speed and accuracy – like manuscript formatting. As such, it’s augmenting and amplifying workforce capabilities rather than replacing cherished roles. This is true largely for repetitive tasks, but it’s also true for more complex ones which are highly bureaucratic, such as finding variances in contracts. These can be highlighted and brought to the attention of the human expert for specific attention, so their intelligence is applied where human judgment is needed, rather than simply in sifting to find difference.
5. Engaging interactive fiction
AI facilitates interaction. So, beyond linear stories, we can have participatory plots that dynamically reinvent plot climaxes, based on reader input. These sorts of collaborative narratives expand creative possibility and engagement, important when you consider the task literature has to compete with movies, music, social media and ‘active lifestyles’.
6. Informed inventory forecasting
AI is really good at predicting! By synthesising indicators from past sales to consumer trends, models predict demand with amplified accuracy. They can optimise supply chains, guiding smarter print runs, reducing waste, and generally increasing sustainability whilst also maximising availability.
7. Quantified marketing effectiveness
Measuring marketing effectiveness is a long-standing puzzle. Determining campaign return on investment often perplexes publishers relying on soft attribution from promotions spanning fragmented channels. Integrating multivariate data, AI marketing mix modelling can isolate and quantifies the impact of differentiated initiatives. Still imperfect, but better!
8. Objective manuscript evaluation
There’s so much good writing that it’s almost impossible for editors to do true justice to all the submissions made. Gems are easily overlooked. AI assessment tools evaluate text consistently and at speed, alerting editors to genre, style, and, importantly, uniqueness. The AI helps with the workload, but is not the arbiter of creativity or originality. Gems can be more certainly found.
9. Rights management automation
AI algorithms parse publishing contracts holistically, and in doing so, relieve humans of the burden of identifying variances, explaining them in context. It easily and simply organises them with consistency and logic. As in the wider world of legal practice, AI simplifies the task of contract evaluation, ensuring humans have time to consider real issues for consideration, and that workloads are focused on high value-adding tasks.
10. Enriched back-list monetisation
Surfacing under-loved, or previously missed, works gets harder as catalogues grow. By matching niche audiences to niche inventory at scale, AI cracks discovery’s long tail, and long-standing problem. It can do so by understanding the true nature of a title, manifesting it in different forms, such as advertising, and showing the title to new audiences in an array of different media channels. This releases value for Publishers, gives greater discoverability to Authors, and greater satisfaction to Readers.