On the surface, book publishing and the news industry seem quite different. We recently completed a project with a European book publisher and were struck by the parallels between the two industries. Both operate under a model where content is king. Capturing the reader’s attention is becoming increasingly difficult as legacy content producers compete with a growing variety of sources of information and entertainment.

That said, in book publishing everything happens more slowly. The time it takes to produce, publish and consume a book is longer than the timeframes we are used to in the news industry. Through a publishing house, an author may take anywhere between 1-3 years to launch a book. Many books are evergreen, with classics being read decades after first publication. Instead, newsrooms can produce over one hundred articles every day, many of which are no longer relevant the next day. 

The slower pace of book publishing can make digital transformation feel less urgent. But this is a misconception – especially with the rise of AI. Last year, a survey in the U.K. found that 36% of book translators and 26% of illustrators have already lost work to AI. Moreover, tools and technology now enable anyone – Taylor Swift included – to publish a book from their bedroom in less than 6 months. This cuts out the middlemen in the process and raises questions about the role and relevance of traditional publishing houses in the future. 

When our client reached out about this project, they had realised that standing still is no longer an option. Specifically, this project was about identifying use cases for AI in this book publisher's business. They wanted to quickly get up-to-speed and find tangible ways to leverage AI in a way that made sense for their business.

How the project ran

The project was designed in three phases: 

• Phase 1: Understanding the business context (~2 weeks): FT Strategies consultants interviewed stakeholders across departments and analysed data to identify the client's strengths, pain points, and needs.

• Phase 2: Identifying relevant AI use cases (~5 weeks): This phase focused on uncovering relevant AI use cases for the client, while inspiring and upskilling their team on AI’s impact on book publishing. We hosted a range of interactive sessions with the publisher’s team, explaining how AI works, showcasing industry examples of AI applications and inviting guest speakers to demystify AI in the industry. A one-day ‘Design Sprint’ workshop engaged the publishing team in identifying use cases using FT Strategies’ 6-exercise methodology.
  
  
• Phase 3: Designing and preparing to launch an AI use case (~2 weeks): Following the Design Sprint, the team prioritised AI use cases based on implementation value and risks. We co-created an actionable plan with milestones and roles to ensure accountability throughout the journey.

What we found

In our project with the European publisher, we uncovered several interesting findings about book publishing and the role of AI in this space. 

1. Less is more - the long tail & how book publishers make money

A small number of books generate the majority of book publishers' revenue. These books are often tied to licensing agreements which we found were ~10% more profitable than the average publishing business. This finding raised a pivotal question for the project: should publishers focus on fewer titles and aim to extend their lifespan, or continue producing the same number of titles, knowing that the long tail will contribute only marginally?

AI can help answer this question. Many AI tools now can assist publishers in extending the lifespan and “liquidity” of their most valuable assets. For instance, publishers like HarperCollins are using tools like ElevenLabs to transform their books at scale into audiobooks so they can be consumed in emerging formats. Dutch publisher Veen Bosch & Keuning is using AI to assist with translation, taking an existing, popular asset and making it easily available somewhere else in the world. 

Extending the lifespan of books doesn’t necessarily require Generative AI. Traditional AI can analyse openly available data to identify opportunities for leveraging existing publisher rights in untapped areas. Tools such as Rightsline are developing this application for organisations managing rights in creative industries. 

2. Build or buy - Should book publishers build AI in-house or buy off-the-shelf?

In the final phase of the project, one key question was whether to build or buy solutions for the chosen AI use cases. For projects with a high degree of customisation or where data privacy and IP were a heightened concern, building a solution (either in-house or with a third party) made sense. We found that most use cases could leverage existing market tools, which can be trialled to start experiments. Alongside a detailed plan for the selected AI use cases, we provided our client with a Vendor Dossier — a long list of available market tools for all ideated use cases, including connections for free trials and a pre-filled specifications template for in-house builds.

Source: Artificial Intelligence & News: Build or Buy?

AI in book publishing - Vendor Dossier

3. Intellectual property concerns - few book publishers are addressing IP infringement concerns.

Unlike the news industry, where many organisations have published AI guidelines and are collaborating on global AI principles, this is uncommon in book publishing. Penguin Random House is among the few publishers with clear AI guidelines. While many experiment with Generative AI, few have published their guidelines, and some, like Bloomsbury Publishing or Tor, face backlash for lacking transparency. 

This is a clear call to action for publishers to start drafting and sharing their AI usage guidelines if they plan to experiment with AI. Our client was given a head start based on the FT’s guideline-setting methodology. Our website offers resources to help you start thinking about these guidelines and getting them live.

Outcomes

After one month of work with FT Strategies, the children’s book publisher walked away with: 

• Bespoke materials and analysis. Including detailed examples of AI use cases specific to book publishing, data analysis of their business, and templates for the Design Sprint methodology to think through and implement AI.
• A clear strategic roadmap for three AI applications. Timeframes, milestones, suggested tools and responsible people (or AI champions) were agreed for each of the selected AI use cases. This roadmap accounted for and addressed with mitigations the potential risks that could arise from AI implementation to avoid backlash. 
• Shared AI vision and team buy-in. Team members gained hands-on experience and understanding of AI and available tools, creating a common and enthusiastic vision of what AI could do for their business. 
  
  

  “It was a pleasure to work with you guys, I am really happy with my choice… open minded, curious people. My compliments again to your work.” 

  Majority Shareholder,  Dec 2024

At FT Strategies, we have deep knowledge of AI, technology & data. Our team of consultants understand the technical capabilities need to future-proof your business. If you want to find out more about services, contact our team for more information.

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About the authors

Inés Luque Calatayud, Associate Consultant

Inés Luque is an Associate Consultant who previously worked in the Business Development department of the Financial Times. She has experience working on subscription funnel analysis and strategic direction support. Inés holds an MSci in Management Science from University College London.

 

Aliyah Itzkowitz, Manager

 

Aliya Itzkowitz is a Manager at FT Strategies, where she has worked with over 20 news companies worldwide. She previously worked at Dataminr, brining A.I. technology to newsrooms and at Bloomberg, as a journalist. She has a BA from Harvard University and an MBA from Said Business School, University of Oxford. She is currently a member of the FT's Next Generation Board.