AI will become normal in publishing, but trust will become scarce. The market will reward those who can prove provenance, quality, legality and human accountability.

2025-11-28 · Source: Pascal’s Substack · Field: Media & Entertainment — Publishing & Journalism, Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Intermediate, long

Summary

A report by BISG and BookNet Canada, "AI Use Across the North American Book Industry 2025," reveals that nearly half of the North American book industry, 45.8% of individuals and 48.0% of organizations, is already using AI, primarily for administrative tasks, marketing, data analysis, editorial support, and metadata. Despite this widespread adoption, there is deep unease, with approximately 72% of open-ended responses expressing negative sentiment regarding AI's legal, ethical, creative, and cultural implications. Key concerns include copyright misuse, hallucinations, low-quality AI-generated books, lack of disclosure, and legal liability. The report highlights a functional divide, with AI used least in rights and licensing, and a structural warning that AI may shift verification costs from publishers to libraries, which are becoming quality gatekeepers for AI-generated content.

Key takeaway

For CTOs and VPs of Engineering/Data in publishing, your AI strategy must integrate robust governance and rights management to build trust, not just efficiency. Prioritize developing clear internal AI policies, investing in machine-readable rights data, and pushing for industry-wide standards for AI-generated content to mitigate risks and maintain brand reputation in a rapidly evolving landscape.

Key insights

AI adoption in publishing outpaces trust and governance, creating an "adoption paradox" with significant ethical and operational challenges.

Principles

• AI redistributes burdens, increasing verification costs.
• Human oversight is key for AI legitimacy.
• Trust requires provenance, quality, legality, and accountability.

Method

Organizations should implement AI governance, start with low-risk workflows, create clear internal policies, build controlled experimentation environments, and train staff on AI failure modes to build trust and accelerate adoption.

In practice

• Use AI for internal search and metadata suggestions.
• Require vendor transparency on data use and storage.
• Invest in provenance metadata and disclosure standards.

Topics

• AI Adoption
• Publishing Industry
• Copyright Concerns
• Content Provenance
• AI Governance

Best for: CTO, VP of Engineering/Data, Executive, Director of AI/ML, Legal Professional, Policy Maker

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Editorial summary, takeaway, and curation by AIssential. Original article published by Pascal’s Substack.