AI is rapidly becoming part of the scholarly publishing conversation. Publishers are applying it to editorial triage, peer review support, production automation, metadata enrichment, and discovery optimization. The pace of experimentation is high, and the expectations are higher. Yet many AI initiatives struggle to scale beyond pilots or to deliver consistent, repeatable value. The issue is often framed as a model problem. In reality, it is far more often a data problem.
Academic publishing sits on decades of accumulated scholarly content, but that content has not always been built with machine use in mind. Data is frequently fragmented across platforms, inconsistently structured, unevenly curated, and governed by varying standards. When AI systems are introduced into this environment, even the most advanced models are constrained by the quality and coherence of the information they are asked to work with.
This is why progress with AI in academic publishing depends less on selecting the latest model and more on strengthening the underlying data infrastructure. AI systems learn patterns, relationships, and context from data. When metadata is incomplete, taxonomies are misaligned, references are poorly resolved, or full-text content lacks structural consistency, AI outputs become unreliable. The result is uneven performance, increased manual intervention, and limited trust from editorial and research communities.
The challenge becomes especially visible when publishers attempt to operationalize AI across workflows. An AI model may perform well in a controlled pilot, but once deployed across journals, disciplines, or legacy platforms, inconsistencies in data structure and curation quickly surface. Editorial decisions, integrity checks, content classification, and discovery recommendations all depend on a shared, trusted knowledge foundation. Without it, AI remains a layer of experimentation rather than a dependable operational capability.
Strengthening data infrastructure means investing in deeply curated and standardized scholarly content. This includes robust metadata frameworks, persistent identifiers, clean reference networks, consistent content structures, and governance models that ensure data quality over time. It also requires treating content enrichment, validation, and normalization as ongoing processes rather than one-time clean-up efforts.
Importantly, this work is less visible than model deployment, but far more foundational. Publishers that focus first on their data ecosystems are better positioned to adopt AI responsibly and sustainably. They gain greater transparency into how AI systems behave, more control over outcomes, and the ability to adapt as models, platforms, and discovery behaviors evolve.
As AI continues to mature, competitive advantage in academic publishing will not come from access to the same general-purpose models available to everyone. It will come from the strength of the knowledge infrastructure those models are built upon. AI can only be as reliable as the data that feeds it. For scholarly publishing, the path forward is clear. Before asking what AI can do, publishers must ensure their data is ready to support it. Know More
Knowledgespeak Editorial Team
