Publishing Meta

Opinion: AI in Publishing – Frameworks or Fear?

When publishers encounter artificial intelligence (AI), their first instinct is often to create a framework. A structure to contain the unknown, to regulate the unregulated. While that sounds responsible, it often feels like fear masquerading as strategy. The question isn’t whether frameworks are necessary—they are—but whether the frameworks actually solve the problems publishers face or simply delay meaningful engagement with AI’s potential.

The publishing industry, like education technology more broadly, has a long history of reacting cautiously to disruptive technologies. In many cases, those reactions stem not from a deep understanding of the technology but from a defensive posture rooted in protecting intellectual property and existing revenue streams. That’s understandable—for industries built on the ownership and distribution of content, the idea of a machine being able to create, remix, or analyse that content introduces existential concerns. But AI is not going to wait for publishers to get comfortable.

Frameworks as a Band-Aid

Let’s be clear: frameworks aren’t inherently bad. They can provide guardrails for ethical use, set boundaries around data privacy, and ensure compliance with regulations. But too often, they’re used as a delaying tactic—a way to look busy while avoiding the hard questions. What kind of data will we need to train AI models? What level of transparency will we demand from AI vendors? How do we handle the inevitable tension between automation and human creativity?

The danger of “fear wearing a lab coat,” to borrow the metaphor, is that it creates the illusion of progress while stifling innovation. A framework that doesn’t address the underlying challenges—not just technical, but cultural and operational—does little more than buy time. And time, in this case, is a finite resource. AI adoption is accelerating across industries, and those who wait too long risk becoming irrelevant.

The Real Risk: Paralysis

If fear of AI’s risks prevents publishers from engaging with the technology at all, that fear becomes self-fulfilling. History offers plenty of examples of industries that waited too long to adapt—think of how the music and film industries initially resisted digital formats, only to find themselves scrambling to catch up. If publishers don’t start experimenting with AI now, they risk being overtaken by competitors who do. Worse, they risk losing control of the narrative around AI, allowing vendors and technologists to dictate how the technology shapes the future of content creation and distribution.

This isn’t just about protecting intellectual property or existing business models. It’s about recognising that AI is already reshaping the landscape of how knowledge is created, consumed, and monetised. In education technology, for example, AI is being used to personalise learning, automate grading, and even create adaptive content. These are not theoretical applications—they’re happening now.

The Power Imbalance

What publishers should be asking themselves is this: Who will control the AI tools that determine the future of publishing? Big tech companies are already consolidating power in AI development, and they don’t necessarily share the same priorities as publishers. For tech firms, AI is a means to scale and optimise; for publishers, it’s a tool to create and curate. That’s not just a difference in function—it’s a fundamental difference in philosophy.

The publishing industry must grapple with the fact that the companies building AI tools are often the same ones building the platforms that distribute content. This creates a dynamic where publishers risk becoming dependent on vendors whose interests may not align with their own. Without a clear strategy for how to use AI—not just frameworks but actual implementation plans—publishers risk ceding their power and influence to those who control the technology.

A Call to Action

The publishing industry doesn’t need to choose between caution and innovation—it needs to balance them. That means building frameworks that are not just reactive but proactive, designed to enable experimentation while managing risk. It means investing in the expertise needed to understand AI beyond the buzzwords. And it means asking the hard questions about how AI will impact not just content creation but the fundamental dynamics of who holds power in the industry.

If publishers don’t start engaging with AI now—not just theorising but actually building, testing, and learning—they will find themselves at the mercy of external forces they cannot control. That’s the real risk, and no framework can protect them from it. Fear is not a strategy. It’s time for publishing to stop reacting and start leading.