Superabundance Needs Superdiscoverability

The question I am asked most often is: which AI marketing tools should we be using? It is a fair question. But it is not the most useful one. The real shift underway is not about tools. It is about how marketing works.

For years, publishing has operated with a structural constraint we have accepted as inevitable. We publish more books than we can market. The result is that a small percentage of titles receive most attention, while the rest of the catalogue becomes invisible.

We call this superabundance. And superabundance needs superdiscoverability.

Changing problems into advantages

Until recently, superabundance was a limitation. AI changes that. Not because it allows us to market every book. AI does something far more valuable: it helps us understand what should be marketed, when and why. It can turn superabundance from a problem into an advantage.

This is where most conversations about AI tools fall short. They focus on speeding up existing tasks. Writing copy faster. Generating more creative. Automating campaign setup. Those use cases are helpful, but they do not address the core problem. The real opportunity is not faster execution. It is better decision-making.

At the heart of this shift is a different way of understanding what is being marketed. Historically, marketing has relied on surface-level signals: genre, comparable titles, broad audience assumptions. AI allows us to go deeper, by analysing a book's themes, tone, emotional drivers and underlying appeal at scale. This moves marketing from broad categorisation to genuine understanding, and that understanding determines which books have commercial potential at a given moment, which audiences are most likely to respond and which angles are worth testing.

In that context, the role of tools becomes clearer. They are not the strategy. They are the infrastructure that supports it. Across the industry, a new layer of book-specific tools has emerged that maps to different parts of this system.

Discovery and tools

Discovery still starts with presence. Your website remains one of your most important marketing assets, not just for search engine optimisation (SEO) but increasingly for generative engine optimisation (GEO). Platforms like Supadu help publishers ensure their catalogues are structured, visible and accessible to both readers and AI-driven discovery systems.

Prioritisation is the next challenge. If you cannot market everything, you need to know where to focus. This is where heat scoring comes in: identifying which titles in a catalogue have the highest potential at any given moment. But this is only half the equation. The other half is acting on that signal quickly. Autonomous advertising tools can take a heat score and immediately deploy media spend behind a title, reaching the right audience, without waiting for a campaign brief. This is where the system starts to close the loop. It is the problem my company, Shimmr AI, is focused on solving across both prioritisation and activation.

Once a title gains traction, metadata becomes critical. Timing matters. Updating keywords, positioning and descriptive copy when a book is performing can materially impact its reach. Tools like Firebrand Technologies' Flywheel are built around this idea of dynamic metadata optimisation.

Discovery is not only about systems. It is also about people. Authors who build direct relationships with their audiences create entirely different discovery dynamics. Platforms like Stck enable authors to develop their own communities and engagement with readers.

AI is also expanding what discovery means by opening access to entirely new audiences. Translation is one example, with tools like Ailaysa lowering the barrier to reaching readers in new languages. AI-generated audio is making content more accessible in different formats. Companies like ElevenLabs enable scalable audiobook creation, while platforms like Beat Technology provide the distribution infrastructure to deliver that audio directly to readers.

Changing mindsets

Many of the most interesting developments come from companies focused on books and publishing, not generic marketing tools repurposed for the industry. They are designed around the unique challenges of catalogue scale, metadata and discovery.

Individually, none of these tools solve the problem. Together, they form something more powerful: a system that can evaluate a catalogue, identify opportunity, activate the right titles and learn from outcomes.

This is a different model from the one most publishers operate today. Marketing has traditionally been campaign-based and frontlist-focused, with limited capacity to revisit titles once the launch window has passed. AI introduces a more continuous approach: not constant marketing of every book, but ongoing evaluation of where opportunity exists, driven by cultural context, seasonal trends and emerging reader signals.

Having access to these tools, however, does not automatically lead to better outcomes. The organisations seeing meaningful impact are not simply using more technology. They are building structured workflows, defining how decisions are made, how experiments are run and how learning is captured and reused. They are treating AI as part of their operating model, not as an add-on.

For the first time, the industry has the infrastructure to navigate superabundance. Not by trying to market everything, but by knowing exactly what to market and when it matters.

The industry's biggest challenge has just become its greatest advantage.

Brooke Dobson is Chief Commercial Officer and Co-Founder at Shimmr AI.

You can connect with her on LinkedIn.