For as long as there have been technology innovators, there have been attempts to “disrupt” the publishing industry.

Some of those efforts have been genuinely useful. Many have not. And almost all of them share one thing in common: they underestimate how idiosyncratic the book business actually is.

Publishing isn’t just another content industry. It runs on long timelines, layered rights, trust, and deeply human judgment. Books are not shipped, priced, marketed, or discovered the way most other products are. Yet year after year, publishing teams are fielding pitches and demos built for industries that look nothing like their own. That’s why smarter AI adoption in publishing requires a different starting point.

AI tools are multiplying fast. New vendors appear all the time, each promising efficiency, insight, or competitive advantage. And in an industry already stretched thin, it’s tempting to believe the solution is simply finding the right tool.

But most tools don’t automatically create better workflows. In practice, without shared understanding, education, and trust, they often create more friction—not less.

This post outlines how publishing teams can move toward smarter AI adoption in publishing by focusing on education, internal clarity, and workflow alignment before adding new tools. By the end, you’ll be able to evaluate whether your organization needs another platform at all—and if it does, how to approach that decision with shared understanding instead of urgency.

The Real Problem Isn’t Tools. It’s Fragmentation.

Across industries, organizations are hitting a breaking point with their tech stacks. Employees toggle between platforms, duplicate work, and spend more time managing systems than doing the work those systems were meant to support.

An excess of tools actually reduces productivity, increases burnout, and fragments institutional knowledge.

This phenomenon is called “tech stack fatigue,” and it happens to organizations in every industry. And it doesn’t just compromise efficiency. It also takes a huge toll on team morale. People who aren’t well-trained to adopt a new system is unlikely to succeed in implementing it, and that feels bad.

Publishing is not immune. In fact, we may be especially vulnerable.

Publishing workflows are already complex, sometimes pretty analog, and deeply human. Editorial judgment, author relationships, long production timelines, seasonal sales cycles, and fragile trust ecosystems don’t lend themselves to plug-and-play solutions.

When tools are layered on top of that complexity without shared understanding, the result is chaos.

What the Ebook Era Taught Us (and What We Ignored)

During the ebook transition, publishing looked outward—to Amazon, to tech companies, to platform logic—for answers. But we didn’t upskill quick enough as an industry; we are very good at passing down the “way we’ve always done it.” We’re not great at learning and integrating new tech.

The result of being stuck in analog during the digital media revolution in the early aughts and 2010s wasn’t just lost pricing power. It was lost data, lost leverage, and lost confidence in the industry’s ability to shape its own future.

One of the biggest failures of that era wasn’t technological—it was educational. Too few people inside publishing were empowered and trained to ask the right questions of vendors. Too many decisions were made reactively, without shared literacy across teams.

AI presents a similar risk. The good news is that we know what won’t work.

If publishing repeats the mistake of adopting AI tools without building organization-wide consensus and understanding, we risk ceding not just data, but decision-making itself to vendors who don’t understand—or prioritize—the realities of this industry.

Why Education Has to Come First

Publishing organizations need to invest in education. Education is the foundation of smarter AI adoption in publishing because it determines how tools are evaluated, implemented, and governed. In training their staff, listening to concerns, and implementing changes based on measurable feedback and goals.

Not hype. Not evangelism.
Literacy.

AI literacy means understanding…

• What can AI do and what can’t it?
• Where is AI already embedded in existing tools?
• What kinds of problems is AI actually good at solving?
• Where must human judgment remain non-negotiable?
• What risks matter in publishing, specifically authorship, rights, trust, and data ownership?

Without this foundation, tool selection becomes guesswork. Teams adopt platforms they don’t fully understand, apply them inconsistently, or abandon them altogether.

Organizations with bloated tool ecosystems are increasingly prioritizing consolidation. Why? Not because innovation failed, but because fragmentation made innovation unsustainable.

Tech rationalization—reducing, aligning, and simplifying tools—is often the key to unlocking real productivity gains.

But consolidation only works when teams know why they’re using what they’re using. And the best move is to understand the internal needs before seeking tools at all, so you never have to consolidate.

That’s where education comes in.

Let Your Teams Tell You What’s Broken

One of the most common mistakes leadership makes during technology transitions is starting with solutions instead of problems.

Publishing professionals already know where the friction lives.

Editors are drowning in administrative overhead that pulls them away from actual editing.
Publicists are rebuilding the same materials across platforms with no systemic support.
Sales teams are juggling disconnected data sources. Operations and production teams are managing schedules that should have been systems years ago.

Before evaluating tools, leadership needs to ask:

• Where are we losing time?
• Where is work being duplicated?
• Where are decisions slowed down by manual processes?
• What tasks feel necessary but add little creative or strategic value?

AI should be evaluated only after those answers are clear.

Otherwise, tools end up solving hypothetical problems while real pain points remain untouched.

Vendors Should Understand Publishing—or They’re the Wrong Partner

Not all AI vendors are created equal. And in publishing, context matters enormously.

A tool designed for generic enterprise workflows may not respect:

• long editorial timelines
• nuanced rights structures
• author sensitivities
• seasonal sales dynamics
• the difference between assisting work and replacing judgment

If a vendor can’t explain how their tool fits into the realities of book publishing—rather than forcing publishing to adapt to their logic—that’s a red flag.

During the ebook era, publishing ceded ground to companies that understood scale but not books. We cannot afford to repeat that mistake.

The right vendors meet publishing where it is. They listen. They adapt. They respect boundaries.

And you can’t assess which vendor is doing that without internal AI literacy.

Fewer Tools Lead to Smarter AI Adoption in Publishing

When organizations invest in education first, something interesting happens.

They don’t need as many tools.

Teams develop shared language. Redundancies become visible. Workflows simplify. Decisions get faster—not because software is smarter, but because people are clearer.

Tool consolidation becomes strategic rather than reactive. AI is applied where it actually helps, not where it looks impressive.

This is how publishing can adopt AI without panic, without eroding trust, and without repeating past mistakes.

Curiosity Over Urgency

The pressure to “do something” about AI is real. But urgency without clarity is how organizations lose control.

Publishing doesn’t need to move fastest. (Good news!)
It needs to move intentionally.

Education, training, and learning across the organization are not delays. They are the work.

Because the future of publishing will be decided by how well your people understand the systems shaping their work—and how confidently they can shape them in return.

That’s the foundation everything else rests on.