from ai to output

With AI, it is becoming a viable publishing proposition. AI-assisted creation, automated production and print-on-demand are lowering the cost of producing more versions in smaller quantities. Rhapsody Media has built its business around precisely this convergence. Its proposition is not simply to automate individual tasks, but to connect creative work, content production and output in one environment. For print and cross-media publishers like the renowned London-based visual arts publisher Thames & Hudson, working like that promises something more valuable than efficiency alone: the ability to make products economical that previously were not.

Rhapsody describes itself today as an AI-enabled creative systems company. Behind that formulation lies a hybrid business model combining creative and production services with proprietary workflow technology and artificial intelligence. Its central platform, Engine 2.0, handles production planning, digital assets, collaboration, approval processes, PDF creation, reporting and increasingly AI-assisted tasks. A high four-digit number of people daily use the system across consumer and contract publishing, catalogues and book production, according to the company.

What makes this remarkable is not another collection of AI tools. The value lies in connecting them to a production process. Generative AI can make images cheaper to create, accelerate adaptation and localisation and assist with text transformation. Yet these gains disappear quickly when people subsequently have to move files manually between editorial systems, layout applications, asset databases, proofing environments and production departments. Software controls the workflow, while services and automation handle an increasing proportion of the work flowing through it.

For publishers, particularly those with significant print output, AI-based asset handling can change the economics of the product portfolio.

Much of the publishing debate surrounding AI has concentrated on whether machines will replace editorial or creative labour. For print-oriented publishers, another question may prove commercially more useful: can AI make more products economically publishable? Can AI help keep the competitors at arm’s length instead of just laying off staff to save money?

Consider localisation. A publisher may have intellectual property that could theoretically address several markets, but translating, adapting, redesigning, checking and producing separate versions may cost more than the expected revenue justifies. AI can reduce some of these upstream costs. Automated workflows can reduce them further. Print-on-demand then removes part of the inventory risk downstream.

The result is potentially significant. Instead of producing one large homogeneous edition, a publisher can contemplate several smaller ones exactly tailored to the needs of more individuals: local-language versions, customised educational materials, regional covers, corporate editions, personalised publications or titles aimed at narrowly defined communities. Print-on-demand is therefore one important part of the equation but just one. Its full economic value emerges when the cost of preparing a print job falls alongside the cost of producing it.

This development favours publishers whose businesses are moving towards greater variety and smaller quantities. Traditional offset economics reward volume. Digital printing and print-on-demand reduce the penalty attached to short runs although production departments can still face virtually the same administrative burden whether they manufacture 200 copies or 20,000.

That makes workflow automation crucial. If metadata, assets, layouts, approvals and print-ready files can be managed automatically, the fixed production cost per title falls. A catalogue publisher can create additional product variants. An educational publisher can update material more frequently. A specialist publisher can maintain slow-selling backlist titles without warehousing thousands of copies. A magazine publisher can repurpose material into special editions or commercial publications. The same infrastructure also serves digital channels. An asset prepared once can feed websites, social media, apps, newsletters or marketing campaigns. Print remains especially interesting because automation can unlock products that previously failed the break-even calculation, while cross-media use extracts still more value from the same underlying content.

In international publishing, localisation may be the most consequential application. The more automatically content can be translated, adapted, checked and reformatted, the easier it becomes to test markets without committing to large initial print runs.

One of publishing’s largest AI problems remains the fact that individual tools are becoming abundant but integration remains scarce. A publisher may already use AI for translation, image generation, metadata, copywriting or content analysis. Connecting those applications to established editorial processes without compromising brand consistency, rights management, quality assurance or production reliability is considerably harder.

An answer is to make AI part of the workflow rather than another application sitting beside it. Its development plans increasingly refer to agentic systems able to coordinate production tasks and to data pipelines connecting creative work with performance information. That could eventually allow a publishing system not only to produce content but to react to demand. A successful cover treatment might trigger variants. Strong demand in one territory could initiate a localised edition. New content might automatically produce marketing assets and output files for several channels. The technological components already exist in partial form. The challenge is combining them into a reliable production environment.

A production platform becomes strategically more valuable when it enables another language edition, preserves a backlist title, supports a print run of 150 copies, creates ten regional catalogue versions or turns one editorial asset into several commercial outputs, to mention just a few items. That is where the convergence of AI, workflow automation and print-on-demand becomes more than an efficiency story.

For decades, publishing production was optimised around making many identical copies efficiently. Rhapsody Media’s business model points towards a different optimisation problem: making many different products efficiently.