Q: What is best practice now?
A: Print automation has matured considerably over the years but the gap between publishers who implement it well – and those who struggle – has less to do with technology than with approach.
There is a common misconception that automation means zero-touch production. And yet the same publishers who want full automation also want granular control over every placement decision. That tension is where implementations struggle.
The most effective deployments begin with a clear-eyed audit of where automation actually belongs in the publication.
The guiding principle is straightforward: let automation handle the volume pages.
These are the structurally consistent sections that – despite not being creatively demanding – can be time-consuming for designers.
The impetus behind print automation becomes giving your designers the time and capacity to focus on the feature-rich pages that genuinely benefit from their artistic judgement. Automation is not intended for highly designed, image-lead pages.
The results of getting this right can be significant. One UK regional publisher produces 47 weekly titles (about 1,500 pages per week). With a central automation unit of just six people, they turn around each paper in under an hour. That productivity is only possible because the scope of automation is well defined and the workflow is tightly integrated. The automated and the designed coexist, and the best implementations make that coexistence seamless.
The second pillar of best practice is content quality.
Automation works best when the content feeding it is clean, consistently structured and properly tagged. You cannot fit 10,000 words onto a page with a half-page ad. The publications that see the strongest results have invested in their content model: story types are defined, priorities are flagged and specific placement rules are applied where they matter.
This requires a genuine shift in editorial mindset. Editors who are accustomed to planning every article on every page will find automation counterintuitive at first. The discipline is to plan the exceptions and trust the system with the rest.
Finally, honest expectation-setting matters across the whole organisation. Print automation removed the mechanical, repetitive work. It does not remove the need for skilled designers. If anything, it makes them more important because preserving the publication’s visual identity and brand requires human judgement that no automation engine can replicate.
Q: How do you see it changing in the future?
A: The most significant shift is the move toward a genuinely unified newsroom. One where a journalist writes a story once, for a digital-first world, but thinks in parallel about how that same content translates into print.
The body copy is the same. What changes is the framing: A writer might supply two headline options, one built for SEO, optimised for search and discoverability online, and one written for a print page, where different conventions apply entirely. The dual-register thinking is becoming a core editorial skill, and the best CMS platforms are beginning to reflect it.
The second shift, and the most transformative one, is the controlled use of AI within the production workflow itself.
AI in the newsroom only works if it operates within the rules of the publication. Every newspaper has an editorial style: conventions governing headline grammar, tone, register, capitalisation, abbreviation, regional language. These rules exist for good reason; they preserve the publication’s voice and its relationship with its readers. Any AI being used in production must be anchored to those rules. That means building AI tools around an editorial style book, not deploying general-purpose language models and hoping for the best.
For example: AI-assisted headline generation, where the system reads the story, understands the available space in the print layout, and proposes candidate headlines calibrated to fit. The sub-editor reviews the options and selects the one that works best. AI does not make the decision, the editor does. That distinction matters. The efficiency gain is real, but it comes with full human oversight at the point of publication.
What will not change is the principle established in the first answer: automation, and now AI, handles the repeatable and the predictable. Human judgement, editorial, creative, ethical, handles everything else.
Q: What are your three top tips?
1. Start narrow, prove it, then expand. The temptation is to automate as much as possible, as quickly as possible. Resist it. Pick one section, ideally one with high volume and consistent content structure, and do it properly. Learn the system, build confidence, demonstrate measurable time savings.
2. Fix your content model before you go live. Print automation is only as good as the content feeding it. If story types are inconsistently applied, metadata is patchy, and journalists are publishing in ways that vary from person to person, the system will struggle. Audit how content is actually structured today, not how it should be in theory, and fix gaps upstream before go-live.
3. When you introduce AI, give it the rules of your publication. AI tools are only useful in a newsroom context if they operate with the editorial standards of the publication. That means building any AI-assisted workflow around your house style – the conventions that govern how your journalists write, how your headlines are constructed and how your publication speaks to its readers.
About Atex
Supporting publishers and media outlets to develop a sustainable business model through innovative technologies and services, Atex solutions allow today’s publishers to address and scale for tomorrow’s challenges. In the dynamic world of news, our goal is to innovate and create solutions that redefine how news is produced, shared, and monetised.
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This article was first published in Issue # 1 of Best Practice in Publishing, a new publication from InPublishing. Click here for links to the other ‘best practice’ articles from the publication.
