by Cassandra Balentine
One popular use of AI in production print is generative layout. Today’s AI-based layout tools continue to evolve and improve.
Ayelet Szabo-Melamed, VP marketing, XMPie, says the technology has improved enormously over the last two years and for many simple applications, the results are already surprisingly good. “However, ‘professional design’ is still a high bar,” he admits.
Layout isn’t simply a matter of making content fit on a page. Szabo-Melamed stresses that designers make judgments about hierarchy, branding, readability, emotional impact and audience expectations. AI can assist with those decisions and increasingly automate routine design tasks, but for high-value customer communications there is still significant value in human oversight.
Piet DePauw, head of marketing, Enfocus, admits that AI-based layout tools have improved quickly, particularly for digital content, presentations, and web-based design. “They can now make useful suggestions around hierarchy, image placement, white space, cropping, and visual balance. For early concepts, rapid mock-ups, or simple marketing assets, this can be valuable.”
Print is more demanding. DePauw cautions that a layout that looks acceptable on screen is not automatically ready for production. It must account for bleed, trim, safe areas, folds, binding, finishing, substrate, color management, image resolution, barcode placement, regulatory text, postal requirements, imposition, and many other factors.
Many general-purpose AI tools are still optimized for screen-based output, including HTML-style layouts, rather than production-ready print files. “So, can AI help make layouts better? Yes. Can it reliably replace professional design and prepress judgement without intervention? Not yet,” shares DePauw.
Naimish Patel, VP of sales, OnPrintShop, agrees, noting that this is an question asked a lot. “The honest answer is yes, but with important caveats.”
Capabilities have advanced significantly and AI layout tools today can analyze content length, hierarchy, and image composition to make reasonable decisions about white space distribution, type sizing, and image crop placement. They understand design principles in a learned, pattern-recognition way. For templated, high-volume work where brand guidelines are defined and the design vocabulary is constrained, they perform well.
However, they still struggle with design judgment under ambiguity. “When a recipient’s name is unusually long, or the offer copy runs to three lines where the template expected two, or the product image has a composition that does not suit the allocated crop zone, AI systems can produce technically valid layouts that look wrong to a trained eye. The rules are followed. The result is not quite right,” comments Patel.
A practical answer for most print businesses is to use AI layout generation as a first draft engine, not a final approval engine. “Let it generate 5,000 layout variants. Flag the outliers statistically. Route flagged pieces for a human review. That hybrid model captures the speed benefit while maintaining quality control at a fraction of the cost of reviewing everything manually,” suggests Patel.
The trajectory is clear. “Each generation of these tools makes better decisions in more edge cases. The circle of human intervention is shrinking. Within two to three years, I expect the manual review threshold for templated VDP to drop substantially,” predicts Patel.
“The future is likely to be collaborative rather than fully autonomous, with AI handling routine layout optimization while designers focus on strategy, creative direction and brand stewardship,” notes Szabo-Melamed.
Sep2026, DPS Magazine
<h2>AI, VDP<h2>
