AI design tools produce visually compelling output with no print physics embedded in them.
Tools like Adobe Firefly, Canva, and AI-assisted brand toolkits generate beautiful layouts, but they lack critical production data. Information like bleed, color separation, or minimum dot constraints for a specific flexo screen ruling rarely come embedded in the file.
SKU proliferation only compounds this issue. A brand running 40 regional variants of a single label, all adapted by AI, sends 40 files to prepress. Each individual file must be checked and made print-ready for a specific press, substrate, and ink system.
Efficiency upstream creates more load downstream. That is Jevons Paradox applied to the packaging supply chain.
Every converter has a print specification. But preflight and a print spec are checking fundamentally different things:
A file can pass every preflight check and still fail on the press it is assigned to.
Common mismatches include rich black built as 4C when the press demands 2C, BWR set for the wrong print direction, multi-ink type falling below the minimum font size, or gradient-and-transparency combinations creating scum dot risk in flexo highlights.
A well-maintained, customer-specific preflight profile can catch these errors, but only if someone has explicitly encoded and maintained the rules. Building those profiles is expensive, requires specialist knowledge, and must be completely redone every time a customer changes their press, substrate, or ink system. Because of this overhead, most shops run a generic profile and rely on operator experience to fill the gap. That reliance breaks down when production variables change—which they frequently do.
FIVE CLASSES OF PREPRESS DECISION
The first class is solvable with a rules engine. Classes three through five require the system to know the production environment: press, substrate, ink system, and barcode standard. A graphics engine connected to the job’s production specification holds that context, while a generic AI model does not.
Adding more preflight rules and sending longer error lists back to designers only adds cycles without fixing the structural problem. The production specification must be the starting point.
If the spec is machine-readable, structured, and linked to the job, the graphics engine can read it the moment the file opens. Every action then gains immediate context. The TAC limit, min dot, and rich black recipe for that specific press and ink system are instantly known.
The Current State: Preflight checks if the file is technically valid and flags errors. It has no knowledge of which press, substrate, or ink system the job is actually going to.
The Future Direction: Esko is linking the production specification directly to the job. The graphics engine reads it at file open, giving every action context. The system can then state: “This font is below spec for multi-ink type on this press. Increase the weight, reduce the ink count, or flag for review.” The operator receives a specific recommendation, retains full control, and every decision is logged.
This is the automated concept Esko is working toward: object recognition trained on packaging file structures, classifying objects by type and production role, paired with structured production knowledge linked directly to the job.
The prerequisite for this future is structuring the specification. Ingesting a specification once per customer is materially less expensive than encoding that same knowledge into custom preflight profiles one rule at a time.
116 prepress professionals confirmed that the file itself remains the core problem. The industry has spent 20 years automating what happens inside the prepress department. The next five years belong to whoever solves the problem of what arrives at the door.
Esko is actively working on the capability described in this article – connecting structured print specification knowledge directly to the graphics editor workflow. If this is a problem you are solving today, we would like to hear from you.
Follow the Esko blog for updates as this develops.
