Bringing AI into Print Production: Connect Three Workflows First

AI (Artificial Intelligence) in print production software is being used for prepress automation, intelligent workflows, and data-based decisions. These were also the focus of a session at FESPA Conference 2026. FESPA, an international organization that runs print industry conferences and exhibitions, says in its session overview that strategic adoption aims to raise productivity without adding staff. The page does not list software examples or results

Mai Strategy defines "AI in print production software" as features built into prepress or production management workflows that use job data to identify conditions and help carry out tasks. A person still needs to check the output against quality standards. Before buying, identify the steps that often involve waiting or rework, then assess what adoption would require. Don't treat a conference topic as a promise of return on investment

The best tasks for a print shop to try first are ones with clear requirements, repeated work, and results someone can review. FESPA's session overview names prepress automation, intelligent workflows, and data-driven decision making, and notes that AI is moving onto the production floor. Mai Strategy recommends matching these ideas to actual shop-floor tasks

Mai Strategy starts by checking for missing files

・Mai Strategy starts at intake: check dimensions, bleed, fonts, and file versions. If information is missing, send the job back for corrections

・Next, sort out estimating: paper, colors, finishing, and delivery date fields should all match the same order record

・Then try scheduling prompts or inspection alerts: let the system flag changes and questions, while production control or inspection staff decide whether to adjust the job

FESPA lists strategic adoption as a goal for its session. Mai Strategy recommends organizing data and handoff rules first, and assigning an owner and checkable output to each of the three adoption areas. The system makes decisions based on the fields and workflows it is given

・Specification checkpoint: standardize fields for stock, dimensions, colors, finishing, delivery dates, and file versions

・Status checkpoint: specify who updates each stage, from awaiting files and prepress completion to press, finishing, and shipping

・Responsibility checkpoint: spell out what the system may flag or do, and who reviews cases with missing information or out-of-spec details

To see results, a small or midsize shop can pilot one workflow and compare the time spent on similar jobs, missing information and rework, and on-time delivery. FESPA's session overview points to higher productivity without adding staff, but gives no figures for expected gains. Mai Strategy recommends using the shop's own baseline to judge results

Mai Strategy says to keep human review in place

・Record the current process first: how long it takes from waiting for files to completing prepress checks, and which errors often lead to a job being sent back

・Keep sign-off in human hands: the person responsible should still approve color, file version, and finished appearance

・Before expanding, check that the pilot improved turnaround time or reduced rework, and that order data is accurate. Then connect the next workflow

Brand clients and designers can reduce repeated questions from production by providing clear specifications. FESPA Conference 2026 covers specialty print, signage, textile, and personalisation, each with different print requirements. Mai Strategy recommends recording finished dimensions, materials, finishing, delivery dates, and the approved version on the purchase order. Design files should identify their version and any open questions. To map handoffs from order to delivery, contact the consulting team at Mai Strategy Knowledge Academy to lay out responsibilities and data fields

・Mai Strategy recommends starting with tasks that are easy to check and often repeated, and validating one workflow first

・The FESPA 2026 session overview Mai Strategy refers to focuses on prepress automation, intelligent workflows, and data-based decisions. It gives no figures for expected gains

・For AI adoption, Mai Strategy recommends setting specifications first, aligning statuses next, and defining responsibilities clearly

・Mai Strategy recommends keeping people responsible for quality sign-off and measuring turnaround time, missing information, and rework first

A print shop does not need to start by talking about AI across the whole facility. Mai Strategy recommends choosing the handoff that gets stuck most often, making sure the approved file, order fields, and owner are all in place, then comparing turnaround time, returned jobs, and rework. If the data still does not line up, spend the next budget on organizing master data and workflows. Automating bad rules just copies the same mistakes to the next job ticket faster

・AI Is No Longer a Gimmick: Why Print Production Software Could Be Your Most Productive Employee