AI cannot tell the truth if the data it relies on is incomplete, inconsistent, or disconnected.
AI will be a major topic at PRINTING United Expo, and for good reason. It can support production print operations, summarize performance, identify anomalies, explain recurring downtime, guide supervisors, and simplify complex data queries. AI can transform a three-hour reporting task into a simple system query.
However, AI does not eliminate the need for disciplined data management.
Many organizations seek AI-driven insights, but their operational data is often fragmented. Job information may reside in one system, machine status in another, and operators may record reasons inconsistently. Spreadsheets are used to fill gaps, while some events go unrecorded or lack context. In such environments, AI may provide fluent answers, but fluency does not guarantee accuracy.
The first rule of production AI is clear: poor data does not yield valuable insights, regardless of processing speed.
Effective AI begins with data capture. Operations require signals that describe events such as machine state, counts, speed, alarms, idle time, waste, job IDs, shift details, operator context, material usage, and process milestones.
Next, data must be normalized, as different machines and departments may describe similar events differently. Correlation is also essential, since the significance of a stop varies by job type, substrate, run length, finishing path, due date, and estimate.
Without that groundwork, AI can misread the operation. It may mistake downstream congestion for press underperformance. It may interpret a scheduling issue as an operator issue. It may miss the distinction between planned and avoidable idle time.
With a solid data foundation, AI becomes significantly more valuable. Production leaders can ask why output dropped and receive a clear summary. Supervisors can track increases in recurring alarms. Continuous improvement teams can identify job families that consistently miss estimates. Executives can determine where recovered capacity would have the greatest financial impact.
AI can also support workforce development. While many organizations rely on experienced staff to recognize operational patterns, AI can help capture and present these patterns in ways that benefit newer managers.
For this reason, the AI roadmap should begin with a discussion on data readiness. Consider these questions: What do we collect automatically? Can we link events to jobs? Are the reason codes consistent? Can we distinguish machine downtime from workflow delays? Do we know the dollar value of recovered minutes?
AI will play a key role in the future of production printing. Companies that provide AI with accurate operational data will gain a competitive advantage.
Visit us at PRINTING United Expo, September 23–25, Las Vegas Convention Center, Booth N6349.