Insight August/September 2026 www.pwemag.co.uk Plant & Works Engineering | 11 inspection systems, and quality records. It links that information across builds and across time. It makes the data usable for traceability, reporting, SPC, root-cause analysis and, eventually, AI. The word “backbone” is important. AI cannot be a floating layer above production. It needs a spine: a reliable model of the process that tells it what the data means. This is where many AM organisations need to rethink their IT architecture. ERP remains essential for business truth. MES remains essential for execution truth. But AM needs a dedicated production-level quality software that owns quality truth (part-level evidence, powder genealogy, process history, inspection results, stability metrics, and repeatable reports.) Only then can AI move from experimentation to operational credibility. SPC before AI One of the mistakes companies make is jumping directly from manual reporting to AI prediction. That skips a crucial maturity step, statistical process control. SPC is not old-fashioned. It is one of the best readiness tests for AI in production AM. If a company cannot define critical-to-quality characteristics, monitor process stability, detect drift, understand variation, and respond through defined control limits and reaction plans, then it is not ready to delegate judgement to AI. It is still learning how to describe its process. SPC forces discipline. It asks, what are we measuring? Why does it matter? How stable is the process within a build and across builds? What input variables appear to influence the outcome? Which variations are normal, and which are signals of risk? These are exactly the questions that make AI useful later. A mature AI strategy in AM should therefore not replace SPC. It should build on it. SPC gives the process its language. AI can then help recognise more complex relationships, accelerate analysis, and suggest earlier interventions. But without SPC discipline, AI risks becoming a black box sitting on top of a grey fog. AI needs labels, and AM often hides them In machine learning, labelled data is precious. In production AM, the labels are often buried. A part may be accepted or rejected, but why? Was the root cause powder condition, laser drift, parameter choice, build layout, post-processing variation, operator error, inspection uncertainty, or a maintenance event? Was it truly scrap, or was it accepted under deviation? Did it fail CT but pass mechanical testing? Did the coupon represent the part accurately? Was the parameter set later revised? These details matter because they turn outcomes into learning signals. If organisations only store final reports, AI learns very little. It sees results, not reasons. It sees pass/fail, not process history. It sees defect categories, not the chain of events that produced them. A quality data backbone turns production into a learning environment. Every build becomes more than an isolated event. It becomes a structured data point in an evolving understanding of the process. That is where AI becomes powerful. The commercial reason to care There is a practical commercial reason to build this backbone before chasing AI, and that is credibility. Customers in regulated sectors do not want experimental intelligence. They want controlled intelligence. They want confidence that recommendations are grounded in traceable evidence and that data can support qualification, audit readiness, and continuous improvement. If a supplier can show that its AI efforts are built on connected powder, process, and inspection data, the conversation changes. AI is no longer a marketing claim. It becomes an extension of quality maturity. This is powerful because the market is becoming more demanding. It is no longer enough to show a successful build. Customers increasingly want to know whether the supplier can reproduce quality, explain variation, prove conformity, and reduce risk as production scales. The companies that can answer these questions with structured data will be in a stronger commercial position than those offering only machine capacity and enthusiasm. What AM leaders should do now The path does not begin with hiring a data scientist and asking for a prediction model. It begins with building the evidence chain. Start with traceability. Link powder, build, postprocessing, and inspection at part level. Standardise how quality data is captured. Reduce manual reporting. Define critical-to-quality attributes. Implement SPC where variation matters most. Make root-cause analysis faster by connecting machine events, maintenance, powder state, and inspection outcomes. Create repeatable reports that do not require forensic spreadsheet work. Then, and only then, ask where AI can add value. Can AI help detect early drift? Can it identify correlations between powder reuse and quality outcomes? Can it suggest more targeted inspection? Can it shorten root-cause analysis by surfacing likely process changes? Can it support parameter development by learning from historical production evidence? These are credible AI questions because they start from a credible data foundation. The future is not AI versus quality management Some people talk about AI as if it will replace quality management. I think the opposite is true. AI will make quality management more important, not less. As algorithms become more involved in production decisions, the need for structured evidence, traceability, governance, and accountability increases. The better the AI, the more important it becomes to understand what it was trained on, what it sees, what it does not see, and why its recommendations should be trusted. For AM, this is a significant opportunity. The industry has always been data-rich, but it has not always been data-disciplined. AI gives us a reason to become disciplined faster. At amsight, our view is clear, the future of AM quality will be data-powered, but only if the data is connected, contextualised and usable. The digital quality backbone is the enabling part. Without it, AI remains a slide in a strategy deck. With it, AI becomes a practical tool for supporting more reliable, lower-risk AM production at scale.. The companies that understand this will move faster. Not because they adopt AI first, but because they build the foundation that makes AI worth trusting. And in production AM, trust is the real technology. For further information please visit: https://www.amsight.de Figure 2: AI is only as credible as the quality backbone beneath it.
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