AI at the Edge: Why Print Cannot Afford to Miss Out
This article was inspired by a FuturePrint Podcast interview. You can listen here.
If you spend any time in and around industrial print, you will already feel it: AI has moved from buzzword to board-level agenda item. Yet in many factories, the reality on the shop floor remains notebooks, ‘tribal knowledge’, and mechanical production lines.
That tension is exactly where 42 Technology operates. In this conversation, Dr Peter Brown, physicist and Chief Commercial Officer, and his colleague Jamie Jeffs, Director of Industrial Instrumentation, set out a pragmatic roadmap for AI in manufacturing and print – and why doing nothing is now the riskiest strategy of all.
At heart, 42 Technology is a bridge between bleeding edge innovation and real-world product development and manufacturing innovation. On one side are chip makers, AI model developers and research labs; on the other are OEMs, print technology suppliers, manufacturers and print production companies who know their processes intimately but don’t have the time or in-house capacity to track every twist of the AI hype cycle. The value 42T offers lies in translating between those worlds - turning abstract capability into working systems that actually improve uptime, yield and margin.
A big focus for 42T is “edge AI” - running AI models close to where data is generated, rather than pumping everything up to the cloud. For industrial print, that might mean sensors and processors embedded in a press, in a coating line or on a critical sub-assembly. Instead of streaming huge datasets to a remote data centre, decisions are made in milliseconds at the machine: detecting anomalies, predicting failures or optimising parameters on the fly.
The appeal is clear. Latency falls. Cyber risk is reduced because sensitive production data stays on site. Plants that are understandably wary of exposing live manufacturing data to the public cloud can still benefit from advanced machine learning and even small language models running on specialised neural processing units. For sectors like pharmaceutical packaging or high-value décor, where validation and IP protection are paramount, this truly matters.
But Brown and Jeffs are deliberately sceptical about “AI for AI’s sake”. Their starting point with any client is disarmingly simple: what problem are you trying to solve, and what financial return would make it worthwhile? In their experience, a surprising proportion of AI initiatives fail not because the technology is inadequate, but because this kind of fundamental question was never properly defined – or because the underlying data is patchy, incomplete or simply not captured at all.
Walk into many factories and you will still find critical process data handwritten on paper and stored in boxes, or logged on local servers and overwritten when disks are full. Even where data exists, it may not be tied to specific machines, batches or maintenance events. Before you can talk credibly about AI, you have to understand what you’re measuring, how you’re measuring it and whether it is reliable. In some cases, conventional analytics can deliver 80% of the benefit without the complexity of full-blown AI.
Where AI does make sense, the opportunities for print are extensive. Vision systems are an obvious starting point. Today’s inspection cameras capture enormous quantities of information, only to have most of it discarded so that traditional algorithms can run in real time. Edge AI can instead learn directly from those rich visual signatures - comparing live output to ideal reference prints, spotting subtle defects, banding or registration drift, and feeding corrections back to the press.
Beyond the press room, AI can be applied up and down the supply chain. For example, printheads are notoriously difficult to manufacture consistently; pigments and other raw materials are variable by nature. Machine learning models trained on multimodal data (vibration, temperature, pressure, images and more) can help stabilise these processes, tightening tolerances and reducing waste. Similarly, condition monitoring and predictive maintenance can shift service strategies from reactive firefighting to planned interventions based on actual equipment health.
A recurring theme is institutional knowledge. Many print operations rely on a handful of experts who “just know” how to coax a difficult machine into running. That is both a strength and a vulnerability. AI, particularly natural-language interfaces backed by domain-specific models, offers a way to capture and democratise that expertise. If you combine feed manuals, historical logs and process know-how into a model focused on a single press platform, then operators can start to ask questions in plain language rather than deciphering cryptic error codes or trial-and-error adjustments.
Crucially, Brown and Jeffs do not see AI as a route to removing people from the loop. They talk instead about AI assistance. Systems that take on the dull, repetitive and fragile parts of the job so that skilled staff can focus on higher-value work. That stance may prove critical in an industry already struggling to recruit and retain production talent, and in a generation of new engineers who expect the same intuitive user interfaces at work that they enjoy on their smart phones.
Technically, none of this happens in isolation. 42 Technology’s model depends on an ecosystem of partners: Synaptics for edge-AI silicon, Grinn for system-level modules, Innatera for neuromorphic “spiking neural network” chips that only wake up when something interesting happens, and Fujitsu’s research labs for cutting-edge algorithms. The consultancy then wraps those ingredients into bespoke systems for clients by choosing the right tool for each job while remaining independent enough to say no when AI is not the answer.
If all this feels overwhelming, Brown’s warning is blunt: the bigger risk is standing still. Chinese and other Asian manufacturers are not waiting politely for Western print and packaging companies to get comfortable. They are investing in AI, automation and robotics now. And in sectors where margins are already thin, the productivity gap will only widen. For European and US businesses, the choice increasingly is not whether to engage with AI, but how fast.
The good news is that there is a rational way forward. Start with a clearly defined problem. Audit your data and instrumentation. Decide what must be done at the edge and what, if anything, belongs in the cloud. Run small experiments, learn, and scale only when the value is clear. Above all, keep humans firmly in the loop.
Look back in three or five years and AI in print may feel less like a sudden revolution and more like a creeping normality. Better uptime here, fewer defects there, a new service model emerging somewhere else. But the groundwork is being laid now. For those willing to engage thoughtfully, the next phase of digital print may be shaped as much by smart AI strategy as by ink and media.
Dr Peter Brown will be speaking at the AI for Industrial Print Conference which is part of FuturePrint Industrial Print 21-22 January in Munich.