AI IN PRINT
Automate print supply management Imagine never having to make another printer procurement request ever again. With real-time consumables tracking and predictive analytics, machine learning algorithms can automate the entire print supply management process, right down to ordering and replenishment. By integrating with vendor APIs, AI can predict a shortage, procure supplies and have them delivered, without IT ever lifting a finger. All you have to do is physically top up the machine. Machine learning can also remember personalised print settings The more you print with machine learning switched on, the better it knows your printing preferences. This might include stuff like paper size, colour settings, duplex printing and document formatting. AI models will incorporate user feedback into these systems over time, becoming more accurate and refined, eventually applying personalised print settings for every user. This has a few benefits: it speeds up the print flow, minimises misprints and formatting errors and improves the overall user experience. AI print services mean reduced waste You might notice a common theme running through these features: efficiency. Cost cutting. Removing or reducing unnecessary printing. Only ordering and using the consumables you actually need. This is great for the printing budget, but it also helps cut down on paper use and e-waste, both of which are major issues in the printing industry. Stats on this are hard to come by – widespread adoption of printing AI is still in
its infancy – but in 3D printing, for example, it’s been shown that AI-based optimisation allows for one ‘free’ print after every 6.67 prints – just from materials that were previously wasted.
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Using machine learning to analyse print patterns is great for users on a granular level, but it’s also fantastic for fleet managers and sysadmins, who need to make sure they’re using their print resources efficiently – and cheaply.
Analysing print usage patterns with machine learning
Using machine learning to analyse print patterns is great for users on a granular level, but it’s also fantastic for fleet managers and sysadmins, who need to make sure they’re using their print resources efficiently – and cheaply. By optimising print queues, and implementing AI-guided load balancing, there will be fewer bottlenecks and performance issues. High volume printing can automatically occur during ‘off-peak’ hours. You can even tweak AI to enforce cost-saving policies like duplex or greyscale printing, quickly identifying departments or users who generate the most waste. Improve tracking and accountability This brings us back to the final benefit: improved accountability. By tracking all print activity across a network in real time, machine learning platforms offer fantastically detailed reports and audit trails. They also consolidate print job data into a centralised logging system, giving sysadmins and IT managers a bird’s eye view of their print environment. Automated audit procedures cut down on man hours and will automatically flag any outliers in terms of wastage, print errors or suspicious network activity. Think of it like having a robot detective, constantly monitoring print flow.
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A version of this article first appeared on the PaperCut website – see more at www.papercut.com
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