4 ways AI can transform the print and imaging industry
May 12, 2023
Article, Artificial Intelligence, Digital Transformation, Security, Trends
Since Quocirca first wrote about the potential of AI in the print industry in 2017, AI has entered the mainstream, impacting our everyday lives from self-driving cars to chatbots. The emergence of ChatGPT in November 2022 heralded a new era in generative artificial intelligence, and is set to disrupt a variety of industries. As it becomes the new reality for businesses, it has the potential to change the future of work. So how can the print industry embrace AI?
Democratisation of AI
The emergence of ChatGPT, created by Open AI, in November 2022 captured the public’s imagination, reaching 100 million monthly users in 60 days. As a form of generative AI based on large language learning models (LLMs), ChatGPT has made AI accessible to everyone. GPT-3 (Generative Pretrained Transformer 3) and GPT-4 are natural language processing AI models that generate human-like text. GPTs and other LLMs can be applied across a range of areas – customer service chatbots, content generation, predictive modelling, automation, coding, analytics, and security.
AI is now on a fast track. Microsoft has incorporated ChatGPT technology into its search engine, Bing, and has introduced Microsoft 365 Copilot, which integrates AI into the Microsoft 365 office suite. Google has made Bard, its generative AI chatbot, available in 180 countries.
As AI becomes more widely accessible to all types of users, it has the potential to transform businesses. The democratisation of AI can help drive innovation, improve efficiency, enhance decision-making, and improve customer experience across every industry.
Applying AI in the print industry
Artificial intelligence can transform a number of areas in the print industry, particularly as edge computing brings AI processing tasks closer to the source, enabling AI-led decisions in real time with minimal latency. All of the following areas have the potential to transform current approaches to manage print services, enabling customers to gain better outcomes from service reliability, reduced risk, and improved efficiency.
1. Service innovation through predictive maintenance
Combining big data and AI allows print manufacturers to drive greater efficiency by performing predictive maintenance more effectively. Today’s multifunction printers (MFPs) are connected IoT edge devices, equipped with a multitude of sensors that generate a wealth of data. Using machine learning (ML), predictive maintenance models make predictions based on usage trends to detect and diagnose faults and estimate when a failure will happen based on the current and past state of the equipment and sensor.
Such AI-based predictive maintenance enables suppliers to optimise fleet maintenance and use field service engineers more effectively through less time on site, reduced costs through lower device downtime, and faster service resolutions. Customers benefit from increased device uptime and better device utilisation. Lexmark, for example, has built in machine learning (ML) and artificial intelligence (AI) models into its Optra IoT Platform
2. Beyond the dashboard with AI-powered analytics
Today’s print analytic dashboards are primarily focused on presenting current and historical data in graphical format. Traditional dashboards typically rely on historical data, and are often static so don’t update in real time. Dashboard creation and maintenance can be labour-intensive and costly.
Advanced analytics, such as predictive and prescriptive analytics, use machine learning to recommend possible outcomes and improve decision-making. This can, for example, help customers go beyond using dashboards to review print usage by device and user to understand print costs. It can also use advanced analytics to uncover opportunities for processes that can be digitised and predict their impact on costs and productivity.
3. Endpoint security
Securing data across end points, including printers, is critical, particularly with the rise of remote and distributed working. AI has the potential to be both a friend and a foe when it comes to security. Next-generation malware already uses AI to behave like a human attacker, identifying targets and evading detection. Connected IoT devices, printers and MFPs are a sitting target for malicious attackers, as are print management software and servers.
This demands advanced security that uses AI to enable devices to self-monitor and self-heal. Instead of occasional device-level patches, machine learning combined with more advanced AI decision-making will help manufacturers and software vendors provide network-level behaviour analytics and real-time anomaly detection.
AI-based authentication and access control can use advanced algorithms and machine learning to identify potential security threats. In addition, zero-day attacks can be mitigated better through AI to analyse activity across a broad and anonymised data set in real time. Protection against such issues can then be applied to devices and environments that need immediate protection. Where a security breach occurs, an effective and rapid response using machine learning in combination with an advanced analytics engine can optimise remediation efforts.
Content security can also be augmented with AI. An AI engine can classify data according to confidentiality level and augment existing data loss prevention (DLP) programmes.
4. Intelligent document processing (IDP)
Although many businesses remain reliant on paper, the acceleration of digitisation initiatives is well underway. Automation of paper-based processes is both a challenge and an opportunity for the print industry – if AI can be embedded in document capture platforms, it will provide a much-needed bridge between the paper and digital worlds.
IDP is an automation technology that uses a combination of AI, ML, OCR and natural language processing (NLP) to capture, extract and process data from a variety of document formats – paper and digital – without human input. Benefits of IDP include increased accuracy, reduced manual processing, increased productivity and improved compliance and security. Examples include Microsoft’s Azure-based Intelligent Document Processing engine, Amazon Web Services’ (AWS) IDP, and Google’s Document AI. Examples in the print and imaging market that use ML and AI include Xerox’s IDP platform and Kofax’s TotalAgility IDP platform.
Conclusion
The print industry must evolve amidst the dynamic and rapidly changing era of the cloud, IoT, and AI. While caution must be taken due to the limitations and risks of AI, to respond to the disruptive potential of AI, traditional vendors must look to partnerships and collaborations while ensuring devices are built with the embedded intelligence to integrate with AI-powered cloud platforms. This will mean moving away from proprietary to open architectures to take full advantage of the speed and scalability that AI can offer.
AI presents opportunities not just for the traditional vendors but also for the channel. Customers will be looking for solutions that embed the AI functionality and solutions available from the devices into their broader IT systems and this integration opportunity is something the channel should be looking to capitalise on.
