AI led Digital Transformation in Print Manufacturing
The manufacturing industry is undergoing a massive transformation, integrating technologies like artificial intelligence, automation, and digitalisation to optimise production and boost efficiency. In print manufacturing, companies adapt to these changes by viewing AI as a tool for growth rather than a threat. In this article, John Kilburg from K12 Printing reacts to digital transformation that is led by artificial intelligence. This article is published in line with the Digital Transformation and AI Awards and Summit. To exhibit at the event, please get in touch at +44 (0)203 931 5827. The Impact of Digital Transformation on Print Manufacturing This isn’t the first major shift in manufacturing. Like many other industries, print manufacturing experienced significant digital transformation with the introduction of computers and other digital technologies. Decades ago, manual processes dominated the industry, limiting efficiency and scope. The introduction of digital tools such as computer-aided design and automated printing presses revolutionised production. This shift allowed for faster workflows, improved precision, and greater customisation. This earlier wave of digital transformation was a cause of discomfort for many in the manufacturing industry due to concern about the new technology replacing jobs. Instead, it reshaped roles without replacing them. It created opportunities for workers to upskill and move into more specialised roles such as graphic designers or other important roles within the print manufacturing industry. Today, many print manufacturers see AI as a continuation of this evolution, offering new ways to enhance their business and empower their workforce. AI’s Role in Print Manufacturing While still early in print manufacturing, AI is poised to bring substantial benefits in certain areas. At K12 Print, AI is being explored cautiously, with a focus on enhancing efficiency and creativity rather than reducing jobs. Shortly, AI is likely to assist in design elements. This would enable designers to work faster and focus more on creativity and strategic decision-making. Another practical implementation is through machine maintenance, using predictive analytics to prevent breakdowns and optimise performance, minimising downtime. Rather than letting technology drive layoffs, companies should be committed to maintaining their workforce and creating opportunities for career advancement. This approach keeps jobs in the country and the community and strengthens the company’s foundation as a tight-knit group that values each employee’s growth. Enhancing Print Quality There are many practical uses of artificial intelligence in print manufacturing. First, AI can be used to analyse and optimise images that are received from the customer. Most customers are not knowledgeable about print files or colour builds. Artificial intelligence can sharpen images and adjust colour in real time. The result of these corrections is high-quality prints in a shorter amount of time which saves money. Also, AI may come into the design elements of print when customers bring forward ideas for us to create. Several nuances go into making their idea a reality and although there are currently real people building off those ideas. AI and Sustainability The manufacturing industry is constantly evolving when it comes to sustainability. Many of the printing tools have become more environmentally friendly over the years especially when it comes to ink and other critical elements in printing. But more changes on the horizon can make printing more sustainable. The use of AI systems can help reduce waste and save on energy consumption. This can help lessen the environmental impact of the printing industry. Additionally, artificial intelligence can use its vast database (learned knowledge) to suggest other areas where printing can transition to eco-friendly materials. A practical implementation of AI could also be in predictive maintenance—monitoring the machines in use and predicting breakdowns so they can be avoided. AI and Cost AI may be cost-prohibitive to smaller print manufacturing companies in the short term. However, as history teaches us, technology tends to become affordable very quickly once it is embraced by the masses. The more we learn to embrace AI rather than fear AI, the faster it will become a tool that propels us forward. Security Every data-driven industry must think about security. And print manufacturing is no different. Another practical implementation of AI in manufacturing is within the company’s security setup. AI can identify patterns in data traffic that can be indicative of a security breach and suggest, or even implement measures to counter the breach. It can also work to prevent security breaches by enforcing user authentication and even monitoring the printing of sensitive or confidential materials. Print Personalisation Although a vast majority of the print orders that come through the shop are custom orders there is still a major market for pre-made ready-to-order looks. AI systems can use available data about customers to customise content based on that data. This can be used in marketing campaigns to create high-response materials targeted to specific audiences. Packaging and direct mail will be big beneficiaries of this. AI Won’t Replace Jobs – It Will Create New Opportunities A common concern in the workplace is that AI will lead to job loss. However, this fear may be overstated. Much like the earlier digital transformation, AI presents an opportunity to elevate both workers and businesses. K12 Print is committed to investing in education and on-the-job training for employees, preparing them to adapt and thrive in a more technology-driven workplace. Upskilling the workforce will ensure that workers move into more rewarding roles. AI will likely take over repetitive tasks, but this shift will allow employees to focus on oversight roles. For print manufacturing, this means workers can explore new career paths in roles such as AI technicians, data analysts, and advanced machine operators. Digital transformation in manufacturing is inevitable, and AI will play a crucial role in shaping the future. Get in touch For event sponsorship enquiries, please get in touch with calum.budge@31media.co.uk For media enquiries, please get in touch with vaishnavi.nashte@31media.co.uk
Transforming Banking with DevOps
This article is published in collaboration with the Digital Transformation and AI Awards and Summit. These are two separate B2B events organised by 31 Media. If you wish to exhibit your tech solutions or to advertise your brand at the event, please get in touch at +44 (0)203 931 5827. Author: Arnab Mitra, programme manager at Banking Industry Architecture Network (BIAN) The banking industry is undergoing a significant digital transformation. The emergence of fintechs and industry disruptors is forcing traditional banks to innovate faster than ever before to remain competitive and address the needs of the digital-first customer. Delivering these solutions effectively and at speed often requires an overhaul of legacy technology and the emergence of new technological-driven processes. Enter DevOps, a combination of practices and tools that is driving the future of the financial services industry. We spoke with Arnab Mitra, programme manager at the Banking Industry Architecture Network (BIAN) about the role of DevOps in banking and the need for industry collaboration to further accelerate transformation. Q1: What challenges have traditionally hindered banks from adopting DevOps, and how has the industry’s perspective shifted to embrace this methodology? Traditionally, banks were slow to adopt DevOps due to regulatory constraints, concerns about data security, and a legacy mindset that is naturally more hesitant towards change and innovation. However, the pandemic accelerated the need for banks to offer digital services, which in turn hastened the adoption of DevOps. As the benefits of DevOps became clear, banks have realised that they needed to embrace this methodology to remain competitive and keep pace with nimbler fintechs, while offering new services that are enabled through best-of-breed technologies. In today’s rapidly evolving IT landscape, DevOps streamlines transformation and enables banks to deliver innovative digital services at speed and scale. With the global drive for transformation, banks recognised that if they don’t transform, they risk being left behind, and at a significant cost. For example, a 2023 IDC Financial Insights survey found global banks are on track to spend $57.1 billion on legacy payments technology in 2028. It’s therefore no surprise that over 80% of financial services firms have embraced DevOps practices, reflecting the widespread adoption and recognition of its benefits in the industry. Q2: What is BIAN’s approach to DevOps? How is this reflected in BIANs offerings? BIAN is built on collaboration across the industry and we use DevOps methodology within our member activities to encourage this. Working groups formed of members from different organisations within banks, technology vendors and consultancies come together to share thoughts, ideas and experiences to collaborate on innovative solutions, for example producing API specifications. Another example is our Coreless Banking initiative, which completed its third iteration last year. BIAN took a DevOps approach to this initiative, which was developed by a collaboration of leading banks and technology vendors, including HSBC, Zafin and IBM. The initiative, which aims to tackle the interoperability challenges banks face, resulted in an API-based services architecture that empowers banks to integrate best-of-breed technology seamlessly. Coreless Banking leverages the DevOps processes of the individual participating members to bring their components (solutions) into a published state, for other participants to use and integrate with. This allows for quick releases when any changes are required for any individual member. At the same time, using the BIAN standard for the API interface specifications means the integrated solution still works. Q3: Can you share any examples of how you’ve implemented DevOps principles on a more practical level? BIAN’s materials, including our Service Domains, are made available on the cloud, allowing members to access and use BIAN APIs for various applications within their organisations. Using BIAN’s framework, external parties can access our materials and create their own CI/CD pipeline, adapting it to their own needs. Members also have access to BIAN tooling, with functionality that allows users to match their APIs with BIAN APIs. In addition to this, we have an automated feedback loop and message modellers which enable rapid updates to BIAN models, once manually approved. Members can compare artefacts with the BIAN model content, helping to ensure APIs are compliant, within our framework. These automated processes guarantee consistent quality across all of our materials, eliminating individual preferences and ensuring regulatory compliance While we have been using DevOps in our approach for many years, we are now exploring how AI is enabling and evolving our DevOps operations. Q4: How is AI transforming DevOps practices and environments within the financial services industry? AI is a true game-changer within the industry. When applied to DevOps, the scope for automation within these environments is huge. BIAN is exploring many potential use cases for the technology. For instance, we’re looking at how we can use AI to generate sample data for Service Domain APIs to create a sandbox with quality test data that developers can use to mock up innovative solutions using these APIs. Additionally, we are piloting an AI-based API mapping app to automate the mapping process by 50-60%, significantly reducing human effort. We are focusing on training our AI-Engine with quality data, and feedback from our members supports the finetuning of this app. This means that members using our model will benefit from streamlined processes and enhanced efficiencies, while BIAN benefits from member feedback which continuously trains and improves our AI models, further supporting our collaborative environment. Q5: How important will DevOps be for the future of banking? Why is collaboration the key to transformation? As banks continue to focus on digital transformation, DevOps practices will be essential for delivering innovative products and services at speed. When development and operations teams work together closely, they can identify and address issues more efficiently, improve communication and ultimately deliver better results. By breaking down siloes – not only within organisations but across the financial services ecosystem – it creates a more cohesive and successful work environment. With the advent of new technologies, including AI, it’s now more important than ever for teams to share ideas about how this technology can be used safely and
Delivering DevOps through Platform Engineering
This article is published in collaboration with the Digital Transformation and AI Awards and Summit. These are two separate B2B events organised by 31 Media. If you wish to exhibit your tech solutions or to advertise your brand at the event, please get in touch at +44 (0)203 931 5827. Author: Fred Lherault, Field CTO, EMEA / Emerging Markets, Pure Storage Delivering on the promise of DevOps through Platform Engineering In software engineering, the Golden Path aims to provide the smoothest way forward via a self-service template for common tasks. It is enabled by platform engineers – who provide developers with the simplest possible internal developer platform and the tools they need to deliver innovation. Here we look at the emerging discipline of platform engineering, and the benefits it brings to application development via easier and faster access to services and resources, in particular using modern data management platforms built on Kubernetes containerised environments. Giving developers what they want When DevOps emerged in the late 2000s, it brought with it key principles of shared ownership, rapid feedback and workflow automation to help deliver on the vision of agile software development. It requires a high degree of autonomy for the developers and in exchange empowers them with the tools they need to be efficient. Automation is one of the key principles of DevOps since the quick pace of changes it drives is incompatible with “human in the loop” workflows. The mode of operations preferred by developers (and many technical specialist roles such as data scientists, AI researchers etc.) can often be boiled down to 3 main asks: Instant access to resources Instant results Full self-service Using the above as the “north star” when building services geared towards technical profiles is a great way to enable innovation and ensure fast adoption. While providing instant resources and results might not be always possible, getting as close as possible to instant will drive greater satisfaction. Platform engineering treats the developer as its primary customer Today, we see the coming of age of DevOps through the rise of platform engineering, a new function for a more mature era in application development, that provides a suite of self-service tools to empower developers. Platform engineering operates behind the veil to provide an easy-to-use, self-service catalogue of services and infrastructure components to support the day-to-day development experience. Best practice platform engineering aims to help application developers get on board and start building faster by providing everything that they need to experiment, develop, test and deploy. The platform made available to these developers often takes inspiration from the services popularised by the public cloud and its mode of operation. It is designed to provide instant access to not just the latest and greatest tools and software that underpin innovation, but also provide easy access to the data itself, protected by pre-determined guardrails and security protocols. Kubernetes and data management The ideal developer-focused platform also includes data management. It may build on top of Kubernetes as the means to orchestrate, deploy, run and scale cloud-native applications as well as to manage the data services required for those applications. Data management capabilities are key to platform engineering because they enable exploration and testing in realistic conditions, for example using an instant copy of production data instead of a somewhat unrealistic synthetic data set. Ideally, the data management capabilities will also be designed with self-service in mind, and deliver access to data in a highly available, reliable, elastic, multi-tenant and secure manner. Portworx from Pure Storage is an example of such a modern data platform. Fully integrated with Kubernetes, it allows the developer to easily get access to persistent data options (including data protection capabilities such as data replication, backup and archiving) but also to data sets themselves through instant data cloning, even enabling the use of self-service instant snapshot creation and restore so that developers may experiment with changes and roll back to previous states quickly and easily. Additionally, Portworx Data Services provides a catalogue of curated data services, including MongoDB, Elasticsearch, Cassandra, Kafka and PostgreSQL, simplifying deployment into just a few clicks or a single API call, so that developers can deploy or scale these data services easily with the optimal data storage configuration and protection. This foundation brings these easy-to-specify toolchains and data services to the developers so that they can easily use them as building blocks, even if they don’t have extensive knowledge of Kubernetes or how to deploy a given database engine in a secure and scalable manner. Platform engineering enables the Golden Path Platform engineering teams are busy working unseen in the background to bring the self-service Golden Path to application development. With Kubernetes as the orchestration framework, and containers and data services as key resources, the platform engineers can finally deliver fully on the vision of increased agility and greater productivity of DevOps. Get in touch For event sponsorship enquiries, please get in touch with calum.budge@31media.co.uk For media enquiries, please get in touch with vaishnavi.nashte@31media.co.uk
Using workflow automations to minimise development downtime
This article is published in collaboration with the Digital Transformation and AI Awards and Summit. These are two separate B2B events organised by 31 Media. If you wish to exhibit your tech solutions or to advertise your brand at the event, please get in touch at +44 (0)203 931 5827. In this article, you’ll learn how you can minimise development downtime with workflow automation. Author: Steve Barrett, VP of EMEA, Datadog Minimising development downtime and disruption through workflow automation DevOps teams are under constant pressure to deliver software applications as quickly as possible. At the same time, DevSecOps and security operations centre (SOC) teams face an ongoing challenge in detecting and remediating constantly evolving security threats. These challenges can be exacerbated by the various complex and often error-prone processes involved in responding to disruptions and changes to an organisation’s systems. A large amount of time can be spent switching between different tools to gather the context required for remediation, and in the manual execution of tasks needed for incident management, significantly prolonging downtime and causing further disruption. It can be hard to prioritise and manually respond to the high volumes of alerts generated by larger and more complex systems, further delaying resolution and increasing the risk of human error. Fortunately, a range of new workflow automation tools are available to support DevOps, DevSecOps and SOC teams, specifically in the observability and real-time monitoring of servers, databases, SaaS tools and services across their organisations’ cloud and IT infrastructure. Automate end-to-end processes Workflow automation helps teams more confidently manage the health of their systems and resolve issues faster, automating and orchestrating complex flows of tasks in response to specific threats, events, and alerts, and allowing teams to incorporate human input into those flows where required. By allowing them to combine monitoring and remediation into a single, streamlined solution, new workflow automation tools can enable DevOps to automate and orchestrate entire end-to-end processes across their infrastructure and tools, helping them to quickly remediate any issues that might arise. Consider alerts, for example. Whether monitoring network health, application performance, or infrastructure resources, DevOps teams must set alerts. By letting them know the moment an issue occurs, an alert allows them to respond in an appropriate and timely manner. But responding to alerts manually can be repetitive and time-consuming: an alert might send notifications in the middle of the night, or engineers might have to restart an application to resolve the issue manually. However, creating a workflow which consists of connected remedial actions that automatically execute when a specific alert is triggered can significantly reduce a team’s mean time to resolution (MTTR). Tackling emerging threats The technology has considerable benefits for DevSecOps and SOC teams, too, enabling them to orchestrate an automated series of actions in response to an alert, and quickly tackle any emerging threats to their system’s security. By chaining together specific actions in a workflow, or actions from integrations such as AWS, Okta and others, teams can configure workflows to trigger a specific alert and automatically execute an important security process, such as blocking a suspicious IP address, performing tier–one triage—such as reviewing and adding context to threats detected by cloud SIEM—or rolling back a code deployment that introduces a vulnerability. An organisation might use Okta for identity and access management, for example, and has a rule in place which detects when a user attempts to access an application without authorisation. Configuring and adding a “Suspend Suspicious Okta User” workflow means that if and when that rule is triggered, the suspicious user will be automatically suspended Workflow automation can even help create new rules that establish whether an alert has detected a real threat or a false positive. Although security signals provide much information, it’s not always enough to indicate whether an alert requires further investigation. By enriching cases with relevant context from the observability data generated through real-time monitoring, teams can better identify and eliminate false positives and determine whether an incident is a malicious event. DevSecOps and SOC teams can also combine new cloud SIEM and automated workflows to automate repetitive security tasks like detecting emerging vulnerabilities or triaging security signals. Traditionally separate automation, SIEM, and case management capabilities can be unified in a single pane of glass, allowing teams to create a centralised workspace for investigating their security signals. Not only does this help teams reduce tool sprawl and spending, but the combined use of cloud SIEM and automated workflows also reduces the burden on security engineers, allowing them to focus on more complex tasks. Streamlining monitoring and troubleshooting Today’s security teams operate in a constantly evolving, increasingly complex, and challenging environment. The use of disparate point solutions only adds to this complexity and can risk an ineffective security posture. Workflow automation helps mitigate this risk, streamlining monitoring and troubleshooting by automating end-to-end processes and executing actions in response to alerts, security threats, and other insights. As well as boosting productivity and saving valuable time, implementing automated workflows in response to security threats allows an organisation’s DevOps, DevSecOps, and SOC teams to focus on the most critical security issues and more quickly and easily detect and defend against potential attacks. Get in touch For event sponsorship enquiries, please get in touch with calum.budge@31media.co.uk For media enquiries, please get in touch with vaishnavi.nashte@31media.co.uk
