In Industry 4.0, Cyber-Physical Systems (CPS) generate vast data sets that can be leveraged by Artificial Intelligence (AI) for applications including predictive maintenance and production planning. However, despite the demonstrated potential of AI, its widespread adoption in sectors like manufacturing remains limited. Our comprehensive review of recent literature, including standards and reports, pinpoints key challenges: system integration, data-related issues, managing workforce-related concerns and ensuring trustworthy AI. A quantitative analysis highlights particular challenges and topics that are important for practitioners but still need to be sufficiently investigated by academics. The paper briefly discusses existing solutions to these challenges and proposes avenues for future research. We hope that this survey serves as a resource for practitioners evaluating the cost-benefit implications of AI in CPS and for researchers aiming to address these urgent challenges.

It's a business pain point most know only too well, and new research confirms that integration challenges are not just a pain, they're slowing companies' digital ambitions and causing infrastructure issues and risks. MuleSoft's eighth annual Connectivity Benchmark Report shows the number of applications in Australian organisations (sorry, New Zealand, there are no Kiwi results in this one) have increased nearly 10 percent in the past year, to 1,032, highlighting the complexity of the digital landscape. But 68 percent of those applications are not integrated with other applications used by the business, creating data silos and the flow on effects, including increased costs, duplicated work, productivity bottlenecks and disconnected experiences. It's a situation that's proving costly – not just in terms of money spent building custom integrations (read on for those eye-watering figures) but also in the slowing of digital transformation efforts – something 84 percent of Australians said was happening, causing infrastructure and major risks as IT budgets come under increased scrutiny. And the cost of failing to complete digital transformation initiatives successfully?

IT operations teams have a lot to juggle. They manage servers, networks, cloud infrastructure, user experience, application performance, and cybersecurity, often working independently of one another. Staffers are more often than not overworked, burdened with excessive alerts, and struggling to solve problems that involve multiple domains. Enter AIOps, a burgeoning field of technologies and strategies that inject artificial intelligence into IT operations in an effort to solve challenges face by IT operations teams by reducing false positives, using machine learning to spot problems before they occur, automating remediation, and seeing a holistic view of the enterprise. According to an October survey of IT leaders conducted by ZK Research and Masergy, 65% of companies are already using AIOps, and 94% say that AIOps is "important or very important" for managing network and cloud application performance.

Editor's Note: Countless companies fail to implement data management and advanced analytics properly -- and that's understandable, given the changing data landscape, its complexity, the rapidly increasing amount of data, and the accompanying integration challenges. In this piece from Data Management at Scale, Principal Architect, Piethein Strenholt, provides principles, observations, best practices, and patterns to overcome these challenges. Advanced analytics focuses on projecting future trends, events, and behaviors. It is the most complex form of value creation because it requires statistical models for newer technologies, such as machine learning and artificial intelligence. While it is getting easier to train and develop accurate models, deploying them into production -- especially at scale -- is a major challenge.

Technological innovations like artificial intelligence, machine learning and deep learning are increasingly becoming the driving force for various industries. And with the world dealing with the current pandemic, AI is playing a considerable role in tackling the rapidly spreading COVID-19 pandemic, right from delivery of services, diagnosing the risk of the outbreak to drug discovery. Utilizing AI technology and advanced conversational tools, several brands, across the world, have enabled their remote workforce to work from home and yet meet the modern-day requirements of their customers. However, in this rapidly challenging environment, few companies are still struggling to make their business resilient. "In 2019, nearly 37% of enterprises implemented AI – depicting an increase of around 270% in the past four years."

When you think about the digital future, you probably think about self-driving cars, disruptors like Uber and Airbnb, and artificial intelligence. What you probably do not think about, however, is factories. Despite their outwardly staid appearance, the industrial and manufacturing industries have been at the forefront of the practical application of technology and automation for decades. This evolution has culminated in what is called Industry 4.0--a vision of the smart factory and the Industrial Internet of Things (IoT). And, I believe, it may be a blueprint for the future of IT across all industries.