Rückert's focus, though, is on more proactive AI applications that can make decisions on their own and control processes. Such AI agents, she believes, will give industry a boost comparable to the erstwhile advances triggered by smartphones and the internet. If a machine breaks down, the agent will check if the same problem has already been experienced in a different Bosch factory, examines handbooks and scans shift logs – before then proposing a possible solution within seconds. For more complex tasks, several agents can be combined, which then communicate with each other. Comprehensive use of such tools, says Rückert, can translate into millions in savings for individual factories.

In the era of Industry 4.0, artificial intelligence (AI) is assuming an increasingly pivotal role within industrial systems. Despite the recent trend within various industries to adopt AI, the actual adoption of AI is not as developed as perceived. A significant factor contributing to this lag is the data issues in AI implementation. How to address these data issues stands as a significant concern confronting both industry and academia. To address data issues, the first step involves mapping out these issues. Therefore, this study conducts a meta-review to explore data issues and methods within the implementation of industrial AI. Seventy-two data issues are identified and categorized into various stages of the data lifecycle, including data source and collection, data access and storage, data integration and interoperation, data pre-processing, data processing, data security and privacy, and AI technology adoption. Subsequently, the study analyzes the data requirements of various AI algorithms. Building on the aforementioned analyses, it proposes a data management framework, addressing how data issues can be systematically resolved at every stage of the data lifecycle. Finally, the study highlights future research directions. In doing so, this study enriches the existing body of knowledge and provides guidelines for professionals navigating the complex landscape of achieving data usability and usefulness in industrial AI.

AI has been rapidly finding industrial applications, such as the use of large language models to automate enterprise IT. Those applications may make the question of actual intelligence moot. The British mathematician Alan Turing wrote in 1950, "I propose to consider the question, 'Can machines think?'" His inquiry framed the discussion for decades of artificial intelligence research. For a couple of generations of scientists contemplating AI, the question of whether "true" or "human" intelligence could be achieved was always an important part of the work.

For many years now, we've heard and read about how "next year" will be the breakout year for AI. And in a way, that's always true – each year marks a new level-up in AI's ability to optimize traditional business operations and streamline work for the better. These year-after-year improvements have also further refined AI, for more fit-for-purpose applications that drive new value in specific use cases. Industrial AI is the latest iteration of this phenomenon, applying AI's processing to specialized applications in a manufacturing world rapidly undergoing profound digital transformations. Just as 2020 and 2021 marked new evolutions in artificial intelligence, 2022 promises to see industrial AI turn a major new corner in how the manufacturing sector draws on AI applications to tackle the problems of today and create new value-adding layers to their organization and ways of working.

The term Artificial Intelligence (AI) has been popularised by companies such as Google and Amazon and often draws connotations of robots, natural language assistance or self-driving cars. Whilst all of these are popular examples of the more interesting use cases for AI, the oftentimes invisible technology is already starting to impact everyday life. In the industrial world, AI is a technology we're increasingly finding uses for and one that will be a big focus for companies in 2022. For industrial applications, AI combines data science with machine learning and domain expertise to perform a wide variety of critical functions that may range from monitoring essential infrastructure to ensuring that machinery continues to operate as optimally as possible. To fully realise the benefits of AI, industrial companies must work to understand which processes can benefit most from AI and focus on a highly specific set of potential operational efficiencies, quality gains and safety enhancements.

Over the past 20 years, artificial intelligence (AI) has significantly transformed industry, taking an organisation's ability to optimise processes and proactively detect and solve problems to a whole new level. As a result of the increasing adoption of digital transformation, AI continues to provide benefits across a range of industrial processes. This has resulted in the extensive use of digital twins – virtual representations of physical objects, systems or factories that are created through data gathered from Internet of Things (IoT) devices, advanced computer systems and digital processes. AI is the brain behind the digital twin. By applying various forms of AI – such as neural networks, computer vision, and machine learning – in different ways, it can create targeted solutions presented in the form of analytics.

Conferences, summits, or events are gatherings of like-minded people and industry peers. These summits bring people together from different geographical regions who share a common discipline or interest to discuss innovations, techniques, unpublished data, or to learn new things from thought leaders that they may not have known previously. Artificial intelligence summits and events have been gaining popularity with the rising demand for technology in the different industries. In this article, we have shared a list of some of the top upcoming artificial intelligence summits and events for 2022 that tech enthusiasts should attend. The ICAIE will be held in January 2022, which will mark its second year as the industry witnesses the development in the field of artificial intelligence in education.

What is your industrial AI readiness? That's a question that's top-of-mind for many industrial executives lately -- and simultaneously one that has not taken on enough importance for many others. While AI, machine learning and other means of automation have swept through industries, including the industrial sector, in recent years, AI still too often gets treated as an add-on technology. But AI isn't something to be tacked onto an existing framework; it has to be treated as the strategy itself. This is especially true for industrial AI.

Industrial digital transformation is critical to achieving new levels of safety, sustainability, and profitability--and "Industrial AI" is a key enabler of that change. Organizations are switching their focus from mass data accumulation to strategic industrial data management, homing in on data integration, mobility, and accessibility--with the goal of using AI-enabled technologies to unlock value hidden in these unoptimized and underutilized sets of industrial data. The rise of the digital executive (chief technology officer, chief data officer, and chief information officer) as a driver of industrial digital transformation has been a key influence on this trend. This has fueled the need for "Industrial AI," a new paradigm that combines data science and AI algorithms with software and domain expertise to deliver measurable business outcomes for the specific needs of capital-intensive industries. Industrial AI disrupts these industries by lowering barriers to adoption, offering new opportunities for industrial organizations to significantly reduce costs, improve efficiency, and transform their operations for the better.

As data accessibility and analysis capabilities have rapidly advanced in recent years, new digital platforms driven by artificial intelligence (AI) and machine learning (ML) are increasingly finding practical applications in industry. "Data are so readily available now. Several years ago, we didn't have the manipulation capability, the broad platform or cloud capacity to really work with large volumes of data. We've got that now, so that has been huge in making AI more practical," says Paige Morse, industry marketing director for chemicals at Aspen Technology, Inc. (Bedford, Mass.; www.aspentech.com). While AI and ML have been part of the digitalization discussion for many years, these technologies have not seen a great deal of practical application in the chemical process industries (CPI) until relatively recently, says Don Mack, global alliance manager at Siemens Industry, Inc. (Alpharetta, Ga.; www.industry.usa.siemens.com). "In order for AI to work correctly, it needs data. Control systems and historians in chemical plants have a lot of data available, but in many cases, those data have just been sitting dormant, not really being put to good use. However, new digitalization tools enable us to address some use cases for AI that until recently just weren't possible." This convergence of technologies, from smart sensors to high-performance computing and cloud storage, along with advances in data science, deep learning and access to free and open-source software, have enabled the field of industrial AI to move beyond pure research to practical applications with business benefits, says Samvith Rao, chemical and petroleum industry manager at MathWorks (Natick, Mass.; www.mathworks.com).