Artificial Intelligence (AI) for Machine Applications research provides an overview of AI technologies, applications, and markets at the machine level. This study provides current market analysis and five-year market and technology forecasts.
Artificial intelligence in machines attracts attention
According to research from ARC Advisory Group, artificial intelligence in machinery is gaining traction as it helps many companies improve productivity and efficiency. The rapid evolution of computing power has enabled AI applications in industrial operations. The combination of edge devices, the integration of AI chips, accelerators, and AI controllers has simplified the process for machine builders to seamlessly add his AI capabilities to machine applications. For end users, AI can improve operational efficiency and reduce overall costs. The combination of advanced computing infrastructure and AI capabilities is ushering in a new era of operational efficiency at the machine level, allowing manufacturers to benefit from data-driven insights into their manufacturing processes.
Artificial intelligence in machine strategy problems
In addition to providing five-year market forecasts, AI for Machinery Applications Market Research provides detailed quantitative current market data to address key strategic questions such as:
Be aware of legal restrictions
Address legal issues that can make or break your application from the beginning of your project. There is an ongoing debate around the world about how to regulate AI. The European Union (EU) currently leads the way in regulating AI around the world. The company has long been working on a corresponding legislative project, the Artificial Intelligence Act (AIA). This has proven difficult because technology is developing so rapidly that sometimes the process of discussion is simply outdated. But now the AI law has been passed by the European Parliament. The document now needs to be agreed with the European Commission and member states in a so-called “trialogue” before it can officially enter into force. An agreement is expected to be reached by the end of 2023. Companies will then have two years to adapt to the changed framework conditions.
Consider the lifecycle from R&D to implementation
The costs associated with implementing AI can be significant and can be a barrier to adoption. End users must factor in the complete cost of ownership of an AI solution, from research and development to model design, implementation, and ongoing maintenance. Additional infrastructure costs, such as additional hardware, storage, and computing power, can further increase the overall cost of the project.
data in an extended sense
As companies develop and implement digitalization strategies, the value of their data increases. Machine manufacturers use data to enhance their equipment, and end users use data to streamline operations and drive strategic decision-making. It is essential for users to establish precise definitions of the technical, business, security, and legal aspects of machine data transparency, ownership, and acquisition. A user may also consider the possibility of charging her OEM for providing access to labeled operational data.
