Category: Vivia

Artificial intelligence is rapidly moving beyond isolated chat interfaces. Modern AI systems can analyze technical information, generate engineering artifacts, support complex decisions, and coordinate activities across multiple tools. However, even the most advanced AI model has limited value when it cannot securely access the systems, data, and workflows engineers use every day. Engineering information is […]

Product Lifecycle Management deployment has traditionally been one of the most complex transformation initiatives an engineering organization can undertake. Implementing a PLM environment involves much more than installing software. Organizations must migrate product data, standardize engineering processes, connect specialized tools, define information ownership, establish configuration rules, validate workflows, train users, and maintain traceability across the […]

Engineering change is unavoidable. Requirements evolve. Regulations and customer expectations change. Components become obsolete. Suppliers modify specifications. Tests reveal weaknesses. Cybersecurity vulnerabilities emerge. Product teams introduce new capabilities, variants, and performance targets. In a complex engineering environment, however, a change rarely affects only one item. A modification to a system requirement may influence architecture, software, […]

Engineering systems are becoming more connected, software-defined, data-dependent, and increasingly influenced by artificial intelligence. A modern product may combine stakeholder requirements, system architecture, embedded software, hardware configurations, simulation models, datasets, AI models, prompts, agent tools, test cases, cybersecurity controls, and compliance evidence. Every one of these elements can change—and even a small modification may affect […]

Engineering organizations generate enormous volumes of information throughout the product and system lifecycle. Stakeholder needs, requirements, architecture models, design files, simulations, risk analyses, test cases, defects, manufacturing records, operational data, and compliance evidence may all contribute to a single engineering program. The problem is that this information is rarely created or managed in one place. […]

Artificial intelligence is becoming deeply embedded in modern engineering workflows. Engineering teams now use AI to analyze requirements, identify inconsistencies, generate test cases, assess risks, summarize technical documentation, recommend traceability links, support design decisions, and accelerate verification activities. These capabilities can significantly improve productivity and engineering quality. However, they also introduce new risks. AI-generated recommendations […]

The EU AI Act changes how engineering organizations classify, design, develop, verify, document, release, and monitor artificial intelligence systems. Organizations that develop AI products, integrate third-party models, deploy AI tools, manufacture AI-enabled equipment, or supply systems to the European market may face different obligations depending on their role and the risk classification of the complete […]

Artificial intelligence is becoming embedded in regulated products, engineering processes, quality-management systems, and high-impact business decisions. Organizations increasingly use AI to analyze requirements, recommend trace links, generate test cases, identify risks, support clinical decisions, detect financial fraud, monitor industrial equipment, and automate complex engineering activities. AI agents can go further by modifying records, triggering workflows, […]

Artificial intelligence is becoming embedded throughout the engineering lifecycle. Teams now use AI to extract and draft requirements, identify defects, recommend traceability links, generate test cases, evaluate risks, analyze proposed changes, and summarize complex technical information. These capabilities can accelerate engineering work, but they also introduce a critical governance question: Can the organization reconstruct and […]

Artificial intelligence is transforming how engineering organizations define requirements, evaluate risks, analyze design alternatives, generate test cases, manage changes, and prepare compliance evidence. AI systems can process large volumes of engineering information, identify relationships across connected artifacts, and produce recommendations far faster than traditional manual methods. However, faster analysis does not automatically result in technically […]