Industrial Agents are not just 'rebranded' large language models! Siemens’ century of industrial expertise is embedded into industrial AI.

Frankly speaking, these Agents are indeed impressive: drafting documents, generating reports, tracking customers, supporting sales decisions—even screening high-potential projects and producing analytical reports for investment firms… But dropping these intelligent Agents directly onto factory shop floors will most likely leave them utterly incapable of delivering any real value. The ‘2025 Report on the Current Status and Future Trends of Industrial Intelligent Agents’ (hereinafter referred to as “the Report”) surveyed over 200 manufacturing enterprises—and the findings were sobering: 43% of enterprises have not yet deployed industrial intelligent agents; only 8% apply them extensively. It’s not that companies don’t want to adopt them—63% are held back by deployment costs, and 46% struggle to find personnel who understand both technology and production lines. Of course, legacy equipment spanning multiple generations, fragmented communication protocols, and the long-standing data silo between Information Technology (IT) and Operational Technology (OT) also pose major hurdles—on top of stringent requirements for autonomous, secure, and controllable data handling. Ultimately, industrial environments have never been about isolated, single-point problems. Rather, they constitute a holistic systems engineering challenge spanning R&D, engineering, manufacturing, quality assurance, and operations & maintenance. Single-point optimization cannot solve systemic challenges—this is precisely why the bar for industrial-grade Agents is far higher than for office-oriented ones. They must comprehend industrial semantics, invoke industrial tools, ingest real-time data, and execute within closed-loop workflows. In other words, every capability must be tightly anchored to actual enterprise operations—not merely a large model wrapped in a superficial UI. For enterprises urgently needing industrial-grade Agents, building from scratch is neither realistic nor cost-effective. As generative AI and intelligent Agent technologies accelerate their entry into industrial domains, Siemens is further integrating its existing industrial software, automation systems, data capabilities, and ecosystem strengths: On one hand, it has launched AI-native products such as the Eigen Engineering Agent; on the other, it leverages Siemens Xcelerator—a unified platform connecting products and partners—to drive scalable industrial AI adoption. Siemens’ In-House Industrial Intelligent Agents: From Advisory Support to Autonomous Execution The Eigen Engineering Agent—Siemens’ first AI agent purpose-built for industrial automation engineering—is now commercially available across the Chinese market. Last month, it received the ‘SAIL Star’ award at WAIC. Unlike typical AI-assistive tools, the Eigen Engineering Agent can independently plan, execute, and verify end-to-end tasks within real engineering systems. Traditionally, electrical engineers complete wiring and hardware design in ECAD tools, while automation engineers must then translate those designs using an entirely separate description system to write control programs. Equipment lists, variable tags, and control logic have long relied on manual, repetitive data entry—meaning every hardware revision triggers time-consuming, error-prone re-synchronization between hardware and software. The Eigen Engineering Agent introduces ECAD integration and automated project generation. It reads mainstream electrical design file formats—including XML and AML—identifies data conflicts, configures connections, and automatically generates PLC variable tags aligned with the actual hardware topology. Engineers can also describe workstations, associated equipment, and operational logic using natural language—and generate fully compliant, development-ready projects in minutes. Real-world results speak volumes: In practice, the Eigen Engineering Agent delivers 2–5× faster execution versus manual workflows, boosts engineering efficiency by up to 50%, and improves overall solution quality by 80%.

2026 In-Depth Strategic Analysis Report on Industrial AI

1.3 GE Vernova, Honeywell, and ABB: Divergent Industrial AI Pathways GE Vernova: Deep Integration of Energy and Industrial AI GE Vernova focuses on integrating AI into energy transition and industrial operations. Its Proficy industrial software suite and APM (Asset Performance Management) platform transform industrial data into automated workflows, reducing operational resource intensity. It holds deep expertise in predictive maintenance—ranking prominently in Verdantix’s 2025 Green Quadrant evaluation of industrial AI analytics software. Honeywell: Focused on Energy Efficiency and Industrial Digitalization Honeywell is undergoing a major strategic restructuring—completing the spin-off of its aerospace business in late 2026 to concentrate on industrial automation and energy software. Its Forge AI software is already deployed at scale in the data center industry, optimizing energy efficiency and cooling costs. Its collaboration with Qualcomm strengthens edge AI and data analytics capabilities, enabling smarter, more autonomous energy operations. ABB: AI-Driven Robotics and Grid Management ABB deeply integrates AI with robotics and automation, offering comprehensive solutions spanning grid management, renewable energy integration, and industrial energy efficiency. Its digital platform, ABB Ability, unifies advanced data analytics, machine learning, and automation—supporting predictive maintenance, intelligent grid management, and real-time operational decision-making. ABB continues to hold global technological leadership in robotics for discrete manufacturing. Vendor Core AI Platform Architectural Characteristics Strategic Ultimate Goal Siemens Siemens Xcelerator + Industrial Copilot Unified three-layer stack: engineering models → production data → edge/cloud; NVIDIA Omniverse digital twin integration World’s first fully AI-driven adaptive factory (industrial AI operating system) Schneider Electric EcoStruxure + Azure AI Three-tier IoT architecture (connected devices → edge control → cloud analytics); software-defined automation End-to-end agentic manufacturing; full supply-chain autonomous operation by 2027 GE Vernova Proficy + APM Industrial-data-driven automated workflows; AI for energy optimization Fusion of energy and industrial AI; reduction of operational resource intensity Honeywell Honeywell Forge Deep integration of software + AI; enhanced edge intelligence Focus on pure-play automation and energy businesses (post-aerospace spin-off) ABB ABB Ability Robotics + AI fusion; intelligent grid management Global leadership in intelligent robotics and AI-powered grid management Part II: Enterprise-Level Strategies of Global AI and Large Model Vendors AI and large model vendors serve as the ‘cognitive engines’ powering industrial intelligence. Their foundational models, Agent frameworks, and cloud infrastructure are becoming indispensable technical bedrocks for industrial applications. 2.1 OpenAI: From Research Lab to Industrial Intelligence Infrastructure Strategic Transformation: Frontier Intelligence Layer + AI Super-Applications

Siemens targets up to 80% factory automation with AI, board member says

15 hours ago

Siemens executive Cedrik Neike announced plans to raise factory automation levels to 70%-80% using AI and digital twins, investing €500 million in the Erlangen plant to enhance productivity.

reuters

2026-10-19

Siemens targets up to 80% factory automation with AI, board member says

Siemens targets up to 80% factory automation with AI, board member says

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