4 Software-Defined Automation (SDA) Vendors Leading the Industrial AI Revolution

The Software-Defined Automation (SDA) market leaders are increasingly determined by the extent to which Artificial Intelligence (AI) can drive cost savings. According to ABI Research, SDA technology revenue attributable to industrial AI will increase from about US$0.5 billion in 2025 to more than US$5 billion by 2035.

AI reimagines how manufacturers interact with Programmable Logic Controllers (PLCs), Supervisory Control and Data Acquisition (SCADA)/Human-Machine Interface (HMI) software, control systems, and computers. With AI, industrial companies can champion paper-free technical and maintenance documentation via Optical Character Recognition (OCR). AI agents are being embedded across manufacturing systems to reduce code development time by up to 50%. And in the far future, Physical AI will enable robotics to navigate more safely and efficiently within the smart factory.

These Industry 4.0 breakthroughs are only brought to the factory floor through innovative technology vendors. ABI Research identifies Siemens, SUPCON, Rockwell Automation, and CODESYS as key companies developing the next wave of SDA.

Table 1: Strategic Positioning in the SDA Market

Siemens

Siemens is aligning its SDA strategy with a broader push into industrial AI. At Hannover Messe 2026, the company introduced Eigen Engineering Agent, an AI-powered system designed to automate Programmable Logic Controller (PLC) engineering tasks and even commission virtual PLCs independently.

From a high-level, Siemens is investing heavily in Industrial Foundation Models (IFMs). Reflecting this, the company acquired Altair Engineering and made a €1 billion commitment to AI Research and Development (R&D). These efforts are already influencing Siemens' SIMATIC portfolio through engineering copilots, AI partnerships within the Siemens Xcelerator ecosystem, and a gradual shift toward goal-based automation.

For Siemens, a key focus is ensuring that AI systems have sufficient industrial context. Contextualized Large Language Models (LLMs) help deliver the predictability and reliability that manufacturers require in mission-critical tasks.

SUPCON

Chinese vendor SUPCON’s Time-Series Pre-Trained Transformer 3 (TPT-2) is its latest and most advanced AI tool. Enabled by integration with the Universal Control System (UCS), TPT-2 is trained on process and operational data to support five key manufacturing processes:

• Simulation
• Control
• Optimization
• Prediction
• Evaluation

Operators can interact with TPT-2 using natural language, eliminating the need for specialized data science skills. For example, instead of manually analyzing thousands of data points, a plant manager could simply ask, "Why has production efficiency dropped this week?" Shortly, they receive an AI-generated explanation with contextualized recommendations.

Early case studies indicate strong Return on Investment (ROI) for chemical manufacturers, reducing chemical consumption and predicting disruptive events before they happen.

Much of the value behind TPT-2 stems from SUPCON’s Universal Control System (UCS), which creates virtualized DCS and PLC environments. The operational data generated from these systems is fed into TPT-2, enabling continuous self-optimization.

Rockwell Automation

Rockwell Automation embraces software-hardware hybridization in its SDA strategy. That is to say, it stands out from other vendors in that it does not support virtualization outside of simulation and testing. Instead, Rockwell Automation maintains specialized control hardware, while its comprehensive software platform supports multiple broad aspects of industrial automation:

• Logix Echo: A virtual controller used only to simulate PLCs for testing and development.
• FactoryTalk Design Studio: Launch of a Generative AI (Gen AI)-powered low-code/no-code tool, as well as a set of agents that simplify PLC engineering.
• FactoryTalk Analytics Pavilion8 and GuardianAI: AI-driven analytics tools that help predict production issues and improve operational performance.
• LogixAI: Software that provides real-time insights and recommendations to optimize industrial operations.

CODESYS

While a much smaller company than the others listed, CODESYS plays a pivotal role in the development of SDA technology. Many of the larger SDA vendors—like ABB, Beckhoff (for TwinCAT 2), Bosch Rexroth, INOVANCE, and Schneider Electric—use CODESYS for either the runtime environment or IDE. Having such a monopolistic edge means that the German company heavily influences innovation cycles in the SDA market.

When it comes to AI, CODESYS is taking a pragmatic approach. First, CODESYS adopted the Model Context Protocol (MCP) to deploy private LLMs for engineering tasks, helping organizations benefit from AI without exposing sensitive data. Second, the company continues to invest heavily in AI R&D. CODESYS's collaboration on Intel’s OpenVINO underscores its commitment to delivering realistic productivity gains. As an open-source inference optimization toolkit, OpenVINO helps tailor AI models in SDA technology environments and automate repetitive PLC tasks.

Conclusion

Vendors are increasingly differentiating themselves through AI-enabled SDA capabilities and virtualized controllers that improve flexibility and efficiency in manufacturing operations. Manufacturers seek to reduce manual labor from repetitive, yet critical tasks, such as coding or simulation. To accommodate this, leading industrial technology vendors broadly embed intelligence into automation product suites historically defined by hardware innovations.

AI agents communicate with one another, taking actions and interpreting the results for human users. In natural language, factory workers can ask their most operationally pressing questions.

Bringing these use cases to the shop floor requires not only contextualized models to build user trust, but an orchestration layer to facilitate communication within a systems-diverse SDA environment. Protocols such as Agent2Agent (A2A) from Google and Agent Network Protocol appear to be temporary tools before leading vendors inevitably develop their own. 

Learn more by downloading ABI Research’s AI in Software-Defined Automation (SDA) report, which is part of the company’s Industrial & Manufacturing Technologies Research Service.