Industrial AI & analytics software

Industrial AI is the application of artificial intelligence to manufacturing and other industrial operations. It uses operational data from equipment, processes, production systems, quality systems, maintenance records, and other sources to help teams understand performance, diagnose problems, predict outcomes, recommend actions, and automate selected workflows.

Unlike many general-purpose AI applications, industrial AI often operates in environments where reliability, timing, security, explainability, and control are critical.

Industrial analytics uses operational data, statistical methods, machine learning, process models, and other analytical techniques to understand and improve industrial performance. Applications can include process optimization, predictive maintenance, anomaly detection, quality analysis, root-cause analysis, forecasting, and operational troubleshooting.

Industrial analytics can include both deterministic models designed for repeatable industrial decisions and AI-based approaches used to identify patterns, generate insights, and support decision-making.

Industrial analytics is a broad set of technologies for analyzing operational data to understand performance, identify relationships, predict outcomes, and optimize processes. Industrial AI extends these capabilities with technologies such as machine learning, generative AI, and AI agents that can interpret information, identify patterns, answer questions, make recommendations, and support or automate actions.

In industrial environments, the two often work together. Deterministic analytics may be appropriate for process control and optimization, while AI can help users investigate complex operational information and make faster decisions.While a PLC performs low-level control at the device level, HMI displays equipment conditions to nearby personnel. SCADA integrates data from many PLCs and sensors across an entire operation, offering supervisory control, alarm handling, data logging, and historical analysis. Together, these technologies form a layered automation architecture that enables efficient, safe, and informed industrial operations.

Generative AI is one type of AI and is primarily designed to interpret and generate content such as language, images, or code. Industrial AI encompasses a broader range of technologies used in industrial operations, including machine learning, predictive models, optimization techniques, deterministic process models, and generative AI.

Industrial applications may therefore use different types of AI and analytics depending on the accuracy, latency, repeatability, governance, and operational requirements of the use case.

Industrial AI and analytics typically require reliable data from equipment, controls, sensors, and operational software such as HMI SCADA, Historian, MES, quality, maintenance, and enterprise systems.

Connecting this information is only the first step. Data also needs sufficient context so analytics and AI can understand relationships among equipment, processes, products, production events, quality results, and other operational conditions.

Industrial AI and analytics can support applications across production, assets, quality, and compliance. Common use cases include production optimization, root-cause analysis, predictive maintenance, anomaly detection, predictive quality, process optimization, energy analysis, alarm analysis, troubleshooting, forecasting, and AI-assisted operational decision-making.

The appropriate analytical or AI technique depends on the use case and the level of reliability, explainability, speed, and automation required.

Industrial AI can help manufacturers identify problems earlier, investigate operational issues faster, predict equipment or process risks, improve production and quality performance, and provide operators and engineers with more relevant information for decision-making.

As capabilities mature from descriptive and diagnostic analytics toward predictive and prescriptive approaches, teams can move from reacting to past performance toward anticipating conditions and taking action before problems occur.

Yes. Industrial AI and analytics can operate on-premises, at the industrial edge, in the cloud, or across hybrid architectures. The appropriate deployment depends on factors such as latency, connectivity, security, data sovereignty, computational requirements, and the operational use case.

Real-time industrial applications may require processing close to equipment, while enterprise analysis may benefit from aggregating information across plants or using cloud computing resources.

Trustworthy industrial AI requires more than an accurate model. Industrial organizations need reliable and contextualized operational data, appropriate security and access controls, visibility into the information used to produce recommendations, governance over how AI is used, and clearly defined boundaries around actions AI systems are permitted to take.

Human oversight is also important, particularly where incorrect recommendations could affect safety, quality, production, or regulatory compliance.Whether managing a complex chemical batch process, tracking production stages in a discrete assembly line, or overseeing HVAC and utility systems in a facility, SCADA enables operators to stay informed, react to issues in a timely manner, and support safe, efficient workflows. Its ability to integrate with other systems and scale across production environments makes it a vital tool for manufacturers looking to drive reliability, compliance, and performance.

Augmented operations use AI and analytics to give operators, technicians, engineers, and other frontline workers additional insight, recommendations, and guidance while people remain responsible for decisions and actions.

Autonomous operations allow software to take defined actions without direct human intervention. The appropriate approach depends on the use case, confidence in the technology, operational risk, governance requirements, and consequences of an incorrect action.