Control systems + automation
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Towards AI-driven process automation As process industries advance towards an AI-driven future, Kobus Vermeulen, Direct Sales Executive, Process Automation at Schneider Electric, outlines four major trends that are set to redefine automation strategies through 2026: hyper automation, AI-first automation, low code/no code platforms, and real-time process intelligence.
Kobus Vermeulen, Schneider Electric.
F rom unified platforms that blend AI, machine learning, and robotic process automation (RPA) for end-to-end optimisation, to AI-driven self-healing operations, ‘citizen developer tools’, and predictive process intelligence, these trends provide a view into how process automation will evolve. Hyper automation: a new era of integration Leading industrial organisations are increasingly incorporating hyper automation into their strategies. This integration of AI, machine learning, and RPA into unified platforms provides end- to-end process optimisation, particularly in complex industrial environments. Platforms like AVEVA Unified Operations Centre and UiPath Hyper automation enable organisations to gain greater visibility and control across operational technology (OT), information technology (IT), and business workflows. The trend towards agentic AI and autonomous operations is also gaining ground. AI agents embedded in industrial software can take over automated tasks such as dashboard creation, alarm management, and predictive analytics. Cloud-native copilots, such as Microsoft Azure Copilot, facilitate lifecycle governance and optimisation of automation agents, creating an environment where real-time decision-making and closed-loop optimisation are achievable. Furthermore, improved process intelligence is enabling organisations to identify bottlenecks more effectively, generating automation-ready workflows through process mining and modelling tools. Integrating these tools with manufacturing
execution systems (MES), computerised maintenance management systems (CMMS), and asset performance management (APM) systems, organisations can achieve real-time execution and create feedback loops that inform continuous improvement. The results are notable: documented benefits include up to a 27% reduction in downtime, 10 to 30% cost savings, and significant gains from predictive maintenance and enterprise visibility. AI-first automation: autonomous operations come into view The shift towards AI-first automation signals a change in how organisations are approaching operational processes. Through 2026, businesses will increasingly rely on AI systems not only to automate tasks but also to predict potential issues and respond automatically. In predicting issues, AI-driven platforms will analyse real-time and historical data from IT systems, OT assets, and IoT sensors, allowing organisations to anticipate failures and intervene proactively. Automated responses and closed-loop control mechanisms enabled by AI will further facilitate operational continuity. We expect to see businesses implementing AI-driven platforms that ensure dynamic load balancing, auto-scaling, and real- time parameter adjustments without human intervention, thus minimising mean time to repair (MTTR). The implementation of self-healing infrastructure will elevate AI-first automation to a new level. AIOps and agentic AI will empower IT and OT systems to self-repair, autonomously
Process intelligence is evolving to become integral to AI-driven automation strategies.
4 Electricity + Control APRIL 2026
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