Industrial Test Automation Trends in Manufacturing

AI agents and physical AI are shifting industrial automation from equipment supply to autonomous, self-optimizing systems. The most mature vendors are moving from pilots to production, with robots navigating complex environments and digital twins optimizing the value chain. This CB Insights brief gives a good view of where the top 20 industrial automation companies stand on AI maturity. Three key trends. 1. Leaders like Siemens Industry and ABB are linking AI systems across design, logistics, manufacturing, and maintenance creating compounding benefits. 2. Optimization dominates near-term priorities, while digital twins are emerging as the backbone for connecting hardware and software. 3. Partnerships with tech companies like Microsoft, Google, and Nvidia are essential, but they create new dependencies that must be managed. Siemens at the top of the ranking, combining copilots, edge platforms, and digital twins. Its work with Microsoft and Nvidia expands capabilities but increases reliance on external tech. Honeywell takes a more focused approach, embedding AI into devices and workflows. Its Qualcomm partnership highlights product-level integration over broad system building. ABB advances through its OmniCore platform and acquisitions such as Sevensense and SensorFact, blending robotics, software, and energy management. Schneider Electric pushes AI in energy management, using digital twins and partnerships with Nvidia, Microsoft, and Itron to extend from factory optimization into grid intelligence. The path forward in industrial AI is moving beyond pilots or isolated tools. It will depend on how well vendors embed AI into their platforms, link technologies across domains, and balance the benefits of external partners with the need for strategic independence. Those that will get it right will turn AI from experimentation into durable advantage. Just as critical is how their customers adopt these technologies. Industrial firms must shift from isolated use cases to embedding AI in design, production, energy, and logistics. Success requires not only advanced tools, but also the data, skills, and processes to make AI scale in complex operations.

As we close out 2025, I’ve been reflecting on the seismic shifts that defined industry, and what they signal for the future. 2025 was a year of compressed transformation. Persistent volatility in energy prices, supply chains, and labor markets accelerated adoption of IoT, AI, edge computing, and 5G. These technologies are no longer optional, they’re the backbone of modern industrial ecosystems. Analysts confirm this trajectory: 🔹 Deloitte reports that 80% of manufacturing executives plan to allocate 20% or more of their improvement budgets to smart manufacturing initiatives, prioritizing real-time visibility and predictive maintenance.  🔹 McKinsey & Company finds that 88% of companies now use AI in at least one function, but scaling remains a challenge - high performers redesign workflows to unlock growth and innovation.  🔹 Market forecasts show industrial automation growing from $206B in 2024 to $378B by 2030 (10.8% CAGR), driven by Industry 4.0, and AI integration.  🔹 Edge computing is surging too, expected to reach $45B by 2033, enabling low-latency analytics and predictive quality control. What does this mean for our industry? Automation is becoming open, software-defined, and decoupled from proprietary hardware, creating a foundation for adaptability, sustainability, and resilience. AI is moving from pilot projects to embedded intelligence, powering predictive maintenance, autonomous operations, and sustainability gains. At Schneider Electric, we see this every day: open, software-defined automation unlocks innovation through openness, interoperability, and flexibility, enabling manufacturers to scale faster and respond dynamically to market shifts. Looking ahead: AI will not just augment operations, it will redefine competitive advantage. From generative design to autonomous workflows, the next wave of industrial transformation is already here. 👉 What are your reflections on 2025, and where do you see the biggest opportunities in 2026 and beyond?  

Manufacturing innovation used to follow a predictable pattern. Build a prototype. Test it. Adjust it. Repeat. Trial and error. But AI is quietly replacing that process with something new. Simulation first manufacturing. One of the most powerful tools enabling this shift is the digital twin. A digital twin is a virtual model of a real world system. Factories, machines, production lines, even entire supply chains can now be simulated digitally before anything is built or changed. Physics informed AI models allow manufacturers to test: • equipment stress • production flow • failure scenarios • maintenance schedules inside simulations. Instead of experimenting on real machines, companies experiment in virtual environments first. The second big shift is happening in quality control. Computer vision systems are now inspecting products with precision that often exceeds human inspection. These systems can detect microscopic defects in: • electronics • automotive components • pharmaceuticals • consumer products Industry reports suggest AI vision adoption for quality inspection has already crossed 40% in some sectors. The third shift is about knowledge. Factories often rely on experienced technicians who carry years of institutional knowledge. But when those experts retire, knowledge can disappear with them. Large language models are now being used to build technical knowledge assistants for manufacturing teams. Technicians can ask systems questions like: “Why does this machine vibrate under load?” “What troubleshooting steps were used last time this fault occurred?” Instead of digging through manuals or calling senior staff, answers appear instantly. And finally, we’re seeing the rise of agentic AI in operations. These systems don’t just analyze information. They execute workflows. For example: • automatically triggering procure to pay cycles • coordinating maintenance scheduling • monitoring supply chain disruptions and recommending actions All with governance and human oversight. Manufacturing has always been about precision. What AI is doing now is extending that precision beyond machines to decisions, operations, and planning. The factories of the future won’t just be automated. They’ll be predictive. #Manufacturing #AI #ArtificialIntelligence #SmartManufacturing #DigitalTransformation #DigitalTwin #Simulation #ComputerVision #QualityControl #PredictiveMaintenance #AgenticAI #DeepTech

We are witnessing a meaningful advance in Embodied Intelligence that directly impacts industrial automation. A recent study, “Human-AI Co-Embodied Intelligence for Scientific Experimentation and Manufacturing” (Lin et al., 2025), demonstrates a cyber-physical-human loop where agentic AI, multimodal sensing, wearable interfaces, and adaptive control jointly guide real manufacturing tasks in real time. 📄 https://lnkd.in/gWYTC4zQ The system fuses human motion data, sensor-actuator signals, and process models to generate context-aware reasoning, real-time planning, corrective feedback and higher accuracy than general multimodal LLMs in flexible-electronics fabrication. For us, the implications are clear: Physical AI will require tightly integrated perception-reasoning-control stacks, human-robot collaboration, and safety-critical robustness to enable the next generation of intelligent manufacturing, adaptive automation, and the Industrial Metaverse. #PhysicalAI #EmbodiedAI #IndustrialAI #SmartManufacturing #CyberPhysicalSystems #HumanRobotCollaboration #Robotics #AgenticAI #DigitalTwin #Industry40 #ManufacturingInnovation #OperationsIntelligence #AdaptiveAutomation #WearableIntelligence #SensorFusion #ControlSystems #siemens

2026 will be the “operational AI + software-defined automation” year in industrial automation. After years of pilots, we’re moving into a phase where AI is expected to deliver measurable ROI on the shop floor: higher OEE, fewer unplanned stops, faster changeovers, and more resilient supply chains. Here are the trends I’m betting on for 2026 — with numbers: 1) Industrial automation keeps growing (and AI is one of the key accelerators) Global industrial automation is projected to reach ~$233.6B in 2026 (up from ~$215.2B in 2025) and is forecasted to more than double over the next decade. 2) Robotics stays the #1 “hard ROI” segment The world installed ~553,000 industrial robots in 2022 (a record pace) and the installed base is already around ~4 million robots globally. In 2026, I expect growth to shift even more toward: cobots (low barrier deployment) AMRs/AGVs (warehouse + intralogistics automation) “physical AI” capabilities (vision + autonomy + safer human collaboration) 3) AI moves from dashboards to decisions (Agentic AI in OT) 2026 is where we’ll see AI transition from “analytics” to actionable, semi-autonomous decision support: predictive maintenance that triggers workflows quality inspection with closed-loop parameter tuning energy optimization that reacts to tariffs and load supply chain agents that detect risk early and propose mitigation This is also directly tied to the IT/OT convergence wave: AI needs clean, contextualized, real-time data to work. 4) Virtual PLC / SoftPLC: the first real “software-defined control” chapter Virtual PLC is still early, but the growth rate is the signal: The Virtual PLC / SoftPLC market is estimated ~ $1.19B in 2026, with long-term forecasts showing ~13% CAGR over the following years. Why this matters: it’s not “just PLC in a VM.” It’s a different operating model: faster deployment & updates easier scaling (instances, redundancy, orchestration) tighter integration with edge compute + industrial AI pipelines more portable automation stacks (less vendor lock-in over time) 5) SCADA/HMI evolves into an industrial data platform SCADA is expected to keep a strong trajectory, with the market projected around ~$13.1B in 2026 and continuing double-digit growth in the years ahead. In practice, SCADA/HMI becomes the “context layer” for AI: tags + events + historian + alarms + workflows. My 2026 “winners” in industrial automation! If I had to pick the branches that will outperform in 2026: - Robotics + intralogistics automation (AMRs, conveyors, picking, palletizing) - Industrial AI for maintenance + quality (fastest ROI use cases) - Edge computing + Virtual PLC ecosystems (software-defined control) - Cybersecurity for OT (because connectivity keeps rising) Are you investing more in AI on the edge, or in Virtual PLC / software-defined architectures? #IndustrialAutomation #Industry40 #IndustrialAI #VirtualPLC #SoftPLC #SCADA #Robotics #IIoT #EdgeComputing #OTSecurity

AI in manufacturing isn't a future trend. It's a $10B+ market in 2026 heading to $230B by 2034. Your competitors aren't waiting to figure it out. Here's what's moving the needle on the shop floor right now:   PREDICTIVE SOLUTIONS ↳ Predict equipment failures before they happen ↳ Cut unplanned downtime with sensor data and ML ↳ Optimize maintenance schedules automatically   SMART PRODUCTION ↳ Computer vision catches defects in real time ↳ Automated assembly lines reduce human error ↳ Quality control runs 24/7 without fatigue   HUMAN-AI COLLABORATION ↳ Cobots handle repetitive tasks — people handle judgment ↳ Training and upskilling become a competitive advantage ↳ Digital twins let you test changes before you make them   AI-DRIVEN SUSTAINABILITY ↳ Peak load management cuts energy costs ↳ Emissions monitoring becomes automatic ↳ Circular economy integration moves from aspiration to system   The gap between companies using AI and those not widens every quarter.   The question is no longer whether to start. It's how much ground you've already lost.   *** Two tools worth knowing:   Retrocausal uses computer vision through off-the-shelf cameras to improve shop floor performance → retrocausal.ai   Gemba Walk AI, my first SaaS, turns Gemba Walk observations into trackable action items...so Kaizen ideas don't die in email threads.   Launching very soon → join the waitlist at gembawalk.ai

AI is no longer just analyzing data. It is starting to run the factory. In the January issue of Photonics Spectra, Andreas Thoss explores how AI is moving from post-process inspection into real-time process control across industrial photonics. From TRUMPF’s AI-supported image processing in hairpin welding to Scansonic MI GmbH’s weld seam evaluation and AUDI AG’s rapid deployment of production-ready models, the shift is already underway. The article also highlights how researchers at Fraunhofer ILT and RWTH Aachen University are combining physics-based models with machine learning to dramatically reduce process optimization time. Workshops led by SPECTARIS brought together industry and research voices from Microsoft, ZEISS Group, TRUMPF, AUDI AG, Precitec Vision, Bystronic Group, Blackbird Robotics, 4D Photonics GmbH, and others to examine what comes next. The message is clear. Data is becoming the most valuable asset in laser-based manufacturing, and AI systems that learn from both physics and production data are beginning to close the loop. Read the full article here: https://lnkd.in/gAnNcc9W #Photonics #ArtificialIntelligence #IndustrialAI #LaserProcessing #QualityControl #Manufacturing #MachineLearning #FraunhoferILT #TRUMPF #Audi #ZEISS

🚀 Industrial Automation 2026: From Control to Intelligence Industrial automation is no longer just about controlling machines. It’s about building systems that think, adapt, and optimize in real time. We are witnessing a major architectural shift in modern facilities: 🔹 Edge computing enabling low-latency decisions 🔹 AI-driven anomaly detection replacing reactive troubleshooting 🔹 Digital twins supporting commissioning before physical startup 🔹 Web-native HMI platforms with scalable, high-performance visualization 🔹 Data-driven maintenance instead of calendar-based servicing The traditional flow used to be: Sensor → PLC → SCADA → Operator reaction Today, it is evolving into: Sensor → PLC → Edge analytics → Predictive insight → Autonomous adjustment 📊 Alarm management is becoming event intelligence. 📈 Dashboards are transforming into decision engines. ⚙️ Commissioning is supported by simulation and data validation. The question is no longer: “Is the pump running?” The real question is: “Is the system performing optimally — and what will happen next?” Industrial automation is entering its intelligence era. And those who understand architecture will shape the future of it. #IndustrialAutomation #SCADA #PLC #EdgeComputing #DigitalTwin #SmartFactory #Industry40 #AutomationEngineering #ControlSystems #Commissioning #ProcessAutomation #FutureOfIndustry

📢𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴 𝗧𝗼𝘄𝗮𝗿𝗱𝘀 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆 Artificial Intelligence (AI) agents are revolutionizing manufacturing by 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗶𝗻𝗴 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀, 𝗲𝗻𝗵𝗮𝗻𝗰𝗶𝗻𝗴 𝗵𝘂𝗺𝗮𝗻-𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻, and steering the industry toward 𝗻𝗲𝗮𝗿-𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀. 📌 𝗦𝗶𝗲𝗺𝗲𝗻𝘀' 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 𝗖𝗼𝗽𝗶𝗹𝗼𝘁: In collaboration with Microsoft, Siemens has introduced the Industrial Copilot, an AI-powered assistant designed to boost productivity and efficiency across the industrial lifecycle. This tool allows users to rapidly generate, optimize, and debug complex automation code, significantly shortening simulation times. 📌𝗕𝗠𝗪'𝘀 𝗛𝘂𝗺𝗮𝗻𝗼𝗶𝗱 𝗥𝗼𝗯𝗼𝘁𝘀: BMW Group has partnered with Figure AI to test the Figure 02 humanoid robot in real production environments at their Spartanburg plant. These robots are designed to perform tasks such as inserting sheet metal parts into assembly fixtures, showcasing the potential of AI-driven automation in manufacturing. ➡️ 𝗦𝗲𝗲 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿𝘀𝗲𝗹𝗳: 𝗠𝗮𝗿𝗸𝗲𝘁 𝗚𝗿𝗼𝘄𝘁𝗵: The global AI in manufacturing market is projected to grow from $𝟯.𝟮 𝗯𝗶𝗹𝗹𝗶𝗼𝗻 𝗶𝗻 𝟮𝟬𝟮𝟯 𝘁𝗼 $𝟮𝟬.𝟴 𝗯𝗶𝗹𝗹𝗶𝗼𝗻 𝗯𝘆 𝟮𝟬𝟮𝟴, reflecting a compound annual growth rate (𝗖𝗔𝗚𝗥) 𝗼𝗳 𝟰𝟱.𝟲%. 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: Early adopters of AI in industrial operations have achieved up to 𝟭𝟰% 𝙨𝙖𝙫𝙞𝙣𝙜𝙨, highlighting the technology's potential to enhance efficiency and reduce costs. ➡️ 𝗔𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀: • 𝗧𝗿𝘂𝘀𝘁 𝗜𝘀𝘀𝘂𝗲𝘀: Concerns about the reliability and decision-making capabilities of AI agents can make stakeholders hesitant to implement them. • 𝗟𝗲𝗴𝗮𝗰𝘆 𝗦𝘆𝘀𝘁𝗲𝗺𝘀: Integrating AI agents into existing infrastructures can be complex and costly, especially when dealing with outdated technology. • 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Scaling AI solutions across large operations presents challenges in terms of consistency and interoperability. ❓ What challenges do you anticipate in adopting this technology? ❓ How do you envision AI agents enhancing operational efficiency in your organization? Source: World Economic Forum #AIinManufacturing #IndustrialAutomation #HumanMachineCollaboration #Siemens #BMW #Industry40 #ManufacturingInnovation