Top 10 Industrial Automation Trends to Watch [2026-2027]

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Executive Summary: What are the Top 10 Industrial Automation Trends?

1. AI & ML Integration: AI and machine learning enable industrial systems to analyze real-time and historical data to optimize production, quality control, and autonomous decision-making at scale. AI-driven robots increase operational efficiency by up to 30% by performing continuous and error-free tasks automatically.
2. IIoT: IIoT connects machines, sensors, and control systems to create continuous data flows that improve visibility, coordination, and performance across industrial operations. Plants that use linked condition monitoring cut down on unplanned downtime by up to 50%.
3. Collaborative & Autonomous Robots: Collaborative and autonomous robots work alongside humans or independently to increase flexibility, precision, and throughput in manufacturing and logistics environments.
4. OT Cybersecurity: OT cybersecurity focuses on protecting industrial control systems and connected assets from cyber threats that can disrupt production, safety, and critical infrastructure. 65% of industrial organizations experienced at least one OT cybersecurity incident in the prior 24 months.
5. Predictive Maintenance: Predictive maintenance (PdM) uses sensor data and analytics to detect early signs of equipment failure, reducing unplanned downtime and maintenance costs. PdM offers 30% reduction in unplanned downtime on monitored welding cells.
6. Sustainable Automation: Sustainable automation applies energy-efficient technologies and resource-optimized processes to reduce emissions, waste, and operational costs in industrial systems.
7. Edge & Cloud Computing: Edge and cloud computing distribute data processing between local devices and centralized platforms to support low-latency control, scalability, and advanced analytics. cloud applications deliver 2.1 times the ROI of on-premises applications, due to lower infrastructure and maintenance costs and faster deployment.
8. Immersive Technologies: Immersive technologies such as AR and VR support training, remote assistance, and simulation by overlaying digital insights onto physical industrial environments. A study shows VR training in manufacturing reduces learning time by 40% and improves knowledge retention by 75% compared with traditional methods
9. Digital Twins: Digital twins create virtual replicas of physical assets or processes to simulate performance, optimize operations, and support data-driven decision-making.
10. 3D Printing: Additive manufacturing enables on-demand, layer-by-layer production of components, supporting customization, rapid prototyping, and localized manufacturing.

Read on to explore each trend in depth – uncover key drivers, current market stats, cutting-edge innovations, and 20 leading innovators shaping the future.

Innovation Map outlines the Top 10 Industrial Automation Trends & 20 Promising Startups

For this in-depth research on the Latest Industrial Automation Trends & Startups, we analyzed a sample of 2134 global startups & scaleups. The Industrial Automation Innovation Map created from this data-driven research helps you improve strategic decision-making by giving you a comprehensive overview of industrial automation trends & startups that impact your company.

Top 10 Industrial Automation Trends [2026-2027]

1. Artificial Intelligence (AI) and Machine Learning (ML) Integration
2. Industrial Internet of Things (IIoT)
3. Collaborative and Autonomous Robots
4. Operational Technology (OT) Cybersecurity
5. Predictive Maintenance
6. Sustainable Automation
7. Edge and Cloud Computing
8. Immersive Technologies
9. Digital Twins
10. 3D Printing

Methodology: How We Created the Industrial Automation Trend Report

For our trend reports, we leverage our proprietary StartUs Insights Discovery Platform, covering 9M+ global startups, 25K technologies & trends, plus 190M+ patents, news articles, and market reports.

Creating a report involves approximately 40 hours of analysis. We evaluate our own startup data and complement these insights with external research, including industry reports, news articles, and market analyses. This process enables us to identify the most impactful and innovative trends in industrial automation.

For each trend, we select two exemplary startups that meet the following criteria:

• Relevance: Their product, technology, or solution aligns with the trend.
• Founding Year: Established between 2020 and 2026.
• Company Size: A maximum of 200 employees.
• Location: Specific geographic considerations.

This approach ensures our reports provide reliable, actionable insights into the industrial automation innovation ecosystem while highlighting startups driving technological advancements in the industry.

Tree Map reveals the Impact of the Top 10 Trends in Industrial Automation

Based on the Industrial Automation Innovation Map, the tree map below illustrates the impact of the Top 10 industrial automation trends. Industrial automation includes the artificial AI and machine learning integration cluster at the core.

This reflects their role in enabling data-driven control, optimization, and autonomous decision-making across production systems. Further, the industrial internet of things (IIoT) expands data acquisition through connected sensors, machines, and assets, forming the backbone for analytics-driven operations.

Predictive maintenance and digital twins translate this data into actionable intelligence by forecasting failures and simulating asset performance. Collaborative & autonomous robots represent execution layers, improving flexibility, precision, and labor productivity on factory floors.

Edge computing and cloud computing support this stack by balancing low-latency processing with scalable analytics and storage. At the application edge, immersive technologies enhance training and remote support, while additive manufacturing enables localized, on-demand, and customized production workflows.

Global Startup Heat Map covers 2100+ Industrial Automation Startups & Scaleups

The Global Startup Heat Map showcases the distribution of 2134 exemplary startups and scaleups analyzed using the StartUs Insights Discovery Platform. It highlights high startup activity in India and the US, followed by Turkey. From these, 20 promising startups are featured below, selected based on factors like founding year, location, and funding.

Want to Explore Industrial Automation Innovations & Trends?

Top 10 Emerging Industrial Automation Trends [2026-2027]

1. AI & ML Integration: AI-driven Robots are 30% More Efficient

AI and machine learning now function as core operational systems in industrial automation. It shifts factories from rule-based control toward continuously learning, self-optimizing production environments.

The global AI in industrial automation is growing at a compound annual growth rate (CAGR) of 18.6% and is expected to reach USD 90.28 billion by 2033. This is supported by the rapid adoption of vision systems, process optimization models, and AI-assisted quality control systems in both discrete and process sectors.

AI-driven robots increase operational efficiency by up to 30% by performing continuous and error-free tasks automatically. It supplements human labor in assembly, handling, and inspection. Further AI-driven human-robot collaboration improves production output by up to 40%.

The integration of AI also reduces the need for human supervision. For instance, the inclusion of deep learning-based vision systems enables faster process changeovers and handles variability.

Additionally, AI-driven automations of quality systems also enhance production output. A McKinsey study observed that an AI-enabled quality system resulted in a two- to three-times increase in productivity. It also offered a 50% improvement in service levels, a 99% reduction in defects, and a 30% decrease in energy consumption.

In recent years, there has been an increasing capital flow towards industrial AI platforms, simulation software, and AI-native automation stacks. ABB, a big player in the industrial automation sector, invested in Landing AI to enhance its vision AI capabilities. Landing’s pre-trained models, smart data workflows, and no-code tools reduce training time by 80%. This accelerates deployment in fast-moving sectors, including logistics, healthcare, and food & beverage

AI integration also requires new approaches in data governance, model lifecycle management, and connection with control systems, which are all important for success. Standardizing AI pipelines across all facilities allows manufacturers to grow faster, get a better return on investment (ROI), and get measurable competitive advantages in cost, quality, and resilience.

Skild AI offers General-purpose Robotic Intelligence

US-based startup Skild AI develops general-purpose robotic intelligence that enables machines to perceive, learn, and act across diverse physical environments. It builds machine learning models that integrate large-scale data and self-supervised training. This creates adaptable intelligence capable of performing complex tasks in dynamic, real-world settings.

Skild Brain is the startup’s core system that serves as a scalable robotics foundation model that allows adaptation across different robot types and task domains without reprogramming.

Skild provides a mobile manipulation platform that abstracts low-level skills such as grasping and navigation into API calls. The startup accelerates robot application development. Its solutions extend to full-stack robotic systems for inspection, data collection, and autonomous operations in industries like construction, manufacturing, and logistics.

Apenum provides AI-powered Control Systems

Polish startup Apenum develops AI-powered control systems that automate industrial automation through a combination of artificial intelligence, computer vision, and robotics. Its flagship bin-picking solution analyzes 3D cloud data generated from low-cost RGB cameras to identify each object’s 6D pose using deep learning algorithms.

The technology integrates strategic path planning to calculate collision-free trajectories, enabling robotic arms to perform precise pick-and-place operations even when items are randomly positioned and lighting conditions fluctuate.

It replaces deterministic programming with an adaptive, non-deterministic approach that enhances flexibility, accuracy, and uptime in manufacturing environments.

2. IIoT: 46% of Manufacturers Use IIoT to Gain Asset Insights

The integration of IIoT in industrial automation allows for real-time visibility, analytics, and remote control of production units. The global IIoT market is growing at a CAGR of 8.1% and is expected to reach USD 286.3 billion by 2029 as connection spreads to older equipment and brownfield buildings.

IIoT turns physical tasks into systems that can be measured and analyzed. Plants that use linked condition monitoring cut down on unplanned downtime by up to 50%, which reduces maintenance costs by 50% and extends machine life by years.

IIoT makes production more flexible and increases quality, not only maintenance. In advanced factories, continuous monitoring of process variables enables tighter control limits, which cuts down on scrap and material wastage.

These systems analyze operating conditions and flag efficiency drops immediately. Machine learning models have achieved 4.4% lower scrap rates by optimizing parameters.

Faster feedback loops help with mass customization and cut down on changeover times. A case study indicates a 65% reduction in changeover time for an automated packaging system by linking the product ID with the machine settings required.

With increasing focus on implementing smart manufacturing technologies, 46% of manufacturers are using IIoT to gain insights into their assets.

Inclue provides Device Intelligence

Indian startup Inclue develops a low-code IIoT platform called Babble, which connects, monitors, and analyzes data from diverse industrial devices and systems in real time.

The platform integrates IoT, AI, and data analytics to enhance industrial automation by linking protocol-agnostic data sources, including Modbus TCP/RTU, OPC UA, and MQTT.

The startup offers unified dashboards that visualize both real-time and historical data. It enables users to define dataflows and apply advanced transformations. It also enables users to configure multi-condition alarms with instant notifications across multiple channels to ensure operational continuity.

Babble also supports granular permission control, encrypted data handling, and comprehensive audit logs to maintain security and compliance.

Holon Robotics builds a Robot Monitoring Platform

Taiwanese startup Holon Robotics develops an AI-native robot monitoring platform. It combines multi-brand robots, sensors, and control systems through its core middleware, HolonOS.

The platform operates as an intelligent communication layer that connects robots, sensors, actuators, and cloud infrastructure. It enables real-time coordination and adaptive automation across industrial environments.

The startup combines built-in algorithms and seventy modular function blocks to streamline tasks such as path planning, data analysis, and AI-based inspection while eliminating the complexity of multi-robot programming. Its control interfaces on rugged tablets, swing-arm panels, or mounts offer intuitive interaction with industrial systems.

Holon Robotics provides robotics management, sensor analytics, and workflow automation in a single operational system. This enables manufacturers and integrators to reduce downtime, optimize robot utilization, and scale automation with consistency and precision.

3. Collaborative & Autonomous Robots: Cobot Market to Reach USD 3.38 Billion by 2030

As manufacturers pursue higher throughput, safer operations, and flexible production systems, industrial automation systems are utilizing collaborative and autonomous robots.

The global collaborative robotics market is expected to reach USD 3.38 billion by 2030, growing at a CAGR of 18.9%. Cobots expand human-robot collaboration on assembly lines, inspection cells, and packaging operations.

ABB installed their Yumi and GoFa cobots on the gas leak detection and electrical test stations of the Electrolux group to eliminate repetitive manual processes. This increased the quality of tests and brought more security to employees. It resulted in an 8% gain in the effectiveness of tests.

Autonomous robots add even more value to intralogistics, inspection, and moving materials. Mobile robots and self-driving forklifts speed up internal transportation, make better use of space, and keep throughput steady in spite of worker shortages.

The global autonomous robot market was estimated at USD 27.5 billion in 2025, growing at a CAGR of 11.4%. The market value is expected to reach USD 82 billion by 2035.

Autonomous mobile robots (AMRs) make up the major portion of this market. AMRs in warehouses reduce labor costs by 30% to 50%, enabling payback in as little as 18 to 30 months.

Investment activity shows that people are becoming more confident in robotics as a way to boost productivity over the long run. Major automotive companies like Ford and Mercedes-Benz are integrating cobots and humanoid robots into their production lines. These robots automate mundane and dangerous tasks and augment the workforce.

Fasta Robotics designs Self-driving Robots

Canadian startup Fasta Robotics develops self-driving robots for warehouses and production lines that perform material handling tasks without human intervention.

The startup integrates advanced artificial intelligence and sensor-based navigation systems. These systems enable real-time obstacle detection, path optimization, and dynamic rerouting across complex industrial environments.

Fasta Robotics uses an adaptive control algorithm that continuously learns from data to improve route efficiency and operational performance. The robots feature a modular design that supports multiple payload types, ensuring flexibility across sectors such as manufacturing, logistics, and fulfillment.

Through continuous remote monitoring and intelligent ambient detection, the technology ensures consistent quality, increased safety, and efficient resource utilization.

SeeNGrip manufactures Grippers for Cobots

SeeNGrip is a South Korean startup that manufactures advanced electric grippers engineered for collaborative robots used in industrial and service automation.

It designs and produces gripper systems that integrate sensing technologies to estimate force and softness. It also enables absolute position reading at power-up and provides self-locking capacity at power-down for stable operation.

The startup’s modular products are segmented into Optimum, Essential, and Economy series. They allow programmable control of gripping position, speed, and force through Modbus RTU or USB communication, supporting compatibility with cobots from Universal Robots, Doosan, Rainbow, and Dobot.

The startup’s proprietary design incorporates lightweight materials such as aluminum and HP PA12 nylon to achieve efficient motion profiles and robust gripping performance across rotational and parallel types.

4. OT Cybersecurity: 65% of Industrial Organizations Report Cyber Attacks

As networking and digitization speed up, cybersecurity becomes a key part of industrial automation. The global OT security market will be worth about USD 47.95 billion in 2031, growing at a CAGR of 13.74%. This is because people are more conscious of the risks of cyber-physical attacks in the manufacturing, energy, and infrastructure sectors.

The business case for OT cybersecurity is becoming easier to measure. In 2025, ransomware attacks demanded an average of more than USD 1 million in ransom, and even if no ransom is paid, the cost of recovery will be USD 1.3 million. According to broader breach statistics, the average cost of a data breach over the world is close to USD 4.4 million. This shows that industrial operators are at a great risk of losing a lot of money.

65% of industrial organizations experienced at least one OT cybersecurity incident in the prior 24 months. The average cost per OT incident was USD 2.8 million, largely due to downtime and lost production.

Cyber catastrophes cause more than just financial losses. They stop production from continuing. OT assaults generally cause more downtime than IT disasters since they need safety checks, manual resets, and equipment validation.

Putting money on segmentation, anomaly detection, and secure remote access cuts down on the average time it takes to recover and stops problems from spreading across production lines.

Rockwell notes that mature OT incident-response and recovery practices shave days or even weeks of downtime off the recovery timeline after an attack. This is achieved by having asset inventories, backups, and OT-aware IR retainers in place.

Instead of seeing security as a cost of doing business, leaders see it as a way to make operations easier. They build cyber risk management into the design of automation architecture. This method helps make digital scaling safer, preserves investments of money, and keeps trust in connected industrial systems.

Steryon streamlines Cyber Risk Management

Spanish startup Steryon offers a cyber resilience risk management platform that addresses vulnerabilities within cyber-physical systems (CPS) across industrial infrastructures.

It collects telemetry, OEM data, and threat intelligence from OT environments. The platform then correlates them through artificial intelligence to generate real-time insights, prioritize mitigation, and automate compliance tasks.

The platform integrates through an agentless, vendor-agnostic architecture, offering both SaaS and on-prem deployment to adapt to diverse organizational needs. It unifies risk visibility, remediation, and compliance under a single operational view. This transforms scattered OT and IT threat signals into actionable, business-aligned intelligence.

The startup provides continuous asset visibility, contextualized risk assessment, and KPI-based performance tracking. This enables OT asset owners to maintain operational continuity, strengthen cyber resilience, and ensure regulatory readiness.

DTSecLabs builds an OT Security Platform

German startup DTSecLabs develops an OT security platform that enables continuous, non-intrusive ethical hacking for industrial environments. It operates by safely replicating real-world attack scenarios in an isolated layer over live production systems, ensuring that security teams test for vulnerabilities without interrupting ongoing operations.

The startup’s product, ProSec, continuously maps and monitors connected industrial assets, simulates attack paths in real time, and ranks risks based on severity to guide remediation priorities.

Through an integrated command center, it consolidates asset visibility, threat insights, and compliance status, streamlining response across operations. It supports major industrial protocols and integrates with SIEM and CMDB systems. It builds resilience directly into existing OT workflows.

5. Predictive Maintenance: Reduces Unplanned Downtime by 30%

Predictive maintenance (PdM) shifts asset management from time-based servicing to condition- and risk-based decision-making. The global predictive maintenance market is growing at a CAGR of 35.1% and is expected to reach USD 47.8 billion by 2029.

PdM platforms combine vibration analysis, thermal monitoring, acoustic sensing, and oil analysis with machine-learning models. These models are either deployed at the edge or integrated into industrial cloud stacks.

For example, ABB utilizes vibration and performance analytics in its IRB welding robots to detect deviations in motion before weld quality drops. This results in a 30% reduction in unplanned downtime on monitored welding cells.

Siemens also offers AI-driven PdM in their robots that monitors motor currents, temperatures, cycle profiles, and positional deviations. It automatically generates machine and maintainer health scores and also provides maintenance recommendations.

Further, for manufacturing companies like General Motors, reported improvements in plant performance with the implementation of PdM. The AI-driven PdM system predicted more than 70% of equipment failures at least 24 hours in advance. This allows more effective distribution of maintenance labor, focusing on at-risk machinery. This extends equipment life by reducing over-maintenance and unnecessary replacements.

This increased rate of adoption is also evident from the predictive AI in the robotics market, which grows 16.8% annually. The global predictive AI in the robotics market is expected to increase by USD 5.25 billion by 2029.

Eight Vectors provides Predictive Robotic Health Insights

Singapore-based Eight Vectors develops an edge SaaS platform that delivers predictive health insights and operational intelligence for robotic and material-handling fleets. ZenitaAI is the startup’s platform that analyzes real-time sensor data from autonomous robots and manual handling equipment to detect early signs of mechanical or behavioral drift.

The platform integrates edge computing, machine learning, and 3D mapping to monitor utilization, identify inefficiencies, and predict component failures before they occur.

It differentiates itself through brand-agnostic compatibility, retrofit sensor boxes for instant activation, and affordable deployment tailored for small and mid-sized operators. The startup enables organizations to maximize uptime, reduce maintenance costs, and extend the lifespan of their robotic assets.

AION Technologies offers AI-Powered Predictive Maintenance

AION Technologies is a startup based in Saudi Arabia that develops an AI-powered predictive maintenance platform called MAINTA. The platform enables industries to monitor and manage equipment health with precision. The system integrates with industrial assets to collect real-time data from sensors, historical logs, and AI-based camera inspections. This creates a unified dataset for continuous assessment.

The startup’s advanced machine learning algorithms analyze operational patterns to detect anomalies and forecast potential failures before they occur. MAINTA provides predictive alerts, detailed reports, and actionable maintenance recommendations that help teams prevent downtime and optimize resource allocation.

Its secure architecture ensures reliable offline functionality, while customizable maintenance schedules and intuitive dashboards streamline decision-making. The startup allows industrial operators to enhance asset reliability, extend equipment life, and lower maintenance costs through data-driven foresight and operational intelligence.

6. Sustainable Automation Solutions: Energy Efficiency can Save USD 6 Million Annually

Industrial establishments aim to become more productive while also using less energy and polluting less. The global industrial efficiency market is expected to reach USD 48.6 billion by 2032, growing at a CAGR of 8.6%.

ABB introduced an ultra-efficient motor for an Indian steel plant that offers 99.13% efficiency. This over a lifetime of 25 years saves 61 GWh of electricity that will cut nearly USD 6 million in electricity costs. Additionally, the payback period for this higher-efficiency design is 3 months.

Smart automation and IIoT-based waste reduction initiatives show 10% to 30% reductions in scrap or material waste in manufacturing lines utilizing automatic optimizations like vision inspection, process analytics, and parameter control. This translates directly into lower embodied energy and CO2 per unit produced, complementing pure energy-efficiency gains.

Sight Machine applied its real-time analytics to a production line with high scrap, which resulted in a 30% reduction in scrap costs within 3 weeks of deployment.

Compliance Robotics offers Lightweight & Flexible Robotics

French startup Compliance Robotics develops lightweight and flexible robotic systems that use soft robotics principles to achieve high energy efficiency and safe operation. Its technology integrates compliant materials with advanced simulation and digital twin modeling to minimize power consumption while maintaining precise control over movement and force.

The startup’s robots deform naturally and distribute mechanical stress efficiently. This reduces the energy required for repetitive industrial tasks. Hollow is the startup’s deformable arm for manufacturing.

The startup combines flexibility, low-power mechanics, and easy human-robot collaboration, offering a generation of sustainable robotic systems that optimize performance and efficiency across agri-food and industrial settings.

All3 enables Sustainable Construction using Robots

UK-based startup All3 develops an AI-driven architecture and robotic fabrication platform for fully custom, sustainable buildings that use renewable structural timber.

It processes project briefs, site constraints, and regulatory parameters through specialized AI agents. It optimizes layouts, material quantities, and energy performance before fabrication, which ensures each structure aligns with strict European sustainability standards.

The startup then uses a structured data layer and real-time manufacturing software to drive robot-powered production cells that execute precision cutting and assembly with 0.2 mm accuracy, which reduces timber waste by about 3% compared with conventional CLT construction and lowers embodied carbon by up to 25% versus reinforced concrete.

Furthermore, the startup integrates plug-and-play connectors, pre-installed MEP systems, and the fully electric Mantis on-site robot to minimize rework, shorten on-site activity, and support safer, low-emission urban infill projects.

As a result, the startup offers a vertically integrated, robot-based construction model that enables verified low-carbon buildings at an industrial scale while maintaining cost levels comparable to mass-produced conventional structures.

7. Edge & Cloud Computing: Cloud Systems Provide 2.1x ROI

The need for processing with low latency, local resilience, and scalable analytics is increasing the importance of edge and cloud computing. This brings AI inference and real-time control closer to machines.

Edge designs cut down on latency compared to processing that solely happens in the cloud. This makes it possible to do closed-loop control, visual inspection, and safety operations without relying on the network. This feature makes yields more stable and cuts down on quality escapes in factories that run at high speeds.

The BMW manufacturing plant utilized an edge-enabled PdM system that reduced maintenance costs by 25% and resulted in a 7% increase in production efficiency.

Similarly, the Siemens industrial edge deployment at an automotive supplier enabled an 18% reduction in scrap and waste thanks to localized analytics and faster feedback. It also resulted in a 12% increment in overall equipment effectiveness.

Cloud platforms work well with edge systems because they allow for fleet-level analytics, digital twins, and centralized optimization. Manufacturers may balance computing costs, data sovereignty, and performance needs with hybrid architectures. Adoption speeds up as manufacturers make industrial edge stacks and orchestration technologies more mainstream.

Nucleus Research found that cloud applications deliver 2.1 times the ROI of on-premises applications, due to lower infrastructure and maintenance costs and faster deployment. This is mainly due to the reduced upfront costs and shorter deployment cycle.

OTee offers a Virtual Industrial Automation Platform

Norwegian startup OTee develops a virtual industrial automation platform that enables engineers to create, program, deploy, and manage virtual PLCs (vPLCs) at scale through a cloud-native environment.

It operates using an IDE based on the IEC 61131-3 structured text standard and deploys seamlessly on any Linux-based hardware while supporting containerized execution via Docker.

The platform integrates a zero-trust cybersecurity framework with role-based access control to secure distributed automation systems. It also provides a unified interface for configuring, updating, and monitoring entire PLC fleets.

Its pub-sub data framework enhances speed, reliability, and interoperability for Industry 4.0 systems, while its open technology architecture eliminates vendor lock-in.

ARES Autonomy provides Swarm Intelligence on the Edge

Irish startup ARES Autonomy develops an AI-powered edge platforms that enable the coordination, control, and autonomy of large-scale drone swarms in complex and GNSS-denied environments.

Its technology integrates advanced swarm intelligence algorithms with edge computing hardware and scalable cloud architecture, offering real-time decision-making and communication across distributed drone networks.

The system employs a swarm mesh network that supports inter-drone communication, anti-jamming capabilities, and GPS-independent navigation, while its modular design ensures vendor-agnostic integration with diverse unmanned systems, including UAS, VTOL, UUVs, and ground vehicles.

8. Immersive Technologies: Reduces Learning Time by 40%

Augmented reality (AR), virtual reality (VR), and mixed reality are becoming more and more helpful for industrial operations, training, and maintenance.

AR-based training cuts the time it takes for new staff to become productive. Remote help also cuts the average time it takes to fix something and the cost of the trip. These gains help with skill shortages and make it easier to share information between places.

The global AR in manufacturing market is set to reach USD 158.6 billion by 2035, growing at a CAGR of 19.66%.

Adoption grows as technology gets lighter, cheaper, and easier to connect to industrial systems. By combining digital twins and IIoT data, manufacturers get better contextual guidance throughout operations.

A study shows VR training in manufacturing reduces learning time by 40% and improves knowledge retention by 75% compared with traditional methods.

The global VR in manufacturing market is growing at a CAGR of 27.2% and is expected to reach USD 38.93 billion by 2032.

Investment patterns favor deployments that are based on specific use cases over generic visualization. Companies put immersive processes first for complicated assembly, safety training, and maintenance, where demonstrable ROI backs up business reasons.

Companies that use immersive processes in their automation systems may train people faster, provide better service, and run their businesses more smoothly.

PZ Robotics enables Immersive Training & Operation

US-based startup PZ Robotics provides an intelligent teleoperation platform that enables human operators to remotely control industrial robots with high precision and real-time haptic feedback.

The startup’s PZ_motion system connects operators to robots through a web-based interface, translating human gestures into robot motion while transmitting tactile sensations that reflect tool forces and torques.

The startup integrates stereo vision and 3D scanning to deliver spatial awareness and geometry verification. Its modular architecture supports AI-assisted functions such as automatic tool alignment and adaptive motion scaling from coarse to fine adjustments.

The system further includes digital twin and virtual reality simulation environments for training and offline validation, supported by the PZ Immersive app built on NVIDIA Omniverse, which merges live sensor data and 3D models into real-time, VR-compatible workspaces. PZ Robotics streamlines complex manufacturing tasks such as drilling, cutting, grinding, and assembly, enabling companies to bridge the gap between skilled human craftsmanship and robotic efficiency.

Carbon Origins advances VR Teleoperation

US-based startup Carbon Origins enables VR teleoperation of heavy construction and mining equipment through a hyper-immersive interface. It streams 360-degree video, spatial sensor data, and AI overlays into a headset so expert operators can control remote robots as if seated in the machine.

The startup’s platform installs robotics and connectivity upfits on skid-steers and other heavy equipment, links them over redundant networks, and fuses data from cameras, LiDAR, radar, ultrasonic sensors, GPS RTK, and IMUs to localize each robot, plan paths, and execute tasks autonomously while remaining under VR supervision.

It uses AI co-pilots to assist human operators, deploys collision avoidance, geofencing, rollover prevention, machine health monitoring, and wireless and on-machine emergency stops to reduce incidents to zero across more than 5,000 operating hours and over 10 commercial projects.

Furthermore, it fields a multipurpose robot platform called Bobby with swappable tooling for construction, material handling, dust control, firefighting, solar field work, snow removal, mowing, and vegetation clearing, while VR eye and head tracking feed advanced annotation pipelines that continuously train and improve task-specific AI models.

9. Digital Twins: Schneider Electric & FANUC Use Digital Twin

Digital twins are becoming more and more useful for design, commissioning, and operations in industrial settings. The global digital twin market is growing at a CAGR of 47.9% and is expected to reach USD 149.81 billion by 2030.

Operational twins connect real-time data with simulation models to make sure that all processes run smoothly. By evaluating modifications in a virtual environment before putting them into action, manufacturers increase the stability of throughput, energy efficiency, and quality. These features lower the risk of running a business and speed up the process of making things better.

Digital twins detect anomalies through real-time condition monitoring and speed up troubleshooting by recreating fault conditions virtually. This also helps with faster root-cause identification and reduces downtime duration. This results in a 20% reduction in unexpected work stoppages while optimizing maintenance schedules.

Companies like Schneider Electric and FANUC are partnering with US facilities to utilize digital twin implementation to drive measurable operational transformation.

Companies that standardize how they use twins over their life cycles see speedier innovation, less risk in capital expenditures, and continued operational excellence.

Cyberwave develops an Asset Replica Platform

Italian startup Cyberwave offers an AI-native robotics infrastructure platform that connects and controls heterogeneous robots, sensors, and digital twins across industrial environments such as factories, warehouses, and maritime operations.

It works by creating synchronized digital replicas of physical assets that continuously exchange telemetry and commands with their real-world counterparts. This enables real-time monitoring, simulation, and remote control.

The startup’s automated asset pipeline ingests CAD and robot descriptions, auto-generates simulation-ready twins, and stores them in a searchable catalog for instant deployment across workflows.

The platform integrates physics-accurate simulations, GPU-accelerated reinforcement learning environments, and sensor-level data generation, ensuring developers validate and optimize systems virtually before deploying them on hardware.

Helpforce AI creates a Virtual World for Robot Learning

US-based startup Helpforce AI develops a virtual world platform for training autonomous robots before their physical deployment in real-world environments. It builds high-fidelity digital twins of client facilities, accurately modeling every corridor, obstacle, and operational scenario using advanced physics simulation and sensor modeling.

Within these environments, robots undergo thousands of training runs powered by reinforcement learning, behavior cloning, and sim-to-real transfer on NVIDIA’s Isaac Sim platform.

The startup’s simulation-first approach eliminates the need for on-site calibration, reducing setup time to six to eight weeks and lowering deployment costs by over 50%. Each robot arrives already equipped with navigation, perception, and interaction models adapted to the client’s specific layout, language, and safety protocols.

The startup’s system continuously refines itself with real-world data, ensuring operational reliability across industries such as logistics, security, manufacturing, energy, and defense.

10. Additive Manufacturing: Market Growing at 23.3% CAGR

Additive manufacturing (AM) allows for flexible production, quick changes, and manufacturing in one place. The global additive manufacturing market is projected to reach USD 88.28 billion by 2030, growing at a CAGR of 23.3%.

In repair and spare-part situations, AM cuts lead times for components. This makes assets more available and lowers inventory costs.

Integrating automation makes AM more consistent and easier to grow. Closed-loop monitoring, in-process inspection, and digital workflows help keep quality high and cut down on scrap. Further, additive systems are connecting to digital twins and MES platforms to meet certification and traceability needs.

Musashi, a Japanese automotive part manufacturer, used 3D printing for their jigs and fixtures, which reduced the lead time from 30 days to 7 days. It also resulted in an overall cost savings of 30%.

Additive manufacturing also reduces raw material use through near-net-shape builds and lattice/lightweight designs.

Further, AM also simplifies supply chains and cuts transport and warehousing costs, since parts can be made closer to the point-of-use on demand.

Dewu 3D offers Selective Laser Sintering

Chinese startup Dewu 3D develops high-precision metal 3D printing equipment based on advanced selective laser melting (SLM) technology. Its system uses a high-intensity laser to selectively melt and fuse fine metal powders layer by layer.

It achieves a forming tolerance within 10μm and a surface roughness as low as 1μm. The technology enables the production of complex geometries with wall thicknesses as low as 30μm and a molding density reaching 99.9%. This makes it suitable for industries such as aerospace, medical devices, and humanoid robotics.

The startup integrates a multi-material powder coating process that supports materials like copper, stainless steel, and high-temperature alloys, allowing for integral parts suited for extreme environments.

Complementing its hardware, Dewu 3D offers proprietary CAx software that combines topology optimization, simulation analysis, and machine learning-based process monitoring to guide end-to-end manufacturing workflows.

Repentium enables Multi-resolution 3D Printing

Austrian startup Repentium develops multi-resolution 3D printing technology that accelerates and enhances industrial additive manufacturing.

It integrates filament-based fused filament fabrication (FFF) with an advanced multiplex process that dynamically adjusts layer resolution within a single print job, enabling precise detailing in complex areas while maintaining rapid throughput in less critical zones.

The startup’s approach increases production speed by up to 13 times without compromising structural quality and supports consistent process reliability across diverse geometries and materials.

Discover all Industrial Automation Trends, Technologies & Startups

Looking ahead, industrial automation is moving toward tightly integrated, data-centric systems where intelligence, connectivity, and autonomy shape operational competitiveness.

As AI-driven control, connected assets, and digital twins mature, factories shift from reactive execution to predictive and adaptive operations. Edge and cloud architectures support this transition by enabling real-time responsiveness alongside large-scale analytics.

At the same time, cybersecurity and sustainability remain structural priorities as industrial environments become more connected and energy-aware. Over the next few years, immersive tools and additive manufacturing will further compress design-to-production cycles, positioning industrial automation as a foundational layer for resilient, efficient, and scalable industrial growth.

The Industrial Automation Trends & Startups outlined in this report only scratch the surface of trends that we identified during our data-driven innovation & startup scouting process. Identifying new opportunities & emerging technologies to implement into your business goes a long way in gaining a competitive advantage.