Manufacturing process automation is the use of control systems, robots, sensors and software to run production steps with less manual effort and fewer errors. In 2026 the new layer is AI: copilots and agents that help engineers write control code and help operators diagnose faults, running alongside the PLCs and robots most plants already have. Humanoid robots are in pilots, not yet in routine production.
Updated October 2026
What changed in 2026
- Robots keep scaling. 542,000 industrial robots were installed in 2024, more than double the figure ten years earlier, and IFR expects installs to pass 700,000 a year by 2028 (IFR, Sep 2025).
- AI is now in daily operations. Manufacturers say 34% of operations are AI-augmented today and expect more than half by 2030; only 18% are still in pilot mode with smart manufacturing (Rockwell Automation, May 2026).
- Copilots became agents. At Hannover Messe 2026 Siemens showed an engineering AI agent for TIA Portal, tested by more than 100 companies (Industrial Production). Schneider Electric says its Industrial Copilot cuts engineering time by up to 50% (vendor figure) (AI Magazine, Apr 2026).
- Humanoids moved from demo to pilot. A wheeled humanoid ran logistics tasks at Siemens' Erlangen electronics plant on NVIDIA's physical AI stack (AI Magazine). IFR still calls mass adoption "uncertain" (IFR position paper via Automation.com).
- Physical AI is on the plan. Nearly a quarter of manufacturers plan to deploy physical AI within two years, more than double today's adoption (Deloitte 2026 outlook via Manufacturing Digital).
What manufacturing process automation covers
Automation is not one technology. It is a stack, and most plants run several layers at once.
| Layer | What it does | Typical examples |
|---|---|---|
| Control | Runs machines and process loops in real time | PLCs, DCS, SCADA, motion controllers |
| Physical automation | Moves, joins, picks and inspects parts | Industrial robots, cobots, AMRs, conveyors, vision cells |
| Execution | Tracks orders, recipes, quality and genealogy | MES, quality management, historians |
| Business process | Handles paperwork around production | ERP workflows, RPA for invoices, orders and BOM updates |
| AI and decisions | Predicts, explains and recommends; now also acts within limits | Predictive maintenance, vision inspection, industrial copilots, AI agents |
The old version of this guide focused on RPA in the back office. That still matters for invoices and bills of materials. But the bigger 2026 story is AI reaching the shop floor and the engineering desk.
Fixed, programmable and flexible automation
Engineers still use three classic types:
- Fixed (hard) automation: dedicated equipment for one high-volume product, such as a bottling line. Fast and cheap per unit, costly to change.
- Programmable automation: equipment you reprogram between batches, such as CNC machines and robots for batch production.
- Flexible automation: systems that switch products with little downtime, such as robot cells with vision and quick-change tooling.
AI pushes plants toward the third type. Vision lets a robot handle part variation, and copilots shorten the time to write and test new control logic.
Where AI fits in 2026
Engineering copilots and agents. Siemens' Industrial Copilot helps engineers generate PLC code and HMI screens in TIA Portal; thyssenkrupp Automation Engineering began rolling it out across its engineering sites from 2025 (Automation.com, Nov 2024). The 2026 step is agents that break a large task into subtasks, gather data and run the plan, with the engineer approving each step.
Operations copilots. These turn a machine error code into plain language and suggest a fix from manuals and spare-parts lists. Siemens runs one on soldering machines in its Erlangen plant, deployed on-premises so data stays on the shop floor (Automation.com).
Vision and predictive maintenance. Cameras check parts at line speed, and models on vibration and current data flag failing equipment early. Both are mature and run best at the edge, close to the machine. See our guide to computer vision solutions and how NexaStack runs edge AI.
Agentic workflows across systems. Deloitte expects agents to help find alternative suppliers, capture retiring workers' know-how and draft shift handover reports (Manufacturing Digital). These are early uses; treat them as assistive, with a person signing off.
Humanoids and cobots: what the pilots show
Cobots and mobile robots are proven. Humanoids are not yet. Public results so far:
| Programme | Task | What is public |
|---|---|---|
| BMW Spartanburg with Figure AI | Loading sheet-metal parts at a welding fixture | Figure says its robots supported production of 30,000+ BMW X3s before the fleet was retired (vendor figure) |
| GXO with Agility Robotics (Digit) | Moving totes from mobile robots to conveyors | 100,000+ totes moved (company-announced) |
| Mercedes-Benz with Apptronik (Apollo) | Delivering assembly kits | Pilots confirmed; no performance data published |
| Siemens Erlangen with Humanoid (HMND 01) | Tote logistics | About 60 tote moves per hour (vendor-reported) |
The pattern: narrow, repetitive material-handling tasks in one cell, closely supervised. Tesla's CEO said in early 2026 that its Optimus robots were doing only a few simple tasks in its factories and were still in research and development (heise). Plan humanoid work as a learning pilot, not a headcount plan.
How to automate a manufacturing process: six steps
- Pick one constrained process. Choose a bottleneck with clear pain: scrap, downtime, a safety risk or a labour gap.
- Measure the baseline. Record OEE, first-pass yield, cycle time and changeover time before you change anything.
- Fix the data path. Connect PLCs, sensors and cameras to an edge layer so models can read real-time data without new cloud round-trips.
- Automate the physical task first, then add AI. A stable robot or vision cell gives the AI clean data to work with.
- Keep a person in the loop. Let copilots and agents recommend; require approval for any action that changes machine settings or recipes. Log every decision.
- Scale with a template. Once one line works, package the models, settings and rules so the next site deploys in weeks, not months.
Benefits and limits
| Benefit | What to watch |
|---|---|
| Consistent quality and fewer manual errors | Models drift as parts, lighting or suppliers change |
| Safer work: robots take dull, heavy and dangerous tasks | New safety cases for mobile robots and humanoids |
| Faster engineering with copilots | AI-written control code needs testing and review |
| Better use of data already collected | Rockwell's study says only 43% of collected data is used effectively |
| Higher uptime with predictive maintenance | 46% of manufacturers had a cyber incident in the past year (Rockwell); more connected assets mean more exposure |
Where it's heading: 2026 → 2030
| When | What it looks like |
|---|---|
| Around 2020 | Robots and PLCs on lines; RPA in the back office; AI limited to vision and maintenance pilots |
| 2026 (now) | 542,000 robots installed a year (2024 data, IFR); 34% of operations AI-augmented (Rockwell); engineering copilots shipping; humanoids in single-cell pilots |
| By 2028 (expected) | More than 700,000 robot installs a year (IFR forecast); agents handle routine engineering and maintenance tasks with human approval (our view) |
| By 2030 (expected) | More than half of operations AI-supported (Rockwell respondents' expectation); humanoids in limited production roles where pilots prove out (our view) |
How NexaStack helps
NexaStack deploys, updates and monitors AI models and agents across plants and edge sites from one control plane. Vision, maintenance and copilot models run next to the machines, and every agent decision is logged and governed by policy, so engineers stay in control as automation scales.
Frequently asked questions
What is automation in a manufacturing process?
It is the use of control systems, robots, sensors and software to carry out production steps with little manual effort. It ranges from fixed lines for one product to flexible robot cells and, in 2026, AI copilots and agents that assist engineers and operators.
What are the latest manufacturing process automation trends in 2026?
The main trends are industrial copilots turning into agents, AI built into a third of operations, steady growth in robot installs, and humanoid robots in narrow pilots. Edge deployment and data governance are becoming the gating factors.
Will RPA be replaced by AI?
Not outright. RPA still handles rule-based tasks such as invoices well. AI agents are taking on work that needs judgement or unstructured data, and the two increasingly run together.
What are the types of manufacturing automation?
The classic three are fixed, programmable and flexible automation. Many guides also list integrated automation, where design, production and business systems are connected end to end.
Are humanoid robots used in factories yet?
Only in pilots. BMW, Mercedes-Benz, GXO and Siemens have run humanoids on narrow material-handling tasks, and IFR says mass adoption remains uncertain.
Scale manufacturing AI with NexaStack
See how NexaStack runs vision, maintenance and copilot models at the edge of your plant, with every agent decision governed and logged. Book a manufacturing AI pilot review →
