Summary
Industrial Next, a San Francisco-based robotics startup, wants to make factory automation more flexible with artificial intelligence. Its robotic workstations use AI models, computer vision, and advanced simulation to learn manufacturing tasks without relying entirely on traditional robot programming. The company says this approach could help factories switch between tasks in minutes instead of spending weeks reprogramming equipment. Industrial Next has also announced a partnership with BMW Group Technology Office to evaluate its technology for manufacturing.
What Happened?
Industrial Next presented its technology at Startup Battlefield during TechCrunch Disrupt 2026, highlighting a challenge that manufacturers have faced for decades: changing the work performed by industrial robots can be slow and expensive.
Many factory robots are designed for specific tasks, such as assembling components or positioning parts. When a product changes, manufacturers may need to update the robot’s programming, adjust equipment, or modify the production line. In some cases, the process can take weeks or longer.
Industrial Next wants to reduce this complexity by building robotic workstations that can learn new tasks using AI. Rather than depending only on manually written instructions, the company’s systems combine visual information, an understanding of the task, and robot movement.
During its presentation, the startup also announced a collaboration with BMW Group Technology Office to evaluate its AI-powered dexterous manipulation technology for manufacturing applications.
How Industrial Next’s AI Robots Work
Industrial Next develops self-contained robotic stations powered by proprietary AI foundation models. These systems are designed to interpret what they see, understand the operation they need to perform, and control their movements accordingly.
One of the company’s main approaches is to train robots in highly detailed simulated environments.
In these virtual environments, robots can practise tasks under conditions intended to resemble real-world physics. This gives the AI an opportunity to learn how objects behave and how precise movements should be performed before the task is carried out on a physical production line.
The company also uses demonstrations to teach new tasks. This approach is intended to reduce the need for engineers to write detailed movement instructions for every operation.
Industrial Next says its robotic stations can be reconfigured, combined, and moved between product lines. If the technology works as intended, manufacturers could adapt existing automation equipment more quickly when production requirements change.
Why Traditional Factory Robots Can Be Difficult to Adapt
Conventional industrial robots are highly effective at performing repetitive operations under controlled conditions. However, they often depend on carefully defined movements and production processes.
Even a small change in a component’s shape, position, or assembly procedure can require adjustments to the robot’s programming and surrounding equipment. These changes can increase costs and interrupt production.
Manufacturers sometimes design products around the limitations of their automation systems because changing the equipment is too difficult or expensive.
Industrial Next believes AI can help address this problem by allowing robots to learn tasks instead of relying exclusively on fixed instructions.
The challenge is that robots need to understand and interact with the physical world. Unlike language models, which can learn from enormous collections of online text, robotics systems have historically had less accessible training data for complex physical tasks.
Simulation offers one possible solution. By creating virtual environments that closely reproduce real-world conditions, developers can train robots on operations that may be difficult, expensive, or time-consuming to demonstrate repeatedly in a physical factory.
Industrial Next Is Already Targeting Delicate Assembly Work
According to co-founder Lukas Pankau, some of Industrial Next’s workstations are already being used in the manufacturing process for a widely used smartphone. He did not disclose the customer.
The reported applications include threading delicate flexible cables and fitting small connector caps. These operations require precise movements and careful handling of components.
Pankau also described a robotic workstation designed to remove protective film from camera modules and buttons. He said the system can perform the task at or above human speed with 100% accuracy, including when it is moved to a different phone model and production line.
These examples demonstrate the types of manufacturing problems the company is trying to solve. However, the reported performance figures come from the company’s own account and should not be interpreted as independently verified results across all factory environments.
Industrial Next hopes to apply its technology to more complicated manufacturing processes in the future, including automotive production.
Who Founded Industrial Next?
Industrial Next was founded in 2021 by Lukas Pankau and Allen Pan, both of whom previously worked at Tesla.
Pankau worked as a lead electrical architect at Tesla before moving to Waymo. Pan was involved in Tesla’s autonomous factory efforts and later worked as a chief architect at autonomous driving company Neolix.
The founders developed the idea after experiencing the gap between advanced vehicle technology and traditional factory equipment. While engineers could use sophisticated software and machine learning to improve vehicle design and autonomous driving, programming industrial robots could still be surprisingly difficult.
Their experience led them to consider how modern AI techniques could make manufacturing equipment easier to configure and use.
After moving to other companies, the two continued discussing the limitations of industrial automation. They eventually decided to build a company focused on making robotic systems more adaptable.
Why the BMW Partnership Matters
The collaboration with BMW Group Technology Office gives Industrial Next an opportunity to evaluate its technology in an automotive manufacturing context.
Vehicle production involves many different operations, from handling components to assembling parts with strict accuracy requirements. More flexible robotic systems could potentially help manufacturers adapt production lines as vehicle designs and processes change.
However, the announcement concerns an evaluation of the technology. It does not, by itself, confirm that BMW has adopted Industrial Next’s systems across its factories or that the technology is ready for every automotive application.
The outcome of the evaluation may help demonstrate where AI-powered manipulation can deliver practical benefits and where further development is needed.
What This Could Mean for Manufacturing
If AI-powered robotic stations become reliable and economical at scale, they could change how manufacturers approach automation.
Potential benefits include:
- Faster production changes: Factories could spend less time adapting robotic equipment when products or processes change.
- Lower reprogramming effort: Engineers may rely less on manually specifying every robot movement.
- Automation of delicate work: AI systems could help automate precision tasks that have been difficult to handle with conventional robotics.
- More reusable equipment: Workstations could potentially be retrained and redeployed across different products or production lines.
The technology could be especially useful in electronics and automotive manufacturing, where product designs evolve and many assembly operations require precision.
Nevertheless, factories will need to assess reliability, safety, maintenance requirements, operating costs, and production speed before deploying such systems at scale. AI-driven automation must perform consistently in real operating conditions, not just in simulations or demonstrations.
What Comes Next?
Industrial Next’s next challenge is to expand its technology to more complex tasks while demonstrating consistent performance in real factories.
The company has ambitious goals. Pankau has described a longer-term vision of using AI-powered robotics to build an entire car with minimal or no human intervention in the assembly process.
Reaching that level would require progress across many areas, including robot coordination, component handling, quality inspection, safety, and production reliability.
For now, the company’s focus on adaptable robotic workstations represents an attempt to make automation easier to change as manufacturing needs evolve. The BMW Group Technology Office evaluation could provide further insight into how well the approach translates to industrial applications.
Conclusion
Industrial Next wants to move factory automation beyond robots that perform only one predefined task. By combining AI foundation models, simulation, and learning through demonstration, the startup aims to make robotic workstations easier to retrain and redeploy.
Its reported electronics assembly applications and collaboration with BMW Group Technology Office highlight the potential of this approach. Whether robots can consistently switch tasks in minutes across a broad range of manufacturing environments remains to be proven, but the company’s work reflects a growing effort to bring AI capabilities into the physical world.