HOW AI IS BECOMING EMBEDDED IN ROBOTICS ENGINEERING
BY CALUM MACDOUGALL, SENIOR MECHANICAL DESIGN ENGINEER, DEXORY
Warehouse robotics adoption is accelerating. The 2026 Intralogistics Robotics Survey, covering organisations across manufacturing, transport and warehousing, wholesale and retail, found that 52 % of organisations are using one or more types of robot.
The commercial market is growing alongside adoption. The global warehouse robotics market was valued at $ 14.7 billion in 2024 and is projected to reach $ 117 billion by 2034. For robotics engineering teams, this growth creates a practical challenge: shortening product development cycles without compromising performance or reliability.
This is where AI comes in. Much of the discussion around AI and robotics has understandably focused on intelligence within the robot itself. But there is another change happening behind the scenes. AI is beginning to influence the engineering processes used to design, simulate and test physical products.
At Dexory, we are testing what this looks like in practice. Building on our existing use of SimScale’ s AInative cloud simulation platform, we have deployed its Engineering AI technology to explore how AI agents can automate parts of our simulation workflows and accelerate engineering analysis.
Moving from manual simulation to agentic workflows
Simulation is fundamental to mechanical engineering because it allows us to understand how a component or assembly is likely to behave under load before committing to physical prototypes or production. But simulation carries its own overhead: defining the load cases and boundary conditions, configuring and meshing each analysis, running it, and interpreting the results- then repeating the cycle as the design evolves.
At Dexory, we are using Engineering AI to help reduce the time engineers spend investigating structural component failures through FEA, and by enabling more simulation iterations during critical design phases. The programme is also intended to automate parameter sweeps, effectively testing multiple combinations of design variables and generating
42 MACHINERY, AUTOMATION, CONTROL & ENGINEERING