The most common failure in automation is focusing on the robot or software. True success is determined by deeply understanding and codifying the entire process, including its environment and inherent variabilities. Getting the requirements right is the core challenge; the technology itself is secondary.
In hardware automation, a "go slow to go fast" approach is essential. Iterations are too slow and costly once hardware is built. Front-loading validation through drawings and simulations avoids major architectural issues that often get buried later due to project momentum or "go fever."
The primary value of AI in bioprocessing is not just automating tasks, but analyzing process data to predict outcomes. This requires a fundamental shift in capital equipment design, focusing on integrating more sensors and methods to collect far more granular data than is standard today.
The software-centric Minimum Viable Product (MVP) model is ill-suited for hardware. Instead of aiming for a 'viable' product, focus on a 'testable' one. This allows for controlled pilot deployments to gather real-world data and iterate before committing to expensive, hard-to-change physical designs.
Before building expensive hardware, validate your automation concept by having a person simulate the robot's functions and limitations. This low-cost method tests the system workflow in a real environment, uncovering hidden requirements and process flaws before a single line of code is written.
The best candidates for automation are rote, repetitive tasks where your brain is disengaged. If a process demands constant thought, adaptation, and complex decision-making, it is highly variable and a poor fit for automation, as you will likely never capture all its requirements.