RUBIKSCORPAI in factories
Where AI actually helps in a factory
AI works best on narrow, measurable problems: counting output, spotting visible defects, watching machine behaviour or showing where a line is stuck. Plants that start with a broad AI plan usually stall.
Start with one operational decision
Choose a problem an operator or manager already recognizes: a count that takes time to collect, a visible defect, an unexplained stoppage or a missing view of shift performance. Write down the current process and its cost. Establish whether a simpler sensor, report or process change would solve the problem. AI becomes useful when the available signals support a specific, measurable task.
What to check
- Observe the task across different shifts, products and operating conditions.
- Confirm the availability, permission to use and quality of camera or machine data.
- Identify who will act on the result and what happens when the system is unavailable.
What to change
Scope a narrow pilot with an acceptance test and a baseline. Agree installation constraints, operator involvement and a manual fallback. Capture representative examples before selecting a model. Run the pilot alongside the current process so its results can be checked without putting unvalidated automation in control of production.
How to evaluate the result
Use an operational metric such as count error, inspection recall, time spent recording data or response time to a stoppage. Include installation, maintenance and review effort in the evaluation.
What the result does not establish
A successful demonstration on a few samples is not evidence of production readiness. Any move into live operation needs a separate decision based on the pilot results, safety responsibilities and ongoing support requirements.
Further reading
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