Simply put, it must be determined whether AI has any benefit at all for the scenario at hand, and whether that benefit is large enough to justify the investment. Despite the hype surrounding AI, the ROI calculation must be done with a cool head. The following questions can help:
● Would AI help achieve better inspection results in the long run?
● How accurate do the inspection results need to be? What are our quality requirements? Are the components relevant to safety?
● What is the error rate of the current inspection method and how much can it be reduced by AI?
Not all AI solutions are the same. In its simplest form, an AI is trained with good parts and anything that deviates is classified as a defect (anomaly detection). For more complex, accurate testing, the AI needs to be trained with much more data on all possible cases. What skills are really required?
The more complex a solution, the higher the training effort. And there must first be enough data for that, including for errors and borderline cases. Therefore, one of the most important questions is: Do we have this data and if not, where do we get it from?
The more the neural network of an AI can do, the more resources it needs. The cost of hardware and computing power (in the cloud) is exponentially higher for an AI solution than for normal software.
So the effort and cost of an AI solution is relatively high and a quick ROI is unlikely. A rule-based algorithm delivers reliable results from day one. An AI, on the other hand, needs to be optimized on the fly. It may take years for the results to reach the required quality. Until then, companies may have to deal with higher costs and poorer results.