The term AI lifecycle is particularly at home in the fields of artificial intelligence, automation and digital transformation. It describes the entire lifecycle of an AI solution - from the initial idea through to daily use and regular improvement.
The AI lifecycle starts with problem definition: a company recognises that there is a problem that can be better solved with artificial intelligence, for example predicting product demand. Data is then collected and prepared, as no AI can work without high-quality data. In the next step, a suitable AI model is developed to fulfil the desired tasks.
After development, the model is tested and checked to see if it works reliably. If everything is OK, the AI is integrated into the company processes and begins to take on real tasks - for example, creating forecasts for the warehouse. But the AI lifecycle does not end there: the AI continues to be monitored and regularly optimised so that it can adapt to new conditions.
The AI Lifecycle ensures that solutions with artificial intelligence remain sustainable, secure and effective.