There is no universal answer to the question of how much time and data are necessary to implement AI applications in the company. Both depend on the intended use. For example, to develop autonomous learning AI and neural networks like ChatGPT, machine learning is necessary: Using a large amount of data, the AI learns to recognize patterns, make decisions on its own, and make better and better predictions over time.
It is usually impossible to obtain a critical mass of data, particularly in the case of special processes in mechanical and plant engineering. But even without machine learning, artificial intelligence can support companies in mechanical engineering. For optimal results, the quality of the data is more important than the quantity.
Content created with ST4 is ideal for this: The structured content in ST4 usually contains much more information (e.g. product mapping) than just plain text. This means, for example, that a large language model (LLM) could be "trained" on content from technical writing to better understand its own machines, which would enable a number of new business cases (e.g., chatbot documentation).
Quality is also the factor that determines the timeframe of an AI implementation. Many technologies for standardized processes deliver passable results after a short time. But the higher the quality requirements, the longer the implementation takes. Companies should therefore be more prepared for long-term processes with many iterations.