09:00 - 10:00
Many companies are exploring paths to become a player in AI business. If there is no experience and expertise in the company already, this means a decision on the right way to go:
- Building up a new AI team inside the company. Experiences are that this can take a very long time.
- Buying a smaller company with already proven expertise. This is becoming a more and more costly approach.
- Founding a start-up outside of the main company and later integrating it. This can evolve to a risky approach.
We propose an embedded approach that allows a quick start. From our experience, the availability of training data on the identified business case is the most challenging bottleneck. Therefore, the combination of good performances on a small amount of data of one’s own business case and the ability to quickly increase data volume are highly important for a quick start. We address the steps of management involvement, definition of business ideas, collection and growths of data for training, and roll-out. We also explain the relation of data volume and capability of the AI model and provide insights on how to address this fact in the process of business development .