Executive Briefing: From Business to AI—The missing pieces in becoming "AI ready"

Description

When organizations begin the transformative process of becoming “AI ready,” a common pitfall is to focus mostly on technical infrastructure. Of course, collecting data and providing the infrastructure to perform scalable data analysis is important, but there are further missing pieces that are easily overlooked.

Mikio Braun highlights three such missing pieces: making the connection between business problems and AI technology, implementing AI-driven development, and running AI-based projects. Join Mikio to learn how to actually “do AI” in an organization.

• Making the connection between machine learning and business problems: A nontrivial transformation of business processes is often necessary before they are able to be used by machine learning methods, including deep learning. Mikio illustrates the importance of data and explains how problems can be rephrased to allow for treatment with AI methods.
• Implementing AI-driven product development: When using AI methods, the data is used not only to inform design decisions but also to train models to solve the problem directly. This leads to a much stronger focus on testing at all stages of product development, from offline A/B testing to production.
• Running AI-driven projects: Running projects based on AI methods poses new challenges to the product and software development cycle. Mikio explains how to integrate both research work and engineering work on such projects.