Artificial intelligence (AI) and machine learning (ML) are talked about as though they are in a distant future. That future is here. We live in a world where technology is fully integrated with how we live. People own smartphones, smart wearables, smart TVs, and so on. With the integration of technology into almost every aspect of our everyday lives, there is an ever-growing, massive amount of data coming from each digital interaction. This data is the critical fuel powering deep enterprise insights that can be used to expand AI capabilities and infuse those capabilities into mission-critical applications and processes to gain a competitive advantage.
With the relatively novice AI and ML technologies in demand by most industries today, there is still confusion around how to most efficiently use these capabilities to realize the most benefits. There is a diverse scope of AI and ML solutions ranging from models that aid in medical discoveries to models that detect fraudulent banking behavior to protect consumers and financial institutions from costly financial breaches. Adding to the complexity of how AI and ML can help enterprises solve their evolving business demands is the growing number of available tools and product offerings to bring these capabilities to life on the IBM Z® platform.
This IBM® Redpaper publication can help you better understand the uniquely advantageous roles AI and IBM Z can play in helping your organization realize your business goals. It introduces some of the most critical AI on IBM Z use cases currently being worked on across multiple industries, and describes component suggestions with high-level reference architectures for the implementation of each use case. We cover the following use cases:
This paper is intended for IT architects to understand the integration and components of the solution at a high level, and IT management and decision makers to better understand how these AI on IBM Z use cases can be applied to help solve your business problems.
Chapter 1. Where artificial intelligence fits into your industry
Chapter 2. Artificial intelligence, machine learning, and data on IBM Z
Chapter 3. Artificial intelligence for IT operations use cases
Chapter 4. Data and artificial intelligence operations use cases
Chapter 5. Use cases for artificial intelligence in chatbots
Chapter 6. Financial services sector use cases
Chapter 7. Public sector use cases
Chapter 8. Healthcare sector use cases
Chapter 9. Retail and insurance sector use cases