Hechler E., Oberhofer M., Schaeck T. Deploying AI in the Enterprise: IT Approaches for Design, DevOps, Governance, Change Management, Blockchain, and Quantum Computing

Apress, 2020. — 343 p. — ISBN13: 978-1-4842-6205-4.Your company has committed to AI. Congratulations, now what? This practical book offers a holistic plan for implementing AI from the perspective of IT and IT operations in the enterprise. You will learn about AI’s capabilities, potential, limitations, and challenges. This book teaches you about the role of AI in the context of well-established areas, such as design thinking and DevOps, governance and change management, blockchain, and quantum computing, and discusses the convergence of AI in these key areas of the enterprise.
Deploying AI in the Enterprise provides guidance and methods to effectively deploy and operationalize sustainable AI solutions. You will learn about deployment challenges, such as AI operationalization issues and roadblocks when it comes to turning insight into actionable predictions. You also will learn how to recognize the key components of AI information architecture, and its role in enabling successful and sustainable AI deployments. And you will come away with an understanding of how to effectively leverage AI to augment usage of core information in Master Data Management (MDM) solutions.What You Will LearnUnderstand the most important AI concepts, including machine learning and deep learning
Follow best practices and methods to successfully deploy and operationalize AI solutions
Identify critical components of AI information architecture and the importance of having a plan
Integrate AI into existing initiatives within an organization
Recognize current limitations of AI, and how this could impact your business
Build awareness about important and timely AI research
Adjust your mindset to consider AI from a holistic standpoint
Get acquainted with AI opportunities that exist in various industriesGetting Started
AI Introduction
AI Historical Perspective
Key ML, DL, and DO Concepts
AI Deployment
AI Information Architecture
From Data to Predictions to Optimal Actions
The Operationalization of AI
Design Thinking and DevOps in the AI Context
AI in Context
AI and Governance
Applying AI to Master Data Management
AI and Change Management
AI and Blockchain
AI and Quantum Computing
AI Limitations and Future Challenges
Limitations of AI
In Summary and Onward
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