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2020 | Buch

AI Introduction Kapitel lesen Erstes Kapitel lesen

Deploying AI in the Enterprise

IT Approaches for Design, DevOps, Governance, Change Management, Blockchain, and Quantum Computing

verfasst von: Eberhard Hechler, Martin Oberhofer, Thomas Schaeck

Verlag: Apress

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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SUCHEN

Über dieses Buch

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 learningFollow best practices and methods to successfully deploy and operationalize AI solutionsIdentify critical components of AI information architecture and the importance of having a planIntegrate AI into existing initiatives within an organizationRecognize current limitations of AI, and how this could impact your businessBuild awareness about important and timely AI researchAdjust your mindset to consider AI from a holistic standpointGet acquainted with AI opportunities that exist in various industries

Who This Book Is For
IT pros, data scientists, and architects who need to address deployment and operational challenges related to AI and need a comprehensive overview on how AI impacts other business critical areas. It is not an introduction, but is for the reader who is looking for examples on how to leverage data to derive actionable insight and predictions, and needs to understand and factor in the current risks and limitations of AI and what it means in an industry-relevant context.

Inhaltsverzeichnis

Frontmatter

Getting Started

Frontmatter
Chapter 1. AI Introduction
Abstract
Artificial intelligence (AI) has been a vision of humans for a long time. Works of fiction have explored the topic of AI from many angles. For instance, Neuromancer, 2001: A Space Odyssey, Terminator, A.I., Star Trek, Alien, Mother, and so forth feature AI in many different manifestations: some human-like and some very different, some serving, some working with, and some even fighting against humans.
Eberhard Hechler, Martin Oberhofer, Thomas Schaeck
Chapter 2. AI Historical Perspective
Abstract
Without us being fully aware of and constantly appreciating it, AI is already impacting us since years, even decades. Therefore, an AI historical perspective doesn’t seem to be a vital consideration any more: AI has established itself as an undeniable fact of life. Its impact is already noticeable to each and every individual and the society as a whole.
Eberhard Hechler, Martin Oberhofer, Thomas Schaeck
Chapter 3. Key ML, DL, and DO Concepts
Abstract
Following AI evolution in the previous chapter, this chapter is devoted to key concepts of machine learning (ML), deep learning (DL), and decision optimization (DO). We don’t go into the details on the 101 of these concepts or mathematical and statistical science behind these areas; instead, we are discussing considerations about their practical application in enterprises or other organizations. It should serve as a high-level introduction for readers with limited knowledge in this space.
Eberhard Hechler, Martin Oberhofer, Thomas Schaeck

AI Deployment

Frontmatter
Chapter 4. AI Information Architecture
Abstract
In this chapter, you will learn about the specific role of information architecture (IA) to deliver a trusted and enterprise-level AI foundation. As an introduction into this topic, we briefly review key aspects of an information architecture (IA) and highlight the logical and physical IA components in the context of AI. These are important to the reader in order to fully understand the impact of AI on an existing information architecture. Any architecture needs to be underpinned with products and offerings. We learn about the key components of AI information architecture and their role in enterprise suitability. We conclude with use cases that illustrate AI in information architecture.
Eberhard Hechler, Martin Oberhofer, Thomas Schaeck
Chapter 5. From Data to Predictions to Optimal Actions
Abstract
The concept of optimizing decisions based on predictions considering additional data and constraints introduced in Chapter 1, “AI Introduction,” is often critical to solve real business problems. Decision optimization (DO) takes predictive insight one step further and guarantees that an optimal combination of business-relevant actions can be taken based on predictive insight with relevant context.
Eberhard Hechler, Martin Oberhofer, Thomas Schaeck
Chapter 6. The Operationalization of AI
Abstract
Developing AI solutions, including the training and deployment of ML/DL models, remains an important and often IT resource-intensive task. The integration of AI artifacts, such as ML/DL models and data engineering modules, into an existing enterprise IT infrastructure and application landscape represents an additional challenge. The productization or operationalization of AI and the inference of AI-based analytical insight into consuming applications are further explored in this chapter, where we focus on the productization and operationalization of AI specifically in an enterprise context. Furthermore, we shed light on the key challenges of operationalizing AI and describe essential goals for an efficient and sustainable productization of AI solutions, particularly ML and DL models and data engineering artifacts.
Eberhard Hechler, Martin Oberhofer, Thomas Schaeck
Chapter 7. Design Thinking and DevOps in the AI Context
Abstract
Design thinking and DevOps are long-standing concepts, well established and leveraged by most leading organizations. A sustainable adoption of AI for these concepts, however, is still lacking. But what exactly do we mean by adopting AI for design thinking and DevOps, and what possibilities do we have anyhow? Design thinking and DevOps methods can certainly be applied to develop AI systems and devices, products and tools, or applications. This is probably a more obvious thought. But can AI and its siblings be leveraged and infused into design thinking and DevOps concepts – and how? What are the prerequisites, challenges, and the benefits to do so? An obvious prerequisite is to first establish a sound design thinking and DevOps infrastructure and culture, prior to introducing AI and ML into these concepts.
Eberhard Hechler, Martin Oberhofer, Thomas Schaeck

AI in Context

Frontmatter
Chapter 8. AI and Governance
Abstract
While AI is already widely leveraged in a vast set of use cases, we will see it become increasingly more commonplace in all industry verticals and affecting our societies. Inference of predictive and ML-driven insight into business processes can be characterized by a great deal of autonomous decision making, which may be perceived by some users as incomprehensible or elusive. Since AI-based decision making ought to be meaningful and human comprehensible, AI comes with a new dimension of governance imperatives designed to ensure transparency, trust, and accountability, taking into account explainability, fairness, and trackability.
Eberhard Hechler, Martin Oberhofer, Thomas Schaeck
Chapter 9. Applying AI to Master Data Management
Abstract
We introduced in Chapter 8, “AI and Governance,” data governance and the use of AI capabilities making data governance smarter. A related capability used by many enterprises is master data management (MDM). Depending on the industry, vertical customer, person, organization, product, supplier, patient, employee, citizen, and asset are typical examples of master data entities. MDM is used to deliver a trusted 360° view of master data which helps many critical operational processes such as customer service, cross- and up-sell, consistent customer experience in a multichannel architecture, or streamlined new product introduction.
Eberhard Hechler, Martin Oberhofer, Thomas Schaeck
Chapter 10. AI and Change Management
Abstract
As AI is increasingly adopted by businesses and society as a whole, there is an emerging need for existing change management practices to be adapted. Change is usually perceived as a threat, causing uncertainty, sentiments, and risks to organizations and individuals alike. However, change comes along with new business and personal opportunities. AI has the potential to accelerate and improve change management and make it more unerringly and human-centric.
Eberhard Hechler, Martin Oberhofer, Thomas Schaeck
Chapter 11. AI and Blockchain
Abstract
Most people believe that the paper from 2008 by Satoshi Nakamoto, a pseudonym used by a yet unknown author, introduced the concept of blockchain. However, the key idea is actually 17 years older. The first mentioning of key blockchain concepts goes back to 1991 when Stuart Haber and Scott Stornetta described the concept of a cryptographically secured chain of blocks for the first time.
Eberhard Hechler, Martin Oberhofer, Thomas Schaeck
Chapter 12. AI and Quantum Computing
Abstract
Richard P. Feynman, a Nobel Prize winner in physics, was a physicist thought leader in the areas of quantum mechanics and quantum electrodynamics. In 1982, he published a research paper with the title “Simulating Physics with Computers”. In this paper, he asks the question if a quantum computer could be built (which he believed to be the case) or if classical computers can simulate the probabilistic behavior of a true quantum system (which he answered with a clear no). This research paper sparked interest in the scientific research community, which started to seriously explore whether or not a quantum computer can actually be built.
Eberhard Hechler, Martin Oberhofer, Thomas Schaeck

AI Limitations and Future Challenges

Frontmatter
Chapter 13. Limitations of AI
Abstract
The promise of AI with its breathtaking range of applications seems to be without limits. To elaborate on limitations of AI may therefore be perceived by some of our readers as a spin in opposite directions. AI is so much associated with accelerating innovation, insight, and decision making that we see its opportunities as immeasurable. And yet, even for AI, there are limits and challenges, as we learn about in this chapter.
Eberhard Hechler, Martin Oberhofer, Thomas Schaeck
Chapter 14. In Summary and Onward
Abstract
In this book, we explained how AI can today be used in enterprises. We dove into key aspects like an AI information architecture (IA) to lay a foundation of data in order to support AI, the AI life cycle to get from data to predictions to optimal decisions and actions, and important AI operations (AIOps) and AI DevOps aspects. We have furthermore elaborated on additional enterprise aspects, such as AI deployment and operationalization challenges, AI in the context of governance, change management, design thinking, and MDM. We have also exposed you to some limitations of AI – including limitations that may persist for the foreseeable future – and some exciting and emerging topics, such as AI in the context of blockchain and quantum computing.
Eberhard Hechler, Martin Oberhofer, Thomas Schaeck
Chapter 15. Abbreviations
Abstract
Architecture building blocks
Eberhard Hechler, Martin Oberhofer, Thomas Schaeck
Backmatter
Metadaten
Titel
Deploying AI in the Enterprise
verfasst von
Eberhard Hechler
Martin Oberhofer
Thomas Schaeck
Copyright-Jahr
2020
Verlag
Apress
Electronic ISBN
978-1-4842-6206-1
Print ISBN
978-1-4842-6205-4
DOI
https://doi.org/10.1007/978-1-4842-6206-1