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The Humachine: AI, Human Virtues, and the Superintelligent Enterprise (Second edition)

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Updated for a post-Covid world, the second edition of this groundbreaking book explains why becoming a Humachine enterprise is the only way forward for a company to maintain a competitive advantage in the age of artificial intelligence (AI).

The first edition of The Humachine offered a foundation for a new form of enterprise, integrating AI technology and human resources to optimize the unique advantages possessed by each. Now, in the face of the 'Great Resignation' and 'botsourcing'-where an activity previously done by humans is replaced by technology-thought leaders Sanders and Wood present a more positive and promising scenario, where an enterprise recognizes human resources as an asset class that possesses skills that cannot be replaced by automation. Enlightened business leaders will look to create synergy between technology and people, enabling the organization to maximize its capabilities by elevating the basis of decision making with closer-to-perfect information and rationality. This book provides a roadmap for how to do this and achieve collective intelligence at the enterprise level: superintelligence. More specifically, it answers these questions, and more.

  • Why must an enterprise achieve superintelligence as a competitive advantage in the age of AI?
  • How can any organization achieve superintelligence by following the 4-I model?
  • What is the step-by-step process an enterprise should follow in becoming a Humachine?
  • What strategies can be used by enterprise leaders to 'futureproof' the Humachine against uncertainty?

All business leaders, executives, and managers at companies wanting to use AI and technology to survive and thrive in this new age, and students of analytics and decision-making, will value this thought-provoking and practical book, rich with case studies.

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Product Details
Routledge
1003850510 / 9781003850519
eBook (EPUB)
11/03/2024
United Kingdom
English
402 pages
Copy: 30%; print: 30%
Previous edition: 2020 Description based on CIP data; resource not viewed.