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Building AI-Native Sentient Organizations: How Organizations Transform into Intelligent, Data-Driven, Autonomous Enterprises Using Data AI, and Organi
Paperback
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ISBN13: 9798196624728
Publisher: Independently Published
Published: May 12 2026
Pages: 168
Weight: 0.89
Height: 0.36 Width: 8.50 Depth: 11.00
Language: English
Publisher: Independently Published
Published: May 12 2026
Pages: 168
Weight: 0.89
Height: 0.36 Width: 8.50 Depth: 11.00
Language: English
The book is organized around the conviction that intelligence is not a feature to be added to an organization - it is a system to be designed into one. The six sections move from the strategic and conceptual foundations in Section 1, through the data and AI strategy disciplines of Section 2, the organizational transformation imperatives of Section 3, the technology architecture of Section 4, the implementation and execution disciplines of Section 5, and finally to the horizon of autonomous enterprise and the future of AI regulation in Section 6. Section 1: The Rise of the Sentient Organization - traces the five eras of enterprise evolution, introduces the Sentient Organization reference model and its five-layer architecture, and establishes the strategic and competitive frame for why transformation is not optional.
Section 2: Building the Data & AI Strategy - presents the portfolio alignment disciplines, the governance operating models, the master data and semantic foundations, and the democratization architecture that together constitute the strategy layer without which technology investment produces demonstrations rather than advantages.
Section 3: Organizational Transformation - examines the organizational archetypes, cultural properties, and leadership disciplines that determine whether AI capability is built, adopted, and sustained. It presents the AI Adoption Lifecycle, the Culture Transformation Framework, and the Executive Governance Model as operating instruments.
Section 4: The Technology Foundation - provides a complete reference architecture for the modern data ecosystem, agentic AI systems, real-time intelligent operations, and the MLOps and AI platform disciplines that govern the model lifecycle from experiment to retirement.
Section 5: Implementation & Execution - delivers the transformation roadmap methodology, a diagnostic framework for the eight recurring failure patterns, and a rigorous value measurement framework that produces board-credible ROI across four value classes and three time horizons.
Section 6: The Future - examines the autonomous enterprise and its operating implications, the converging global regulatory framework, and the disciplines of the continuously adapting organization that will thrive in an environment evolving faster than any fixed strategy can anticipate. Who This Book Is For
Section 3: Organizational Transformation - examines the organizational archetypes, cultural properties, and leadership disciplines that determine whether AI capability is built, adopted, and sustained. It presents the AI Adoption Lifecycle, the Culture Transformation Framework, and the Executive Governance Model as operating instruments.
Section 4: The Technology Foundation - provides a complete reference architecture for the modern data ecosystem, agentic AI systems, real-time intelligent operations, and the MLOps and AI platform disciplines that govern the model lifecycle from experiment to retirement.
Section 5: Implementation & Execution - delivers the transformation roadmap methodology, a diagnostic framework for the eight recurring failure patterns, and a rigorous value measurement framework that produces board-credible ROI across four value classes and three time horizons.
Section 6: The Future - examines the autonomous enterprise and its operating implications, the converging global regulatory framework, and the disciplines of the continuously adapting organization that will thrive in an environment evolving faster than any fixed strategy can anticipate. Who This Book Is For
- CEOs and Board Members - seeking a strategic framework for AI investment decisions, risk appetite setting, and the governance obligations that AI at scale creates.
- CIOs, CTOs, CDOs, and CAIOs - responsible for the architecture, platform, governance, and data strategy that AI-native operation requires.
- Enterprise Architects and Data Platform Leaders - designing the reference architectures, data ecosystems, and AI platforms described in Sections 2 and 4.
- Transformation Leaders and Programme Directors - building and executing the transformation roadmaps, managing the failure patterns, and measuring the value described in Section 5.
- AI Product Managers and Senior Practitioners - translating executive intent into AI products, governing their lifecycle, and sustaining their business performance in production.
- Academics and Researchers - in information systems, organizational behaviour, and AI governance seeking a practitioner-grounded account of enterprise AI transformation at scale.
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