AI Success Beyond Perfect Data : Bridging enterprise AI strategy with agentic, data-driven LLM pipelines (English Edition)

Description AI has become the cornerstone of innovation in modern enterprises. However, the common misconception that perfect data is a prerequisite often stalls initiatives. This book challenges that narrative, proving that AI success is possible even with messy, incomplete, or evolving data landsc...

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Bibliografische gegevens
Hoofdauteur: Hossain, M. Maruf
Formaat: Livre numérique
Taal:Anglais
Gepubliceerd in: New Delhi : BPB Publications 2025.
Paris : Cyberlibris
Online toegang:Accès Université d'Orléans et IFPM
Opmerking: Couverture. https://static2.cyberlibris.com/books_upload/300pix/9789365899313.jpg
Cyberlibris (ScholarVox) corpus Informatique
Autres localisations: Voir dans le Sudoc
Edition sous un autre format:• AI Success Beyond Perfect Data, Bridging enterprise AI strategy with agentic, data-driven LLM pipelines (English Edition), Dr. M. Maruf Hossain, New Delhi, BPB Publications, 2025, 1 vol. (650 p.), 978-93-6589-931-3
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245 1 0 |a AI Success Beyond Perfect Data :  |b Bridging enterprise AI strategy with agentic, data-driven LLM pipelines (English Edition)   |c Dr. M. Maruf Hossain. 
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500 |a Couverture. https://static2.cyberlibris.com/books_upload/300pix/9789365899313.jpg 
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505 0 |a Section 1: Crafting the Strategic Foundation for AI Value -- Section 2: Ethical AI, Automation, and Implementation Decisions -- Section 3: Mastering Generative AI and Emerging AI Paradigms -- Section 4: Leading Enterprise AI Transformation -- 1. AI Leader's Strategic Mindset for Value Creation -- 2. Navigating AI Project Challenges and Measuring Success -- 3. Building Pragmatic Data Strategy and Foundation -- 4. Operationalizing Data Science for Business Success -- 5. Enterprise AI Platform -- 6. Ethical AI and Responsible Automation -- 7. Navigating Data Challenges without Synthetic Shortcuts -- 8. Business Imperative of GenAI and LLMs -- 9. Strategic Dilemma of AI Procurement -- 10. Leading and Overcoming Pitfalls in AI Automation -- 11. Ensuring Trustworthy AI Through Guardrails -- 12. Harnessing RAG and Prompt Engineering -- 13. Unlocking Next Frontier with Hybrid and Agentic AI -- 14. Next-Gen AI Applications -- 15. Evolving CDAO and AI-Decision Culture -- 16. Communicating AI Value to Executives -- 17. Scaling AI Initiatives and Managing Change -- 18. AI Governance and Ownership Bibliography 
506 |a L'accès en ligne est réservé aux établissements ou bibliothèques ayant souscrit l'abonnement. Cyberlibris 
520 |a Description AI has become the cornerstone of innovation in modern enterprises. However, the common misconception that perfect data is a prerequisite often stalls initiatives. This book challenges that narrative, proving that AI success is possible even with messy, incomplete, or evolving data landscapes. The book offers a comprehensive roadmap for leveraging data science, automation, and generative AI. It begins with the principles of data science excellence, addressing how to extract value and insights from existing data. It then addresses automation, the deployment of ethical AI, and team leadership. Finally, it explores generative AI, such as its business potential, limitations, and how to balance LLMs with traditional AI techniques. Practical frameworks, real-world case studies, and success metrics are woven throughout the text. By the end of this book, readers will have a clear, actionable strategy to drive measurable AI outcomes, regardless of their current data maturity. This guide empowers leaders to drive innovation and business transformation through AI. What you will learn: Learn how to implement AI without perfect data quality. Discover practical frameworks to overcome organizational silos; Examine the ethical implications of automation and generative AI; Gain leadership insights to structure and scale data science teams; Balance predictive AI with LLMs for real results. Who this book is for: This book is for business executives and aspiring data leaders responsible for implementing data-driven strategies and AI in their organizations. It will also benefit data scientists and IT professionals aiming to bridge strategy and execution in enterprise AI adoption 
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