Enterprise AI: Strategic Blueprint for Purple People
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Enterprise AI: Strategic Blueprint for Purple People

Investing with Intent: Transforming Large Organizations Through Strategic AI Deployment

About the Book

"Enterprise AI: Strategic Blueprint for Purple People" is the definitive compendium for leaders navigating the complex voyage of AI integration within expansive organizations. This guide is the beacon for 'Purple People'—the vanguard at the confluence of business strategy and technological innovation—charting a course through the AI adoption odyssey with strategic prowess and informed insight.

Unveiling a comprehensive framework tailored for the intricacies of large-scale enterprises, this book is the answer to the pressing challenge of prioritizing AI investment amidst a constellation of autonomous units and global operations. It transcends conventional approaches, offering a strategic lens to assess AI readiness and impact with unmatched precision.

At the heart of this transformative tome lies the Enterprise AI Transformation Framework (EAITF), a beacon of innovation that guides leaders to make data-driven decisions about where to deploy AI resources for optimal effectiveness.

The AI Effectiveness Index, born from the pioneering application of Principal Component Analysis (PCA), is a groundbreaking metric that quantifies the potential of AI within each decision domain. Coupled with practical Python code demonstrations, the book is not merely a guide but a hands-on toolkit for bringing the EAITF methodology to life.

"Enterprise AI: Strategic Blueprint for Purple People" is more than a publication—it's a strategic ally for enterprises poised to redefine their operations through AI. It's a manifesto for the Purple People leading the charge into a new era of intelligence and innovation.

Embrace this guide as your compass in the digital age, ensuring that AI is not just implemented but integrated with conviction and strategic foresight. Transform the potential of Enterprise AI into a strategic advantage—with "Enterprise AI: Strategic Blueprint for Purple People" by your side.

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    • Digital Transformation
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About the Author

Mahmudur R Manna
Mahmudur R Manna

Mahmudur R. Manna is a technologist and innovator whose illustrious career in computing began in the early '90s. As a Teaching Assistant at his father's computer training institute, he facilitated the mastery of applications such as WordPerfect, Lotus 123, and dBase IV for executives. His passion for technology led him to pursue A-levels in computing, and before graduation, he developed a Data Warehousing and ETL studio using Java Swing and Oracle 9i, garnering national recognition.

In the mid-2000s, Manna's expertise expanded with the J2EE-based Pentaho BI suite and OLAP, earning him a spot among the top three Data Warehousing experts on the Odesk marketplace globally. His journey took him to Europe, where he and his family spent time as he delivered successful solutions for prominent organizations and Governments, merging the Liferay Collaboration Platform with the Pentaho BI Suite, leveraging his deep knowledge in J2EE and Data Warehousing.

Later, Manna engaged with the Data Model Resource Book—a library of universal data models for all enterprises—to architect an ERP product based on Apache OFBiz for large manufacturing firms. The early 2010s marked his foray into entrepreneurship, developing a Cloud-based Digital Workplace SaaS Platform in distributed architecture. Despite stiff competition, he invested six years into this venture, a testament to his dedication and belief in innovation.

Transitioning from entrepreneur to consultant, Manna has advised on organizational transformation, specializing in Microservice architecture with Kubernetes and ML ecosystems. He has been instrumental in reshaping the digital strategies of public companies and recently has built and been leading a product development unit for a Big 4 firm.

Author of "Enterprise AI: Strategic Blueprint for Purple People," Manna distills his extensive experience into a strategic guide for organizations embarking on AI transformation. His work is characterized by a blend of technical expertise and business insight, making him a venerated thought leader in the tech community.

Beyond his professional pursuits, Manna is an avid music enthusiast, enjoying writing and composing songs. He cherishes spending time with his wife and daughter, finding joy and inspiration in the harmonies of his personal and professional life.


Table of Contents

    • Enterprise AI: Strategic Blueprint for Purple People
    • Abstract
    • Introduction
    • Chapter 1: Enterprise
      • 1.1. What is an Enterprise?
      • 1.2. Business Lifecycle Overview
      • 1.3. Cognitive Functions in Business Lifecycles
      • 1.4. Non-Cognitive Functions in Business Lifecycles
    • Chapter 2: Automation and Intelligence
      • 2.1. The Synergy of Automation and Intelligence
      • 2.2. Automation in Non-Cognitive functions in Business Lifecycles
      • 2.3. Intelligence in Business Decision-Making (Cognitive Functions in Business Lifecycles)
      • 2.4. Enhancing Intelligence with AI
    • Chapter 3: Decision Classifications and Types
      • 3.1. Decision Classifications
      • 3.2. Types of Enterprise Decisions
      • Conclusion
    • Chapter 4: Framework for Enterprise AI Transformation
      • 4.1. Decision Domain Management
      • 4.2. Transforming Enterprise Data
      • 4.3. AI Ecosystem by EAITF
    • Chapter 5: Profiling Decision Domains for AI Readiness
      • 5.1. Decision Domain Profiling: The Decision Domain Inventory
      • 5.2. Scoring Decision Domains
      • 5.3. Identifying Measurable Criteria
      • 5.4. Aggregating Scores into Domain Profiles
      • 5.5. Preparing for PCA Analysis
      • Conclusion
    • Chapter 6: Leveraging Principal Component Analysis to Derive the AI Effectiveness Index
      • 6.1. Introduction to Principal Component Analysis (PCA)
      • 6.2. The PCA Process
      • 6.3. Selecting Principal Components
      • 6.4. Deriving the AI Effectiveness Index
      • 6.5. Using the AI Effectiveness Index
    • Chapter 7: Implementing EAITF with Python
      • 7.1. Introduction
      • 7.2. Seed Data Preparation
      • 7.3. Data Standardization
      • 7.4. Weighting Scores
      • 7.5. Calculating Correlation Matrices
      • 7.6. Eigenvalues and Principal Component Loadings
      • 7.7. Deriving the AI Effectiveness Index
      • Conclusion
    • Conclusion: Charting the Course for AI-Driven Transformation
    • References

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