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Making AI Work for People : A Framework for Designing and Building Impactful AI-Powered Applications.
Asmaa. Ibrahim
ISBN 1394392117
1st ed.
utgivning
Newark : John Wiley & Sons, Incorporated, 20261 online resource (354 pages)
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Making AI Work for People : · ISBN 1-394-39209-5 (Print:)Sammanfattning
Design AI applications that inspire trust, solve real problems, and put people first Making AI Work for People: A Framework for Designing and Building Impactful AI-Powered Applications by Asmaa Ibrahim offers software engineers, product managers, and app designers a comprehensive framework for creating AI-powered applications that truly serve.
Innehållsförteckning
Cover -- Half Title Page -- Title Page -- Copyright -- Contents -- About the Author -- Acknowledgments -- Introduction -- Chapter 1: The Trillion-Dollar Challenge -- Stats About AI-Powered System Development in Enterprise -- The Investment-Reality Gap -- Where the Money Goes -- Reality Check for ROI -- Different AI, Different Story -- Common Failure Patterns of AI Systems Development -- The FOMO Syndrome -- Technology-First Thinking -- When People-First Actually Works -- The Human Resistance Wall -- The Black Box Trust Deficit -- The Late Compliance Time Bomb -- The Scale Nightmare -- How Verizon Actually Scaled Without Exploding -- Data Quality Disaster -- The ROI Mirage -- When the Numbers Actually Add Up -- The Pattern Cascade -- The Hidden Costs of AI Systems Failure -- Direct Losses and Opportunity Costs -- Trust Erosion -- Competitive Disadvantage -- Team Morale and Retention -- The Compound Effect -- What Actually Works -- The 20% Who Get It Right -- When the Numbers Actually Add Up -- Introducing the PRESS Framework -- What Winners Do Differently -- Why These Five Matter Together -- PRESS Flips Everything -- Time to Get Real -- Summary -- Time for Your AI Reality Check -- The Questions That Really Matter -- Where You Probably Stand -- Your Next Move -- Failure Isn't Your Destiny -- References -- Chapter 2: Understanding AI Economics and Assessing Your Readiness -- Meet Your Companions -- MegaRetail -- RegionalTelco -- CommunityBank -- The Real Economics of AI -- The 70/20/10 Rule -- The J-Curve Reality -- Building Business Cases That Last -- The PRESS Readiness Assessment -- Instructions for Completing the Assessment -- People-First Assessment -- Interpreting a People-First Score (Average of Ten Questions) -- Responsible Assessment -- Interpreting a Responsible Score (Average of Ten Questions) -- Explainable Assessment., Interpreting a Explainable Score (Average of Ten Questions) -- Safe Assessment -- Interpreting a Safe Score (Average of Ten Questions) -- Sustainable Assessment -- Interpreting a Sustainable Score (Average of Ten Questions) -- Calculating Your PRESS Profile -- Your Organizational Mindset -- Understanding Your Scores -- MegaRetail: Technical Strength, People Challenge -- RegionalTelco: Cultural Strength, Infrastructure Limitation -- CommunityBank: Balanced Across All Dimensions -- Critical Thresholds -- Your Readiness, Your Path Forward -- Summary -- References -- Chapter 3: Identifying AI Opportunities Through the PRESS Lens -- Starting with Real Problems -- The Observation Advantage -- The Ethnographer's Method -- How AI Opportunities Have Evolved -- Understanding Human-AI Collaboration Modes -- Selecting the Right AI Type for Collaboration Success -- Assistive: AI as a Capability Enhancer -- Suggestive: AI as Recommendation Engine -- Autonomous: AI Acts, Human Monitors -- Collaborative: Human-AI Partnership -- Adaptive: AI Learns from Human Feedback -- When to Use Each Mode -- Choosing the Wrong Mode: When Collaboration Becomes Chaos -- Traditional Opportunity Identification -- The Hidden Economics of AI Inference -- The Unspoken Reality of Inference -- Your Inference Economic Checklist -- Applying PRESS Lenses to Filter Opportunities -- People-First Filter: Which Collaboration Mode Enhances Humans Best? -- Responsible Filter: Can You Implement This Mode Ethically? -- Explainable Filter: Can Users Understand This Collaboration? -- Safe Filter: What Are the Risks of This Autonomy Level? -- Sustainable Filter: Can You Support This Mode Long-Term? -- Selecting Your Winning Use Case -- Balancing Impact with Readiness -- Journey of the Three Companies' Decisions -- From Opportunity to Implementation -- Summary -- References., Chapter 4: Design Your People-First AI Systems -- The People-First Design Philosophy -- Why People-First Design Drives Success -- Building on Your Chosen Collaboration Mode -- Setting the Foundation for Adoption -- How Each PRESS Principle Shapes People-First Design -- Collaboration Mode Key Design Considerations -- Assistive Mode Design Requirements (RegionalTelco's Path) -- Suggestive Mode Design Requirements (CommunityBank's Path) -- Autonomous Mode Design Requirements (MegaRetail's Path) -- User Interface Implications -- Control and Oversight Mechanisms -- CommunityBank's Suggestive Controls -- MegaRetail's Autonomous Controls -- Feedback and Learning Loops -- The Ten-Week Validation Journey -- What Makes This Approach Different from Traditional Ones? -- Your Roadmap -- Internal Validation (Weeks 1-6) -- Design Phase (Weeks 1-2): Building Your Foundation -- Dogfood Phase (Weeks 3-4): Internal Reality Check -- Alpha Phase (Weeks 5-6): Friendly User Validation -- External Validation (Weeks 7-10) -- Beta Phase (Weeks 7-10): Preparing for Reality -- Maintaining Momentum Through Your Journey -- From Validation to Technology Selection -- From Design to Production at Scale -- Summary -- References -- Chapter 5: Measuring Success by AI Type -- The Measurement Challenge -- How PRESS Shapes What You Measure -- Understanding the Limitations of Traditional Metrics -- Define Your North Star Metric -- Your North Star Through PRESS -- The Five Steps to Select Your North Star -- Validating Your North Star -- Metrics by AI Type -- AI Metrics Consist of Three Distinct Layers -- The Unified Measurement Framework -- Traditional ML: Precision Within Constraints -- GenAI: Measuring Creative Chaos -- The Metrics That Actually Matter -- Specialized AI: Regulatory Requirements Drive Measurement -- Connecting the Layers -- Measurement by Collaboration Mode., Assistive Mode: Measuring Augmentation -- Autonomous Mode: Measuring Independence -- Suggestive Mode: Measuring Influence -- Adaptive Mode: Measuring Evolution -- Collaborative Mode: Measuring Partnership -- PRESS-Driven Implementation Examples -- MegaRetail's Automated Measurement Machine -- RegionalTelco's Crowdsourced Chaos -- CommunityBank's Democracy of Data -- Trends in Evolution -- Phase 1: Survival Metrics -- Phase 2: Optimization Metrics -- Phase 3: Leadership Metrics -- Mistakes to Avoid -- Mistake 1: Measurement Theater -- Mistake 2: Metrics Misalign with PRESS -- Mistake 3: Wrong North Star Selection -- Mistake 4: Ignoring the Middle Layer -- Mistake 5: Measurement Overconfidence -- How These Mistakes Trigger Chapter 1's Failures -- Summary -- References -- Chapter 6: Continuous Evaluation -- The Dual Evaluation Framework -- How Your Metrics Trigger Evaluation -- Benchmarking: Systematic AI Type Evaluation -- Product Evaluation Through PRESS Flywheel -- Three AI Types, Three Evaluation Journeys -- MegaRetail's Traditional ML Journey -- The Data Processing Challenge -- Hierarchical Evaluation Architecture -- A/B Testing at Massive Scale -- RegionalTelco's Hybrid Challenge -- The Handoff Evaluation Problem -- GenAI Evaluation Under Resource Constraints -- Consistency Across Systems -- CommunityBank's Compliance-First Evaluation -- Edge Evaluation Constraints -- Specialized System Evaluation -- Regulatory Evaluation Requirements -- Building Your Evaluation Pipeline -- Foundation Level, Manual but Systematic -- Growth Level, Semi-Automated Evaluation -- Human-in-the-Loop Evaluation -- Training Non-Technical Evaluators -- Evaluation Calibration Exercises -- Incentive Alignment -- Evaluation Tools and Platforms -- Open-Source Evaluation Frameworks -- Commercial Platform Capabilities -- Custom Evaluation Infrastructure., Evaluation Cadences and Governance -- Daily Evaluation -- Weekly Evaluation Cycles -- Monthly Evaluation Reviews -- Quarterly Strategic Evaluation -- Future-Proofing Evaluation -- Preparing for New AI Paradigms -- Building Adaptive Evaluation -- Integration with Organizational DNA -- Embedding Evaluation in Roles -- Making Evaluation Competitive Advantage -- Creating Evaluation Culture -- Summary -- References -- Chapter 7: Data Foundation Through PRESS -- Why Your AI Type Changes Everything -- Poor Data Foundations Impact -- How PRESS Scores Detect Data Challenges -- Your Data Reality Check -- Data That Actually Serves Humans -- When More Dashboards Create Less Understanding -- Building Trust Through Radical Transparency -- Why Users Abandon Data Collection -- Data Ethics Made Practical -- AI-Type Ethics Challenges -- Privacy Frameworks That Scale -- How RegionalTelco Caught Bias Affecting Rural Customers -- Making Data Decisions Clear -- The Stakeholder Explanation Challenge -- Explainability Requirements by AI Type -- CommunityBank's Regulatory Explainability Approach -- Safe as Your Data Quality Foundation -- The 80/20 Rule -- Quality Concerns by AI Type -- Early Warning Systems -- Prevention Beats Firefighting. Every time -- The Vendor Data Challenge -- Building for the Long Game -- The True Cost Escalation -- Stage 1: Prevention at Entry (1) -- Stage 2: Correction After Propagation (10) -- Stage 3: Business Impact (100) -- Why Organizations Underinvest in Prevention -- Sustainability Challenges by AI Type -- Build Once, Scale Often -- Future-Proofing Data Strategies -- Migration Without Disaster -- Who Owns Your Data Quality? -- The Three Governance Models -- Examples of Team Structure -- Clarity of Roles -- Your Data Action Plan -- Week 1: Assess Your Current State -- Weeks 2-4: Address Immediate Gaps by AI Type., Months 2-3: Build Your Foundation.
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Medverkan och funktion
Asmaa. IbrahimIdentifikator
ISBN 1394392117Indirekt identifierad av
ISBN 1394392095har titel
Making AI Work for People : A Framework for Designing and Building Impactful AI-Powered Applications.upplageuppgift
1st ed.utgivning
Newark : John Wiley & Sons, Incorporated, 2026copyright
©2026omfång
1 online resource (354 pages)Annat bärarformat
Making AI Work for People : · ISBN 1-394-39209-5 (Print:)kontrollnummer
3q97197d1g6jbb4vResursens ID / Permalänk: https://libris.kb.se/3q97197d1g6jbb4v#it
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