Making AI Work for People : A Framework for Designing and Building Impactful AI-Powered Applications.

Format:

Book

Author/Creator:

Ibrahim, Asmaa.

Language:

English

Physical Description:

1 online resource (354 pages)

Edition:

1st ed.

Place of Publication:

Newark : John Wiley & Sons, Incorporated, 2026.

Summary:

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.

Contents:

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

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

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

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

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

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

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.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

1-394-40646-0

1-394-39211-7

9781394392117

OCLC:

1577548783