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11.1 Diagnosing Pilot Purgatory: Technical, Measurement, and Organizational Causes of Failure to Scale

I. Pilot Purgatory: What the Evidence Shows

II. Technical Causes: Integration, Data, and Technical Debt

III. Measurement Causes: Model Metrics without Business Baselines

IV. Organizational Causes: Ownership, Adoption, and Change

Section Summary

11.2 Designing Pilots That Produce Evidence: Stages, Success Criteria, Controls, and Decision Gates

I. Four Stages, Four Questions

II. The Pilot Charter: Hypothesis and Success Criteria

III. Comparison, Population, Conditions, and Duration

IV. The Decision Gate

11.3 Staged Rollout: Shadow Mode, Limited Release, Phased Expansion, and Rollback

I. Why Stage a Rollout

II. The Stages: Shadow Mode, Limited Release, and Phased Expansion

III. Promotion Criteria, Hard Stops, and Rollback

IV. The Rollout Plan: Owners, Integration Points, Metrics, and Rollback Conditions

11.4 Integrating AI into Systems of Work: Workflow Integration, MLOps and LLMOps Essentials, and Security Controls

I. Embedding Output in Systems of Work

II. MLOps Essentials

III. LLMOps: Operating Generative AI Systems

IV. Security and Access Controls

11.5 Ownership and Operating Models: Business and Technical Owners, AI Team Structures, Funding the Run Phase, and Vendor Dependencies

I. Business Owners and Technical Owners

II. Operating Models: Centralized, Hub-and-Spoke, and Federated

III. Funding the Run Phase

IV. Owning Vendor Dependencies

11.6 Managing the People Side of AI Deployment: Role Redesign, Reskilling, Job Security, and the ADKAR and Kotter Models

I. Why Adoption Is a Management Problem

II. Diagnosing Individual Adoption: The ADKAR Model

III. Leading the Organizational Change: Kotter's Eight Steps

IV. Role Redesign, Reskilling, Job Security, and Frontline Involvement

11.7 Labor Relations and Worker Voice: Bargaining Obligations, Joint Committees, Advance Notice, and Just Transition

I. The US Legal Framework

II. What US Unions Have Negotiated

III. Institutionalizing Worker Voice: Joint Committees

IV. Job Displacement and Just Transition

11.8 Measuring Adoption after Rollout: Active Users, Usage Depth, Adoption Cohorts, and Links to Business Results

I. From Access to Habit: What to Count

II. Adoption Cohorts and Warning Signals

III. Capturing the Time Saved

IV. Linking Use to Business Results, and Controlling Its Cost

Knowledge Check

Knowledge Check:

Knowledge Check II. Technical Causes: Integration, Data, and Technical Debt

Knowledge Check III. Measurement Causes: Model Metrics without Business Baselines

Knowledge Check IV. Organizational Causes: Ownership, Adoption, and Change

Knowledge Check II. The Pilot Charter: Hypothesis and Success Criteria

Knowledge Check III. Comparison, Population, Conditions, and Duration

Knowledge Check IV. The Decision Gate

Knowledge Check II. The Stages: Shadow Mode, Limited Release, and Phased Expansion

Knowledge Check III. Promotion Criteria, Hard Stops, and Rollback

Knowledge Check IV. The Rollout Plan: Owners, Integration Points, Metrics, and Rollback Conditions

Knowledge Check I. Embedding Output in Systems of Work

Knowledge Check II. MLOps Essentials

Knowledge Check III. LLMOps: Operating Generative AI Systems

Knowledge Check I. Business Owners and Technical Owners

Knowledge Check II. Operating Models: Centralized, Hub-and-Spoke, and Federated

Knowledge Check II. Diagnosing Individual Adoption: The ADKAR Model

Knowledge Check I. From Access to Habit: What to Count

Are you struggling to move your AI initiatives from the lab to the real world. Welcome to 'Deploying AI: Pilots, Integration, and Adoption,' a comprehensive course designed to help you overcome 'pilot purgatory' and successfully scale AI solutions across your organization.

Are you struggling to move your AI initiatives from the lab to the real world? Welcome to 'Deploying AI: Pilots, Integration, and Adoption,' a comprehensive course designed to help you overcome 'pilot purgatory' and successfully scale AI solutions across your organization. While many AI projects fail at the deployment stage due to technical debt, misaligned metrics, or organizational resistance, this course provides a proven blueprint for end-to-end AI integration. You will learn how to design evidence-based pilots with clear success criteria, execute staged rollouts (including shadow mode and phased expansion), and seamlessly integrate AI into existing workflows using MLOps and LLMOps best practices. Beyond the technical aspects, this course dives deep into the human element of AI adoption. You will explore operating models, funding strategies, and change management frameworks like ADKAR and Kotter's Eight Steps to ensure your workforce is ready for role redesign and reskilling. Furthermore, we cover critical topics such as labor relations, worker voice, and measuring post-rollout adoption to link AI usage directly to tangible business results. Whether you are a business leader, technical manager, or AI strategist, this course equips you with the tools to navigate the complexities of AI deployment, foster user adoption, and drive sustainable business value.

Chapter 11: Deploying AI: Pilots, Integration, and Adoption

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            <p>Transitioning an artificial intelligence model from a controlled testing environment into a live, value-generating business system is one of the most significant hurdles organizations face today. Many promising AI initiatives fall victim to "pilot purgatory," a state where technical debt, misaligned measurement metrics, and organizational resistance prevent successful scaling. This course provides a comprehensive roadmap for navigating the complex journey of AI deployment, ensuring that models not only function technically but also integrate seamlessly into existing systems of work. By examining the root causes of failure to scale, learners will discover how to design robust pilots that produce actionable evidence, establish clear success criteria, and implement definitive decision gates.</p>

<p>Beyond the initial pilot phase, successful AI integration requires a highly strategic approach to deployment and change management. This curriculum delves deeply into the operational realities of scaling AI, covering staged rollout techniques such as shadow modes and phased expansions, alongside essential MLOps and LLMOps practices. Furthermore, the course addresses the critical human element of AI adoption. Through established frameworks like the ADKAR and Kotter models, learners will explore role redesign, reskilling, and effective leadership during organizational transitions. By integrating perspectives on labor relations, worker voice, and post-rollout adoption metrics, this course equips professionals with the holistic strategies needed to sustainably embed AI into an organization and drive measurable business results.</p>
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