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9.1 The Pilot-to-Production Gap: Reading the Failure Evidence

Lecture Video Intro

Lecture Video 9.1

I. Three Numbers, Three Populations

II. What Is Actually Robust

III. Failure and Abandonment

IV. The Pilot Graveyard and Purgatory

V. The Adoption Illusion

VI. Ownership and Kill Criteria

9.2 The 10-20-70 Allocation: Where the Work Actually Is

Lecture Video 9.2

I. What the Rule Says, and What Kind of Claim It Is

II. Why the Direction Is Well Supported

III. The Inversion, and Why It Happens

IV. Effort, Not Budget

V. What the Seventy Per Cent Actually Is

VI. Measuring It

9.3 The Baseline Discipline: Measuring What You Cannot Measure Later

Lecture Video 9.3

I. Why the Baseline Is Missing

II. Process and Outcome

III. What the Denominator Omits

IV. What the Numerator Omits

V. Attribution Without a Holdout

VI. Kill Criteria That Bind

9.4 From Productivity to Earnings: Where the Value Goes

Lecture Video 9.4

I. What the Shift Evidence Shows

II. Activity and Earnings

III. The Efficiency-Revenue Gap: Two Explanations

IV. Cost Per Outcome

V. Value Decay: Two Mechanisms

VI. The Cadence

9.5 Staged Release: Governance Gates for Systems That Fail Quietly

Lecture Video 9.5

I. Why This Differs From Software Release

II. Shadow Mode, and What It Actually Costs

III. Canary Deployment and the Sample Size Problem

IV. A/B Testing and Ground Truth Lag

V. Rollback Triggers That Fire

VI. Gates and Ownership

9.6 Maintenance: Managing Silent Decay

Lecture Video 9.6

I. Silent Decay

II. Four Sources of Drift

III. The Monitoring Stack, and Why All Four

IV. Triage Before Retraining

V. What to Change

VI. Ownership

9.7 The Kill Decision: Stopping Well

Lecture Video 9.7

I. Purgatory, and Why It Persists

II. The Criterion

III. Why Criteria Do Not Bind

IV. The Two Errors

V. Organisational Warning Signals

VI. Killing Well

9.8 Change Management as a Workstream: Diagnosing the Adoption Gap

Lecture Video 9.8

I. The Gap, and How to Read It

II. Change Saturation

III. ADKAR

IV. When ADKAR Is the Wrong Instrument

V. Unsanctioned Use as a Requirements Signal

VI. Sentiment Baselines

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Are you struggling to move your Artificial Intelligence projects out of the testing phase. You are not alone.

Are you struggling to move your Artificial Intelligence projects out of the testing phase? You are not alone. The journey from a successful AI pilot to a fully integrated, value-generating production system is fraught with hidden challenges. "Managing AI Initiatives from Pilot to Production" is a comprehensive course designed to bridge this critical gap. Many organizations find their AI initiatives stuck in "pilot purgatory," suffering from silent decay, adoption illusions, and misaligned metrics. This course provides a robust framework to navigate the complexities of AI implementation, focusing on the reality that 70% of the effort lies in organizational change and process integration, not just the technology.

You will explore the critical 10-20-70 allocation rule, learn how to establish baseline disciplines to measure true value, and understand why productivity gains often fail to translate into bottom-line earnings. We will dive deep into governance gates for systems that fail quietly, utilizing staged releases, shadow modes, and canary deployments. Furthermore, you will master the art of maintenance by managing silent decay and drift, and crucially, learn how to make the difficult "kill decision" to stop failing projects well. Finally, the course emphasizes change management as a core workstream, utilizing frameworks like ADKAR to diagnose and overcome adoption gaps. Whether you are a project manager, AI strategist, or business leader, this course equips you with the actionable insights needed to ensure your AI investments deliver sustainable, measurable business value.

Chapter 9: Managing AI Initiatives from Pilot to Production

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            <p>Transitioning artificial intelligence projects from isolated experiments into fully integrated, value-generating systems is one of the most significant challenges facing modern organizations. Far too many promising AI initiatives fall into the pilot-to-production gap, trapped in a state of organizational purgatory where they fail to deliver measurable business outcomes. This course provides a comprehensive framework for navigating this complex journey, starting with a hard look at the evidence behind pilot failures, abandonment, and the illusion of adoption. By examining the critical 10-20-70 allocation rule, you will discover where the actual work of AI implementation lies—shifting the focus from mere budget allocation to the intensive human and operational effort required for true integration. Furthermore, you will learn the indispensable practice of baseline discipline, ensuring you capture the essential metrics and performance indicators before deployment that become impossible to measure accurately after the fact.</p>

<p>Beyond the initial deployment, sustaining and capturing the value of AI requires rigorous governance, continuous oversight, and strategic foresight. This curriculum delves deeply into the mechanics of translating raw productivity gains into tangible financial earnings, addressing the efficiency-revenue gap and the underlying mechanisms of value decay. You will explore the intricacies of staged releases—utilizing shadow modes, canary deployments, and A/B testing—to safely govern systems that often fail quietly rather than loudly. Because AI models interact with dynamic real-world data, managing silent decay and drift is paramount; thus, you will be equipped with the knowledge to build robust monitoring stacks and triage maintenance effectively. Finally, the course tackles the critical organizational elements of AI adoption, from establishing binding kill criteria to decisively stop failing projects, to leveraging change management frameworks like ADKAR to overcome change saturation and permanently bridge the adoption gap.</p>
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