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7.1 Reframing as Prediction: The Economic Lens

Lecture Video Intro

Lecture Video 7.1

I. What AI Reduces the Cost Of

II. Unbundling the Decision

III. Substitutes, Complements, and Where the Framing Breaks

IV. Reframing, and Its Limits

V. The Accuracy Threshold, and What It Actually Asks

7.2 Point and System Solutions: The Architecture of Disruption

Lecture Video 7.2

I. Three Tiers, Sorted by Independence

II. Rules as Glue

III. The Threshold, and Its Missing Term

IV. The Blank Slate, and Why the Scaffolding Exists

V. Where a Redesign Moves the Base Rate

VI. Task-Level and System-Level Thinking

7.3 The Prioritisation Framework: Scoring Impact Against Effort

Lecture Video 7.3

I. Identification and Prioritisation

II. The Matrix as a Communication Device

III. Named Drivers, and What They Do Not Fix

IV. Independent Scoring

V. The Structural Bias

VI. Gates and Zones

7.4 The Good First Project: Profiling the Sweet Spot

Lecture Video 7.4

I. What a First Project Is For

II. Tasks, Not Jobs — and When That Stops Being Right

III. The Sweet Spot

IV. Why Expected Value Is the Wrong Test

V. Demonstrations, Owners, and Business Pull

VI. Keeping It a First Wave

7.5 Valuing Decision Quality: Building a Defensible Business Case

Lecture Video 7.5

I. Four Channels, and the Overlap Between Two of Them

II. The Formula That Cannot Go Negative

III. Saying What the Discount Means

IV. Quality, Revenue, and Risk

V. Business Capacity, and When Minutes Do Not Consolidate

VI. J-Curves, Horizons, and Sensitivity

7.6 The J-Curve and Pilot Sprawl: Navigating the Between Times

Lecture Video 7.6

I. The J-Curve, and Why It Cannot Fail

II. The Costs Nobody Budgets

III. The Activation Gap

IV. Sprawl and Its Tells

V. Why Nobody Kills a Pilot

VI. Concentration Discipline

7.7 Advantage Against Table Stakes: Building the AI Moat

Lecture Video 7.7

I. Rented and Owned

II. VRIN, Applied Honestly

III. Flywheels and the Closure Condition

IV. Embeddedness, From Both Sides

V. Velocity, Constrained Assets, and Trust

VI. What the Cases Actually Show

7.8 Decision Owner's Final Word: Sequencing the Portfolio

Lecture Video 7.8

I. A Roadmap Is a Sequence

II. The Horizon Problem

III. The Dependency Principle

IV. Concentration and Diversification

V. The Investment Committee Conversation

VI. Closing the Loop

Knowledge Check

Knowledge Check: 1: AI Opportunity as a Prediction Problem

Knowledge Check: 2: Decision Components

Knowledge Check: 3: Limits of the Prediction Framing

Knowledge Check: 4: Accuracy Thresholds

Knowledge Check: 5: Point Versus System Solutions

Knowledge Check: 6: Rules as Operational Glue

Knowledge Check: 7: Task-Level and System-Level Evaluation

Knowledge Check: 8: Impact-Effort Prioritisation

Knowledge Check: 9: Independent Scoring

Knowledge Check: 10: Selecting a First-Wave Project

Knowledge Check: 11: Business Pull and Project Ownership

Knowledge Check: 12: Building the Business Case

Knowledge Check: 13: Capacity and Realizable Savings

Knowledge Check: 14: The J-Curve and Activation Gap

Knowledge Check: 15: Avoiding Pilot Sprawl

Knowledge Check: 16: Building and Sequencing an AI Advantage

Are you ready to move beyond the hype and start making strategic, high-impact decisions about Artificial Intelligence. "Strategic Evaluation of AI Opportunities" is a comprehensive course designed for business leaders, product managers, and strategists who need to identify, evaluate, and prioritize AI initiatives effectively.

Are you ready to move beyond the hype and start making strategic, high-impact decisions about Artificial Intelligence? "Strategic Evaluation of AI Opportunities" is a comprehensive course designed for business leaders, product managers, and strategists who need to identify, evaluate, and prioritize AI initiatives effectively. In today's rapidly evolving technological landscape, understanding the economic lens of AI—specifically reframing AI as a prediction problem—is crucial for uncovering genuine business value. This course takes you deep into the architecture of disruption, helping you distinguish between point and system solutions, and equipping you with a robust prioritization framework to score impact against effort.

You will learn how to profile the "sweet spot" for your first AI project, ensuring you target specific tasks rather than entire jobs, and build a defensible business case that accurately values decision quality, revenue, and risk. Furthermore, the curriculum tackles the realities of implementation, guiding you through the inevitable "J-Curve" of productivity and teaching you how to avoid the dreaded "pilot sprawl." Finally, you will discover how to build a sustainable AI moat using VRIN principles and flywheels, and how to sequence your AI portfolio for long-term success. Whether you are presenting to an investment committee or leading a digital transformation, this course provides the analytical tools and strategic frameworks necessary to turn AI potential into measurable competitive advantage. Join us to master the strategic evaluation of AI and lead your organization's next wave of innovation.

Chapter 7: Strategic Evaluation of AI Opportunities

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            <p>Artificial intelligence is fundamentally reshaping the economic landscape by drastically reducing the cost of prediction. "Strategic Evaluation of AI Opportunities" provides a rigorous framework for leaders and decision-makers to identify, assess, and capitalize on these shifts. Rather than viewing AI merely as a technological upgrade, this course reframes it through an economic lens, teaching you how to unbundle complex decisions and understand the intricate balance between substitutes and complements. By exploring the architecture of disruption—from isolated point solutions to comprehensive system-level redesigns—you will learn how to evaluate accuracy thresholds and determine where AI can genuinely move the baseline of your business operations.</p>

<p>Beyond theoretical frameworks, the curriculum emphasizes actionable strategies for portfolio sequencing and project execution. You will master an impact-versus-effort prioritization matrix to pinpoint the "sweet spot" for your crucial first AI initiatives, ensuring they generate tangible business pull rather than falling into the trap of pilot sprawl. The course delves deeply into valuing decision quality, guiding you to build defensible business cases that account for hidden costs, capacity constraints, and the inevitable J-curve of implementation. Ultimately, you will discover how to transition from leveraging table-stakes technology to constructing a sustainable AI moat, utilizing flywheels and embedded trust to sequence a robust, long-term AI portfolio.</p>
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