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6.1 Discovery Lenses: Using Value-Stream Mapping and the Horizontal Capabilities Catalog to Find Candidates

I. The Discovery Gap: Why More Ideas Do Not Mean More Value

II. Top-Down and Bottom-Up Discovery

III. Value-Stream Mapping as a Discovery Lens

IV. The Horizontal Capabilities Catalog

V. Normalizing the Pipeline: Strong and Weak Use Cases

6.2 Screening Foundations: Applying "Must-Pass Gates" for Data Readiness, Decision Ownership, and Compliance

I. Gates before Scores

II. The Data Readiness Gate

III. The Decision Ownership Gate

IV. The Legal, Privacy, and Ethics Gate

V. The AI Use Case Intake Process

6.3 The Value-Feasibility Matrix: Scoring Candidates on a 1–5 Scale across Ten Weighted Drivers

I. From Gut Plots to Named Drivers

II. The Five Value Drivers

III. The Five Feasibility Drivers

IV. The Anchored Rubric

V. Strategy-Set Weights

6.4 Profile of a Quick Win: Identifying "Data-Rich, Process-Heavy" Tasks That Build Momentum in 90 Days

I. The Profile of a Viable First Project

II. The Data-Rich, Process-Heavy Heuristic Revisited

III. Timeboxing the First Project

IV. Measuring the Win

V. From Quick Win to Strategic Bet

6.5 Portfolio Horizons: Balancing the Portfolio across Deploy (0–6m), Reshape (6–18m), and Invent (18m+) Categories

I. Deploy, Reshape, and Invent

II. How Much in Each Category? Two Views

III. Staging Commitments under Uncertainty

IV. Sequencing: Early Projects Build Later Capabilities

V. Deploy Is Never Finished

6.6 Capability-Based Sequencing: Using Dependency Mapping to Ensure Early Wins Build Foundations for "Strategic Bets"

I. Beyond the Ranked List: Dependency Mapping

II. Sequencing in Waves

III. Matching Model Complexity to Data Maturity

IV. Reusable Assets

V. Staging the Largest Bets

6.7 The "Own vs. Orchestrate" Filter: How the Build vs. Buy Decision Tree Interacts with Resource Prioritization

I. From Build vs. Buy to Own vs. Orchestrate

II. The Decision Questions

III. Build to Learn and Build to Run

IV. The Hybrid: Buy the Core, Build the Differentiating Layer

V. Total Cost of Ownership and Its Effect on Priority

6.8 Defending the Rank: Using Independent Scoring and Pre-committed Strategy Weights to Defeat "Scoring Theater"

I. Recognizing Scoring Theater

II. Procedural Safeguards

III. Governance: Who Proposes, Who Scores, Who Decides

IV. Defending the Ranking to Competing Sponsors

V. The Value-Realization Loop

6.9 Case Study: The Underwriting OS: Prioritizing a "Lighthouse Case" in Commercial Insurance to Move from Manual Queues to an AI Nerve Center

I. From Inbox to Nerve Center

II. Why Domains Rather than Scattered Use Cases

III. Choosing the Lighthouse Case

IV. The Three Lanes and the Controls That Govern Them

V. Build, Buy, and the Insurer's "Secret Sauce"

Knowledge Check

Knowledge Check:

Knowledge Check 1: Discovery Gap

Knowledge Check 2: Discovery Approaches

Knowledge Check 3: Value-Stream Mapping

Knowledge Check 4: Horizontal Capabilities

Knowledge Check 5: Gates Before Scores

Knowledge Check 6: Data Readiness

Knowledge Check 7: Decision Ownership

Knowledge Check 8: Compliance Screening

Knowledge Check 9: Value-Feasibility Scoring

Knowledge Check 10: Anchored Rubrics

Knowledge Check 11: Quick-Win Profile

Knowledge Check 12: Measuring a Quick Win

Knowledge Check 13: Portfolio Horizons

Knowledge Check 14: Dependency Mapping

Knowledge Check 15: Own vs. Orchestrate

Knowledge Check 16: Hybrid Sourcing

Knowledge Check 17: Defending the Rank

Knowledge Check 18: Lighthouse Case

In today's rapidly evolving technological landscape, generating artificial intelligence ideas is the easy part; identifying which of those ideas will actually drive measurable strategic value is a far more complex challenge. 'Prioritizing AI Use Cases for Strategic Value' is a comprehensive, expertly designed course created to bridge this critical discovery gap.

In today's rapidly evolving technological landscape, generating artificial intelligence ideas is the easy part; identifying which of those ideas will actually drive measurable strategic value is a far more complex challenge. 'Prioritizing AI Use Cases for Strategic Value' is a comprehensive, expertly designed course created to bridge this critical discovery gap. It empowers business leaders, product managers, and corporate strategists to transform a chaotic, overwhelming pipeline of AI concepts into a structured, high-impact portfolio. Throughout this immersive learning experience, you will explore proven, industry-tested frameworks for discovering, screening, and sequencing AI initiatives. You will learn exactly how to apply strict 'must-pass gates' for data readiness, decision ownership, and legal compliance to filter out weak candidates early. Furthermore, you will utilize a robust Value-Feasibility Matrix to objectively score projects, and identify 'data-rich, process-heavy' quick wins that can build crucial organizational momentum within just 90 days. Moving beyond simple prioritization, the curriculum delves deep into managing portfolio horizons—balancing short-term deployments with long-term inventions—and capability-based sequencing to ensure that early wins lay a solid foundation for larger strategic bets. Complete with a detailed, real-world case study on transforming commercial insurance underwriting into an AI-driven nerve center, this course equips you with the governance tools needed to defeat 'scoring theater' and confidently defend your AI roadmap. Join us to master the art of AI prioritization and ensure your organization's investments deliver scalable, long-lasting success.

Chapter 6: Prioritizing AI Use Cases for Strategic Value

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            <p>Organizations often struggle not with a lack of artificial intelligence ideas, but with an overwhelming influx of them—a phenomenon known as the discovery gap, where more ideas do not necessarily equate to more business value. To harness the true potential of AI, leaders must move beyond scattered experiments and adopt a rigorous, strategic approach to selecting and prioritizing use cases. This course provides a comprehensive framework for identifying, evaluating, and sequencing AI initiatives that align directly with core business objectives. By leveraging discovery lenses such as value-stream mapping and horizontal capability catalogs, decision-makers can uncover high-impact candidates while filtering out distractions through strict "must-pass gates" concerning data readiness, decision ownership, and legal compliance.</p>

<p>As the curriculum unfolds, you will learn to apply a robust Value-Feasibility Matrix, scoring candidates across weighted drivers to objectively rank potential projects and defeat subjective "scoring theater." The journey emphasizes the importance of securing early momentum by targeting data-rich, process-heavy quick wins within a 90-day window, which in turn builds the foundation for larger strategic bets. Furthermore, the course explores how to balance an AI portfolio across immediate deployments, medium-term reshaping, and long-term inventions. Through capability-based dependency mapping, "own versus orchestrate" decision frameworks, and a real-world commercial insurance case study, you will gain the actionable insights needed to transform manual workflows into an intelligent, AI-driven nerve center.</p>
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