Strategic AI Implementation Insights
Conceptualizing the Design Space of Artificial Intelligence Strategy: A Taxonomy and Corresponding Clusters
Discover how leading enterprises define, implement, and evaluate their AI strategies to achieve competitive advantage and drive innovation.
AI Strategy: Driving Transformative Growth
The effective design and implementation of an AI strategy are crucial for navigating market shifts and resource reallocations driven by AI's autonomy, learning, and inscrutability. Understanding the design space enables organizations to formulate a strategic response to these challenges, fostering innovation and sustainable competitive advantage.
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Dimensions
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Characteristics
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Strategy Clusters
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The Scope layer defines activities under an organization's control, focusing on responsibility and accountability for AI strategy. It covers how strategic ownership, organizational anchoring, life cycle management, governance level, control mechanisms, and data governance framework are structured.
| Dimension | AI-induced Shift | Key AI Strategy Considerations |
|---|---|---|
| Strategic Ownership | From narrow to pervasive AI applications |
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| Life Cycle Management | From narrow to pervasive AI applications |
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| Data Governance Framework | From manual to data-driven and inscrutable decision-making |
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The Scale layer addresses how organizations ensure the availability of human capacity and technological resources for AI applications. This includes knowledge acquisition methods (training, hiring, contracting) and technology sourcing approaches (make, hybrid, buy).
83%
of Technology Navigators rely on Hiring for knowledge acquisition.
The Speed layer focuses on the time and sequence of establishing AI use cases. It explores different approaches for use case identification (systematical, experimental) and use case expansion (one-to-many, many-to-one) in a complex and dynamic AI environment.
Enterprise Process Flow
Use Case Identification
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Pilot Programs
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Value Validation
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Organizational Rollout
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Continuous Optimization
The Source layer describes mechanisms for gaining value from AI. It includes dimensions like technology aspiration (established, cutting-edge, bleeding-edge), business model impact (complementing, extending, renewing), risk tolerance (high, limited, minimal), value creation (frontstage, backstage, front- & backstage), and value recipient effect (replacing, reinforcing, revealing).
JPMorgan
JPMorgan Chase actively researches and deploys bleeding-edge AI technologies, positioning itself as a Technology Navigator in the financial sector, embracing high risk for high reward.
Learn More about JPMorgan AIQuantify Your Potential AI Impact
Use our ROI calculator to estimate the potential cost savings and efficiency gains your organization could achieve with a tailored AI strategy.
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Your AI Strategy Journey
Our proven roadmap guides you through the essential phases of designing and implementing an effective AI strategy tailored to your enterprise's unique needs.
Phase 1: Strategic Assessment
Define AI vision, identify core business challenges, and assess current capabilities.
Phase 2: Taxonomy & Cluster Analysis
Map your current state using the taxonomy and benchmark against industry clusters.
Phase 3: Design & Prioritization
Formulate a tailored AI strategy, prioritize use cases, and define governance.
Phase 4: Implementation & Scaling
Deploy pilot projects, refine based on feedback, and scale successful initiatives.
Phase 5: Continuous Optimization
Monitor performance, adapt to technological shifts, and ensure long-term value.
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