Ai innov mgt : Ch10 prep & planning (stg 1)

Summary: Chapter 10 - Phase 1: Preparation and Planning

Overview

This chapter introduces Phase 1 of the AI Innovation Management Framework, establishing the foundation for successful AI initiatives through systematic preparation across multiple dimensions.

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Core Components of Preparation and Planning

1. Assessing Organizational Readiness

Five Dimensions of AI Readiness:

· Data Readiness: Availability, quality, accessibility, governance, and infrastructure

· Technical Infrastructure: Computing resources, cloud/on-premise decisions, development tools, integration capabilities, scalability

· Talent and Skills: Technical expertise, domain knowledge, hybrid skills, leadership literacy, change management capabilities

· Organizational Culture: Data-driven decision-making, experimentation mindset, collaboration, change tolerance, risk appetite

· Leadership and Governance: Executive sponsorship, strategic alignment, governance structures, resource commitment, ethical frameworks

Assessment Process: Six steps from assembling a cross-functional team to communicating results and developing a readiness plan.

Case Example: DBS Bank's successful transformation began with a comprehensive readiness assessment that identified AI-specific skill gaps and cultural barriers, which they addressed through education and training initiatives.

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2. Building the Business Case

Key Components:

· Problem Definition: Current state, desired future state, AI justification, strategic alignment

· Value Proposition: Revenue impact, cost reduction, risk reduction, customer experience improvement, competitive advantage, strategic options

· Investment Requirements: Technology, talent, data, change management, opportunity costs

· Timeline and Milestones: Pilot, scale-up, full deployment, value realization phases

· Risk Assessment: Technical, data, adoption, regulatory, and competitive risks with mitigation strategies

· ROI Analysis: Multiple time horizons and scenarios; includes NPV, IRR, and payback period calculations

· Alternatives Analysis: Comparison against doing nothing, traditional solutions, and buy vs. build options

Narrative Approach: Tell a compelling story starting with "why," using concrete examples, proactively addressing concerns, showing momentum, and connecting to strategy.

Case Example: Salesforce's Einstein AI business case focused on customer value, competitive positioning, and strategic options, securing executive support for what became a highly successful enterprise AI initiative.

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3. Securing Leadership Buy-In

Leadership Concerns:

· Uncertainty and risk

· Technical complexity

· Cost and ROI

· Organizational disruption

· Ethical and reputational risks

· Competitive pressure

Buy-In Strategies:

· Educate leaders through briefings, demos, and site visits

· Frame AI as a strategic imperative (competitive necessity, customer expectations, operational efficiency, future-proofing)

· Start small with pilot projects to demonstrate quick wins

· Build a coalition of champions across functions and engage board members

· Address concerns directly with specific mitigation plans

· Align incentives with AI success metrics

Case Example: Microsoft's CEO Satya Nadella personally championed AI, articulated a clear strategic framework, committed billions in investment, reorganized to create an AI group, and promoted a growth mindset culture.

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4. Establishing Governance Structures

Governance Framework Components:

· Structure: AI Steering Committee, Center of Excellence, Project Governance, Ethics Committee

· Decision Rights: Clear allocation of strategic, project approval, technical, risk, and operational decisions

· Policies and Standards: Development standards, ethical AI principles, risk management, data governance, model governance

· Processes: Project approval, risk assessment, model review, monitoring and reporting, incident response

· Roles and Responsibilities: CAIO, data scientists, engineers, business owners, compliance, ethics, IT operations

Balance Strategy: Hybrid approach combining centralized governance/infrastructure with decentralized execution (hub and spoke model).

Case Example: DBS Bank's PURE Framework (Progressive, Unbiased, Responsible, Explainable) includes an Ethics Committee, clear policies, structured processes, accountability mechanisms, and transparent reporting.

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5. Resource Allocation and Budgeting

Investment Categories:

· Infrastructure (computing, data, development tools, production)

· Talent (hiring, training, consulting, retention)

· Data (acquisition, cleaning, labeling, governance)

· Projects (pilot, production, maintenance)

· Change Management (communication, training, process redesign)

Budgeting Approaches:

· Centralized, Distributed, Hybrid, or Venture Capital model

Resource Allocation Principles:

· Prioritize based on value and feasibility

· Invest in foundational capabilities (high leverage)

· Start small, scale fast

· Plan for long-term commitment

· Balance exploration and exploitation

· Monitor and adjust investments

Case Example: Google's multi-billion dollar AI investment spanning research, infrastructure, talent, product integration, and platform development positions them as an AI leader.

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6. Creating the AI Vision and Strategy

Vision Characteristics:

· Aspirational yet realistic

· Specific and actionable

· Aligned with business strategy

· Customer-centric

· Achievable with available resources

Strategic Components:

· Strategic objectives (customer experience, efficiency, innovation, decision-making, risk management)

· Focus areas based on importance, AI suitability, data availability, feasibility

· Approach choices (build vs. buy, cloud vs. on-premise, centralized vs. distributed, partnerships)

· Phased roadmap with milestones and flexibility

· Capability building (technical, talent, process, cultural)

· Governance and risk management integration

· Success metrics and KPIs

Communication Strategy: Leaders must consistently communicate vision; engage employees, customers, and stakeholders transparently.

Case Example: Microsoft's three-pillar AI strategy (infuse AI into every product, build AI platforms, develop responsible AI) provided clear direction and enabled successful AI transformation.

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7. Setting Objectives and KPIs

KPI Types:

· Business Impact: Revenue, cost, customer, operational, risk metrics

· AI Performance: Accuracy, precision/recall, F1 score, AUC-ROC, MAE/RMSE

· Adoption: User adoption, usage frequency, feature utilization, satisfaction

· Operational: Latency, throughput, availability, cost per prediction

· Governance and Risk: Bias metrics, explainability, privacy compliance, security incidents

SMART Criteria: Specific, Measurable, Achievable, Relevant, Time-Bound

Balance: Include both lagging indicators (outcome-based) and leading indicators (predictive activities)

Dashboard Approach: Executive, operational, and technical dashboards with regular updates and alerting

Case Example: Netflix tracks comprehensive KPIs for its recommendation system including streaming hours, retention, recommendation accuracy, diversity, viewing percentage (~80%), latency, and availability.

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Key Takeaways

1. Preparation is foundational - Rushing into AI implementation without adequate planning creates costly obstacles

2. Assessment reveals gaps - Honest evaluation across data, infrastructure, talent, culture, and leadership dimensions is essential

3. Business cases must tell a story - Combine quantitative analysis with compelling narrative

4. Leadership alignment is critical - Address concerns proactively and build coalitions

5. Governance enables scale - Clear structures, policies, and processes enable responsible AI growth

6. Resource allocation requires balance - Invest in foundations while funding projects

7. Vision and strategy provide direction - Clear objectives and KPIs guide execution and demonstrate value

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Connection to Subsequent Phases

The Preparation and Planning phase establishes the foundation for:

· Phase 2: Idea Generation and Evaluation

· Phase 3: Implementation and Execution

· Phase 4: Monitoring and Optimization

"With the foundation in place, organizations are ready to move to Phase 2: Idea Generation and Evaluation, where they will identify specific AI opportunities and select the most promising initiatives to pursue."