AI CAPITAL ALLOCATION

AI Capital Allocation Framework

Strategic Resource Planning for Enterprise AI Initiatives

A comprehensive 3-phase methodology for prioritising, planning, and executing AI investments with clear accountability and measurable outcomes

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Core Principles

Honesty Over Optimism

Realistic assessment of capabilities and constraints leads to better outcomes than aspirational planning.

Value Before Volume

Focus on delivering measurable business impact from fewer initiatives rather than spreading resources thin.

Ownership & Accountability

Every initiative must have a named owner with authority and capability to deliver results.

Data-Driven Decisions

Use quantitative scoring and metrics wherever possible, but acknowledge qualitative strategic factors.

3-Phase Implementation Methodology

Establish a comprehensive scoring methodology that balances business value, technical feasibility, and strategic alignment.

Value Scoring Matrix (0-10 Scale)

Score initiatives across Business Impact (revenue, cost savings), Strategic Alignment (competitive advantage, future positioning), and Market Timing (urgency, window of opportunity).

Key Insight: Use quantitative metrics where possible, but accept qualitative assessments for strategic factors.

Key Metrics:
Business Impact Score
Strategic Alignment Score
Market Timing Score

Technical Feasibility Assessment

Evaluate data availability and quality, technical complexity, team capabilities, and infrastructure readiness. Rate from 1 (impossible) to 10 (trivial).

Key Insight: Be brutally honest about current capabilities. Over-optimism here leads to project failures.

Key Metrics:
Data Quality Index
Technical Complexity Rating
Team Readiness Score

Risk-Adjusted Prioritisation

Combine value and feasibility scores with risk factors including regulatory compliance, competitive response, and execution dependencies.

Key Insight: The highest value opportunities are often not the right starting point if risk-adjusted probability of success is low.

Key Metrics:
Risk-Adjusted ROI
Probability of Success
Strategic Option Value

Common Pitfalls & Solutions

Initiative Proliferation

Starting too many AI projects simultaneously, leading to resource dilution and delivery delays

Solution:

Strict portfolio capacity planning and quarterly go/no-go decisions

Capability Overestimation

Assuming current team can deliver AI capabilities without additional skills or resources

Solution:

Honest skills gap analysis and realistic recruitment/training timelines

Value Measurement Lag

Waiting too long to measure business impact, leading to continued investment in low-value initiatives

Solution:

Quarterly value delivery checkpoints with hard stop criteria

Technical Debt Accumulation

Rushing to proof-of-concept without considering production scalability and maintenance

Solution:

Technical feasibility scoring includes production-readiness factors

Typical Implementation Timeline

Weeks 1-4

Prioritisation Phase

  • • Initiative inventory and scoring
  • • Technical feasibility assessment
  • • Risk-adjusted prioritisation
  • • Stakeholder alignment sessions

Weeks 5-8

Resource Planning

  • • Detailed cost estimation
  • • Skills gap analysis
  • • Recruitment planning
  • • Portfolio capacity planning

Weeks 9-12

Execution Setup

  • • Owner assignment and briefing
  • • 90-day sprint planning
  • • Governance framework setup
  • • Execution launch

Core Outcome: Clear, accountable execution plan for AI capital allocation

Framework by The AK Dispatch. Based on enterprise AI implementations across multiple sectors and scales.

Applied across 10+ organisations with combined AI budgets exceeding €100M