Frameworks Reference Sheet

AI-Driven Business Innovation Masterclass

AI-Driven Business Innovation

Strategic Frameworks Reference Sheet

Executive Education Curtin Business School

Your quick-reference guide to the five strategic frameworks

Five Strategic Frameworks

This reference sheet provides quick access to the five frameworks taught in the AI-Driven Business Innovation Masterclass.

What You’ll Find:

Framework 1: AI Transformation Matrix - Classify AI initiatives by strategic value - Four quadrants: Optimize, Enhance, Revolutionize, Transform

Framework 2: Data Value Pyramid - Assess your organisation’s data maturity - Five levels from collection to autonomous operations

Framework 3: Three Horizons Model - Balance your AI portfolio across time horizons - Manage current operations while building the future

Framework 4: AI Investment Model - Evaluate AI proposals systematically - Strategic fit, feasibility, ROI, and risk assessment

Framework 5: Innovation Adoption Framework - Understand barriers to AI adoption - Plan for organisational change management

How to Use This Guide

During the workshop: - Keep this on your desk for quick reference - Use it during exercises to apply frameworks - Annotate with your own insights and notes

After the workshop: - Pin near your workspace - Reference when evaluating AI opportunities - Share with colleagues on your AI team

🌐 Digital Version: https://exec-ed.github.io/ai-business-innovation/

📧 Questions: michael.borck@curtin.edu.au

Framework 1: AI Transformation Matrix

Purpose: Classify AI initiatives to understand strategic value

Two Dimensions:

Dimension 1: Focus

Process Focused: Automate/optimize existing processes (efficiency)
Strategic Focused: New capabilities, products, business models (differentiation)

Dimension 2: Magnitude

Incremental: Improve what exists (10-30% improvement)
Transformational: Fundamentally change how business operates (10x change)

Four Quadrants:

	Incremental	Transformational
Process	Optimize - Automate tasks, reduce costs	Revolutionize - Reinvent core processes
Strategic	Enhance - Improve products/services	Transform - New business models

Examples: - Optimize: Automate invoice processing, chatbot for FAQs - Enhance: AI product recommendations, personalised marketing - Revolutionize: Fully autonomous supply chain, lights-out manufacturing - Transform: AI-as-a-Service new revenue stream, platform business model

Use this when: Prioritizing AI initiatives in your portfolio

Framework 2: Data Value Pyramid

Purpose: Understand data maturity and AI readiness

Five Levels (bottom to top):

Level 1: Data Collection

Raw data from systems
Example: Transaction logs, sensor data, customer interactions

Level 2: Data Integration

Connected data from multiple sources
Example: Customer 360 view, unified inventory system

Level 3: Descriptive Analytics

“What happened?”
Example: Dashboards, reports, KPIs

Level 4: Predictive Analytics

“What will happen?”
Example: Demand forecasting, churn prediction, maintenance alerts

Level 5: Prescriptive Analytics / Autonomous Operations

“What should we do?” → System acts automatically
Example: Dynamic pricing, auto-replenishment, self-optimizing systems

Key Insight: You can’t skip levels. AI requires strong data foundations.

Use this when: Assessing organisational readiness for AI initiatives

Framework 3: Three Horizons Model

Purpose: Balance AI portfolio across timeframes

Horizon 1: Optimize the Core (70% of investment)

Timeframe: 0-12 months
Goal: Improve current business
AI Examples: Process automation, chatbots, predictive maintenance
ROI: Quick, measurable, low risk

Horizon 2: Build Emerging Business (20% of investment)

Timeframe: 1-3 years
Goal: Develop new capabilities
AI Examples: AI-enhanced products, new customer experiences, platform services
ROI: Medium term, moderate risk

Horizon 3: Create the Future (10% of investment)

Timeframe: 3-5+ years
Goal: Transformational innovation
AI Examples: New business models, AI-native ventures, autonomous operations
ROI: Long term, higher risk, potential for breakthrough

Portfolio Balance: - Too much H1: Short-term thinking, miss opportunities - Too much H3: Burning cash on uncertain futures - Sweet spot: 70/20/10 allocation (adjust for industry)

Use this when: Building your AI investment portfolio

Framework 4: AI Investment Model

Purpose: Calculate ROI and make data-driven investment decisions

Value Categories

1. Cost Reduction - Labour savings from automation - Efficiency improvements - Error reduction

2. Revenue Growth - New products/features enabled by AI - Better conversion rates - Premium pricing for AI-enhanced offerings

3. Risk Reduction - Fraud detection savings - Compliance automation - Better decision-making reduces costly mistakes

4. Strategic Positioning - Competitive advantage - Market share gains - Option value (platform for future initiatives)

Investment Calculation

Total Value = Cost Reduction + Revenue Growth + Risk Reduction + Strategic Value

ROI = (Total Value - Investment Cost) / Investment Cost × 100%

Evaluation Criteria

Minimum thresholds: - Horizon 1 initiatives: ROI > 200% in Year 1 - Horizon 2 initiatives: ROI > 150% over 3 years - Horizon 3 initiatives: ROI > 100% over 5 years (or strategic must-have)

Risk-adjusted: - Low confidence in value → require higher ROI - High certainty → accept lower ROI

Use this when: Evaluating competing AI investment proposals

Framework 5: Innovation Adoption Framework

Purpose: Implement AI strategically across the organisation

Phase 1: Knowledge Building (3-6 months)

Activities: Education, workshops, vendor briefings, pilot identification
Goal: Build literacy and identify opportunities
Investment: Low (1-5% of AI budget)

Phase 2: Pilot Experimentation (6-12 months)

Activities: Small pilots, proof-of-concepts, learn by doing
Goal: Validate technical feasibility and business value
Investment: Medium (10-20% of AI budget)

Phase 3: Selective Deployment (12-18 months)

Activities: Scale successful pilots, build capability, early production deployments
Goal: Deliver initial business value
Investment: Growing (30-40% of AI budget)

Phase 4: Scaling Operations (18-36 months)

Activities: Broader rollout, platform building, centre of excellence
Goal: Systematic value delivery
Investment: Peak (40-50% of AI budget)

Phase 5: Comprehensive Transformation (36+ months)

Activities: AI-native processes, continuous innovation, ecosystem partnerships
Goal: AI as core competency
Investment: Sustained (30-40% of AI budget, shifts to R&D)

Key Insight: Most organisations fail by trying to skip to Phase 4/5. Respect the journey.

Use this when: Planning multi-year AI transformation

Quick Decision Framework

When evaluating any AI initiative, ask:

1. Classification (Transformation Matrix)

Where does this sit: Optimize, Enhance, Revolutionize, or Transform?
Is this the right type of initiative for our strategy?

2. Data Readiness (Data Value Pyramid)

What level are we at today?
What level does this initiative require?
Can we bridge the gap?

3. Portfolio Balance (Three Horizons)

Which horizon is this in?
Does our portfolio balance need this?
Are we over-invested in one horizon?

4. Investment Return (AI Investment Model)

What’s the total value across all categories?
What’s the ROI?
Does it meet our threshold?

5. Implementation Readiness (Innovation Adoption)

What phase are we in organizationally?
Is this initiative appropriate for our maturity?
What capabilities do we need to build?

If you can’t clearly answer these 5 questions, you’re not ready to invest.

Common Strategic Mistakes to Avoid

Mistake 1: AI for AI’s Sake - ❌ Wrong: “We need to do AI because everyone else is” - ✅ Right: “This AI initiative solves business problem X and delivers ROI of Y”

Mistake 2: Skipping Data Foundations - ❌ Wrong: “Let’s jump straight to AI” - ✅ Right: “First fix data quality, then build analytics, then AI”

Mistake 3: All Horizon 1 (Short-term thinking) - ❌ Wrong: 100% investment in quick wins - ✅ Right: 70/20/10 balance across horizons

Mistake 4: All Horizon 3 (Moonshots only) - ❌ Wrong: “We’re building AGI and reinventing the industry” - ✅ Right: Mix of quick wins, growth, and transformation

Mistake 5: No ROI Discipline - ❌ Wrong: “It’s strategic, we can’t quantify the value” - ✅ Right: “Here’s the value across 4 categories, here’s the ROI calculation”

Mistake 6: Trying to Scale Before Piloting - ❌ Wrong: Phase 1 → Phase 4 (skip learning) - ✅ Right: Follow all 5 phases, earn your way to scale

Integration: How the Frameworks Work Together

Strategic Planning Process:

Assess current state (Data Value Pyramid)
- Where is our data maturity?
Classify opportunities (AI Transformation Matrix)
- What types of initiatives are we considering?
Balance portfolio (Three Horizons Model)
- Do we have the right mix across timeframes?
Evaluate investments (AI Investment Model)
- Which initiatives have best ROI?
Plan implementation (Innovation Adoption Framework)
- What phase are we in? What’s next?

Decision-Making Process:

For each AI proposal: - Classify it (Matrix) - Check data readiness (Pyramid) - Assign to horizon (Three Horizons) - Calculate ROI (Investment Model) - Validate against maturity (Adoption Framework) - Then decide: Fund, defer, or reject

Your Action Plan

This week: 1. Map your current AI initiatives on the Transformation Matrix 2. Assess your data maturity on the Value Pyramid 3. Calculate portfolio balance across Three Horizons

This month: 1. Evaluate top 3 AI proposals using the Investment Model 2. Identify what phase you’re in on the Adoption Framework 3. Make portfolio rebalancing decisions

This quarter: 1. Build AI governance using these frameworks 2. Train leadership team on the frameworks 3. Implement decision criteria for all AI investments

These frameworks are tools, not rules. Adapt them to your context, but use them consistently to make better strategic decisions about AI investments.

Quick Reference Summary

When to Use Each Framework:

🎯 AI Transformation Matrix - Use when: Prioritizing AI initiatives in your portfolio - Question it answers: “Where does this initiative fit strategically?” - Output: Classification (Optimize/Enhance/Revolutionize/Transform)

📊 Data Value Pyramid - Use when: Assessing organisational AI readiness - Question it answers: “Are we ready for this AI initiative?” - Output: Current data maturity level (1-5)

🔭 Three Horizons Model - Use when: Building a balanced AI portfolio - Question it answers: “Are we investing across all time horizons?” - Output: Portfolio distribution (H1/H2/H3)

💰 AI Investment Model - Use when: Evaluating specific AI proposals - Question it answers: “Should we fund this AI project?” - Output: Go/No-Go decision with rationale

🚀 Innovation Adoption Framework - Use when: Planning AI implementation - Question it answers: “What barriers will we face and how do we overcome them?” - Output: Change management plan

Common Mistakes to Avoid

❌ Don’t: Only invest in Horizon 1 (Optimize) ✅ Do: Balance portfolio across all three horizons

❌ Don’t: Skip data readiness assessment ✅ Do: Check Data Value Pyramid before committing resources

❌ Don’t: Evaluate AI in isolation ✅ Do: Use all frameworks together for comprehensive view

❌ Don’t: Ignore adoption barriers ✅ Do: Plan for change management from the start

Additional Resources

Digital Materials: - 🌐 Website: https://exec-ed.github.io/ai-business-innovation/ - 📊 Interactive Tools: AI Investment Checklist, ROI Calculator - 📚 Prompt Library: 1000+ strategic AI prompts - 🎯 Leadership Assessment: Evaluate your AI readiness

Further Reading: - AI Metrics and ROI Indicators - LLM and AI Agents Strategic Guide - Five Key AI Capability Domains - Future AI Trend Analysis

Stay Connected: - 📧 Email: michael.borck@curtin.edu.au - 💼 LinkedIn: [Your LinkedIn]

Strategic Frameworks Reference Sheet

AI-Driven Business Innovation Masterclass

Your guide to strategic AI decision-making

Executive Education | Curtin Business School

📧 michael.borck@curtin.edu.au

🌐 https://exec-ed.github.io/ai-business-innovation/