Section 6
AI Opportunity & Use Case Portfolio
Discover, assess, review, prioritize and roadmap AI opportunities as an enterprise portfolio.
AI4EA treats AI opportunities as an enterprise portfolio rather than a collection of independent experiments. Candidate use cases progress through: Discover → Assess → Review → Prioritize → Roadmap.
6.1 Discovery and Assessment
Opportunities should be discovered from business processes, customer journeys, enterprise capabilities, operational challenges, information-intensive activities, strategic initiatives and decision processes. Discovery begins with the problem or opportunity rather than a predetermined AI technology.
6.2 AI4EA Use Case Priority Score (UCPS)
UCPS provides a consistent decision aid for comparing candidate use cases. Organizations may adjust weights to reflect industry, strategy, regulation and risk appetite. Risk is inversely treated so higher inherent risk reduces overall priority.
| # | Dimension | Key Question | Weight | Scoring Guidance (1 = Low, 5 = High) |
|---|---|---|---|---|
| 1 | Strategic Alignment | Does the use case directly support strategic objectives and value themes? | 15% | 1 = No or weak alignment · 5 = Strong direct alignment |
| 2 | Business Value | What measurable business benefit is expected? | 25% | 1 = Minimal value · 5 = Significant, measurable value |
| 3 | Technical Feasibility | Can it be delivered with available technology and skills? | 15% | 1 = Not feasible · 5 = Highly feasible |
| 4 | Data Readiness | Is suitable, trusted and accessible data available? | 15% | 1 = Data not available or poor quality · 5 = High-quality, accessible data |
| 5 | Organizational Readiness | Can the organization adopt the resulting change? | 10% | 1 = Significant resistance / not ready · 5 = Highly ready |
| 6 | Risk & Compliance | What is the regulatory, ethical, security and operational risk? | 10% | 1 = Very high risk · 5 = Very low risk |
| 7 | Time to Value | How quickly can value be demonstrated? | 10% | 1 = Long-term (> 12 months) · 5 = Short-term (< 3 months) |
6.3 Portfolio Classification
The quantitative score should be complemented by a portfolio view that balances business value and implementation feasibility.
Strategic Bet
High value · Low feasibility
Invest and develop enablers
Prioritize
High value · High feasibility
Execute and scale
Defer / Reassess
Low value · Low feasibility
Monitor or reconsider
Quick Win
Lower value · High feasibility
Execute early
Implementation Feasibility →
Primary outcome
A governed portfolio of AI initiatives linked to measurable enterprise priorities.
Core artifacts