Idea Evaluation Frameworks

Idea Evaluation Frameworks

Detailed frameworks for scoring, filtering, and selecting creative ideas.

Table of Contents

1. Core Dimensions
2. NAF Framework
3. ICE Scoring
4. RICE Scoring
5. Weighted Decision Matrix
6. Impact/Effort Matrix
7. Timing & Process
8. AI-Specific Evaluation

Core Dimensions

Most frameworks agree on three fundamental dimensions:

NAF Framework

Origin: Synectics creative problem-solving tradition (George Prince)

Process:

1. Create table with ideas in rows, N/A/F in columns
2. Score each idea 1-10 on each criterion
3. Sum and rank

Criteria:

• Novelty -- Is it new for this situation?
• Attractiveness -- Does it completely solve the problem?
• Feasibility -- Can it be put into practice?

Interpretation:

• Feasibility 80%+ (8/10): Worth trying
• Feasibility < 50% but N+A high: Turn concerns into new problems to solve
• Low on all three: Implementation will disappoint

Why NAF over gut feel: People slip into "either/or thinking" when selecting ideas. NAF forces structured evaluation while keeping the creative spirit.

ICE Scoring

Origin: Sean Ellis / GrowthHackers

ICE Score = Impact x Confidence x Ease

Best for: Quick experiments, growth hacking, early-stage ideas Weakness: Subjective, prone to guesstimates

RICE Scoring

Origin: Intercom

RICE Score = (Reach x Impact x Confidence) / Effort

Best for: Data-driven roadmap planning, product teams with metrics

Weighted Decision Matrix

Process:

1. List all options (ideas)
2. Define criteria (customer impact, feasibility, strategic fit, etc.)
3. Assign weights as percentages (must sum to 100%)
4. Score each option 1-5 on each criterion
5. Multiply score x weight
6. Sum weighted scores per option
7. Rank by total

Example:

Best for: Complex decisions with multiple stakeholders

Impact/Effort Matrix

         High Impact
              |
   Big Bets   |   Quick Wins  <-- Do these first
              |
  ------------|------------
              |
   Avoid      |   Fill-ins
              |
         Low Impact

  High Effort      Low Effort

Quadrants:

• Quick Wins (High Impact + Low Effort): Do immediately
• Big Bets (High Impact + High Effort): Worth investing if resources allow
• Fill-ins (Low Impact + Low Effort): Do when time permits
• Avoid (Low Impact + High Effort): Deprioritize or discard

Timing & Process

The Separation Principle

Generate first, evaluate later. Most important timing rule.

• During divergent phase: No judgment. Zero.
• Create a transitional phase between generating and judging
• Different mindset required: take a break, or evaluate next day

Why Premature Evaluation Kills Creativity

• People self-censor
• Teams get stuck on one idea
• Feasible/safe ideas win over novel ones

Stage-Gate for Ideas

Stage 1: Diverge      → Gate 1: Quick screen (NAF/dot vote)
Stage 2: Develop      → Gate 2: Thorough evaluation (matrix/RICE)
Stage 3: Prototype    → Gate 3: Go/No-go based on evidence

Different Ideas Need Different Processes

• Incremental improvements: Standard criteria work
• Disruptive ideas: Don't penalize low feasibility too early -- disruptive ideas often look impractical initially

AI-Specific Evaluation

The Novelty-Feasibility Trade-off

Research shows two patterns depending on domain:

• Some domains: AI ideas are more novel but less feasible
• Other domains: AI ideas are more feasible but less novel

Key insight: The direction depends on domain, AI system, and prompts.

Evaluating AI-Generated Idea Batches

Beyond individual idea quality, measure set-level diversity:

AI "Safe" Ideas Problem

AI defaults to high-feasibility, moderate-novelty ideas because:

• Trained on existing text (synthesizes known patterns)
• Without novelty prompting, converges to "optimal"
• Lacks lived experience for truly novel insights

Mitigations:

• Explicitly prompt for novelty
• Use AI for quantity, human judgment for quality
• Apply NAF to separate genuinely novel from merely competent
• Use multiple prompting strategies for diversity

Human-AI Evaluation Flow

The emerging consensus (2026):

1. AI generates quantity + feasible ideas quickly
2. Humans recognize novelty + apply domain intuition
3. Best outcomes from iterative collaboration: AI generates -> human evaluates -> AI refines -> human selects