My Take on RICE Scoring (And Why I Modified It)

Every PM knows RICE scoring: Reach × Impact × Confidence / Effort. It’s clean, quantitative, and supposedly objective. But after using it for 3 years across two companies, I’ve found it falls short in two critical ways.

The Problem with Standard RICE

Issue 1: Technical complexity isn’t effort

RICE conflates “effort” (engineering time) with difficulty. But a 2-week project that requires rearchitecting your auth system is fundamentally different from a 2-week feature that’s just UI work.

The risky, complex work deserves more scrutiny than the effort number alone suggests.

Issue 2: Strategic alignment is binary

Standard RICE doesn’t account for how well a feature aligns with company strategy. A feature might score high on RICE but be tangential to your core mission. You end up optimizing for metrics instead of strategy.

My Modified Framework: RICE-S

I added a Strategic Alignment multiplier (0.5x to 2x) that forces explicit conversation about company priorities.

Scoring:

• 2x: Critical to company OKRs
• 1.5x: Strongly supports strategy
• 1x: Neutral or maintenance work
• 0.5x: Nice-to-have, not strategic

And I split Effort into two components:

• Time: Pure engineering weeks
• Complexity: Technical risk factor (1x to 3x)

Formula: (Reach × Impact × Confidence × Strategic) / (Time × Complexity)

Real Example

We were deciding between two features:

Feature A: Advanced filtering

• Reach: 5,000 users
• Impact: Medium (2)
• Confidence: High (90%)
• Strategic: 1x (maintenance)
• Time: 2 weeks
• Complexity: 1x (straightforward)

Score: (5000 × 2 × 0.9 × 1) / (2 × 1) = 4,500

Feature B: AI-powered recommendations

• Reach: 10,000 users
• Impact: High (3)
• Confidence: Low (40%)
• Strategic: 2x (core to new product direction)
• Time: 2 weeks
• Complexity: 3x (new ML pipeline)

Standard RICE Score: (10000 × 3 × 0.4) / 2 = 6,000 ✓ Build this

RICE-S Score: (10000 × 3 × 0.4 × 2) / (2 × 3) = 4,000 ✗ Feature A wins

Standard RICE would prioritize the AI feature. But RICE-S reveals the true trade-off: high strategic value is offset by significant technical complexity and low confidence.

This sparked the right conversation: “Are we ready to invest in ML infrastructure?” (Answer: not yet.) Feature A shipped first, and we planned a proper ML foundation for next quarter.

When This Framework Breaks Down

RICE-S works well for mid-sized feature decisions. It breaks down for:

• One-off asks from executives: No framework saves you from “the CEO wants this”
• Technical debt: Often scores low but is critical
• Exploratory work: Hard to estimate reach/impact before you build

For those cases, I reserve 20% of roadmap capacity as “strategic discretion”—no scoring needed.

The Real Value of Frameworks

Here’s the truth: the specific numbers in RICE don’t matter that much. What matters is that everyone is forced to think about the same dimensions.

The strategic multiplier alone has been invaluable. It makes implicit prioritization explicit. When a stakeholder pushes for their pet feature, I can ask: “How does this tie to our H1 OKRs?” If they can’t answer, the 0.5x multiplier tells the story.

Frameworks aren’t magic. But they turn debates about “what feels right” into structured conversations about trade-offs. And that’s worth the overhead.