Building a Quick Estimation Template When You Have Almost Nothing to Go On

The 4 PM Estimation Crisis

Last month, my PM dropped 73 backlog items on my desk at 4 PM. "Need estimates by tomorrow's planning meeting." Sound familiar?

Here's what I did: I grabbed the scariest item—a machine learning integration nobody on the team had touched before. I rated it:

• Innovation: 8 (we've never done ML)
• Scope: 7 (new infrastructure, data pipelines, APIs)
• People: 7 (data scientists, backend devs, DevOps, plus external vendors)

That's 22 points. With my multiplier of 5 for high complexity, I got 110 → mapped to 89 story points.

Took me 90 seconds. By 6 PM, I'd estimated all 73 items. Here's the framework I built and how you can adapt it for your team.

The Framework

I needed something simple: rate each factor on a 1-10 scale, add them up, apply a multiplier. That's it.

Addition gives a manageable range (3-30) vs. multiplication (1-1000). I can explain it in 30 seconds and calculate it in my head.

Task: Rate 8+7+7 = Sum 22, Apply ×5 = Result 110 points

The Three Core Factors (Deep Dive)

Innovation: Have we done this before?

• 1-3: Routine work with templates (login forms, standard bug fixes)
• 4-6: Similar work needing adaptation (new payment provider, different framework)
• 7-10: New territory requiring R&D (first ML model, blockchain integration)

Ask: "How much Googling vs. copy-pasting from previous work?"

Scope: How big is the work?

• 1-3: Small, isolated change (button color, single validation)
• 4-6: Multiple components (dashboard page, API + form)
• 7-10: System-wide changes (database migration, entire microservice)

Ask: "How many files/systems will we touch?"

People: Coordination complexity?

• 1-3: Solo or single team
• 4-6: 2-3 different skill sets with handoffs
• 7-10: Many teams, diverse skills, external vendors

Ask: "How many different specialties and integration points?"

Multipliers Based on Complexity

Complexity doesn't scale linearly. A sum of 5 might double in effort, but a sum of 22 explodes due to context switching, integration testing, stakeholder delays, and communication overhead.

Mapping to Different Formats

The framework generates consistent relative values that map to any output format. Working in Agile? Map to Fibonacci. T-shirt sizes? Create bands. Need hours? Use a conversion factor (but keep it hidden from stakeholders).

The mapping can be a simple lookup table or nested IF statements. Let AI write these tedious formulas for you.

Mapping to Output Formats

The raw score maps to any format your team uses:

• Fibonacci: 3-6→3, 7-10→5, 11-14→8, 15-18→13, 19-22→21
• T-Shirt: XS (3-6), S (7-9), M (10-13), L (14-17), XL (18-21), XXL (22+)

Getting Your Team On Board

Week 1: Estimate your next sprint solo. Track actuals and calculate variance.

Week 2: Share results with the team. Walk through examples and ask what they would've rated differently.

Week 3: Have the team rate 5 items independently, then compare and discuss differences.

Common Pushback:

Week 2: Share results with the team. Walk through examples and ask what they would've rated differently.

Week 3: Have the team rate 5 items independently, then compare and discuss differences.

Common Pushback:

• "This is too simple" → Try it for one sprint and track accuracy
• "My tasks are unique" → Rate them as high Innovation, that's what the scale is for
• "People will game it" → Calibration exposes inflated ratings when actuals don't match
• "We use Planning Poker" → Use this for initial estimates, then validate with Planning Poker

Focus on Defendable, Not Perfect

The goal isn't perfect estimates—it's estimates you can explain and adjust. When someone challenges your numbers, you can show:

• The specific Innovation, Scope, and People ratings
• Why you rated each factor that way
• How changing any rating affects the estimate
• Which completed work validates your approach

This transparency builds trust even when estimates are wrong.

Python Implementation

Here's a complete working implementation you can adapt:

def estimate_task(innovation, scope, people):
    """
    Calculate task estimate using Innovation, Scope, People framework.
    
    Args:
        innovation (int): 1-10, have we done this before?
        scope (int): 1-10, how big is the work?
        people (int): 1-10, coordination complexity?
    
    Returns:
        dict: Contains sum, multiplier, raw_score, and fibonacci_points
    """
    # Calculate sum
    sum_factors = innovation + scope + people
    
    # Apply progressive multipliers
    if sum_factors <= 9:
        multiplier = 2.5
        complexity = "Low"
    elif sum_factors <= 15:
        multiplier = 4
        complexity = "Medium"
    elif sum_factors <= 20:
        multiplier = 5
        complexity = "High"
    else:
        multiplier = 6
        complexHistory

Work backwards from 5-10 completed items:

1. Rate a completed item (Innovation + Scope + People)
2. Calculate: Actual Effort ÷ Sum = Implied Multiplier
3. Average across several items
4. That's your team's baseline multiplier

No history? Start with ×3 and adjust after a few completions

How This Compares to Other Methods

Key insight: This framework sits in the sweet spot—faster than Planning Poker, more structured than gut feel, and doesn't require extensive historical data.

After six months of 10% accuracy, I got cocky. A "simple" mobile redesign: Innovation 3 (done mobile before), Scope 5 (just UI), People 2 (one team). Sum of 10, ×4 multiplier = 40 hours.

It took 6 weeks.

What I missed:

• Our mobile dev had left—should've been Innovation 7, not 3
• "UI changes" required rewriting state management—Scope 8, not 5
• "One team" was designer + junior dev + consultant—People 5, not 2

Rate based on your team's current reality, not the task description. Key insight: This framework sits in the sweet spot—faster than Planning Poker, more structured than gut feel, and doesn't require extensive historical data.

Build in Automatic Reality Checks

Add a column for "actual effort" and calculate variance automatically. Use conditional formatting to highlight when estimates are off by more than 50%.

Track patterns:

• Are innovative tasks always underestimated?
• Does your team consistently underrate People complexity?
• Do certain types of work always blow up?

These patterns help you adjust ratings going forward, not just multipliers. The framework improves as you learn your team's blindspots.

Keep Cognitive Load Low

SpDefendable Over Perfect

The goal isn't perfect estimates—it's estimates you can explain. When challenged, show the specific Innovation/Scope/People ratings and why you chose them. Transparency builds trust even when you' | Use AI to Handle Tedious Parts

AI assistants excel at:

• Generating Excel formulas for your ISP logic
• Creating validation rules and conditional formatting
• Building reference tables and lookup functions
• Writing calibration calculations
• Producing multiple format outputs from the same data

Don't waste time on formula syntax. Describe what you want and let AI write the implementation. See the LLM Prompts section below for specific examples.

Embrace Continuous Refinement

Your first version will be wrong. The framework gives you something to be wrong WITH, which is infinitely better than being wrong without structure.

After each sprint/milestone/project:

1. Compare estimates to actuals
2. Look for systematic bias in Innovation, Scope, or People ratings
3. Adjust either rating definitions or multipliers
4. Document what you learned

Within 3-4 cycles, your accuracy improves dramatically. The framework evolves with your team's reality.

The Psychology Matters

Having a framework changes the conversation from "how did you guess that?" to "let's discuss these ratings." It moves you from defending random numbers to discussing specific complexity factors.

Stakeholders can engage with "I rated People complexity as 7 because we need frontend, backend, DBA, and DevOps coordination" in a way they can't with "my gut says 3 weeks."

LLM Prompts to Get You Started

AI Prompts to Build Your Framework

Copy these prompts to generate implementation details:

Initial Framework:

"Create an Excel formula that takes 3 factors (Innovation, Scope, People) rated 1-10, adds them, applies progressive multipliers, and maps to Fibonacci."

Spreadsheet Template:

"Generate a Python script using openpyxl for an estimation template with headers, data validation (1-10), automatic calculations, and conditional formatting."

Calibration Formula:

"Write an Excel formula that calculates implied multiplier from actuals and provides an average recommendation."

Tips for Success

Track patterns: Are innovative tasks always underestimated? Do you underrate People complexity? Adjust future ratings based on blindspots.

Keep it fast: More than 2 minutes per estimate means it's too complex. You should estimate 50 items in under an hour.

Use AI for formulas: Let AI generate Excel formulas, validation rules, and lookup tables. Don't waste time on syntax.

Refine continuously: Your first version will be wrong. Compare estimates to actuals after each sprint and adjust. Within 3-4 cycles, accuracy improves dramatically.

Be transparent: Show the ratings openly. It changes conversations from "how did you guess?" to "let's discuss these factors."

Real Calculation Examples

Let's walk through exactly how the math works with concrete numbers:

Copy these prompts to generate implementation details:

Initial Framework:

"Create an Excel formula that takes 3 factors (Innovation, Scope, People) rated 1-10, adds them, applies progressive multipliers, and maps to Fibonacci."

Spreadsheet Template:

"Generate a Python script using openpyxl for an estimation template with headers, data validation (1-10), automatic calculations, and conditional formatting."

Calibration Formula:

"Write an Excel formula that calculates implied multiplier from actuals and provides an average recommendation.

Example Calculations