Decision engine

Decision Engine

Apply four analytical frameworks to any product decision, then synthesize into a single prioritized recommendation with confidence scoring and pre-mortem analysis.

Output

Save to outputs/decision-[topic]-[YYYY-MM-DD].md

When to Use

• Quarterly prioritization with competing initiatives
• Ship / iterate / sunset decisions for features
• Build vs. buy vs. partner evaluations
• Any decision where you need structured reasoning, not gut feel

What You'll Get

Framework	What It Evaluates
Impact × Confidence Matrix	Revenue impact weighted by execution certainty
Strategic Alignment	Weighted scoring against company goals
Second-Order Effects	Downstream consequences (positive and negative)
Pre-Mortem Analysis	"It's December and this failed — what went wrong?"

Plus:

• Synthesized Recommendation with confidence score
• Actionable Next Steps with owners and dates
• Risk Register with mitigation strategies

Process

Step 1: Define the Decision

I'll ask:

"What decision are you facing? List the options you're considering (2-4 options work best). Include any context about constraints, timeline, or strategic priorities."

Step 2: Impact × Confidence Matrix

For each option, I'll score:

• Impact (1-10): Revenue potential, user value, market differentiation
• Confidence (1-10): Technical feasibility, team capability, timeline certainty
• Weighted Score = Impact × Confidence / 10

Step 3: Strategic Alignment

I'll score each option against your company's strategic pillars:

• Growth & Revenue (weight: 30%)
• Customer Retention (weight: 25%)
• Technical Excellence (weight: 20%)
• Market Positioning (weight: 15%)
• Team Development (weight: 10%)

Step 4: Second-Order Effects

For each option, I'll map:

• Positive cascades: What else gets better if this succeeds?
• Negative cascades: What breaks or gets harder?
• Opportunity costs: What can't you do if you pick this?

Step 5: Pre-Mortem

For the top option:

"It's 6 months from now. This initiative failed. What went wrong?" I'll generate 4-5 failure scenarios with probability estimates and mitigations.

Step 6: Synthesis

I'll combine all four frameworks into:

• Recommended option with overall confidence
• Key conditions that must be true for this to work
• Decision reversibility — how easy is it to change course?
• Next steps with specific owners and timelines

Demo Scenario: Quarterly Prioritization

Input:

"We need to pick one initiative for Q2. Options: (A) AI-powered analytics dashboard, (B) Enterprise SSO + compliance features, (C) Mobile app v2 redesign. We're a B2B SaaS with 200 enterprise customers, $15M ARR, trying to move upmarket."

Sample Impact × Confidence Matrix:

Option	Impact	Confidence	Weighted	Rationale
A: AI Analytics	8	6	4.8	High differentiation but unproven tech stack
B: Enterprise SSO	7	9	6.3	Clear requirements, proven patterns, direct revenue
C: Mobile Redesign	5	7	3.5	Nice-to-have, not blocking deals

Sample Pre-Mortem (Option B: Enterprise SSO):

Failure Scenario	Probability	Mitigation
SSO integration testing takes 3x longer due to customer IdP variations	35%	Start with top 3 IdPs only (Okta, Azure AD, OneLogin)
Compliance certifications (SOC2 Type II) delayed by audit backlog	25%	Begin audit prep in parallel, don't gate launch on cert
Enterprise deals close but at lower ACV than modeled	20%	Pre-negotiate 2 pilot deals before committing Q2 scope
Team morale drops working on "boring" infrastructure	15%	Frame as career growth; SSO expertise is marketable

Tips

1. Include constraints — Budget, timeline, team size, technical debt
2. Be honest about unknowns — "We're not sure if X" is better than guessing
3. 3 options is ideal — 2 feels binary, 4+ gets noisy