Making Decisions: Frameworks and Techniques for Tech Leaders
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Making Decisions: Frameworks and Techniques for Tech Leaders

January 21, 2026
14 min read
Jonas Höttler

Making Decisions: How Tech Leaders Make Better Decisions Faster

Jeff Bezos distinguishes between "One-Way-Door" and "Two-Way-Door" decisions. At Amazon, the rule is: Two-Way-Door decisions can be made by anyone, quickly and without bureaucracy. One-Way-Door decisions need more deliberation.

Most teams treat all decisions like One-Way-Doors – and become slow as a result.

Why Making Decisions Is So Hard

The Paradox of Choice

TOO FEW OPTIONS:
→ Bad decisions because alternatives are missing

TOO MANY OPTIONS:
→ No decision because analysis paralysis sets in

THE SWEET SPOT:
→ 2-4 clearly defined options

The Most Common Decision Errors

ErrorDescriptionExample
Analysis ParalysisEndless analyzing without deciding"One more meeting, then we'll decide"
Confirmation BiasOnly seeking info that confirms your opinionFramework X is great → Only read positive articles
Sunk Cost FallacyHolding onto bad decisions because of already invested resources"We've already invested 6 months..."
GroupthinkEveryone agrees, nobody questionsSilence at "Does anyone have concerns?"
Recency BiasOverweighting recent experiences"Microservices didn't work at company X, so never again"
Authority BiasAdopting the authority figure's opinion"The CTO says X, so we do X"

Framework 1: Reversible vs. Irreversible Decisions

The Two-Door Rule (Amazon)

One-Way-Door (Irreversible):

  • Hard or impossible to undo
  • High cost if wrong
  • Need thorough analysis

Two-Way-Door (Reversible):

  • Easy to undo
  • Low cost if wrong
  • Can be made quickly
EXAMPLES IN TECH:

ONE-WAY-DOOR:
- Completely change database architecture
- Switch programming language
- Migrate cloud provider
- Fire a team member
→ Analyze thoroughly, get input, take time

TWO-WAY-DOOR:
- Try a new testing framework
- Change meeting format
- Introduce a feature flag
- Adjust code style
→ Decide quickly, try, adjust

The 70% Rule

Jeff Bezos' approach: Decide when you have 70% of the information you'd like to have.

AT 50% INFORMATION:
→ Too early, too risky

AT 70% INFORMATION:
→ Sweet spot: Enough knowledge, not too slow yet

AT 90% INFORMATION:
→ Too late, opportunity missed

Why 70%? The effort for the last 30% of information increases exponentially, while insight gained decreases.

Framework 2: RAPID for Complex Team Decisions

RAPID defines clear roles for the decision process:

RoleMeaningTask
R - RecommendGive recommendationAnalyzes options and proposes
A - AgreeAgreement requiredMust agree (veto right)
P - PerformExecuteImplements the decision
I - InputProvide inputIs consulted, doesn't have to agree
D - DecideMake decisionFinal decision

RAPID in Practice

EXAMPLE: Introduce new CI/CD tool

R (Recommend): Senior DevOps Engineer
   → Analyzes tools, creates recommendation

A (Agree): Security Team, Finance
   → Must agree for security/budget reasons

P (Perform): DevOps Team
   → Implements the tool

I (Input): Developer teams
   → Give feedback on requirements

D (Decide): Engineering Manager
   → Makes final decision

Why RAPID Works

  1. Clarity: Everyone knows their role
  2. Speed: No endless consensus-finding
  3. Accountability: One person decides
  4. Inclusion: Input is heard but doesn't block

Framework 3: 10/10/10 for Emotional Distance

When a decision is emotionally charged, the 10/10/10 method helps:

Ask yourself:

  1. How will I think about this decision in 10 minutes?
  2. How will I think about it in 10 months?
  3. How will I think about it in 10 years?

Example

DECISION: Should I tell the team the project
          will probably fail?

10 MINUTES:
→ Uncomfortable, conflict, bad mood

10 MONTHS:
→ The team had time to adapt. Trust is higher
  because I was honest.

10 YEARS:
→ Nobody remembers the specific project.
  But: Reputation as honest leader remains.

DECISION: Yes, communicate openly.

Framework 4: OODA Loop for Fast Decisions

The OODA Loop comes from the military and works for fast, iterative decisions:

    ┌──────────────┐
    │   OBSERVE    │  ← Observe situation
    └──────┬───────┘
           ↓
    ┌──────────────┐
    │    ORIENT    │  ← Understand context
    └──────┬───────┘
           ↓
    ┌──────────────┐
    │    DECIDE    │  ← Make decision
    └──────┬───────┘
           ↓
    ┌──────────────┐
    │     ACT      │  ← Act
    └──────┬───────┘
           │
           └────────→ Back to OBSERVE

OODA in Tech Daily Life

EXAMPLE: Production Incident

OBSERVE:
- Error rate rising to 5%
- Latency tripled
- Since last deployment

ORIENT:
- Last deployment was Feature X
- Feature X has database query
- Query has no index

DECIDE:
- Rollback the deployment

ACT:
- Execute rollback
- Check monitoring
- → Back to OBSERVE

SECOND LOOP (if problem remains):
- OBSERVE: Error rate still high
- ORIENT: Not the deployment
- DECIDE: Involve database team
- ACT: ...

Framework 5: Pre-Mortem

Instead of analyzing after failure (Post-Mortem), imagine BEFORE the decision that it failed.

Process

1. Formulate decision:
   "We're migrating to Kubernetes"

2. Imagine time jump:
   "It's 1 year later. The migration failed."

3. Questions:
   - Why did it fail?
   - What did we overlook?
   - Which risks materialized?

4. Analysis:
   - Everyone writes reasons individually
   - Go through together
   - Prioritize risks

5. Adjust decision:
   - Build in mitigations for top risks
   - Or: Reconsider decision

Why Pre-Mortems Work

  • Bypasses groupthink (expressing concerns is legitimized)
  • Activates different thinking (failure mode instead of success mode)
  • Discovers blind spots before the decision

Framework 6: Eisenhower Matrix for Prioritization

Not every decision needs equal attention.

                    URGENT              NOT URGENT
              ┌─────────────────┬─────────────────────┐
   IMPORTANT  │    DO           │      PLAN           │
              │                 │                     │
              │ - Prod incident │ - Architecture      │
              │ - Security bug  │ - Team development  │
              │ - Deadline      │ - Strategy          │
              ├─────────────────┼─────────────────────┤
   NOT        │   DELEGATE      │     ELIMINATE       │
   IMPORTANT  │                 │                     │
              │ - Status emails │ - Unnecessary mtgs  │
              │ - Routine tasks │ - Perfectionism     │
              └─────────────────┴─────────────────────┘

Applied to Decisions

IMPORTANT + URGENT:
→ Decide immediately, even with less info

IMPORTANT + NOT URGENT:
→ Take time for thorough analysis

NOT IMPORTANT + URGENT:
→ Delegate or quick 2-minute decision

NOT IMPORTANT + NOT URGENT:
→ Don't decide, eliminate

Making Decisions in Teams

Consensus vs. Consent

Consensus: Everyone must agree

  • Pro: High acceptance
  • Con: Very slow, lowest common denominator

Consent: Nobody has a serious objection

  • Pro: Faster, better decisions
  • Con: Objections must be qualified
CONSENSUS:
"Is everyone in favor?" → One person against = blocked

CONSENT:
"Does anyone have a serious objection?"
→ Only "This harms us" counts, not "I'd do it differently"

Disagree and Commit

Amazon's principle: After the decision, the whole team commits – even those who were against it.

PROCESS:
1. Open discussion with all perspectives
2. Decision is made
3. Everyone commits to execution
4. No "I told you so" if it goes wrong

IMPORTANT:
- Only works with psychological safety
- Prerequisite: Everyone felt heard

RFC Process for Technical Decisions

Request for Comments (RFC) for larger technical decisions:

RFC STRUCTURE:

1. PROBLEM
   What's the problem we want to solve?

2. CONTEXT
   What's the current situation?

3. OPTIONS
   What alternatives exist?
   Pro/con for each option

4. RECOMMENDATION
   What do we propose and why?

5. RISKS
   What can go wrong?

6. TIMELINE
   When do we need a decision?

PROCESS:
- RFC is written and shared
- Team provides comments (async)
- Discussion meeting if needed
- Decision is documented

Avoiding Decision Fatigue

What Is Decision Fatigue?

The more decisions you make, the worse they get.

SYMPTOMS:
- Postponing decisions
- Making impulsive decisions
- Choosing status quo
- Avoiding decisions

Strategies Against It

1. Important decisions early in the day

NOT: Architecture meeting at 5 PM
BETTER: Architecture meeting at 10 AM

2. Eliminate routine decisions

- Same clothes (Steve Jobs approach)
- Same morning routine
- Standing meetings instead of "When works?"
- Templates for recurring decisions

3. Batching

- All emails at once
- All 1:1s on the same day
- Collect decisions and make them together

4. Set defaults

INSTEAD OF:
"Which framework do we use?"

BETTER:
"Our default is React. Anyone who wants
to deviate must justify it."

Documenting Decisions

Why Document?

  1. Preserve context: In 6 months you won't remember why
  2. Onboarding: New team members understand the history
  3. Learning: What worked, what didn't?
  4. Accountability: Who decided?

Architecture Decision Records (ADR)

# ADR-001: Use PostgreSQL as Database

## Status
Accepted

## Context
We need a database for the new application.
Requirements: ACID, JSON support, good performance.

## Decision
We use PostgreSQL.

## Rationale
- JSON support for flexible schemas
- Proven performance
- Team has experience
- Costs are low

## Alternatives Considered
- MongoDB: Rejected due to ACID requirements
- MySQL: Rejected due to weaker JSON support

## Consequences
- Ops team needs to build PostgreSQL knowledge
- Backup strategy must be defined

## Date
2026-01-21

## Deciders
@jonas

Common Decision Situations in Tech

Technology Selection

FRAMEWORK:
1. Clearly define requirements (before research!)
2. Evaluate 2-3 candidates
3. Proof of concept for top candidate
4. Pre-mortem: What if it goes wrong?
5. Decision + documentation

ANTI-PATTERN:
- "This is trendy" as only criterion
- Evaluating 10 options in parallel
- Endless research without decision

Build vs. Buy

QUESTIONS:
1. Is it our core business?
   → Yes: More likely build
   → No: More likely buy

2. Are there good solutions on the market?
   → Yes: More likely buy
   → No: More likely build

3. Do we have the capacity?
   → Yes: Build possible
   → No: Buy or wait

4. Total Cost of Ownership?
   → Factor in maintenance, updates, training

Refactoring vs. Feature

QUESTIONS:
1. How often is this code touched?
   → Often: Refactoring pays off
   → Rarely: Feature first

2. How painful is the code?
   → Bugs, slow development: Refactoring
   → Works: Feature first

3. Can you refactor incrementally?
   → Yes: Parallel to feature
   → No: Explicit decision needed

Conclusion: Decisions as a Skill

Good decisions aren't talent, they're a skill that can be trained.

Most Important Principles:

  1. Check reversibility: Two-Way-Door = decide fast
  2. 70% is enough: Don't wait for perfect information
  3. Clarify roles: Who decides? (RAPID)
  4. Manage emotions: 10/10/10 for distance
  5. Iterate quickly: OODA for dynamic situations
  6. Anticipate risks: Pre-mortem before the decision
  7. Document: For context and learning

The one question for today:

Which decision are you putting off – even though it's actually a Two-Way-Door?

Make it. Now. You can adjust later.


Want to understand how you communicate decisions and involve your team as a leader? Our guide to Servant Leadership shows a leadership style based on trust and transparency. See also: Decision Fatigue and Cognitive Biases in Project Management.

#Decision Making#Tech Leadership#Frameworks#Productivity

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