ScenarioPlanner

Category: Planning | Module: mycontext.templates.free.planning

Develops multiple future scenarios using the classic 2×2 matrix methodology from Shell's scenario planning tradition. Identifies the two most critical uncertainties as axes, constructs four distinct scenarios, analyzes implications for each, finds strategies that are robust across scenarios, and establishes observable signposts.

When to Use

• Strategic planning (1-5+ year horizons)
• Market entry decisions
• Technology investment under uncertainty
• Industry disruption analysis
• Organizational change planning
• Any decision with significant future uncertainty

Quick Start

from mycontext.templates.free.planning import ScenarioPlanner

planner = ScenarioPlanner()

ctx = planner.build_context(
    topic="The future of AI in enterprise software development",
    timeframe="2025-2030",
)
result = ctx.execute(provider="openai")
print(result.response)

Methods

build_context(topic, timeframe="5 years", context=None)

Parameters:

Parameter	Type	Default	Description
topic	str	""	Topic or decision domain to scenario-plan
timeframe	str	"5 years"	Planning horizon
context	str | None	None	Current situation, relevant trends, constraints

execute(provider, topic, timeframe="5 years", context=None, **kwargs)

result = planner.execute(
    provider="openai",
    topic="Future of remote work policies for technology companies",
    timeframe="2026-2031",
    context="Post-pandemic, AI productivity tools emerging, talent market shifting",
)

The 2×2 Scenario Methodology

The pattern identifies two critical uncertainties — the most important, most uncertain factors that will shape the future. These become the two axes of a matrix, producing four distinct scenarios:

                    HIGH Uncertainty 2
                          ▲
                          │
     Scenario C  ─────────┼───────── Scenario A
                          │
LOW ◄──────────────────── ┼────────────────────► HIGH
 Uncertainty 1            │          Uncertainty 1
                          │
     Scenario D  ─────────┼───────── Scenario B
                          │
                    LOW Uncertainty 2

Example axes for AI in enterprise:

• Axis 1: AI capability speed (Gradual → Exponential)
• Axis 2: Regulatory environment (Permissive → Restrictive)

10-Section Analysis

1. Current State Assessment — Present situation, key trends, momentum
2. Critical Uncertainties — Two uncertainty axes with range and impact explanation
3. Four Scenarios — Name, description, key characteristics, driving forces, probability %, early indicators
4. Implications Analysis — For each scenario: opportunities, threats, required capabilities
5. Robust Strategies — No-regret moves, hedging, shaping actions, adaptive strategies
6. Scenario-Specific Strategies — Optimal approach if each scenario materializes
7. Signposts & Triggers — Observable indicators that signal which scenario is emerging
8. Preparedness Plan — Immediate actions, monitoring plan, contingency plans
9. Scenario Comparison Table — Side-by-side on likelihood, impact, and strategic fit
10. Strategic Recommendations — Most likely, preferred, and how to shape the future

Examples

Technology Investment

result = planner.execute(
    provider="openai",
    topic="Investment strategy for quantum computing capabilities",
    timeframe="2025-2035",
    context="Fortune 500 manufacturing company, $50M R&D budget",
)

Market Entry

result = planner.execute(
    provider="anthropic",
    topic="Expansion into Southeast Asian markets for B2B SaaS",
    timeframe="3 years",
    context="Current: US/EU focused, $20M ARR, 60-person team",
)

Workforce Planning

result = planner.execute(
    provider="gemini",
    topic="Future of remote vs. hybrid work for our engineering organization",
    timeframe="2026-2029",
    context="Currently 70% remote, offices in SF and NYC, 200 engineers",
)

Robust vs. Scenario-Specific Strategies

Key insight

The most valuable output is often the "robust strategies" — actions that create value regardless of which scenario unfolds. These are your no-regret investments.

Strategy Type	Description	Example
No-regret moves	Good in all scenarios	Build API-first architecture (valuable in all AI futures)
Hedging	Reduce downside in bad scenarios	Diversify customer concentration
Shaping	Influence which scenario emerges	Engage regulators proactively
Adaptive	Designed to pivot	Keep options open, avoid lock-in
Scenario-specific	Only valuable in one scenario	Aggressive hiring if AI is slower

Signposts

Signposts are concrete, observable events that indicate which scenario is unfolding. They enable the monitoring plan:

Signpost: "Major LLM provider announces API pricing cut > 80%"
- Signals: Scenario A (rapid AI adoption)
- When to check: Monthly monitoring of provider pricing
- Trigger: Accelerate AI integration roadmap

Signpost: "EU AI Act enforcement begins with first $10M+ fine"
- Signals: Scenario C (high regulation)
- When to check: Quarterly regulatory news review
- Trigger: Activate compliance contingency plan

API Reference

Method	Returns	Description
build_context(topic, timeframe, context)	Context	Assembled context
execute(provider, topic, timeframe, context, **kwargs)	ProviderResponse	Execute planning
generic_prompt(topic, timeframe, context_section)	str	Zero-cost prompt string