LLM-Assisted Scenario Generation & Strategy Simulation

Problem class

Traditional scenario planning is too slow for modern disruption. Building 3–5 robust scenarios with quantified implications requires weeks of analyst effort, limiting planning frequency and comprehensiveness.

Mechanism

LLMs ingest environmental scan data, competitive intelligence, and historical analogues to generate draft scenario narratives and stress-test strategic assumptions. Human strategists curate and validate AI-generated scenarios rather than building from scratch. Simulation engines model strategic options against each scenario, quantifying range-of-outcomes and identifying robust strategies that perform across multiple futures.

Required inputs

• Environmental scan data and competitive intelligence feeds
• Historical scenario libraries and analogue databases
• Current strategic plan and resource commitments
• Industry-specific driving forces and uncertainty registers
• Human expert judgment for scenario validation

Produced outputs

• AI-generated draft scenario narratives with driving-force matrices
• Quantified range-of-outcomes for strategic options per scenario
• Robust strategy recommendations performing across multiple futures
• Assumption sensitivity analyses identifying critical vulnerabilities

Industries where this is standard

• Management consulting firms using AI-augmented strategy engagements
• Energy companies modeling energy-transition scenarios at higher frequency
• Financial services firms stress-testing strategies under multiple regimes
• Large technology companies modeling competitive and regulatory futures

Counterexamples

• Accepting LLM-generated scenarios without expert validation risks hallucinated assumptions entering strategic plans; the Harvard/BCG study found AI degraded performance 19–23% outside its capability frontier.
• Using AI to generate more scenarios without improving decision linkage amplifies analysis paralysis; fewer well-connected scenarios outperform voluminous unlinked ones.

Representative implementations

• Harvard/BCG study of 758 consultants found AI-assisted strategy work completed 25% faster with 40% higher quality on analytical tasks.
• McKinsey's internal Lilli AI tool reduced strategy research and planning from weeks to hours by scanning the firm's full knowledge base.
• BCG reports AI "future-built" companies achieve 5× revenue increases and 3× cost reductions versus peers.

Common tooling categories

Large language model APIs, scenario simulation engines, strategic planning platforms with AI modules, assumption stress-testing tools.