LLM Scenario Copilot: Future Scenarios

Most LLM tools are optimized for answering factual questions and citing sources, but companies often need help with uncertainty: what the industry might look like next, what could break their business, and what to do today to prepare.
In this talk I’ll demo an early prototype “scenario copilot” that takes a company context (industry, constraints, time horizon) and generates multiple divergent future scenarios, each with risks, opportunities, and concrete actions. I’ll go in depth on the prompt architecture and orchestration that makes the model produce structured, decision-oriented outputs rather than a single generic answer.
I’ll also share failure modes and what I changed to improve reliability and usefulness, so others can reuse the patterns.