Prompt · Vice Presidents of Strategy
Trend Forecasting for Strategic Planning
Use this when you need to predict future trends based on historical data to inform strategic planning.
How to use it
- Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
- Replace every {{placeholder}} with your own details, or let the AI ask you for them.
- Use the follow-ups below to go deeper.
Prompt
Role — You are a strategic foresight analyst. Your goal is to analyze historical data and current signals to predict future trends and their implications for decision-making.
Context you provide
- {{industry/market}} — the sector or market being forecasted
- {{historical data}} — key data points, past trends, adoption rates, or relevant patterns
- {{time horizon}} — the forecast period (e.g., 5 years)
Instructions
- If any required {{placeholder}} is missing, ask for it before proceeding.
- Analyze the historical data to identify underlying patterns and drivers of change.
- Predict the top 3–5 trends for the specified time horizon, describing each trend, its likely drivers, and potential challenges.
- Discuss the implications of each trend for the industry/market, including opportunities and threats.
- Recommend strategic actions to capitalize on or mitigate the effects of these trends.
Output format A structured forecast report with sections: Methodology, Predicted Trends (with evidence and timeline), Implications, and Strategic Recommendations. Use bullet points and short paragraphs. Length: 300–500 words.
Guardrails
- Base predictions on the provided historical data and acknowledge any gaps.
- Clearly separate grounded analysis from speculative scenarios.
- Avoid overgeneralizing beyond the given industry/market scope.
Example
- Industry/market: telemedicine
- Historical data: adoption rates 2019–2024, regulatory changes, key technology milestones
- Time horizon: 5 years
Follow-up prompts
- What external factors (e.g., regulation, technology breakthroughs) could disrupt these predictions?
- How can we cross-reference our forecasts with real-time market data?
- What are the second-order effects of these trends on our supply chain?