From the course: How to Measure Anything in AI: Quantitative Techniques for Decision-Making
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Diffusion and adoption of AI
From the course: How to Measure Anything in AI: Quantitative Techniques for Decision-Making
Diffusion and adoption of AI
- Any rapidly changing condition that will be impactful for your business is something you might be interested in forecasting. You may already be forecasting such things as the chance and magnitude of supply chain disruptions, commodity prices, or rising labor costs, but the forecast is only useful if it can inform a decision. Let's discuss how you can make those forecasts and review a couple of important metrics you can use to inform decisions. All forecasts are based on historical data. Forecasts based on historical data aren't perfect, but we only know that because of historical experience. Even the errors of forecasts are part of historical data. If we can explain why a forecast might be wrong, then we've identified another variable to track and improve the forecast. For example, maybe historical progress would produce a poor forecast because we've misestimated the growth in computing power, so how would our forecast change if this was more than we expected or less than we…
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