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Ensuring you don't get caught out in the rain

AI-driven forecasting ensures your business doesn't get caught in the rain without an umbrella

Just like weather forecasts, AI-driven business forecasting is a means to an end. Rather than purely focusing on the accuracy of your forecasts, it's more important to focus on how those forecasts are improving your decision-making and business outcomes.

There are few certainties in life and, even with the help of AI, no sales forecast is going to be 100 per cent accurate every time. If you assume that your forecasts will always be completely accurate, and don't make any contingency plans, then you've left the business exposed to an unacceptable risk.

The role of AI-driven business forecasting isn't to tell you exactly what will happen next week, month or year. Instead, forecasting's job is to predict the range of likely outcomes, so you can prepare for them.

Umbrella gif boris johnson
Knowing how to use the forecast is half the battle.

At the end of the day, the exact accuracy of your forecasts isn't really important. What's far more important is how those forecasts inform your decision-making and business outcomes. They're what really matter, the things which affect the bottom line.

Think about when you study the weather forecast to plan your weekend. It doesn't really matter whether the prediction is out by a degree or two either way. That level of accuracy isn't important. It's more useful to know the rough temperature range and the likelihood of rain.

You don't check the weather forecast because you need to know exactly what the temperature will be on Sunday. You check the forecast so you know whether to wear shorts, take a coat or carry an umbrella. The goal is to ensure you're not caught unprepared by unexpected hot, cold or wet weather – that's the outcome which actually matters.

The weather forecast might not always be perfect, but it's a lot more reliable than simply assuming the weather on Sunday will be the same it was all week. Or the same as this time last year. The weather bureau draws on a lot more than historical data when honing its weather predictions, from weather station readings to satellite images.


It's the same when it comes to AI forecasting for your business. The first step is to create a baseline by looking at your historical data to create a "naive" forecast. Based on what's happened in the past, this is what's likely to happen in the future.

The naive forecast reveals the consistency and predictability of an outcome. From here, AI-driven forecasting's goal is to improve on that naive forecasting by drawing on extra data points.

When you're faced with an ocean of data, the trick is to find the right data points with correlation and relevance to what you're trying to predict. It could be anything from your web traffic and promotions to the weather forecast and upcoming public holidays. Finding meaning and business insight within all that data is one of AI's strengths.

The aim is not just to improve the accuracy of the forecast but, more importantly, to hone the breadth of what could possibly happen, so you're not caught unprepared.

Focusing solely on the accuracy of your forecasts is to miss the big picture. AI-driven business forecasting is a means to an end, the real goal is not to predict the exact future but to empower the business to optimise its efforts tso it's prepared for anything.

So, is it time you came in out of the rain?


Want to know more about inventory management and replenishment? Head here to find out how AI can help you avoid stock-outs, or here to learn all about the things you need to consider before implementing AI. You can also see our platform in action via our case studies - including how we helped a wholesale distributor reduce stock-outs by 24%.Once you’ve had your fill of content from our blog, why not drop us a line here.

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