The quest for the accurate forecast 

Published on February 28, 2025 within Article

An accurate forecast is a good forecast. Or is it always right? It's a little more complicated than that – and above all more interesting!

If we take the example of the weather forecast, most people would probably agree that it should absolutely be accurate. But what about the revenue and cost forecasts that organizations and companies produce: What function do they actually serve? Is an accurate forecast for the business always equal to a good forecast? 

We have asked management consultants Knut Fahlén and Hugo Bluhme a few questions to hear what they think about the purpose of the forecast and how it should be designed to create as much value as possible. When is an accurate forecast not a good forecast?

Knut, what would you say is the big difference between a weather forecast and the economic forecasts that companies and organizations produce? 

The big difference is that we cannot influence the weather, which is the basis for the weather forecast.” Even if our hope is that it will be sunny this weekend, we are completely powerless to change the situation. Most people would consider an accurate weather forecast to be a good forecast because it gives us the opportunity to adapt accordingly.

With a forecast in a company, there is probably – at least if we extend the time perspective – an opportunity to implement activities to change the situation to what we want to happen – that is, to close the gap between the forecast and our set goal. If we take it to the extreme, few people want the forecast that shows bankruptcy to turn out to be an accurate forecast. However, such a forecast is extremely valuable for being able to act in time.

Unfortunately, it is unusual for organizations to use forecasts in this way.

So what is a good prognosis in your eyes? 

A good forecast is one that gives decision-makers insights into the future that lead to action. This requires that we dare to see reality exactly as it is. We don't do that if we talk about "stretching a forecast" - then we mix up the concepts. A CASE can be stretched – a forecast should always be realistic.

How do you create a realistic forecast, it doesn't seem that simple?

Predicting the future is not easy. Especially not now in a world that is increasingly characterized by uncertainty and rapid change. That is precisely why those organizations that have the ability to use forecasting correctly have a competitive advantage.

The difficulty in most cases lies not in the methodology and technology, but in mindsetThere is no "one size fits all" and all businesses need to do their homework and understand their external environmental factors and internal revenue and cost drivers.

In addition, management needs to accept that we cannot actually fully predict the future. It may sound obvious, but sometimes I think we reason as if management and finance functions had a crystal ball at their disposal. Read more about How Beyond Budgeting changes the way we look at forecasting.

You talk about methods and techniques, what are these?  

To simplify, there are basically four approaches to forecasting:

1. Human judgments  
2. Historical data and statistical models
3. Driver-based models (Read more)
4. Predictive and AI-based models

In the first approach, the traditional bottom-up perspective is dominant, where the forecast is designed in the worst case as an updated budget. The problem with this method is that it is very time-consuming, but also that it involves subjective assessments that risk introducing bias, such as optimism, conservatism or groupthink.

But the method should not be overlooked. Bottom-up forecasts, where experts and employees close to the business contribute their assessments, complement data-driven models and provide a more nuanced picture.

An important principle is to agree on whether or not actions should be included in the forecast. If the organization chooses to do so, I recommend full transparency about what effects the actions are expected to have, as they often involve uncertainty.

We see that far too few organizations use data models of various kinds in their forecasting work. Here, the development and application of AI has in a short time provided even greater opportunities for realistic forecasts. Read more about how companies implement data-driven forecasts and rolling forecasts for better decision-making.

How can AI be used in a forecasting context?  

There is definitely leverage with AI, says Hugo Bluhme, but it should be seen as one technology among many and the quality of the solution depends to a large extent on which AI technology and what basic data you use.

For example, using machine learning algorithms, we can improve the accuracy of forecasts by identifying non-linear relationships in large data sets. Prediction with ML models and pre-trained neural networks via decision trees and regression models can help anticipate complex changes that are not obvious with traditional methods.

There is also a place for language models as assistants in forecasting. They can help you test curves, clarify insights about your data, or help you code the solution you are looking for more efficiently.

What advice would you give to an organization that wants to improve its use of forecasts?  

First, the organization needs to agree on what the forecasts will be used for, Knut begins.

I agree, Hugo continues. Based on the purpose, the next step is to design the forecast model and choose appropriate methods.

Yes, Knut continues. The best way is to analyze the business logic and the revenue and cost structure to identify relevant data that decision makers can act on. A hybrid model often provides the most effective forecast.

Hugo completely agrees, once you have clarified the business logic, it becomes much easier to identify areas where investment in advanced prediction will have a direct return.

A hybrid model – what could one look like? 

One option could be to use statistical models to understand trends and supplement them with driver-based models that include influenceable factors. Predictive models, such as AI, improve accuracy and identify unexpected patterns. And last but not least, human judgment to secure strategic and market-specific insights.

Any last messages on the way? 

I want to encourage people to dare to test their skills, says Hugo. Learning by doing, quite simply. With the right expertise in place, it is no big deal to develop a few test versions that you test in a critical situation over a period of time. I also want to emphasize the danger of working too zealously with accuracy as a quality measure. A good forecast is one we act on. If the forecast for, for example, sales is too low, we will of course act and hopefully change the outcome to a better scenario. Then following up on the outcome according to the old forecast as a quality measure is doing the work a disservice.

It's a good approach, Knut continues. Changing the mindset fundamentally – the very idea of what the forecast is and should be used for – is a bigger challenge than the technology itself. It's easier to do if the change is allowed to emerge through gradual insights and conclusions about what works.