AI Forecasting Techniques: How They Work, Models & Challenges

One of the most incredible AI forecasting success stories I know is from Target, the Minneapolis-based retail giant.

Back in 2002, when Target hired statistician Andrew Pole, Target’s revenue was $44 billion. By 2010, its revenue had soared to $67 billion. Target achieved this by using customer data to forecast buying behavior and corner the baby-on-board market.

Luckily, it’s been over a decade since 2010, and the democratization of AI means we can all predict customer needs, build a loyal fanbase, and drive revenue growth. That’s the power of AI forecasting, and in this article, I’ll show you how it works.

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After all, the biggest advantage of AI is its ability to process large, complex datasets — both structured and unstructured — and to deliver accurate results. Structured data is quantitative, like dates and numbers. Unstructured data is qualitative, subjective, and interpretive, like images.

In forecasting, AI runs these datasets through algorithms and connects the dots to reveal patterns that help businesses stay competitive.

AI forecasting techniques have benefits in areas such as workforce management automation. According to McKinsey, companies in telecom, power, gas, and healthcare can have 10-15% cost savings by using AI forecasting engines to automate 50% of workforce-management tasks.

The outcome, which was echoed by Ronald Schmelzer, co-host of the AI Today Podcast, is that AI forecasting helps businesses make better decisions. Kathleen Walch, Ronald’s co-host, agrees and adds that AI can forecast product performance and suggest optimal pricing.

The strength of AI forecasting means businesses can get accurate answers to critical questions. Think questions like:

What will our sales forecast figures look like next quarter?
How can we optimize our supply chain to adapt to changing demand?
What future trends should we be aware of in customer preferences?
What potential risks do we face, and how can we address them?

How Does AI Forecasting Work?

AI forecasting is ideal for industries with established products and ample historical data. Both variables make it easy for AI models to produce accurate predictions. The prediction process typically involves four key steps.

Here is how it works.

Step 1: Data Collection and Processing

Businesses can collect and feed data into their AI forecasting tools in several ways. They can get data from their CRM, ERP, web analytics tools, social media platforms, IoT devices, etc. Depending on your industry, you may need to consider external factors that can significantly impact the accuracy of your predictions and business outcomes.

After gathering the data, you need to clean it. This means normalizing values, removing duplicates, and applying techniques like smoothing and outlier detection. While some AI tools can handle this automatically, I advise you to have a human analyst review the processed data. A quick scan can catch any errors that could lead to misleading predictions.

Step 2: Model Selection and Training

Once you have clean, structured data, you need to choose the right AI forecasting model to use. This depends on your data type and your target prediction — whether it’s categorical or numerical.

You should also evaluate your forecasting goals by deciding whether to make short-term or long-term predictions. For example, ARIMA (AutoRegressive Integrated Moving Average) is simpler and may work better for shorter predictions, and LSTM (Long Short-Term Memory) is complex and can handle intricate relationships.

Once you select a model, feed it with historical data to train it. This process helps the model learn patterns and relationships, minimizing errors for accurate predictions.

Step 3: Forecasting

In this step, the AI model receives new data, such as the latest sales figures or customer behavior insights. The model then generates predictions based on the patterns it learned during training. For instance, it might train using Q3 sales data to predict sales for Q4.

HubSpot’s predictive lead scoring software is a good example of an accurate AI forecasting tool. It analyzes countless historical and current data points to identify the best leads. Also, it becomes smarter over time, helping sales teams anticipate customer behavior and make data-driven decisions.

Step 4: Evaluation and Decision-Making

This step assesses the accuracy and reliability of the forecasting model. After an event, you compare the model’s predictions with the actual results.

To measure accuracy, I suggest using metrics like:

Mean absolute percentage error (MAPE), which shows accuracy as a percentage.
Root mean squared error (RMSE), which shows an error in the same units as the original data.

Visualizations like time series plots or residual plots can also help you compare predicted values with actual outcomes, making it easier to spot biases. You can then use these results to refine your forecasting model. This might involve adjusting parameters, selecting different features, or choosing another AI forecasting model.

AI Forecasting vs. Traditional Methods

Traditional forecasting methods used data from surveys and consumer insights combined with expert judgment and mathematical formulas to improve the accuracy of business predictions.

However, traditional methods relied on historical data and often missed sudden market shifts. Plus, collecting consumer insights is time-consuming and costly, and human bias often affects predictions. These weaknesses in traditional forecasting are where AI shines.

With AI, your business can make quick and accurate predictions using both historical and real-time data. I believe AI forecasting is the future because it’s automated, cost-effective, and scalable, allowing teams to analyze large datasets and uncover complex patterns quickly.

AI Forecasting Techniques or Models

Neural Networks

Neural networks are AI forecasting models designed to mimic how our brain works. They comprise interconnected nodes, or neurons, organized into layers:

An input layer for receiving data.
One or more hidden layers for processing.
An output layer for generating predictions or classifications.

This model functions like a basic computer system. The input layer takes relevant data, like historical sales figures or customer demographics. This data moves through the hidden layers, where neurons apply mathematical transformations. Finally, the output layer produces results, whether a single numerical value or a category for classifications.

Example of a Neural Network

Let’s say a retail company wants to carry out accurate product demand forecasting to improve its inventory management. If they decide to use the neural networks AI forecasting mode, first, they must gather historical sales data, including sales by date, promotional events, seasonal trends, and external factors like holidays.

Next, they’ll clean this data to ensure accuracy. Then, the input data goes into the model, where each neuron in the input layer sends information to each of the hidden layers for processing. After training, the neural network uses new data, usually about promotions and recent sales trends, to forecast future demand. These predictions help the company adjust inventory levels to meet customer demand efficiently.

Support Vector Machines (SVMs)

This AI forecasting technique distinguishes between two data categories, such as predicting yes or no answers. The SVM model is common for tasks, such as sentiment analysis and spam detection.

With clean data in the SVM model, mathematical formulas separate the input into two groups. The model then identifies key features of each category and creates a dividing line between them called a hyperplane. With this boundary, the SVM can analyze new data and determine which of the two categories it fits into based on its features.

Example of the SVM Model

A financial institution wants to prevent customer losses by predicting fraudulent credit card transactions. The bank will gather, clean, and insert historical transaction data, like transaction amounts, locations, merchant categories, and user profiles, into the model.

The model then uses mathematical formulas to identify key features and separate the data into two categories: fraudulent and non-fraudulent. It creates a dividing line and maximizes the distance between these two categories to reduce noise. When the bank inputs new transaction data, the model compares its features to those of each category to determine if it is fraudulent or not. This lets the institution flag suspicious transactions, ‌enhancing its security and minimizing losses.

Long Short-Term Memory (LSTM)

The LSTM model learns and remembers information from long sequences of data. This model suits predictions that rely on context as its structure retains and automatically updates its memory cells.

Example of the LSTM Model

Let’s say I run a manufacturing company that gets unplanned machine breakdowns, resulting in lost revenue. With the LSTM model, I can predict maintenance schedules and reduce the likelihood of machine failures.

To start, I will collect the performance data of all machines, including operational hours, temperature, and vibration levels. Next, my team will feed this data into the LSTM model, which will then discover patterns and identify events that preceded machine failure. For example, the model might notice that high vibrations paired with long operational hours signal an upcoming breakdown.

Once trained, the model analyzes real-time data and accurately predicts when a machine needs maintenance. This proactiveness reduces downtime, cuts maintenance costs, and improves overall efficiency.

Decision Trees

Think of this AI forecasting model like a tree. Each internal node (the point where new branches originate) represents a data feature. Each branch represents decision rules, and leaves show likely outcomes.

The decision tree divides input data into features using methods like Gini impurity, entropy, or mean squared error (MSE) for regression. It keeps splitting its nodes and branches until each attribute reaches an outcome.

When we insert the test data into the model, the model breaks the data into attributes and compares these to the attributes it has in the tree. It then traces these attributes until it reaches its outcome and declares it as its final prediction.

Example of a Decision Tree Model

A company with many employees wants to assess performance to identify those suitable for promotions or training.

The HR team can input employee data into the model and set a target prediction — e.g., performance rating (high, medium, or low). The decision tree splits the data into features such as years of experience, education level, department, and attendance records, comparing them with the target prediction.

As the tree develops, the model identifies employees who meet the target. It might also find a pattern that links previously promoted employees, such as higher education levels and more years of experience in a particular department, participation in a certain training program, etc. With these predictions, HR makes informed decisions about promotions, training, and talent management.

Random Forest

The Random Forest model combines several decision trees to predict more accurately. Unlike the decision tree model, where each internal node has a different data subset, in the random forest, we train each tree on a different subset of data. This helps the model avoid learning random fluctuations or noise from different subsets that can lead to incorrect predictions.

What I find most interesting is how this method randomly allocates data subsets to each decision tree to ensure the trees don’t all learn the same patterns, adding diversity to the forest. Afterward, the model makes a single prediction by aggregating the predictions of each tree, either by averaging (for numerical values) or majority voting (for binary or multi-choice outcomes).

Example of the Random Forest Model

A telecom company wants to reduce churn by predicting which customers might leave. First, they‘ll gather historical data on customer behaviors, including service usage, support interactions, billing history, demographics, and past churn instances. They’ll also consider indicators like the frequency of support calls, customer tenure, and payment types (monthly or annually).

Next, they input this data into the random forest model, and it allocates each attribute to different trees to train and identify patterns. After training, the company inserts fresh data for testing and the model will pass this data to the trees. The company then uses these insights to implement targeted retention strategies to reduce churn.

Challenges of Using AI to Forecast

Lack of Human Expertise

Organizations invest in advanced AI tools but often overlook hiring skilled people who understand how to use them.

“Forecasting tools excel at making predictions. They’re not great at interpretations. And definitely not at translating predictions into actionable business strategies. You need humans,” says Rahul Gangulee, the chief supply chain officer at Tricord International.

I agree with Gangulee and firmly believe capable hands should lead on all things AI. The right team serves as a guardrail that ensures your forecast is accurate.

Data Quality and Availability

To get the best AI forecasting results, you need to have high-quality data that is relevant and accessible. Unfortunately, many businesses struggle with data issues like inaccuracy, inconsistencies, and missing information. This is common, as real-time and accurate data can be tough to find, especially in industries where data come from different sources. It also means companies must clean their data and check for errors.

The words of Justin Croft, vice president of cross solutions architecture at QueBIT, sum this up perfectly: if your AI forecasting model is giving false predictions, you should probably check the data you’re feeding it.

Model Complexity and Interpretability

Another challenge? Walch puts it best when she says, “A too-complex forecasting model doesn’t equate to an accurate model.”

I agree. I found it challenging to understand how some models I discussed in this article functioned, even after experts explained them in simple terms. I can only imagine how tough and time-consuming it must be for a data analyst to trace how these models reach their predictions. This complexity can erode trust in the model and result in wasted resources.

I recommend businesses balance their need for accuracy with simplicity. Organizations can benefit from clearer and easier-to-implement predictions by selecting simpler models.

Using AI for Forecasting

AI forecasting methods can help you stay ahead of the competition. You can achieve significant results with ample historical data, a skilled data analyst, and an accurate model.

However, it‘s important to use predictions from forecasting tools carefully. In the Target story I mentioned earlier, the company predicted a customer’s pregnancy before they had even told their loved ones. Does this raise concerns about privacy and the use of personal data? Of course. Though Target didn’t have any issues, their story is a cautionary tale for companies to handle personal information responsibly when using AI forecasting tools.