The Role of AI in Business Forecasting and Decision-Making

AI plays a crucial role in business forecasting and decision-making by enabling granular, automated micro-decisions, which can be managed effectively with human-AI interaction.

The Role of AI in Business Forecasting and Decision-Making
Written by
Oliver Palnau
Published on
Aug 1, 2023
Read time
4 min
AI Strategy

The Role of AI in Business Forecasting and Decision-Making

In the rapidly evolving digital landscape, businesses are increasingly leveraging the power of artificial intelligence (AI) and machine learning (ML) to gain insights, improve their products and services, and make more informed decisions. A critical component of this process is the ability to make millions of decisions each day about a single customer, product, supplier, asset, or transaction. These granular, AI-powered decisions, often referred to as micro-decisions, require a complete paradigm shift - a move from making decisions to making decisions about decisions.

The Power of Micro-Decisions

Micro-decisions require some level of automation, particularly for real-time and higher-volume decisions. Automation is enabled by algorithms - the rules, predictions, constraints, and logic that determine how a micro-decision is made. These decision-making algorithms are often described as artificial intelligence (AI). The critical question is how do human managers manage these types of algorithm-powered systems?

The Spectrum of Management Models

There are four main management models that vary based on the level and nature of the human intervention. These are:

  1. Human in the Loop (HITL): In this model, the human is doing the decision making and the machine is providing only decision support or partial automation of some decisions or parts of decisions.
  2. Human in the Loop for Exceptions (HITLFE): Most decisions are automated in this model, and the human only handles exceptions. For the exceptions, the system requires some judgment or input from the human before it can make the decision.
  3. Human on the Loop (HOTL): Here, the machine makes the micro-decisions, but the human reviews the decision outcomes and can adjust rules and parameters for future decisions. In a more advanced setup, the machine also recommends parameters or rule changes that are then approved by a human.
  4. Human Out of the Loop (HOOTL): In this model, the machine is monitored by the human. The machine makes every decision and the human intervenes only by setting new constraints and objectives. Improvement is also an automated closed loop - adjustments based on feedback from humans are automated.

The Importance of Monitoring and Measurement

Regardless of how much human involvement there is, every micro-decision-making system should be monitored. Monitoring ensures the decision-making is good or at least fit for purpose now, while also creating the data needed to spot problems and systematically improve the decision-making over time. It's also critical that you measure decision-making effectiveness. At least two metrics should be captured that are focused on decision-making effectiveness. Additionally, you should always capture information about how the system made the decision, not just the actual decision made. This allows both the effective explanation of bad decisions and the matching of suboptimal outcomes to the specifics of the way the decision was made.


The role of AI in business forecasting and decision-making is rapidly evolving. As businesses strive to make more granular, AI-powered micro-decisions, they must also navigate the complex landscape of managing these algorithm-powered systems. By understanding the different ways they can interact with AI and picking the right management option for each of their AI systems, businesses can unlock the full potential of AI in their decision-making processes.