Decision Intelligence: AI-Powered Decision Making Tool

Business + Tech Solution, Tech Look-out, Tech Tips + Tricks
showcasing how AI shaped Decision Making Process

Various digital solutions commonly involve data analysis, which determines the success of a wide variety of digital innovations. In fact, nowadays many businesses rely heavily on data for decision-making. This led to the emergence of a new discipline so-called “Decision Intelligence (DI)”.

In this article, we’ll discover what decision intelligence is and how it could help businesses create a better outcome in decision-making.

Defining Decision Intelligence: AI-Powered Decision?

To fully understand the concept, let’s first clearly define a decision. Cambridge dictionary classifies a decision as a choice that you make about something after thinking about several possibilities.

Whether in life or business, we are confronted with decisions every day. Our decision is usually influenced by a combination of several external and internal factors. That’s why it is difficult to imagine a holistic view of the situation since it does not take into account all of the influencing factors.

However, it will be different if you involve the role of Artificial Intelligence (AI). Unlike humans who consider limited data and information, AI systems could process and analyze massive amounts of data in real-time. This will enable us to find the possible decisions based on the data and parameters set initially.

To put it simply, decision intelligence is the commercial application of AI to help businesses value creation through a data-driven process with low risk.

This technology is crucial to the AI era as it covers the skills needed to lead AI projects responsibly such as design objectives, metrics, and safety nets to allow for scaling automation.

In fact, it also has been listed as one of Gartner’s 12 top strategic technologies trends for 2022 as it emerges as a solution that can connect decision support, decision management, and complicated systems applications.

How Does It Work?

The following technologies and algorithms are used to support decision intelligence:

  • Machine Learning (ML) – The algorithms in ML work with structured data and make suggestions or decisions according to certain parameters. For instance, banks use anti-fraud systems to prevent fraud, in which, if a suspicious IP address attempts to access their banking app, the system requires additional user authentication.
  • Deep learning – The next phase of machine learning is deep learning. In this case, a decision machine takes previous decisions and outcomes into consideration when making new suggestions.
  • Visual decision modeling – Although AI decision-making is a reliable starting point, decisions are still made by business owners and/or their employees. That is why visual decision models offer options and outcomes to decision-makers.
  • Continuous and dynamic system – The advantage of decision intelligence lies in the ability to quickly build a complex business logic based on the available data and the final objective.
  • Analytical prediction – AI systems make decisions based on accurate predictions. For instance, predicting retail prices or effectively optimizing retail operations. Decision intelligence makes these suggestions based on a multitude of factors, including price changes, predicted demand, and lots of customer behavior insights.

How Decision Intelligence Benefits Business

As Decision Intelligence encompasses all the combinations of artificial intelligence, machine learning, deep learning, operations research, statistics, simulation, businesses can become more data-driven, resilient, optimized, and cost-effective in their decision-making.

However, despite the fact that decision intelligence relies on large input data and is not subject to cognitive biases, these systems still need human validation, especially when the choices made can cause conflicts of interest or values.

So, this is what decision intelligence is all about! It combines different perspectives on decision-making and enables businesses to become data-driven, yet also take into account any relevant information when choosing the next right step.


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