Decision Intelligence vs. Artificial Intelligence

Both terms are often used synonymously. However, DI is an important tool from the large toolbox of artificial intelligence.

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August 25, 2022
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Decision Intelligence

When words sound similar, definitions can be difficult to express and this has been the case for Decision Intelligence and Artificial Intelligence. It is not uncommon for both subjects to be considered as the same thing but it can’t be further from the truth. DI is a vital aid to the successful utilization of AI: it is needed for the commercial application of AI for decision-making processes in companies.

AI is an old dream whilst DI is a young business tool

The idea of an artificial intelligence, a thinking machine, has fascinated people since ancient times. It has roamed through literature (Gulliver’s Travels, The Wizard of Oz) and the sciences for centuries, but Alan Turing was the first to explore the huge potential of AI more deeply and wrote a sensational essay about it in 1950: “Computing Machinery and Intelligence”. The progress of AI being witnessed by the world since the 2010s is built on the combination of massive amounts of data and high-performance computational power. This allows insights to be generated faster and more efficiently than would ever be possible for humans.

The dream of all human intelligence – to draw conclusions from the basis of all available information in order to take reasonable and wise actions – has been difficult to simulate for a long time, let alone to replace. The young discipline of DI leverages the strengths of AI-based analytics and other advanced technologies to make decisions based on existing data too so, in this respect, the scope of DI is more specialized than that of AI. Whilst Artificial Intelligence can be found in all aspects of life, Decision Intelligence (so far) primarily supports (business) decisions primarily within organizations. As such, DI is the commercial tool of AI, the business instrument for business success.

What are the benefits of DI?

Successful decisions rely on understanding actions vs. outcomes, but there are major differences between human and technology-based – i. e. artificial intelligence – decision-making. It would be ideal for humans to always act rationally and logically, but in reality it is practically impossible for them to because of their natural limits when it comes to absorbing and processing information. AI, on the other hand, is objective and not at all sensitive to any emotional influences and, additionally, DI utilizes a combination of methods (decision mapping, decision theories, etc.) and technologies (machine learning, automation, etc.) to support and improve business decisions on the basis of data. DI makes it possible to continuously evaluate decision results and optimize them through a feedback system, and thus enables you to scale your business intelligence efforts – to enable data-informed business decisions – throughout the entire organization.

The advantages of DI at a glance:

  • Faster decisions
  • No logical errors
  • Data-driven decisions
  • Multidimensional problem solving ability
  • Not emotionally engaged
  • Supports the engineering team
  • Assesses higher level goals

How do people and machines work together with DI?

There are three levels at which Decision Intelligence can support business decisions.

  • Decision Support: Supported decision-making is the first level at which machines provide basic tools, such as analytics and data exploration, to support human decision-making. These decisions, as such, are made entirely by humans.
  • Decision Augmentation: At the second level, machines play a more proactive role in the decision-making process. They analyze the data and make recommendations and predictions that are reviewed and confirmed by human decision-makers or modified in collaboration with the machine.
  • Decision Automation: The third stage is automated decision-making, which further reduces human involvement in the decision-making process. At this level, machines take over both the decision and the execution autonomously whilst humans maintain an overview, monitor any risks and unusual activities and then regularly review the results to improve the system.
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