An emerging discipline that has started coming into focus is Decision Intelligence (DI). It is a new academic subject involving all aspects of selecting between a variety of options.
Quantellia’s chief scientist and co-founder, Lorien Pratt tends to see Decision Intelligence through the lens of Artificial Intelligence and calls it “Multi-link AI”.
Pioneer of DI field, Lorien Pratt, Cassie Kozyrkov from Google, Vishal Chitrath and Chuck Davis have recognized that working in collaboration can result in the desired outcome. They can form a better understanding and improve Decision-making through DI support.
What comes under the umbrella of DI?
The three best Approaches of Applied sciences, Social sciences and Managerial sciences and Data Science amalgamated to provide the basis to Decision intelligence.
This means that neuropsychologists, Data Analysts, Statisticians, Economists, Social scientists, teachers, leaders and many more comprise this set.
In the context of decision making and recommendations across enterprises along with Machine learning and Artificial intelligence assistance, Decision intelligence works as new augmented BI. And it is considered the future of Artificial Intelligence.
Lets dive into the understanding of it’s basic terminology.
Decision-Making
Decision Making is all about choices, obtaining enough relevant information, and making the right decisions accordingly.
Artificial Intelligence was introduced to reduce human error. And Decision intelligence is the expected future of AI.
For instance, when a student chooses between his two or more favorite subjects for pursuing higher studies, he collects all the relevant information related to the subjects and then asks queries to seniors (experts) and eventually makes decisions with human intelligence. The whole procedure is referred to as Decision-making.
let’s discuss the limelight of this article;
What is Decision Intelligence?
According to Gartner Glossary,
Decision intelligence is a practical domain that frames multiple disciplines constituting multiple techniques of decision-making that fuse to design, align, model, execute, monitor, and tune decision processes and decision models.
Decision intelligence is a new practical academic discipline that has the framework of engineering behind it.
In the simplest terms, Decision intelligence is developed to utilize data and machine learning to create real things and improve the world around us.
Google is enabled to use machine learning and algorithms by throwing examples (obviously by its master human) to relate or differentiate between available data. Similar to the cat vs. non-cat notion presented by the chief data analyst of Google.
It is our decisions that generate our possible actions. Decision intelligence is bringing intelligence to computer productivity. So it’s a decision executed by the computer. The computer decides intelligently that what basic possible choice would you like and the Human leader would make a selection out of it.
Decision Intelligence Taxonomies
Researchers in different fields of Applied sciences have developed two varying approaches for learning Decision intelligence. One largely overlaps with applied data sciences (Quantitative side) and the other is primarily an invention of social and managerial sciences (Qualitative side).
While summing up, I want to add that Decision intelligence is a bridge that links the technology with the human pattern of thoughts before making a decision and the general truth of every innovation that it is a reflection of its creator.
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The invention of Decision intelligence doesn’t put a full stop to the need for humans as work-engine rather it makes it easier to command and play a better leadership role. And this is certainly a reason for AI leaderships to inculcate Decision Intelligence.
Author Info:
My name is Akhunzada Younis Said. I am a software project manager in HAZTECH, a software engineering graduate and a content writer. I love working with Linux, Data science and open-source software.
