Decision intelligence is a subfield of artificial intelligence that is concerned with making vital decisions for a business model as humanly as possible using years of collected data. In a more precise manner the definition of decision intelligence would be-
“A unified field of best applied data science, social science, and managerial science used to make the business models more robust using data.”
It is the adoption and application of decision making at individual and mass levels. It includes decision analysis, risk, cost benefit and effectiveness analysis, decision theory and much more. Decision Science provides a unique perspective on informed decision making.
To begin with, we need to understand what “decisions” are we talking about. For example, suppose A banking company with locations in 53 countries needs to upgrade its telecommunication technology. However, it seems highly costly because an action taken in one database has consequences on another one, and the company can’t easily connect these cause-and-effect chain links. With a decision intelligence solution, the company can gain an understanding of these chains of events and can minimize their costs while upgrading its telecommunication technology. Similarly, decisions like supply chain management, bill optimizations etc are the use cases for applied decision intelligence.
Principles of decision science models are:
Every decision should contribute, directly or indirectly, to the outcome with the intention of elevated customer experience. For example, in customer relationships, relevance is often connected with the optimal action that can be taken to obtain, retain, or extend customer relationships in any business.
Businesses can opt for using black-box models at the price of understanding how these models work. Decision traceability, intelligibility, and explicit dependencies are vital not just for business stakeholders but also customers and all the regulatory approvals involved.
Ensuring the stability of decisions in the light of complex and continuously evolving processes is essential to reliability. By stability, we mean the ability to detect harmful biases and security breaches while being able to fail gracefully while encountering uncertain situations.
The main benefits of the decision intelligence are:
- Making more accurate decisions that provide better outcomes.
- Making faster decisions.
- Eliminating errors like biases.
- Accommodating the benefits of human judgments like intuitions.
Why can’t we deploy human resources for decision science rather than decision intelligence models?
The right answer to this question is that the margin of error in case of humans to perform decision science is too large to depend on it completely. When we talk about decision science, we don’t mean to infer results for say, 60 columns of data but instead thousands and millions of it. Now, for humans to do that and to do it ethically all the time is something we could not ask for. Even the wisest of the humans commit mistakes that can cost us a huge amount in our business models.
Is decision intelligence really worth it?
We can analyse any situation or field in two ways.One being its quality(robustness) and the other being Quantity(accuracy).
- Decision intelligence in Quality:
A correct decision science model should check all the boxes of correctness with respect to the field of Economics, Philosophy, Psychology, Neuroeconomics,behavioral sciences, Design and many more. There is no question that if a model checks all these boxes, its quality would be up to the mark.Now,
- Decision intelligence in Quantity:
Since, Decision intelligence is already a subfield of ever-growing Artificial intelligence, we can make it out that the models would be quite accurate, given that the data present should be varied and large enough. Even if it’s not, Decision intelligence would definitely breakthrough in future and can be proved most versatile.
The scope of Future
In the future, decision intelligence will impact businesses in two different ways:
- With higher computational power, AI systems can support managers to make fast, informed, and accurate decisions by offering the most profitable options.
- AI agents can make decisions on their own, with the attributes and capabilities of a person running a department.