“Oman and Zanzibar Strengthen Bilateral Ties: Focus on Economic, Cultural, and Health Cooperation”
by Khushbu Verma
We explore decision intelligence, an emerging discipline that is a must-have an asset for HR leaders in the AI era.
From people analytics and automation to predictive analysis and digital assistants, HR is finally on par with other business functions in the implementation of AI and data analytics. And, while HR is no stranger to these technologies, decision-making HR leaders still rely on human emotional judgment when it comes to business decision-making.
A study by MIT Sloan Management Review and The Boston Consulting Group suggests that although 85% of CEO’s believe that AI has the potential to drive exponential value to their businesses, only a few admit on incorporating AI capabilities in their decision-making processes and operations.
AI applications such as predictive analysis provide in-depth insights to leaders via algorithms. But the complexities of business decisions outgrow the potential limits of artificial intelligence. These decisions are also impacted by social, managerial and emotional sciences. And so, the need of the hour is a powerful combination of these sciences that can potentially replicate the workings of a human brain’s neural networks.
This powerful combination has recently emerged as an academic discipline known as Decision Intelligence. In this article, we will explore the applications of Decision Intelligence in HR and how to use them. But before we do that, let’s dig deeper into what is decision intelligence.
What Is Decision Intelligence?
Decision intelligence is an academic discipline that enhances data science with theories from social science, decision theories, and managerial science. By turning information into better actions, decision intelligence manifests the power to improve lives, businesses and the world around them.
This approach to supporting business leaders in complex decision-making goes beyond the quantitative science of mathematical calculations and machine learning algorithms. It augments these operations with a touch of human behavior and decision-making tendencies, creating a more humane blend of quantitative and qualitative sciences.
Cassie Kozyrkov, Decision Intelligence Scientist and Head of Decision Intelligence at Google, suggests that it is a vital science for the AI era, covering the skills needed to lead AI projects responsibly and design objectives, metrics, and safety-nets for automation at scale. Comparing decision intelligence with the analogy of a kitchen, she goes on to say that “if research AI is building microwaves and applied AI is using microwaves, decision intelligence is using microwaves safely to meet your goals and using something else when you don’t need a microwave. The goal is always the starting point for decision intelligence.”
