Amount of data generated by companies today is monumental. Data is driving force as well as primary modeling mechanism for businesses and organizations. With advent of artificial intelligence and machine learning, we are now able to process enormous data to draw conclusions, which was unthinkable few years ago.
AI enables Predictive Intelligence which can be effectively used for better customer experience and business insights. In many aspects, AI has been a game changing phenomenon in recent times. It has resulted in a paradigm shift with respect to business decisions, implementation of technology, and user experience.
The core idea behind AI is to augment human capabilities. As a human, we constantly strive to overcome our intrinsic limitations with the help of tools and technologies. AI is only an extension of this quest to combat our challenges as well as progress further to new levels of technological capabilities.
Augmenting machine learning
Even though AI is reinventing technology and empowering lives, we need awareness about its limitations. The main limitation of AI is that it derives conclusions from the provided data. This is the only source of knowledge for AI based systems. Which means, any biases and discrepancies in the data will be replicated in results. If vital decisions are made just on basis of data science, chances of massive-level errors cannot be denied. We need additional perspective which decision intelligence can offer.
Decision Intelligence engineering augments machine learning with behavioral science, decision theory, and managerial science etc. DI utilizes decision science in forming decision making frameworks and scalable practices for applying machine learning and AI.
Decision science is a field that studies psychology, neuroscience, and economics to understand how humans make decisions. However, it does not include technological perspective and automated decision-making. Similarly, data science cannot analyze how humans think to come to a decision. Decision Intelligence combines both ideologies to offer optimum solutions for high-level business scenarios.
Decision intelligence is both; a new as well as old discipline. Many of its elements such as analyzing assumptions, usage of logic to support arguments, applying critical thinking to assess a decision, and understanding impacts of bias are old. Yet, the understanding that these elements can form a structure that can provide benefits to organizations in better decision making is new.
In an automated decision-making, a decision maker may commit errors despite the available data-driven conclusions. As a decision maker, you must assess which mistakes you can afford to live with.
For example, an organizational decision to launch a particular product in a new geographic location based on data driven conclusions may backfire since aspects of decision making and business implementation are based on abstract elements such as opportunity costs, employee morale, intellectual capital, brand recognition and other factors which cannot be captured in terms of data-based models.
The required balance can be achieved through inclusion of human behavioral insights in automated systems to design futuristic and optimum solutions.