Role	Entity
Author	Joseph Byrum
Collection Name	The Intelligent Enterprise
Publishers	– MIT Sloan Management Review – IISE – INFORMS OR/MS Today – INFORMS Analytics
Years Published	2018-2024
Main Subject of the Series	Intelligent Enterprise
Has Parts	– Leading the Intelligent Enterprise – Progress Towards the Intelligent Enterprise – Engineering the Intelligent Enterprise – How the Intelligent Enterprise will Drive Innovation – The Intelligent Enterprise – AI: The Path to the Intelligent Enterprise – Winning as the Intelligent Enterprise – Lockdown Lessons for the Intelligent Enterprise

Extract from IISE (Joseph Byrum)

Basic artificial intelligence algorithms can process mountains of data and match highly complex patterns. More sophisticated expert systems can look at these data to formulate possible explanations for why things might be happening. The fuller implementation of AI is what executives need to manage a market that is chaotic, complex and in a perpetual state of flux.

Business leaders have a limited amount of time to make critical decisions with only partial or even contradictory data in hand. Even for the best leaders, identifying factors that have the greatest impact on success often end up involving quite a bit of guesswork. Big data was supposed to solve this problem by providing a scientific basis for decisions, but in many ways it has made the situation worse due to data overload. Executives frequently complain of being data rich and insight poor, meaning they have plenty of information but it’s not useful without a clear idea of what to do with it.

Properly implemented, AI offers an alternative to luck and guesswork, with systems that can make it possible to build a scalable, insight-driven enterprise. One approach to making this happen would be to model the intelligent enterprise on the classic decision-making framework known as the OODA Loop, which stands for observe, orient, decide and act. The OODA Loop is a method for managing complexity and bringing order to chaos so an executive has greater situational awareness and understanding to make better informed decisions.

Briefly explained, the loop begins by “observing” data without filtering against preconceived notions of what the data are supposed to mean. This step looks at facts straight up, free from the coloring of analysis. The idea is to absorb as much high quality information as possible to gain an understanding of the current situation, even though the data can be incomplete or contradictory.

Making sense of the data is the task left to the orient stage, which formulates theories to explain the observed facts. As the facts change, successful orientation means coming up with new interpretations that serve as potential explanations for the situation. It’s possible, even desirable, to come up with multiple explanations, even if they might seem implausible at the time. The point is to come up with multiple options.

It’s the task of the next stage to decide which explanation and which course of action best fit the data. The choice between interpretations of the facts sets up possible courses of action to achieve the desired goal. This is why having more options from which to choose can be beneficial, enhancing the ability to pivot quickly when the data take a turn that might be considered unexpected to those who are unprepared.

In the final stage, it’s time to act by executing the choice and evaluating the results. This method then “loops” because every choice affects the field of action – that means it’s time to return to the beginning and observe the impact of each choice after it has been made. The process repeats until the desired results are fully achieved.

The OODA Loop is a systematic approach to thinking through problems in a time-constrained environment in a way that forces constant evaluation and reevaluation of options and results. This keeps decision-makers from becoming complacent and assuming what has worked in the past will work again in the future. Instead, this new way of doing things helps the manager adapt quickly to confusing, chaotic and constantly changing circumstances by developing a greater awareness of what is happening, and why.

AI systems in the intelligent enterprise would automate this process by analyzing data and using expert systems to formulate potential courses of action. Human operators would take this information and make decisions about which way to act. The AI systems would continually track conditions and results and provide updates in real time. This would provide needed context and factual backing for the decision-making process, resulting in higher quality choices. While this would seem to make AI the star of the intelligent enterprise, it is actually just half of the puzzle. Humans have an equally important role.

Read the full article here.