How the Intelligent Enterprise will Drive Innovation

How the Intelligent Enterprise will Drive Innovation
ISE Magazine April 2021 Volume: 53 Number: 4
By Joseph Byrum
https://www.iise.org/iemagazine/2021-04/html/byrum/byrum.html

The economic powerhouses of the future are the ones investing in artificial intelligence (AI) today. Machine learning and deep learning techniques are well-suited to creating self-adjusting algorithms that optimize business processes. Businesses across a number of industries are betting big that this technology will achieve greater business efficiency.

But this represents just the starting point for what AI should be able to ultimately accomplish. A good way to explore the full range of possibilities is to imagine what an enterprise would look like if it were designed from the ground up to use AI to optimize not just the core business functions, but every aspect of operations from grounds maintenance to the executive suite. The presumption is that the whole company – call it the intelligent enterprise – would become something greater than the sum of its fully optimized parts.

As we’ll see, the intelligent enterprise of the future has the potential to become a creative engine ideally suited to driving innovation in the decades ahead.

AI creates order from chaos

Basic AI 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.

Humanity’s role in the intelligent enterprise

Many imagine a future in which artificial intelligence finally replaces humans by doing all of our work so we can lie back on the beach and enjoy the fruits of machine labor. While this may seem superficially appealing, such a future is as unrealistic as it is undesirable. Besides the inevitable overcrowding on the beaches this would cause, it would not be an efficient use of resources.

AI, and machines in general, are good at some things and terrible at others. Algorithms have unmatched vigilance that allows them to monitor the smallest details around the clock without complaint. Machines never forget and are unmatched when it comes to numeric computation.

On the other hand, AI is woefully inadequate when it comes to recognizing nuance or subtle contextual changes. Machines are very literal devices with no ability to detect irony or appreciate a simple joke. Joke recognition itself isn’t too relevant at the enterprise level (though it could actually be important for automation in the human resources department), but recognizing nuance is critical to understanding complex adaptive systems in general. For example, understanding the subtle shades of human behavior is essential for identifying the potential underlying causes of market shifts that are rooted in the collective decisions of its human participants.

Where machines are weak, humans often excel. Our brains are hard-wired to instantly recognize familiar faces in a crowd and detect emotions from slight shifts in tone or facial movements. Our ability to appreciate nuance contributes to our ability for creative expression that machines simply lack.

Likewise, where machines excel, humans are weak. We’re terrible at multiplying 10-digit numbers and remembering details with precision. Humans also become bored easily with repetitive tasks. So if our goal is to drive across Kansas on a flat, straight road on a sunny day, AI is going to do a much better job than a human. But if the task is driving on a snowy day in Boston, you’re much better off if a human takes the wheel.

The intelligent enterprise is designed to take advantage of the complementary nature and the symbiosis of man and machine. From biology, we know different species of living things have founds ways of living in harmony with one another. Commensalism is the term used to describe what happens when one side gets most or all the benefit in a symbiotic relationship but the other side doesn’t mind being used. Orchid flowers, for example, often attach to trees to access the sunlight they need to survive. They aren’t parasites since the tree is not harmed by the flower’s presence but the tree does not derive any particular benefit.

A better arrangement is known as mutualism, which describes species that form a partnership allowing both sides to benefit. In the wild, for example, zebra herds are often spotted teaming up with ostriches. This peculiar collaboration between bird and mammal starts to make sense if one evaluates the relative strengths and weaknesses of each. The ostrich has mediocre senses of hearing and smell, but superior eyesight. Zebras are the opposite, with terrible vision and great senses of hearing and smell. By grouping together, they draw upon a superior sensory defense that greatly enhances their joint chances of survival against predators.

Optimizing humans to deal with AI

For man and machine to work in similar harmony, human employees will have to make the most of their strengths. That means workers in the intelligent enterprise should come from a different background, upbringing and education to maximize diversity of thought. Each new hire would be expected to bring new perspectives and insights to the table to avoid one of the most common pitfalls of large organizations: groupthink. As we saw, having multiple perspectives is a key aspect of success when using the OODA Loop, and drawing upon diversity through hiring decisions can deliver those unique perspectives.

But it takes more than cognitive diversity to build an effective team. The workers of the intelligent enterprise would need to draw from a common foundation of training that’s rigorous enough to allow the workers to extract maximum value from AI assistance. This would mean taking individuals who could be from opposite extremes on the educational spectrum – say a liberal arts graduate who studied history instead of math and an electrical engineer – and sending them through a common set of programs like Certified Analytics Professional and Project Management Professional.

While this is obviously a tougher path for the historian to take, it ensures the unique perspective that this calling has to offer can be effectively communicated to other team members. It creates a common language that every other employee can understand. You keep the benefits of cognitive diversity without losing the scientific rigor.

Organized in this fashion, humans can work well with one another and with machines, as the workforce is primed to bring new perspectives to every situation. The human operators can then make the most of their creativity when using these tools at every level of the organization. All of these factors combine to make the intelligent enterprise a unique entity that’s more than the sum of its parts. By honing the adaptability and understanding of the relationship between man and machine, the intelligent enterprise of the future will drive innovation beyond anything we’ve seen before.