Artificial Intelligence Boosts Better Decision-Making

In short: Making better decisions is the key to success. Digital technology, especially Artificial Intelligence, is rapidly decreasing the cost of making better decisions in all walks of life. This will have a major impact on the way products and services are being designed, manufactured and sold. It will create new jobs and opportunities, but also obsolete many traditional ones, transforming industries and lifestyles.

Should I marry Susan or Karen, should I buy this house or that, should I take up this job or the other one, should I buy Google stock or Amazon, should I take a vacation this summer in Sicily or in Corsica, should I rent a BMW X5 or Audi Q5, should I take the highway or stay on the country roads to reach faster …. Should I hire John or Sandra, should we partner with Microsoft or with Apple, Should I outsource assembly to India or to China, should we target a broad customer base or focus on a special high margin segment …. We are perpetually making decisions about a desired future outcome in all walks of our lives.

Making better decisions is the key to success — both in personal and in business life. A better decision means choosing the option that leads to a greater reward, one that has considered all angles, and is faster. Humans have always tried to figure out the best way to making a better decision. At the core, faced with uncertainty, decision-making is really about predicting the future outcome with the highest reward.

Historically, we prayed and asked God to lead us to the best decision. Many still do. We asked priests and learned men in the hope that they had a better insight into the future. We asked friends, family members and contacts for their views. With a bit more sophistication, we started looking at surveys, statistics and consultant opinions. However, most decisions were made based on personal experience and gut feel.

Digital technologies and especially the Internet made decision-making more professional and easier — at least, at first. We gained access to reports, opinions, research, comparisons, social media, and an enormous amount of additional data — allowing us to make data-based decisions. That is when it started becoming difficult again. The human brain has a fairly limited capacity for processing information from multiple inputs and gets overwhelmed with too much data, leading to a paralysis in decision-making. This is where many of us find ourselves today when we have to make a decision. Either we do not decide at all, or we decide based on just 3–5 factors (often ignoring the significant ones), or just surrender to our gut feel.

Data provides objectivity in decision-making and forces us to look at various aspects and angles. However, too much of it overwhelms our decision-making abilities. There is a lot of talk of being overrun by data explosion. With thousands of new papers published on medical research every year, no medical professional can be expected to read and digest the new insights and use them in their daily job. It is not just a matter of available time, but also the capacity of our brain for inputs and actively using them. This is true for most professions — lawyers, tax consultants, and engineers. Is there a way to objectively make decisions always factoring in all available information?

Artificial Intelligence (AI) systems do not have the same limitations of our brain. AI can process millions of inputs, without ignoring a single one. And it can do it extremely fast, without taking rest, without getting tired, without getting distracted, or becoming emotional. AI is an excellent complement to our brain in decision-making. AI is essentially a prediction machine, providing a fairly accurate prediction of the outcome for a given input situation. Machine Learning has provided modern AI systems with the ability to self-learn from thousands of case data provided.

Machine Learning systems are essentially trained to recognize patterns in data, image, and video. They are trained to predict a specific outcome based on the input patterns provided. Once they are trained, they are very quick and accurate in predicting the outcomes in data patterns. The data pattern can be in the form of images of crops in a field to determine the best harvest times, or MRT images of patients to detect a defective organ, or insurance application claims for detecting fraud, or financial data of a company to forecast trends in future performance.

Humans suffer from decision-paralysis or poor decisions when dealing with too much data. In contrast, AI systems love to consume as much data as provided and improve their performance with it. The prediction provided by AI system can be directly accepted as a decision. Alternatively, human judgment can be applied to it before calling it a decision. A surgeon can take the final decision to operate or not, based on the predicted diagnosis of the AI system from the medical data of the patient.

Since AI systems are essentially computer systems, they are subject to Moore’s law of exponentially declining costs and improvement in performance. Standardized hardware using 3D Graphics components for neural processing has enabled better decision-making with AI cheaper and more attractive. This trend will continue. AI is now being applied to a vast number of new areas, where lots of data can be made available for training the AI system. Data is the raw material for AI and for making better decisions.

Internet of Things (IoT) is becoming very popular for automatically managing complex control systems. In essence, IoT is an AI system. Its front-end consists of an array of sensors providing real-time data from sources needed as inputs for the control system, like temperature, location, pressure, motion or an image. This data, in its digital form, is fed into an AI based decision-making system, which decides the best action based on the provided input data. IoT systems are used in a variety of applications like for optimized cooling in a data center, running self-driving cars, deciding which segment of crops to harvest and so on.

Companies like IBM, Microsoft, and Google, are setting up AI IoT Cloud platforms for their customers, who can bring in data from their sensor networks. Customers develop control algorithms within the IoT Cloud. The IoT platform provides AI and additional algorithm libraries that are needed for control and decision-making. IoT platform providers are acquiring vast amounts of sensor data to make their platforms even more attractive. They will be able to provision almost any sensor data needed by their customers on a subscription basis. As a result, creating IoT systems will become significantly easier and faster.

Data, in essence, is a digital representation of the reality in the world. As humans, we take our daily decisions based on sensing the reality with our senses and using our brain for deciding and acting upon the data we sense. Internet of Things (IoT) system works very similarly — with a notable difference. They can decide based on all the thousands of cases they are trained on, 24x7, undistracted, without break or sleep.

Drones have become a very useful sourcing tool for IoT data. Drones can capture high-quality images and videos of specific areas at a very low cost. Images and video are excellent sources of data patterns for AI. Describing the content or the subtle uniqueness in images and video clips is often difficult for humans — but not for AI. Machines have become better than humans at pattern recognition and can learn to differentiate subtle patterns in images without human assistance.

As the cost of better decision-making with AI drops steeply, AI will get integrated into almost all products, services or processes. This will make them a lot smarter and more competitive. Anything without AI would look like a marketing campaign without Internet. Better decision-making is the key to success. And AI is the key to consistently making better decisions.

For more on AI: AI&U — Translating Artificial Intelligence into Business


Contact: Sharad Gandhi, Christian Ehl,

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Technology philosopher and strategist for creating business value with digital technologies