The use of artificial intelligence is changing the way we work. It can be used to improve creativity, speed up decision making, and enhance the ability to process vast amounts of data. In short, AI has the potential to transform business. However, there is still a lot of uncertainty about the best ways to use it.

According to a new Accenture report, AI has the potential to boost profitability by $2.2 trillion by 2035. But how should businesses prepare for this transformation?

The retail industry is undergoing a transformation that is fueled by data. Retailers are turning to AI to improve customer engagement, increase revenue, and identify new opportunities. Developing AI systems will help retailers create an environment where customers can easily interact with their products. Affective computing, also known as “emotion AI”, lets computers read body language and facial expressions.

The Internet of Things (IoT) is a rapidly expanding network of physical objects, embedded technology, and IT endpoints that generates large volumes of unanalyzed data. As more and more devices are connected, it will become essential for organizations to develop intelligent processing for their unique scenarios.

The goal of AI is to develop systems that can learn and perform tasks reliably and efficiently. The processing power required to develop an AI system is huge. Therefore, it is important to choose an AI initiative strategically.

One of the most important challenges for AI designers is balancing conflicting values. To make a system that is reliable, it is important to incorporate non-discriminatory information. For example, a recommendation engine can be used to provide advice to employees. Also, it is important for AI to be unbiased when generating data.

Using an API (application programming interface) to add image recognition capabilities to a home security system is a good example. Alternatively, an AI-based sales tool can be used to guide sellers to make better deals.

Many studies show that AI can be beneficial for organizations. Its ability to identify patterns and trends in massive amounts of data can allow organizations to improve their operations. These types of systems can also be used for fraud detection and other applications. They can also be utilized to automate tasks that humans would otherwise have to perform.

In addition, these systems can also help identify new leads. Unlike human workers, these machines can be trained to learn and adapt without human intervention. This gives them an advantage in the market.

Another benefit of AI is that it can perform repetitive learning through data. For example, a computer can be trained to recognize virus RNA sequences in just 27 seconds. Similarly, an AI-based recommendation engine can be used to suggest career paths and learning content.

Some examples of AI-based systems include driverless vehicles and smart plugs. Moreover, it is possible for AI to process vast amounts of data from genomics, social media, and other public and private sources.

A key component of preparing for an AI-powered future is to have a clear definition of what AI is. Organizations should not exclude IT and data leaders when they are developing an AI definition. And while organizations are not required to develop a single, universal definition, there are a few common principles that can be applied across all industries.