The field of exploration that focuses on getting computers to complete tasks we’d previously assumed only a human could do.
Artificial intelligence is often treated as a catch-all term to encompass a variety of problem solving technologies. Frequently, it is associated with tasks such as visual perception, speech recognition and decision-making. It’s often found in business applications such as fraud detection, chatbots and real-time threat detection.
In a business context, AI is typically understood to include things like machine learning, deep learning and pattern recognition. On occasion, the term AI is used as a marketing term to give technology the appearance of being advanced.
What is it?
Philosophers, psychologists and anthropologists might argue over a definition of intelligence — never mind what constitutes artificial intelligence. But from a business perspective, it typically means the use of computer-based systems that can learn — and often continue to learn — from data, how to accomplish certain tasks rather than having to have a programmer specify the exact steps ahead of time.
While the notion of artificial intelligence has been around for some time, we’re increasingly seeing systems that are able to complete tasks that we’d previously assumed required human intelligence to solve. Famous examples of AI include chess- and Go!-playing computers, digital assistants and self-driving cars.
The most common uses of AI in business are based on machine learning and deep learning systems. But we’re also seeing applications of AI in areas such as agriculture, where better insights into weather patterns can help with crop watering; in retail, where companies can improve their supply chain forecasts; and in aviation, where AI-designed flight paths can help reduce congestion in the skies over airports.
What’s in for you?
The promise of AI is that it will enable your enterprise to automate a wide range of business processes, including some decision making, reducing your costs and delivering better results.
What are the trade offs?
While AI technology is advancing at remarkable speed, with innovative applications emerging all the time, there are limits to its use in the enterprise. Because of the way most businesses have evolved, there is a limit to the number of business problems that are suited to AI. For instance, as a bank, you wouldn’t use AI to raise capital for you but you might use it to improve your ability to predict movements in financial markets.
Many of the organizations that are making impressive advances through the use of AI were created to be data companies — the likes of Google, Facebook, Amazon and Alibaba. These companies were built to gather and analyze data. Here, AI might be able to predict user behavior to benefit advertisers, or perhaps identify images that breach platform rules.
But for more traditional organizations, legacy infrastructure may make it more difficult to collect and analyze data in general.
In most cases, AI applications in the enterprise are based on machine learning systems. These are highly dependent on training data sets and can produce bad results if the training data contains bias or if the training data differs from the data being scored.
However, you may also find the term AI plastered across technologies to make them sound more impressive than they really are. If you invest in that technology it may fail to meet your expectations.