What is artificial intelligence (AI)? How does it work? What can it do for my company? Those are all questions our AI teams hear a lot. We’ve got answers.
AI is usually defined as computer systems that can perceive the digital or physical world, process what they perceive and, in most cases, take the kind of action that normally requires human intelligence. For business, there are big benefits available from machines that can see or hear or sense, draw conclusions, then make smart choices and act on them.
Most business applications fall into three categories. Some make it easier for people to access important information. Some provide insights to support faster and better-informed decisions. Some automate increasingly complex activities. Some do all of the above.
What follows are the most common types of AI applications for business, brief explanations of how they work and a look at how AI manages to be so intelligent.
AI can intelligently read, see, listen and sense to find the data and generate the insights that your business needs.
AI can help people make decisions by providing highly accurate, continually improving forecasts and models — and by putting the right information in front of the right person at the right time.
For most business applications, the brain of AI is machine learning: techniques that allow computer programs to perform better (“learn”) as they receive more data. Machine learning, as used in business today, most often works in three main ways.
In the most straightforward machine learning, humans give AI labeled historical data and outcomes. An example could be the history of a factory tool’s operating temperature, operating speed, maintenance record and malfunctions, with explicit correlations made.
With enough data of this kind, AI can make predictions even for situations different from what the historical data shows. It might predict, for example, that the tool would malfunction even at slower speeds if the temperature were higher. As AI becomes operational and receives new data from its own sensors, it will continue to improve the accuracy of its predictions.
Many companies have unlabeled, poorly organized data which the right kind of machine learning can structure and interpret. A retail chain, for example, might have reams of data on sales of different products in different stores at different prices as well as on economic conditions and the weather.
Unsupervised machine learning could read through all this data, identify patterns and correlations, and then (for example) offer demand forecasts to help improve stocking and pricing strategies.
Machine learning can hone its skills in a fast-changing environment, improving through the feedback that this environment (or human teachers) give it. This feedback reinforces correct decisions.
A securities firm, for example, might ask AI to guess commodity prices based on a set of variables. At first, the AI is often wrong, but it receives feedback by comparing its forecasts to real-world prices. It learns from its own pattern of correct and incorrect guesses until its accuracy improves.
AI can act: It can automate even complex tasks that traditionally require human intelligence. The following examples of current, real-world solutions are just a beginning. The complete list of AI-enhanced automation is far longer — and growing every day.