The organizations that will do best in the age of artificial intelligence will be good at finding opportunities for AI to help employees do their day-to-day jobs better, and will be able to implement those ideas quickly. They will be clear about where to deploy machine learning, and where to avoid it. Alongside their investments in technology, they will remind their teams of the importance of human specialties: supporting colleagues, communicating well, and experimenting with novel ideas. To be ready for pervasive AI, an organization’s whole team will need to be ready too.

So, what should all of your employees be learning about AI? There are three important questions that any member of your team should be able to answer: How does artificial intelligence work? What is it good at? And what should it never do? Let’s look at each in more detail:

How does it work? Team members who aren’t responsible for building an AI system should nonetheless know how it processes information and answers questions. It’s particularly important for people to understand the differences between how they learn and how a machine “learns.” For example, a human trying to analyze one million data points will need to simplify it in some way in order to make sense of it — perhaps by finding an average, or creating a chart. A machine learning algorithm, on the other hand, can use every individual data point when it makes its calculations. They are “trained” to spot patterns using an existing set of data inputs and outputs. Because data is fundamental to a machine’s ability to provide useful answers, a manager should ensure that her team members should have some basic data literacy. This means helping people to understand what numbers are telling us, and the biases and errors that might be hidden within them. Understanding data — the fuel of AI — helps people to understand what AI is good at.

What is it good at? Machine learning tools excel when they can be trained to solve a problem using vast quantities of reliable data, and to give answers within clear parameters that people have defined for them. My expenses software is a perfect example: it has the receipts of its millions of users to learn from, and it uses them to help predict whether a cup of coffee from Starbucks should be categorized as travel, stationery, or entertainment. Learning what machine learning is good at quickly helps someone to see what machine learning is not good at. Problems that are novel, or which lack meaningful data to explain them, remain squarely in the realm of human specialties. Help your employees to understand this difference by showing them tools they already use that are powered by AI, either within the organization or outside it (such as social media advertising or streaming service recommendations). These examples will help team members to understand AI’s enormous potential, but also its limitations.