At its core, artificial intelligence has been with us for a long time. By 1951, there were proof of concept programs, including one from the University of Oxford that played checkers, followed shortly by one that played chess. By 1956, Dartmouth had enough interest to stage a summer workshop to discuss the relationships between man, language, and machines and how those elements could drive problem-solving. The workshop attracted the top thinkers in information theory, natural language processing, neural networks, and cybernetics, laying the infrastructure for AI as we know it today.

Getting from theories to practical uses took some time. Computing power had to be sufficient to execute the many operations that go into AI interactions. Libraries of data—the information used by the AI programs—needed to be built and normalized so programs could access, analyze, and produce responses. For the last seventy years, computing power, storage options, and analytics have evolved, leading to the development of better user interfaces and more diverse algorithms that drive practical uses of artificial intelligence in the print industry.

If you are looking at your team and wondering if you can augment them with a bot, the answer is will likely be yes. There are AI-infused platforms for almost every business segment, many with names you know, like Google’s Cloud Learning Machine, IBM’s Watson, Microsoft Azure Machine Learning Studio, and Salesforce Einstein. These platforms and dozens of others can meet the brief for businesses wanting to add faster analysis of business data.