Company Machine Learning Ready

In recent years, there has been a staggering surge in interest in intelligent systems as applied to everything from customer support to curing cancer. Simply sprinkling the term “AI” into startup pitch decks seems to increase the likelihood of getting access to funding. The media continuously reports that AI is going to steal our jobs, and the U.S. government seems as worried about the prospect of super-intelligent killer robots as it is about addressing the highest wealth disparity in the country’s history. Comparatively, there has been very little discussion of what artificial intelligence is, and where we should expect it to actually affect business.

When people talk about AI, machine learning, automation, big data, cognitive computing, or deep learning, they’re talking about the ability of machines to learn to fulfill objectives based on data and reasoning. This is tremendously important, and is already changing business in practically every industry. In spite of all the bold claims, there remain several core problems at the heart of Artificial Intelligence where little progress has been made (including learning by analogy, and natural language understanding). Machine learning isn’t magic, and the truth is we have neither the data nor the understanding necessary to build machines that make routine decisions as well as human beings.

That may come as a disappointment to some, and potentially disrupt some very expensive marketing campaigns. But the likelihood of self-directed, super-intelligent computational agents emerging in the foreseeable future is extremely low — so keep it out of the yearly business plan for now. Having said that, an enormous amount can already be achieved with the machinery we have today. And that’s where forward-thinking managers should be focusing.

Over the next five to 10 years, the biggest business gains will likely stem from getting the right information to the right people at the right time. Building upon the business intelligence revolution of the past years, machine learning will turbocharge finding patterns and automate value extraction in many areas. Data will increasingly drive a real-time economy, where resources are marshaled more efficiently, and the production of goods and services becomes on-demand, with lower failure rates and much better predictability. This will mean different things for different industries.

In services, we will not only get better at forecasting demand, but will learn to provide the right product on a hyper-individualized basis (the Netflix approach).

In retail we will see more sophisticated supply chains, a deeper understanding of consumer preferences, and the ability to customize products and purchase experiences both on- and off-line. Retailers will focus on trend creation and preference formation/brand building.

In manufacturing there will be an evolution towards real-time complete system monitoring, an area known as “anomaly detection.” The components will become increasingly connected, allowing for streams of real-time data that machine learning algorithms can use to reveal problems before they happen, optimize the lifetime of components, and reduce the need for human interventions.

In agriculture, data will be used to decide which crops to grow, in what quantities, in what locations, and will render the growing process more efficient year after year. This will create more efficient supply chains, better food, and more sustainable growth with fewer resources.

In short, AI may be a ways off, but machine learning already offers huge potential. So how can managers incorporate it into daily decision-making and longer-term planning? How can a company become ML-ready?