“While discussions surrounding AI’s eventual overthrow of humanity can be fun and interesting,” writes TX Zhou, Managing Partner at Karlin Ventures, “they often overlook the little revolutions happening right now.” The little revolutions to which Zhou refers are taking place in labs and workplaces pursuing narrow artificial intelligence applications; specifically, applications involving machine learning. He explains:
“As the debate centers on the future, AI is already doing a lot right now to help human employees do their jobs better and more efficiently, improving the customer experience. This current AI revolution comes in the form of machine learning, a market that’s already worth billions and is growing at an annual rate of nearly 20 percent.”
McKinsey & Company analysts Dorian Pyle and Cristina San José (@crissanjo) explain, “Machine learning is based on algorithms that can learn from data without relying on rules-based programming. It came into its own as a scientific discipline in the late 1990s as steady advances in digitization and cheap computing power enabled data scientists to stop building finished models and instead train computers to do so. The unmanageable volume and complexity of the big data that the world is now swimming in have increased the potential of machine learning — and the need for it.” Although machine learning is increasingly found in business settings, Rick Delgado (@ricknotdelgado) insists, “Machine learning is working its way into everyday life.” Pyle, San José, and Delgado all agree that the rise of machine learning in the Big Data Era is no coincidence. There is a symbiotic relationship between big data and machine learning. Delgado explains:
“Machine learning is closely associated with big data in that it requires collecting data on a grand scale in order to work well. Years ago, this was a difficult proposition, but one major improvement within the technological sphere is the ease with which data can now be gathered. Information can be taken from simple clicks on an internet browser or swipes on a phone. This has also occurred with the rise of the internet, big data tools like Apache Spark, and cloud computing, where that information can be sent to back to data centres and analysed with ease. Machine learning is also becoming more commonplace in part because consumers’ expectations have changed in recent years. It’s not enough for companies to simply provide a device or app that offers a convenient service; consumers now want those devices to respond accurately to their needs and wants. In other words, they want their gadgets to know how they behave, think, and feel. While it may have been possible to program devices to do this before, the programming would have been time-consuming and laborious, and even then it wouldn’t be accurate enough to satisfy consumer tastes. With machine learning, however, devices and programs can actually learn and anticipate, creating more individualised experiences for each person. Consumers have always loved customised products, and machine learning can turn every device they own into a personalised item.”
Because we are still early in the Big Data Era, companies are still trying to figure out how best to use data and machine learning. Pyle and San José insist that the first step is to develop a coherent strategy. They explain:
“C-level executives will best exploit machine learning if they see it as a tool to craft and implement a strategic vision. But that means putting strategy first. Without strategy as a starting point, machine learning risks becoming a tool buried inside a company’s routine operations: it will provide a useful service, but its long-term value will probably be limited to an endless repetition of ‘cookie cutter’ applications such as models for acquiring, stimulating, and retaining customers. … The people charged with creating the strategic vision may well be (or have been) data scientists. But as they define the problem and the desired outcome of the strategy, they will need guidance from C-level colleagues overseeing other crucial strategic initiatives. More broadly, companies must have two types of people to unleash the potential of machine learning. ‘Quants’ are schooled in its language and methods. ‘Translators’ can bridge the disciplines of data, machine learning, and decision making by reframing the quants’ complex results as actionable insights that generalist managers can execute.”
Although quants and translators are important, companies may not need as many of them as they might think. Cognitive computing systems, like the Enterra Enterprise Cognitive System™ (ECS), a system that can Sense, Think, Act, and Learn®, can be programmed with subject matter expertise. Enterra’s approach empowers the business expert by automating the statistical expert’s and data expert’s knowledge and functions, so the ideation cycle can be dramatically shortened and more insights can be auto-generated. Even some of the business expert’s logic can be automated to help tune and re-analyze the data. To learn more about that subject, read my article entitled “Cognitive Computing: Complementing the Quants.”
Pyle and San José recommend a cautious approach when implementing machine learning projects. “Start small,” they write, “look for low-hanging fruit and trumpet any early success. This will help recruit grassroots support and reinforce the changes in individual behavior and the employee buy-in that ultimately determine whether an organization can apply machine learning effectively.” I agree with that advice. We always recommend beginning with a pilot or proof-of-concept project. Zhou offers some other recommendations as well. “Like any new tech,” he writes, “machine learning still needs the right people behind it to work properly.” He then recommends a few things to keep in mind when employing machine learning in your business. They are:
- Help it learn. Ensure you have a good grasp on the algorithm your machine uses, and continually refine it to optimize its learning ability.
- Encourage employee buy-in. Teach employees how to understand and make use of these new predictions, otherwise the best analyses will be redundant. Show employees that machine learning results in tangible improvements so they can see its incentive in action.
- Keep an open mind. It’s not just employees who must jump on the bandwagon. There are three stages to machine learning: description, prediction, and prescription. It’s easy for C-level officers to get comfortable with the first two stages, but the third often gets some pushback because it requires teaching an old dog new tricks. With machine learning, you have to trust the system — even if it might contradict traditional methods.
- Don’t forget the human touch. While this tech can help with decision-making, it doesn’t negate the need for human interaction. Ensure someone regularly still calls or surveys customers. Machine learning should be a complement to understanding your customers, not a substitute for person-to-person communication and live feedback.
- Use machine learning to directly help customers. Companies such as Amazon use sophisticated engines to improve customer recommendations, but more businesses should embrace similar technologies for their clients. Machine learning can improve the customer experience on the front end as well as the back, making recommendations and identifying common pain points to avoid.
Zhou concludes, “Whether we welcome our new AI overlords in the future or not, one thing is clear today: Organizations can and should use the intelligence of big data to aid employees and improve the customer experience.” Pyle and San José agree, “The winners will be neither machines alone, nor humans alone, but the two working together effectively.”
 TX Zhou, “Machine Learning Will Transform Business: How to Benefit,” EnterpriseTech, 14 October 2015.
 Dorian Pyle and Cristina San José, “An executive’s guide to machine learning,” Insights & Publications, June 2015.
 Rick Delgado, “Machine learning is becoming commonplace,” Dynamic Business, 4 November 2015.