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The central goal of artificial intelligence (AI) is to create smart machines. As an umbrella term, AI refers to many different fields of study, which include – but are by no means limited to – robotics, machine learning, neural networks, natural language processing and the type of AI that’s often explored in science fiction: artificial general intelligence.
AI is not a new discipline. However, advances in computational power and the big data phenomenon have propelled AI technologies into a new realm, where smart machines are predicted to be the “most disruptive class of technologies over the next 10 years” by Gartner; and Forrester forecasts that AI will attract three times more corporate investment during 2017.
Machine learning is emerging as a key focus area for AI researchers, developers and investors alike, due to its many potential applications. This sub-category of AI goes beyond creating rule-based systems to developing algorithms that can be trained to learn from data – and identify patterns, connections and insights – without being programmed to work towards these specific conclusions.
AI and machine learning are already impacting many areas of our lives, from the “Hey, Siri” and “OK, Google” interactions that help us find information or navigate our environments, to the online recommendations we get from Amazon, Netflix and many others. In this environment, a growing number of companies are asking how AI and machine learning will disrupt their industries, and how they can harness these technologies to remain competitive – both today and in the near future.
One key area where AI and machine learning can create value in companies today is the acceleration of the decision-making process. A greater volume and variety of data, more affordable data storage solutions and greater computational processing power are all giving machine learning technologies the ability to analyze vast data sets in a way that delivers more accurate results, more swiftly. Due to the size and complexity of these data sets, machine learning can help unlock value from all this data in a way that humans cannot. As a result, machine learning is now able to guide better business decisions and more intelligent courses of action with minimal human intervention.
Many companies leverage business process automation technologies to drive operational efficiency. However, decision-making steps tend to be a stumbling block in even the most well-designed and powerfully automated workflows. Organizations can be slow to make decisions. Perhaps the people responsible are overloaded with work, they’re agonizing over the decisions assigned to them, or there are too many decision-makers involved in one approval step. Such scenarios create bottlenecks in business processes that can impede productivity and profitability.
Today, it’s possible for organizations to set up a machine learning framework that analyses not only business data, but also typical trends in approval processes, in order to automate well-structured decisions. For example, when faced with a choice, question or work item requiring approval, machine learning tools can inform decision-makers what their typical answers have been in the past for that specific situation. These tools can also provide intelligence on decision-making trends over time. Once the machine learning technology has reached a certain threshold of correct suggestions, it can even make these decisions automatically without the need for human intervention. This dramatically speeds up decision-making to accelerate processes and workflows across the organization.
In one to two years from now, early adopters may be able to combine machine learning capabilities with other technologies and interfaces, and perhaps have a two-way dialogue with the smart machine to discuss and authorize approvals and decisions.
While managers may not be fully replaced by algorithms, machine learning technologies could provide valuable management guidance and support. For example, Gartner forecasts that by 2018, over three million workers internationally will be supervised by a “roboboss” in areas of work that are rules-based and where performance can be monitored in an automated way. At the same time, “virtual career coaches” could provide real-time advice to many workers (more than a human manager could), to improve performance across the enterprise.
In five to ten years, there are two possible scenarios:
Machine learning is enabling companies to optimize and accelerate a wide range of business processes to meet their digital transformation goals for the short- and medium-term.
That said, the capabilities of AI and machine learning are still developing and it’s important to understand the limitations of this technology. However, if your organization remains hesitant, you could miss the valuable opportunities that today’s machine learning technology offers your business.