Many are claiming that we are in the era of automation. Discussions around the self-driving car are heating up, IBM Watson is making headway, and the Internet of Things is making quite the splash in all aspects of our lives, from the smart home to smart cities. But what is automation’s role in the workplace?
A recent report from McKinsey and Company says that nearly half of work activities “could be automated using already demonstrated technology.” With more businesses looking to adopt advanced tools that increase efficiency, the real value is still in the hands of humans, which hold the key in the form of decision making. Enterprises and decision makers, therefore, are better served focusing on a blended approach to automation, called augmented intelligence.
Automation in the Enterprise
Though automation has been a key trend for some time now and the idea of building human-like intelligence in the form of autonomous technology (artificial intelligence) sounds appealing, it can impose a business risk as well.
Due to the significant increase in collected data and the increased complexity of the reasoning process itself, performing investigative analytical tasks has become more challenging. The simple goal of any analytics effort is to leverage information to improve business processes. This can involve a variety of techniques to identify and track multiple hypotheses as well as gathering evidence to validate correct hypotheses and eliminating ones that offer no business value.
Executives and decision makers, therefore, require robust ways to handle decision making and knowledge-gathering tasks in open environments, helping them to extract actionable intelligence from constantly evolving data types.
By having a visual decision-support system that keeps users in the analytics loop, it empowers them to identify intelligence that warrants attention. Algorithms read and analyze textual data and determine the who, what, when and where.
But when it comes down to artificial intelligence, AI has difficulty placing and understanding human needs. A human can use his intelligence to determine the “why” and decide on the appropriate course of action, whereas AI is not able to make appropriate suggestions or recommendations in the area that’s needed. For automation to work in the enterprise, it will be more of a facilitator than the anticipated decision maker.
Augmented Intelligence in Action
Augmented intelligence is a blended approach that leverages human decision-making expertise alongside powerful computing abilities. A good example that demonstrates the power of this blended approach is IBM’s chess playing machine Deep Blue, and chess grandmaster Garry Kasparov’s tournaments. After being defeated by Deep Blue, Kasparov began exploring the interplay between man and computer and how each could affect the outcome of a game. Kasparov designed a tournament to determine which grouping would garner superior results – solely humans, solely computers, or a combination of humans and computers. Hundreds of matches were played. Kasparov found that teams consisting of weaker human players using superior computing generally outperformed teams consisting of stronger human players using inferior computing.
This shows how combining the speed and processing power of a computer with a human’s interpretive capabilities leads to superior results. And this is augmented intelligence in its basic form – computational algorithms helping to put the human end user in the best position to make an informed decision.
Automation can help enterprises unlock insights in modern analytics efforts when there is harmonization of the two.
Decision Makers in the Driver Seat
Tools such as an interactive visualized front end allow virtually anyone to interact with data, empowering many people in an enterprise to consume analytics. The interactive visualizations are designed to assist with one of the most fundamental concepts in investigative analysis, the four W’s (who, what, when, where). All visualizations are built to depict each of the four W’s, while the exploratory, relation-probing nature of the system allows analysts to build their own conclusions on the “why.” Most importantly, by augmenting intelligence through deep computing processes, the “why” can be turned into specific action.
Automation is a rising trend that will continue to proliferate not only in the enterprise, but in all areas of our lives. As the amount of collected data increases, actionable insight will be essential, and the most effective way that enterprises can achieve this is by combining powerful computing capabilities with an end-user’s interpretive abilities. By harmonizing the two, enterprises can make better decisions, augment fact finding, and present information for the most value.
Derek Wang is the founder and CEO of Taste Analytics. He earned his Ph.D. in information technology from the University of Northern Carolina at Charlotte and has a broad range of knowledge about scalable data analytics from projects with Microsoft, Xerox, PARC, and Bank of America.
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