When approaching the topic of advanced technological innovation, involving concepts such as artificial intelligence, one of the main resistances one encounters is related to the idea that, somehow, advanced decision-making tools can entirely replace human actions. The latest market trends, however, show us that this idea is already, in many ways, outdated. In recent years, in fact, the concept of augmented intelligence has emerged, where human intelligence is not replaced, but is supported and joined by artificial intelligence.
In practice, the computational and analytical capabilities typical of artificial intelligence sit alongside human decision makers, facilitating them in decision-making and organizational processes. There are many fields of application, but in general any area where large amounts of data need to be collected, aggregated and correlated can benefit from augmented intelligence-based solutions.
Augmented intelligence: definition and market
According to Gartner’s definition , “Augmented intelligence is a design method for a human-centered partnership model in which people and artificial intelligence (AI) work together to improve cognitive performance, including learning, decision making and new experiences.”
In manufacturing, it is possible to collect and analyze seemingly disconnected data and obtain correlations and connections, which can be used for future decisions, even relying on data from the entire supply chain. For example, knowing that a variation in the quality of a supplied raw material has an impact on product service requests with a three- to six-month fallout, it is possible to anticipate a remediation policy or plan a recall campaign.
But customer service can also take advantage of augmented intelligence, for example, by providing marketing or sales with a clear and concise dashboard of customer habits and potential critical issues, giving employees all the tools to anticipate possible inquiries and improve user satisfaction.
The tools of predictive analytics
Although not strictly within the academic definition of augmented intelligence, there are some strategies that are good examples of how entrusting data management to digital tools leads to important benefits. We consider two of them as examples.
It is about the predictive maintenance, which, through data collection and analysis, performs intelligent monitoring, predicting failures, breakdowns, and disruptions. The predictions and analysis provided make, for example, the work of logistics offices that have to organize and plan interventions much more straightforward and manageable.
A second area in which it is possible to benefit human decision makers is undoubtedly that of the Predictive Quality Analytics. In this case, data collection and especially data processing are aimed at process optimization, particularly in the field of manufacturing. Through machine learning and artificial intelligence tools, it is possible to highlight the causes of waste and quality declines, correlate them, and obtain a new set of information on which to base strategies for product improvement and optimizing consumption.
Augmented Governance: the best of two worlds
In light of this information, and especially in light of the results obtained by companies that have decided to adopt solutions based on augmented intelligence, a new management idea is emerging: Augmented Governance. It is a hybrid model in which management strategy is established through collaboration between the computational and correlation capabilities typical of artificial intelligence and human decision-making and discretionary capabilities. Data, in short, even in the absence of an entirely data-driven become a prime stakeholder within the decision-making chain, providing decision makers with the necessary analytical support. The advantage of using augmented intelligence systems to adopt augmented governance policy lies primarily of the quality and in the depth that this type of data analysis can achieve.
The use of data and information to support decisions, in fact, is not a new concept per se, but by its very nature it is all the more effective the better the source data and the more in-depth the layers of analysis that can be performed with it. Augmented intelligence and predictive analytics undoubtedly constitute the most refined model of data analysis currently available.
At Regesta LAB, we believe inAugmented Intelligence, which can integrate data with statistical-mathematical models and machine learning algorithms, with the goal of making companies increasingly competitive and data-driven.
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