Collective intelligence: Collaborating with digital workers
In his 2004 book “The Future of Work,” Thomas Malone, renowned organizational theorist and founding director of the MIT Center for Collective Intelligence, noted that new information technologies and inexpensive communications make it possible to distribute decision making more widely in organizations – supporting a shift from traditional centralized organizational chains-of-command to loose workplace hierarchies, democracies and markets. Malone uses Wikipedia as an example of how a group of loosely connected individuals, each with specialized knowledge, can collaboratively create an output that is exponentially more valuable than their individual contributions. This concept of shared or group intelligence that emerges from collaboration is an example of collective intelligence.
Fast forward nearly two decades and we’ve witnessed expanded examples of collective intelligence, whether it is the continued development of open-source software, the crowd-powered features of the Waze navigation app, or platforms like Innocentive that extend innovation challenges beyond the walls of a traditional organization. Each of these illustrate how technology and communications have created or augmented value from the diverse thoughts and inputs from diverse individuals. The organization of people, centered not around a direct-reporting chain but around a process, project, problem or opportunity, has delivered significant business value – increasing scalability, filling knowledge gaps, accelerating processes and reducing operational costs.
Enter Artificial Intelligence
Though Artificial Intelligence (AI) is more than 60 years old, it is only over the last decade or so that it has finally outlived its hype and began delivering on its promises. Today, big data is supported by the affordability of massive, scalable data storage solutions – economics that have spawned incredible growth in advanced analytics and machine learning capabilities. Accompanied by technological advancements that drastically increased computing power, AI use cases are now prevalent across nearly every financial services business function and across a growing number of industries.
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