Keeping Humans in the Loop: Pooling Knowledge through Artificial Swarm Intelligence to Improve Business Decision Making

Abstract

This article explores how a collaboration technology called Artificial Swarm Intelligence (ASI) addresses the limitations associated with group decision making, amplifies the intelligence of human groups, and facilitates better business decisions. It demonstrates of how ASI has been used by businesses to harness the diverse perspectives that individual participants bring to groups and to facilitate convergence upon decisions. It advances the understanding of how artificial intelligence (AI) can be used to enhance, rather than replace, teams as they collaborate to make business decisions.

On the current state of combining human and artificial intelligence for strategic organizational decision making

“…However, Metcalf et al (2019) raise the concern that the training of AI will be difficult, as data are constantly changing and complex in nature. They thus deem humans to be necessary to ensure the quality of information and interpretation, which is also supported by other researchers' findings (Shollo and Galliers 2016;Bohanec et al 2017a, b;Terziyan et al 2018).…”

“…Therefore, ''while humans have access to both explicit and tacit knowledge, lack of access to tacit knowledge and the reliance on historical data from which patterns can be identified are major limiting factors of AI (…)'' (Metcalf et al 2019: 2). Some researchers even provide evidence that groups are capable of including some of these aspects through discussion (Shollo and Galliers 2016;Bohanec et al 2017a;Metcalf et al 2019).…”

“…Researchers agree that AI has the potential to collect large amounts of information from numerous sources, leverage sharing, and facilitate interpretation, implying that using it for the knowledge management task can increase speed and efficiency (Acharya and Choudhury 2016;Shollo and Galliers 2016;Blasch et al 2019;Butner and Ho 2019). However, AI is said to be unable to solve the inherent challenge of making implicit data available that stakeholders and decision makers are not willing or able to provide, although initial possibilities for overcoming this challenge have been proposed (Terziyan et al 2018;Colombo 2019;Metcalf et al 2019). The quality of implicit information is thus further believed to depend on humans and can only be evaluated and framed through human discussion (Rousseau 2018).…”

HuGoS: A Multi-user Virtual Environment for Studying Human–Human Swarm Intelligence

“…The second category of virtual environments are those developed primarily to study the mechanisms of collective human behavior. For collective decision making, the UNUM platform [40,41], also referred to as Swarm AI R , supports a group of participants that collaboratively explore a decision space [26]. For physical coordination, a Unity implementation supports human-like avatars with first-person view for the study of crowd behaviors [32,45,53].…”

Artificial Intelligence, Machine Learning and the Reconstruction of Employee Psychology

“…Metcalf et al (2019) indicate that there are several business decisions wherein the intersection of human judgement and AI becomes imperative. AI relies on ML, which in turn relies on codified data.…”