Collective intelligence itself – sometimes described as the ability to think at scale – is created when a group of diverse people work together, often with the help of technology, to mobilise a wider range of information, ideas and insights in order to solve a problem. 

It presents a combination of machine intelligence – AI, data, and so on – with human sensitivities of imagination, instinct, emotion, judgement, reasoning, knowledge, experience and learning.

It’s based on the premise that intelligence is distributed, and that when people work together on a problem they can become more than the sum of their parts. 

Different people each hold different pieces of information and contribute different skills that, when combined, create a more complete picture of a problem and how to solve it.

As exceptional as machines may be at the large-scale processing that can amplify certain organisational capabilities, appliances driven by machine learning still remain weak at spotting the non-obvious patterns evident to humans.

Collective intelligence’s intellectual force, therefore, results from bringing together machine-learning capabilities, data and uniquely human judgement within a “collective brain” capable of outperforming any of its individual components.

Drawing on what it knows, collective intelligence presents the capacity of groups to make progressively better decisions – to choose what to do, and with whom to do it – through a combination of human and machine capabilities.

Thus, it’s those businesses that upgrade their human capital at the same time as investing in smart AI that will get the biggest benefits from collective intelligence driven by machine learning. 

And it’s those businesses least prepared for its disruptive potential to which the ascendancy of precisely designed and focused collective intelligence represents its greatest existential threat.

Some useful collective intelligence reference sources

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