Technology has not only brought new opportunities to businesses that they never thought possible, it’s also given birth to a vast collection of confusing and overused buzzwords.
Collective Intelligence (CI) is one that you're likely to hear much more of. Here's what it means, and how it might change the way you approach business decision making.
What is collective intelligence?
Collective intelligence refers to the combination of machine intelligence (AI, data, machine learning etc) with human intelligence (emotions, thoughts, experiences etc). In short, from a decision making point of view, it's the 'wisdom of the crowds', with robots.
For businesses, that essentially means linking all the sources of information and insight - whether it be data sets, customer experience or judgements of stakeholders - into a ‘collective brain’ that can outperform any of its individual components.
So how are businesses using it?
The idea of using collective sources of information to make decisions is nothing new, it’s just that over the last decade the development of technology has increased the number of intelligence sources, and our ability to tap into them.
Practical uses can range from simple citizen science to more large scale data intelligence.
For the last decade Lego has mobilised thousands of enthusiastic fans to develop product ideas. NASA runs regular challenges, sometimes offering financial rewards, to encourage the public to help innovate anything from computer codes to space suits.
Siemens "works on the assumption that the collective intelligence of its engineers will be smarter than its managers" and has adopted a systematic approach that enables its engineers to vote on where the organisation should spend its internal development budget.
But that doesn't mean it is the domain of globe-spanning organisations. For businesses of all sizes, collective intelligence is about thinking what information you need to be successful, and to identify whether there are any sources that are currently being overlooked within the day-to-day operations.
Why is it important?
"In some ways this is obvious, but it’s often not the way that businesses think," says Geoff Mulgan, author of Big Mind and CEO of innovation charity Nesta, which established the Centre for Collective Intelligence to explore and develop research into CI.
If you break down the conventional "components of intelligence" that are available to businesses - observational data, live models, analysis and prediction, memory, empathy, motor coordination, creativity, judgement and wisdom - technology has had a much bigger impact on some of these components than others. This means that when it comes to making decisions, organisations are often tend over reliant on certain sources, most notably data.
"A healthy organisation should have a reasonable balance of these things when it makes decisions," says Mulgan. "It’s about tapping into those resources of intelligence that they might normally be overlooking."
What other ways is it being used?
Our ability to gather intelligence at scale is being used to look for solutions to some of the world’s biggest problems. Take climate change, for example. Taxes, regulations and new business models only provide part of the solution, as you also need to work out how to change the lifestyle choices of everyday people.
Nesta has also been using CI to study how labour markets might develop over time, combining information from over 4 million job adverts with the perceptions of experts, jobseekers and AI, to forecast skills shortages and demand over the next ten years.
But be careful…
In the age of misinformation, the inability to identify facts from perceptions is the biggest threat to the widespread development of collective intelligence, as are the polarising effects of political events like Brexit that stop people talking to each other.
Simply mobilising the crowd or data streams is not enough. Mulgan warns that collective intelligence requires "careful design, curation and orchestration" - this may differ from business to business.
"Any one method on its own could be a little bit misleading," says Mulgan. "So you need people who are sophisticated and can use different methods and use judgement - a lot of this boils down to judgement, rather than just following what the data says."
Image credits: Busakorn Pongparnit via Getty