Getting Smarter With Collective Intelligence 


AI-enabled collective intelligence helps organisations to curate insights and nurture innovations

Did you know Lego has thousands of fans to develop its product ideas? And NASA encourages people to help innovate computer codes and space suits?

Almost every human accomplishment achieved is not by a single individual but by groups of people working together. Also, algorithmically aggregating two or more opinions usually outperforms the average, and sometimes even the best individual. Think groundbreaking technology like Google Maps — a service developed with help from map enthusiasts as well as data from its 1 billion users.

In the last few years, a wave of digital technologies has made it possible for organisations to think at large scale, and Collective intelligence (CI) has emerged. On a slightly more sophisticated level, CI is the combination of machine intelligence (AI, data, machine learning) with human intelligence (emotions, thoughts, experiences).

A Harvard Business Review study found 86 per cent of executives saying frontline workers need better technology-enabled insight to make good decisions in the moment. As data is streaming from all aspects of our lives in unprecedented amounts, to filter and analyse it, organisations are using CI, enabled by AI.

When it comes to making decisions, organisations often tend to be overly reliant on data, but decisions should be based on a reasonable balance of observational data, analysis and prediction, empathy, creativity, judgement and wisdom. It’s about tapping into resources of intelligence — AI and human capabilities — to curate insights and bring agility and efficiency to problem solving and decision making.

Additionally, CI, which covers a wide range of participatory methods, including crowdsourcing, open innovation, prediction markets and citizen science, and is used to look for solutions to some of the world’s biggest problems such as climate change and help organisations to uncover new ways to address strategic priorities.

Human-machine collaboration

How are organisations leveraging human-machine collaboration to make decisions?

In many ways, organisations, big and small, are increasingly realising the value of CI.

Take for example, Waze, a “community-driven” traffic and navigation app used by over 115 million drivers worldwide. It draws on complementary aspects of human and machine intelligence and uses AI to learn the day-to-day patterns of its users and map out potential travel routes, which is supplemented with real-time crowdsourced information by the app’s users.

French collective intelligence pioneer bluenove has built Assembl, a CI platform, to help organisations rapidly identify insights. The CI platform draws on natural language recognition to highlight recurring concepts and keywords in debates, allowing users to follow trends of discourse in real-time.

Using Assembl, a financial services firm engaged more than 11,000 local employees in a conversation to co-create the company’s development strategy in Africa, across business models, products and M&A, among many others. After the discussion, employees were able to present many strategic priorities to the board, and 15 were endorsed

Similarly, RATP, an urban mobility specialist present in 13 countries, was able to co-construct its brand purpose statement, using Assembl, following several weeks of consultations with 7,000 participants and 138,000 contributions.

Make well-informed decisions

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.

Properly managed, AI can also help public institutions become more efficient and less prone to biases and discrimination. In many citizen participation projects, the main challenge isn’t to gather citizen input —  it’s to analyse it. CitizenLab, a citizen participation platform that analyses citizen contributions and helps local governments to tap into the CI of their constituents to make better, well-informed decisions, has developed its own Natural Language Processing (NLP) technique. This technology, based on ML and AI, analyses large amounts of unstructured citizen input (ideas, comments, votes) and quickly extracts the main insights. The algorithms are capable of understanding what comments are about, grouping similar ideas into clusters, placing them on a map and highlighting the main topics discussed on the platform.

Dataminr, an AI platform for early signals of high-impact events and emerging risks, integrates non-traditional data sources, including updates posted by citizens on social media, to monitor how situations unfold in real-time and create summaries, which are sent as alerts.

Many well-established CI initiatives, such as Zooniverse, home to large citizen science projects, started implementing AI into their community platforms. Wefarm, a farmer-to-farmer digital network that crowdsources expertise to help farmers to solve problems, is an integrated AI and CI platform.

American startup Unanimous AI has built an online platform that helps guide group decisions. Their Swarm AI platform connects hybrid human-machine groups to make decisions and predictions together in real-time. Its algorithms, trained on data about behavioural dynamics of groups rather than on the subjects, moderate the interaction of a group of individuals deciding between a set number of options, and then its neural network model uses the interaction dynamics of the participants to generate a conviction index. Swarm AI has been deployed in decision‑making in commercial settings and research environments. Credit Suisse has used the platform to help investors forecast the performance of Asian markets; Disney has used it to predict the success of TV shows.

Also, Factmata has built an AI moderation system and enlisted more than 2,000 experts to analyse online content for things including bias, credibility or hate speech. They then used this analysis to train a natural-language processing system to automatically scan web pages for problematic content.

Nurturing innovations

When everyone collaborates and shares ideas, it can go on to make ground-breaking changes. Not just NASA and Lego, other companies are using CI to move their business forward and nurture innovations.
Take for example, Adobe. Its Kickbox program, a model of CI, was launched to drive innovation from within, where every employee can take an active role in the company’s innovation process by submitting and validating their ideas.

Their program saw such high levels of success that Adobe and Swisscom open-sourced key parts of their Kickbox program and made them available in the Kickbox Foundation. Everyone, from students to managers and innovation gurus, can now validate their ideas.

Today, several organisations use the Kickbox program, including 3M, Cisco, Caterpillar, MasterCard, P&G, Johnson & Johnson and Siemens Energy.

Even Starbucks, with more than 33,833 stores in 80 countries, can find it difficult to keep up with changes in product, process or mindset. For that reason, in 2018, Starbucks started harnessing the power of CI via its Innovation lab, bringing ideas to action.

In the small amount of time since the lab began, more than 130 projects have been developed and tested, and dozens have already made their way into stores.

In April 2021, Starbucks opened an innovation lab on Arizona State University’s campus. Anchoring an innovation centre on a college campus gives Starbucks access to ground-floor research and insight into Gen Z interests before scaling new products or processes to market.

A report by Nesta identified a number of ways AI could enhance our collective intelligence. This includes helping make better sense of data, finding better ways to coordinate decision making, helping us overcome our inherent biases and highlighting unusual solutions that are often overlooked.

To build a frictionless business, having access to data is not enough. Neither is replacing humans with machines through leveraging AI. The processes that organise an enterprise must be completely reimagined. It is the CI of people that can build successful enterprises, with AI playing a big role. Clearly, the narrative is shifting from competing against machines to collaborating with them. Given the complex, multifaceted challenges businesses face, harnessing CI effectively is essential, more because it nurtures innovation.

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