Collaborative intelligence is distinguished from collective intelligence in three key ways: First, in collective intelligence there is a central controller who poses the question, collects responses from a crowd of anonymous responders, and uses an algorithm to process those responses to achieve a (typically) "better than average" consensus result, whereas collaborative intelligence focuses on gathering, and valuing, diverse input. Second, in collective intelligence the responders are anonymous, whereas in collaborative intelligence, as in social networks, participants are not anonymous. Third, in collective intelligence, as in the standard model of problem-solving, there is a beginning, when the central controller broadcasts the question, and an end, when the central controller announces the "consensus" result. In collaborative intelligence there is no central controller because the process is modeled on evolution. Distributed, autonomous agents contribute and share control, as in evolution and as manifested in the generation of Wikipedia articles.

Collaborative intelligence characterizes multi-agent, distributed systems where each agent, human or machine, is autonomously contributing to a problem solving network. Collaborative autonomy of organisms in their ecosystems makes evolution possible. Natural ecosystems, where each organism's unique signature is derived from its genetics, circumstances, behavior and position in its ecosystem, offer principles for design of next generation social networks to support collaborative intelligence, crowdsourcing individual expertise, preferences, and unique contributions in a problem solving process.

Four related terms are complementary:

Collaborative intelligence is a term used in several disciplines. In business it describes heterogeneous networks of people interacting to produce intelligent outcomes. It can also denote non-autonomous multi-agent problem-solving systems. The term was used in 1999 to describe the behavior of an intelligent business "ecosystem" where Collaborative Intelligence, or CQ, is "the ability to build, contribute to and manage power found in networks of people." When the computer science community adopted the term collective intelligence and gave that term a specific technical denotation, a complementary term was needed to distinguish between anonymous homogeneity in collective prediction systems and non-anonymous heterogeneity in collaborative problem-solving systems. Anonymous collective intelligence was then complemented by collaborative intelligence, which acknowledged identity, viewing social networks as the foundation for next generation problem-solving ecosystems, modeled on evolutionary adaptation in nature's ecosystems.

Although many sources warn that AI may cause the extinction of the human species, humans may cause our own extinction via climate change, ecosystem disruption, decline of our ocean lifeline, increasing mass murders and police brutality, and an arms race that could trigger World War III, driving humanity extinct before AI gets a chance. The surge of open source applications in generative AI demonstrates the power of collaborative intelligence (AI-human C-IQ) among distributed, autonomous agents, sharing achievements in collaborative partnerships and networks. The successes of small open source experiments in generative AI provide a model for a paradigm shift from centralized, hierarchical control to decentralized bottom-up, evolutionary development. The key role of AI in collaborative intelligence was predicted in 2012 when Zann Gill wrote that collaborative intelligence (C-IQ) requires "multi-agent, distributed systems where each agent, human or machine, is autonomously contributing to a problem-solving network." Gill's ACM paper has been cited in applications ranging from an NIH (U. S. National Institute of Health) Center for Biotechnology study of human robot collaboration, to an assessment of cloud computing tradeoffs. A key application domain for collaborative intelligence is risk management, where preemption is an anticipatory action taken to secure first-options in maximising future gain and/or minimising loss. Prediction of gain/ loss scenarios can increasingly harness AI analytics and predictive systems designed to maximize collaborative intelligence. Other collaborative intelligence applications include the study of social media and policing, harnessing computational approaches to enhance collaborative action between residents and law enforcement. In their Harvard Business Review essay, Collaborative Intelligence: Humans and AI Are Joining Forces – Humans and machines can enhance each other's strengths, authors H. James Wilson and Paul R. Daugherty report on research involving 1,500 firms in a range of industries, showing that the biggest performance improvements occur when humans and smart machines work together, enhancing each other's strengths.

Collaborative intelligence traces its roots to the Pandemonium Architecture proposed by artificial intelligence pioneer Oliver Selfridge as a paradigm for learning. His concept was a precursor for the blackboard system where an opportunistic solution space, or blackboard, draws from a range of partitioned knowledge sources, as multiple players assemble a jigsaw puzzle, each contributing a piece. Rodney Brooks notes that the blackboard model specifies how knowledge is posted to a blackboard for general sharing, but not how knowledge is retrieved, typically hiding from the consumer of knowledge who originally produced which knowledge, so it would not qualify as a collaborative intelligence system.

In the late 1980s, Eshel Ben-Jacob began to study bacterial self-organization, believing that bacteria hold the key to understanding larger biological systems. He developed new pattern-forming bacteria species, Paenibacillus vortex and Paenibacillus dendritiformis, and became a pioneer in the study of social behaviors of bacteria. P. dendritiformis manifests a collective faculty, which could be viewed as a precursor of collaborative intelligence, the ability to switch between different morphotypes to adapt with the environment. Ants were first characterized by entomologist W. M. Wheeler as cells of a single "superorganism" where seemingly independent individuals can cooperate so closely as to become indistinguishable from a single organism. Later research characterized some insect colonies as instances of collective intelligence. The concept of ant colony optimization algorithms, introduced by Marco Dorigo, became a dominant theory of evolutionary computation. The mechanisms of evolution through which species adapt toward increased functional effectiveness in their ecosystems are the foundation for principles of collaborative intelligence.

Artificial Swarm Intelligence (ASI) is a real-time technology that enables networked human groups to efficiently combine their knowledge, wisdom, insights, and intuitions into an emergent intelligence. Sometimes referred to as a "hive mind," the first real-time human swarms were deployed by Unanimous A.I. using a cloud-based server called "UNU" in 2014. It enables online groups to answer questions, reach decisions, and make predictions by thinking together as a unified intelligence. This process has been shown to produce significantly improved decisions, predictions, estimations, and forecasts, as demonstrated when predicting major events such as the Kentucky Derby, the Oscars, the Stanley Cup, Presidential Elections, and the World Series.