Artificial intelligence is changing society because it affects how people access services, find information, communicate, work, participate in decisions and understand one another. The larger question is how society itself should be organised when people can use AI to interpret information, coordinate action and explore solutions at a scale that was previously difficult. The opportunity is not simply a more automated society. It is a society with greater collective intelligence.
Every society has a centre: its institutions, laws, public services, norms, infrastructures and established ways of making decisions. It also has an edge: people whose needs are not well served, communities whose experience is missing from official data, problems that cross institutional boundaries, and possibilities for cooperation that existing procedures have not yet recognised. AI can help society see patterns and coordinate responses, but it can also reproduce exclusion, concentrate power or make decisions difficult to challenge. The paradigm shift must move from passive systems that process people towards institutions that help people understand, participate and improve the systems around them.
- Use AI to improve public understanding and service access, not simply reduce human contact.
- Design public systems around contestability, privacy, transparency and accountable human authority.
- Connect fragmented data and institutions carefully while respecting rights and context.
- Use AI to help communities identify problems, compare options and evaluate outcomes.
- Build collective intelligence by including people affected by decisions.
Society is a network of shared systems
Society is not only the sum of individuals. It is the network of relationships and institutions through which people cooperate, disagree, exchange resources and solve shared problems. Transport, housing, education, healthcare, energy, communications, courts, emergency response, libraries and local organisations all depend on people coordinating over time.
AI can make some parts of these systems easier to analyse. It may help identify service bottlenecks, summarise public feedback, translate information, detect unusual patterns or support staff with routine documentation. But the social outcome depends on the rules and institutions around the tool. A faster process is not necessarily fairer. A prediction is not automatically a decision. An automated service that excludes someone who cannot use a smartphone may be efficient for most users and inaccessible to those who need it most.
The centre and edge of society
The centre consists of established institutions and standard operating procedures. These provide consistency, continuity and rights that should not depend entirely on individual discretion. The edge consists of needs and experiences that do not fit neatly into standard categories: people with unusual circumstances, new social risks, underserved communities and emerging forms of participation.
An institution can become so focused on its own processes that it sees only the information it was designed to collect. A housing agency may see applications and waiting times but not all the informal pressures a household faces. A school may see assessment results but not every barrier affecting learning. A health service may see appointments but not the transport, language or caregiving circumstances that shape access.
Moving to the edge means asking what the system is not seeing. It means allowing people to explain their circumstances, correct records, challenge automated conclusions and contribute to how services improve. AI should help institutions listen more intelligently, not give them a stronger excuse to stop listening.
From service automation to human agency
Automation can make a form easier to complete or a public answer easier to find. Yet a service is not successful merely because it has moved online. People may need a human explanation, an appeal route, language assistance or an exception process. The quality of public service depends on whether a person can actually resolve the problem.
A stronger model uses AI to prepare staff for better conversations, help residents understand requirements, make information accessible and identify recurring service failures. A person should still know when a machine is involved, what information is being used and how to reach a responsible human. The institution should provide a route to correct errors and seek review where an automated process affects a meaningful interest.
Human agency also includes the ability to decide how much digital interaction is appropriate. Some people prefer self-service. Others need a counter, phone call, paper process or support from a trusted organisation. A modern society should not confuse digital efficiency with the elimination of choice.
Collective intelligence is more than aggregated data
Collective intelligence emerges when people combine different knowledge, question assumptions, coordinate action and learn from outcomes. It is not simply a large dataset or a poll. Data may show where a problem occurs, but local residents may understand why it happens. Professionals may understand technical constraints, while frontline workers may know how procedures behave in practice. Researchers may contribute methods for testing explanations.
AI can help make large bodies of information more navigable, summarise recurring themes and compare possible interventions. But aggregation can erase minority experiences. A frequently mentioned problem may not be the most serious one. A quiet group may face a substantial barrier without appearing often in feedback data. Institutions should combine analysis with consultation, field observation and clear ways for people to correct the record.
Collective intelligence requires disagreement to remain visible. A system that only returns a single averaged answer may hide important trade-offs. Public decisions often involve competing values: cost, speed, privacy, accessibility, resilience and fairness. AI can help explain options, but society still needs a deliberative process for deciding between them.
Trust, legitimacy and government communication
Public trust is not created by saying that an AI system is advanced. It is earned when people can understand its role, see that it is used for a legitimate purpose and obtain a meaningful explanation or correction when something goes wrong. High-impact decisions need stronger safeguards than low-risk administrative assistance.
AI may support a caseworker by summarising documents, but the caseworker and institution remain responsible for the decision. An algorithmic score should not quietly become a substitute for a lawful standard. A model’s confidence does not establish fairness, accuracy or authority. If a person is denied a service, the institution must explain the grounds and provide an appropriate review process.
The OECD’s November 2026 report on government communication describes fragmented information consumption, divisive public discourse and the growing influence of recommendation algorithms, with AI adding another layer of change. See OECD, Reforming Government Communication for the Age of Algorithms. Public communication should help people understand policy and participate in debate, not manipulate them through undisclosed targeting or synthetic consensus.
Social cohesion, work and care
Societies contain differences in language, income, age, belief, geography, family experience and political outlook. Recommendation systems may help people find communities and information, but may also narrow the range of perspectives they encounter. AI-generated content can scale stereotypes or create misleading impressions about what other groups believe. Social cohesion is strengthened when people can see their experiences reflected, understand decisions and trust that others are subject to fair rules.
AI will change tasks inside many occupations, but society should not treat people as interchangeable inputs to a productivity calculation. Work provides income, identity, relationships, learning and a sense of contribution. People need opportunities to learn new methods and participate in shaping how work is redesigned. A fair response includes adult learning, job redesign, transition support and a discussion about how productivity gains are distributed.
Healthcare and social care involve relationships, vulnerability, consent and professional standards. AI may help summarise records or identify patterns, but it cannot obscure who is responsible for a decision. Care systems should use AI to give professionals more capacity to listen and make sound decisions, not eliminate human contact where it is needed.
The city as a laboratory for collective problem-solving
A city contains many systems that can be studied together: transport, housing, drainage, parks, schools, public health, energy, waste, accessibility and cultural life. AI may help combine public datasets, identify patterns and simulate alternatives. The important work is defining the problem correctly and checking whether the proposed improvement helps the people affected.
A student group could investigate whether a public route is easy for a first-time visitor to navigate, using approved public information and supervised observation. Residents might contribute views about a shared space. Researchers could examine heat exposure or pedestrian access using suitable data. Each participant contributes a different kind of knowledge.
Data should not be treated as a complete substitute for lived experience. A map may show a footpath but not whether it is comfortable at a particular time. A service statistic may show average waiting time but not the experience of someone who needs additional assistance. A collective-intelligence approach combines quantitative evidence with human context.
Education becomes society’s capability engine
Education is where people learn to participate in this new social arrangement. They need subject knowledge, language, numeracy, source evaluation, digital literacy and ethical judgement. They also need opportunities to work with others on problems that cross the boundaries of a single subject or institution.
The eduKate series begins with AI and the Paradigm Shift in Education. The practical assignment design in AI-Powered Assignments helps learners use AI to widen inquiry, while From the Centre to the Edge explains how teachers can preserve foundations and guide students towards discovery.
Students can investigate public information, model a local problem, compare possible interventions and present a reasoned proposal. They should use approved public sources, protect personal information and distinguish a classroom model from an official recommendation. This prepares them to participate thoughtfully in civic life rather than merely consume information about it.
A model for AI-enabled public problem-solving
A public problem-solving cycle begins with people identifying an issue and explaining why it matters. The institution defines a legitimate scope and gathers appropriate evidence. AI may help organise information, identify patterns or generate alternatives. People affected by the issue review whether the analysis reflects their experience. Professionals test proposals, decision-makers explain the trade-offs, and outcomes are monitored after implementation.
The process should preserve a record of the data used, assumptions made, AI assistance, human decisions and review mechanisms. When the system is wrong, the institution should identify the source of the error and correct it. When a proposal fails, the failure should inform the next iteration. This is a learning institution rather than a black box.
The paradigm shift is from using AI mainly to make existing institutions faster towards using it to help society understand its own problems and improve its shared systems. A society becomes more intelligent when its institutions can listen, learn and correct themselves.
Collective intelligence requires better questions
When people discuss AI in society, they often begin with a service or a task that could be automated. A more useful starting point is a question that matters to a community. Why do residents find a process confusing? Why does a transport connection work for one group but not another? Which public information is difficult to understand? What changed after a new service was introduced? AI can help organise the evidence, but the question should come from the people who understand the context and experience the consequences.
Good questions also separate symptoms from causes. A long queue may be caused by staffing, scheduling, unclear eligibility rules, inaccessible forms or a sudden change in demand. A model that predicts the busiest time does not explain which cause should be addressed. The investigation needs relevant data, frontline knowledge and observation. It may require speaking to people whose experience is not captured in administrative records.
Collective intelligence improves when different kinds of expertise are allowed to challenge one another. Technical specialists can explain system constraints. Residents can explain how a service works in daily life. Researchers can test whether an apparent pattern is robust. Public officials can clarify the legal and budgetary limits. AI can help these groups work with large amounts of information, but it should not silently decide which contributions count.
From a passive user to an active participant
A passive model gives people a finished answer or a fixed digital process. A participatory model gives people ways to understand how the process works, question a result and contribute information that may improve it. This distinction matters when AI affects benefits, education, employment, housing, healthcare or access to public services. People should not be forced to accept an opaque result merely because a system produced it quickly.
Participation must be meaningful, not decorative. A consultation that collects feedback but never explains what happened to it does not create collective intelligence. Institutions should identify which decisions are open to input, publish the constraints, explain what changed and provide reasons when a suggestion is not adopted. AI may help summarise submissions, but those summaries need review so that minority concerns are not erased by the most frequent themes.
People also need alternative routes to participate. A digital form may work for many residents but exclude someone who needs language support, accessibility assistance or a conversation. A society that values collective intelligence should not treat access to one interface as a condition of being heard. The system should adapt to different people rather than require every person to adapt to the system.
Public institutions as learning systems
An institution becomes more capable when it can detect a failure, investigate why it happened, correct the process and explain the improvement. AI can support the analysis of complaints, service patterns and operational data. Yet a learning institution must preserve records of what was changed and why. Otherwise, it may repeat the same mistakes or become unable to distinguish a successful intervention from a coincidental change.
Public organisations should treat AI as one component in a wider system that includes data governance, professional expertise, legal authority, human review and public accountability. A model may identify a pattern, but the institution must decide whether the pattern is relevant and whether action is justified. Where decisions affect rights or access to essential services, people need clear explanations and an effective route to appeal or correction.
Learning also requires humility. A system may not have enough information. A dataset may not represent the whole population. An apparent improvement may have shifted the burden onto another group. A responsible institution should be prepared to say what remains unknown and what further evidence it needs. The capacity to correct an assumption is a sign of strength, not weakness.
Social resilience in an AI-rich environment
Resilience is not simply the ability to keep digital services running. It includes whether people can obtain reliable information during a disruption, whether communities can help one another, whether essential services have alternatives and whether institutions can recover from a technical or human failure. AI may help identify risks or coordinate resources, but resilience depends on people, relationships, infrastructure and tested procedures.
Society should avoid creating single points of failure. A public service that depends on one model, one cloud provider or one digital identity route may become difficult to operate when the system is unavailable. Human staff, offline procedures, backups, accessible communication and clear recovery plans remain important. The same principle applies to knowledge: important public information should not exist only inside a conversational interface that may change or disappear.
Social resilience also means resisting manufactured division. AI can produce convincing messages at scale, but communities need trusted local institutions, media literacy and public communication that is transparent about sources. People should be able to disagree while retaining a shared basis for checking facts. The purpose is not to make every citizen think alike. It is to preserve enough trust and common understanding for society to solve problems together.
A practical centre-to-edge society project
A school, community group or public agency can begin with a modest project. Identify a service or shared space that people find difficult to use. Gather public information and consent-based observations. Ask AI to organise possible explanations and propose questions that the group may have overlooked. Check each suggestion against actual evidence, interview appropriate participants and identify the constraints. Then propose a change and specify how its effect will be measured.
The group should publish not only its recommendation but also its assumptions, sources, limitations and the people consulted. It should explain which AI suggestions were accepted, changed or rejected. After a trial, it should compare the outcome with the original condition and record unintended effects. This is how a small project can teach a society to use AI as a means of inquiry rather than a substitute for participation.
The broader paradigm shift is from passive systems that process people towards collective systems that help people understand and improve their shared world. AI may increase the reach of analysis, but legitimacy and direction come from people and accountable institutions. A society becomes more intelligent when more of its members can contribute to how it learns.
Continue the AI and Civilisation Paradigm Shift Series
This article belongs to a wider series about how AI changes the way civilisation creates, learns, governs, remembers and understands itself. Begin with How Super Intelligence Works and the educational foundation, AI and the Paradigm Shift in Education.