Operationalizing human–AI collaboration in city intelligence systems

Cities face compounding shocks, however, many “smart city” initiatives stall because data and pilots do not translate into enforceable, revisable governance. This research conceptualizes City Intelligence Systems (CIS) as an ecosystem governance capacity and explains cross-city variation through the Complementary–Competitive Quintuple Helix (CCQH), focusing on how helix actors operationalize Human–AI collaboration while balancing collaboration and rivalry. Using a comparative multiple-case design among San Diego, Seoul, Hangzhou and Doha, the analysis draws on public documentary evidence and digital trace ethnography. Each city is reconstructed as an instrument chain across an Evidence–Solutions–Rules–Legitimacy–Environment–Feedback loop, then compared configurationally using four CCQH levers of commons stewardship, teaming dossiers, governance dials, and outcome amplification. Findings show equifinality that CIS maturity follows multiple pathways rather than a single ladder. The most diagnostic translation layer is governance dials of procurement, standards, and contractual artifacts that convert shared data and experiments into enforceable requirements and accountable scaling. Legitimacy mechanisms are necessary but can become symbolic without auditability and enforceability; orchestration can scale capability but need be paired with contestability and environmental signal surfaces to remain credible. Therefore, CIS should also be assessed as a measurable governance-learning loop embedded in documentation ecosystems, not only as a technology level.