Implementing artificial intelligence in the public sector

Implementing artificial intelligence requires institutional capacity, yet mismatches across public-sector tiers are often neglected. The central government typically has stronger capacity, centralised information systems, larger budgets, and direct access to national digital strategies, which increases AI readiness. In contrast, local government is fragmented and resource-constrained; AI readiness is reduced by non-interoperable systems, shortages of experts, and dependence on external vendors. National strategies often lack concrete municipal support mechanisms (shared platforms, targeted funding, training), leaving smaller municipalities reactive. This paper reviews and examines stakeholder inclusion in AI implementation and identifies key adoption factors. The analysis assumes both national and local digital strategies that specify rationales (service quality, process optimisation, transparency), employee training on ethics and digital skills, funding models, mechanisms to enhance municipal agility, and active involvement of citizens, experts, and other stakeholders. An exploratory case study was conducted using document analysis and focus groups, with interaction intensity assessed across the collaborative process.