What AI solutions can effectively support the missions of the public sector? To help answer this question, a team of researchers compiled a selection of around 100 AI tools. The goal: to assist public stakeholders; including local governments; in quickly identifying operational products tailored to their specific needs.
This selection is not exhaustive. It gives a quick overview of available AI solutions and the ecosystem behind them. The goal is to help public actors find technologies that can support their missions.
The initiative is led by government bodies and focuses on French and European AI tools. It targets national, regional, and local administrations. In total, 435 applications were submitted by 388 companies. After review, around 100 solutions were chosen and mapped. The data comes from the companies themselves, and some did not apply in time; this explains why the study is not complete.
The selection followed several criteria. The most important was proven impact on public service (ideally backed by data). Other key factors included how mature the solution is, how well it fits with existing IT systems, and whether it meets standards for security, data protection, and accessibility.
š Beyond Generative AI
While the overview includes generative AI tools, it goes further. Filters allow users to explore solutions by functional category (e.g., conversational agents, retrieval-augmented generation, computer vision, transcription, fraud detection), thematic area (e.g., legal, health, culture), type (technological component or full application), and hosting method (e.g., SecNumCloud, on-premise).
Examples of use cases relevant to local governments include:
- A legal assistant for municipal clerks,
- Secure internal chatbots for drafting and sorting emails,
- Document analysis and summarization tools to speed up administrative processing,
- Computer vision modules for analyzing video feeds in urban security contexts.
ā ļø Economic Models Still Misaligned
The analysis highlights a wide and varied range of AI solutions that seem, at first glance, to meet public institutions’ functional needs. Yet, many remain overly generic and fail to address the nuanced, complex demands of the public sector.
Before meaningful deployment, the economic models behind these solutions must be examined. Many still rely on traditional B2B frameworks; especially per-user licensing and high integration costs; which limit scalability and long-term sustainability.
Theremore, during procurement, public entities must be especially vigilant: clearly define their functional and technical needs, draft strong contractual clauses to protect sensitive data and ensure regulatory compliance, and assess vendorsā financial and operational stability to avoid service disruption and lock-in.
Ultimately, successful adoption hinges on aligning technological relevance with a sound procurement strategy.
š¤ What Comes Next?
Several follow-up actions are planned. One is to organize targeted āspeed meetingsā between selected vendors and public bodies to help clarify needs and constraints.
A dedicated procurement strategySeveral follow-up actions are planned. First, targeted āspeed meetingsā will connect selected vendors with public bodies. These meetings aim to build mutual understanding of needs and constraints.
Next, a dedicated procurement strategy for AI is being developed. It will include standard clauses and clear acquisition paths, designed in collaboration with key authorities.
Importantly, this framework could also serve as a reference for local governments as they move toward AI adoption. for AI is also being developed. It will include standard clauses and clear acquisition paths, in collaboration with key authorities.
This framework could also guide local governments in their own AI adoption efforts.
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In a previous article, we explored how artificial intelligence is shaping the evolution of our smart cities. In this study, we present a case study focused on AI integration within the public sector. These steps mark a significant milestone; but they also raise a critical question: how can we ensure that these mechanisms lead to coherent, secure, and context-appropriate adoption of AI across public services?