AI in Ticino’s public administration: innovation or digitized problems?

Context

TL;DR

• Ticino combines advanced research and slow bureaucracy
• USI and SUPSI are points of reference for the Canton
• Talents from Ticino look to the federal institutes of technology
• AI accelerates processes, but does not replace responsibility

Key facts

• Author → Elio Del Biaggio
• Institutions → USI and SUPSI
• Education beyond the Gotthard → Federal Institutes of Technology in Zurich and Lausanne
• AI capabilities → analyzing documents and automating repetitive tasks
• Risk → digitizing old problems

The paradox of the public administration

Elio Del Biaggio describes Ticino as a territory that has research, innovative companies, qualified professionals and young people trained at the best academic institutions in Switzerland and around the world. The contrast emerges when the focus shifts to the public administration: slow procedures, redundant steps, duplications, forms, authorizations and systems that struggle to communicate with one another. Technology is advancing, but bureaucracy, according to the author, still seems to reason with the logic of paper.

According to the picture outlined by Del Biaggio, USI, that is, the Università della Svizzera italiana, represents a significant presence for the Canton. At SUPSI, the Scuola universitaria professionale della Svizzera italiana, a leading role in applied education and in connecting skills, innovation and the world of work is acknowledged. Del Biaggio also mentions young people from Ticino who choose internationally renowned institutions, including the Federal Institutes of Technology in Zurich and Lausanne, described as among the best in the world.

The challenge of returning

Looking beyond the Gotthard is not presented as a problem. The problem, Del Biaggio writes, would be failing to offer, once their education is complete, a Ticino capable of making use of the skills acquired. The conditions that allow those skills to become innovation, new businesses, qualified jobs and better public services are therefore part of a territory's ability to build its own future.

The difficulty, the reasoning continues, is also retaining talent. Many find more attractive professional and salary conditions and work environments elsewhere. The result identified is the loss of skills needed to modernize the territory and society. The tema degli annunci di lavoro thus connects to the text's central question: how can training and technology be transformed into concrete public outcomes?

Operational details

The missing check

The issue raised does not concern merely the presence of a digital tool. For those who live or work in Switzerland, the benefit of AI in public administration depends on the ability to transform a slow process into a faster and less redundant one. If duplications, unnecessary authorizations, and systems unable to communicate remain, the shift from paper to a digital solution does not solve the problem: it preserves it in a different form. This is the scenario the author summarizes as the risk of digitizing old problems.

The practical comparison is therefore between two organizations. The first retains redundant steps and uses technology without rethinking the work. The second identifies repetitive activities, entrusts AI with document analysis or decision support, and checks whether procedures actually become faster. AI can do a great deal, but it does not replace the expertise, common sense, and responsibility of those who use it.

The human factor

This point directly affects the organization of public-sector work. Whoever decides how a tool is used must engage with the processes; whoever rethinks them must address the duplications; whoever verifies the results must take responsibility for the judgment. In the text, this chain is linked to the selection of people: choosing on the basis of affiliation, connections, or convenience, rather than competence, leads to modern tools being managed by an organization that has remained old-fashioned.

This also brings out the political dimension highlighted by the author. Deciding whom to value, which processes to rethink, and whom to hold accountable is not a technical detail. The same logic applies to the relationship between training and the region: if people with these skills do not find attractive professional conditions, salaries, and working environments, the region loses resources that could help modernize services and society. If, instead, it succeeds in making good use of them, it can turn them into innovation, new businesses, and qualified jobs. For the reader, the concrete measure is not the nominal presence of AI, but the quality of the results and of the conditions that enable those skills to produce them. This is also the point at which aziende che assumono's research comes into play.

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Key points

A four-step framework

The text does not provide an already defined operational project. However, it offers a useful sequence of questions for evaluating a public project without confusing technology with innovation.

1. Map the process. List the slow procedures, redundant steps, duplications, forms, and authorizations that weigh down the work. Also point out the systems that struggle to communicate with one another. The goal is to understand where the problem lies before introducing a tool.

2. Separate tasks and responsibilities. Determine which activities can be analyzed or automated and which decisions must remain entrusted to people capable of exercising good judgment and taking responsibility. The source presents AI as support for documents, repetitive activities, the speed of procedures, and decision-making—not as a substitute for expertise.

3. Choose the skills. The person tasked with deciding how to use AI, rethink processes, and monitor results must be selected based on competence. Affiliation, knowledge, or convenience are not the criterion indicated by the text.

4. Check the result. After the intervention, it is necessary to ask whether the processes are truly faster, whether the systems communicate better, and whether the organization values those who know how to do the work and rewards those who deliver results. These are the checks that prevent having highly modern tools inside an old organization.

From training to service

For an administration, this framework links innovation to the ability to transform skills into better public services. For those who work or are training, it brings the question back to professional and salary conditions and to work environments capable of valuing what they have learned. The operational step is to measure skills, responsibilities, and results, not merely to declare the adoption of AI.

The final question remains the one posed by the author: is the Public Administration using AI to change processes, or merely to digitize problems that already exist? To compare the value of skills and responsibilities with income, use calcolatore stipendio.

Source: tio.ch