Top 5 Jobs in Government That Are Most at Risk from AI in Italy - And How to Adapt

AI is reshaping Italy's public sector fast: the national AI strategy (2024–2026) spotlights public administration modernization and skills, while real projects - from customer-service chatbots for Reddito di cittadinanza to tax‑fraud tools that cross‑reference bank and asset records - show how automation can speed service delivery and spotting evasion but also risk desk jobs and front‑line roles if human oversight is trimmed (Italian Strategy for Artificial Intelligence 2024–2026, How Italy's Government is Using AI).

Local pilots - like Rome's sensors that “listen” for leaking pipes - illustrate productive uses, yet legislation and ethics (AI Act and national bills) insist on human‑in‑the‑loop, transparency and reskilling.

Public servants and IT teams can turn risk into advantage by learning practical promptcraft and tool‑use; the AI Essentials for Work syllabus (Nucamp) lays out those workplace skills for non‑technical staff, so careers evolve with the machines instead of being replaced by them.

Bootcamp details: AI Essentials for Work - Length: 15 Weeks - Cost (early bird): $3,582 - Registration: Register for AI Essentials for Work (Nucamp) and AI Essentials for Work syllabus (Nucamp).

The shortlist was built by blending hard evidence and sector signals relevant to Italy's public sector IT landscape: tasks that are highly routine or data‑centric, clear signs of existing automation uptake, legal incentives that accelerate deployment, and firm‑level heterogeneity that predicts displacement risk.

Quantitative cues came from studies of automation adoption and employment effects in Italy (which show divergent impacts by firm size and sector; see the firm‑level analysis at RePEc), while country‑level statistics helped calibrate exposure (Italy's administrative automation rate is still lower than some peers, per a 2025 industry roundup).

Policy and platform shifts - notably the 2023 Public Procurement Code that opens full life‑cycle automation - weighted jobs tied to tenders and cross‑database triage more heavily.

Practical tests included whether tasks already appear in government pilots or Nucamp use cases (document summarization, pseudonymized cross‑database analysis), and whether a platform failure could meaningfully halt service (recall hospital procurements stalled when a national platform went down).

Those filters yielded the five roles profiled below, prioritizing where automation could most quickly reshape day‑to‑day IT and clerical work.

“while in the previous Code we had digitization solely related to the presentation and evaluation of bids, this new Code envisages the possibility of automating the entire life cycle of public contracts. The government really took sides and chose automation.”

Frontline citizen‑service staff - counter clerks at the Ministry of Labour and call‑centre operators - face the fastest, most visible changes as Italy experiments with chatbots for services like the Reddito di cittadinanza: these tools can cut waits and triage routine queries but often hit limits on complex, equity‑sensitive cases, sending callers back to menus or FAQs instead of a human who can make discretionary exceptions (see the Ministry's chatbot rollout and risks at Democracy Technologies).

That brittle handoff matters because Italy's emerging legal framework - from the EU AI Act to the national AI Bill - explicitly demands transparency, “human‑in‑the‑loop” oversight and obligations to inform workers about deployed systems, so managers can't simply replace judgment with automation (details on the bill's principles and worker protections at DLA Piper).

Practical adaptation will hinge on rapid reskilling in triage, promptcraft and audit roles so street‑level bureaucrats keep the humane discretion that resolves messy, real‑world cases rather than leaving citizens stuck talking to a machine.

“Using AI for chatbots is an underestimation of what AI can do,” says Marco Bani.

Administrative clerks and data‑entry staff in public administration are squarely in the crosshairs because their day‑to‑day is dominated by repetitive, document‑heavy work that generative AI and RPA excel at - think bulk form processing, transcription, and routine eligibility checks - so tools that do fast “document summarization and automated triage” can speed throughput but also shift the burden of error correction onto already stretched teams (document summarization and automated triage tools for public administration).

Evidence from public‑sector studies warns that automation often makes jobs harder, not easier: clerks end up policing AI outputs, wading through “millions of pages of documentation” and fixing hallucinations or mistaken denials rather than being freed from work (see the Roosevelt Institute scan of AI in public administration), so Italian IT managers should pair any rollout with clear data‑governance rules, human‑in‑the‑loop checkpoints and staff training.

European regulatory analysis underscores the same theme - risk‑based rules, transparency and procurement governance are rising priorities that will shape how GenAI is adopted across administrations (generative AI regulatory awakening in the US and EU and its impact on public administration).

Practical interventions that work in IT teams include strong prompt‑audit practices, vendor clauses on data provenance, and upskilling clerks to a supervisory role - monitoring, validating and translating AI outputs back into humane, equitable decisions rather than letting automation quietly erode those skills.

“Failures in AI systems, such as wrongful benefit denials, aren't just inconveniences but can be life-and-death situations for people who rely upon government programs.”

Tax auditors and routine compliance analysts in Agenzia delle Entrate and Guardia di Finanza teams are being reshaped by AI that can trawl filings, bank records and property registries to surface VAT anomalies - tools like VeRa and recent AI‑powered chatbots (built with Sogei) already prioritise cases so aggressively that VeRa reportedly flagged one million high‑risk VAT cases in 2022, a vivid signal that routine sampling may give way to algorithmic triage (AI-powered VAT tools at Agenzia delle Entrate).

For Italy's IT leads and auditors the practical risk is twofold: first, analysts can be pulled away from judgement‑heavy follow‑ups into proofreading model outputs and chasing false positives; second, legal and privacy guardrails - GDPR, national rules and evolving AI governance - mean deployments must embed DPIAs, strict data‑provenance clauses and strong pseudonymisation or access controls before cross‑database analytics run in production.

Upskilling should therefore prioritise model‑validation, prompt auditing and secure data‑linkage techniques so teams supervise, not simply obey, automated risk scores; see the new European guidance on safe pseudonymisation to design workflows that both catch evasion and protect citizens' rights (EDPB pseudonymisation guidelines).

“pseudonymised data must not be regarded as constituting, in all cases and for every person, personal data for the purposes of the application of Regulation 2018/1725, in so far as pseudonymisation may, depending on the circumstances of the case, effectively prevent persons other than the controller from identifying the data subject in such a way that, for them, the data subject is not or is no longer identifiable.”

Junior legislative drafters and research assistants in Parliament are prime candidates for AI augmentation - and for AI pitfalls - because their work depends on precise cross‑references, legislative history and tonal judgment that current LLMs still mishandle.

Tools can speed routine drafting, generate neat summaries and surface helpful precedents, but models also hallucinate, compress long legislative records beyond reliable context windows, or even fabricate plausible‑sounding facts, so a neat first draft can hide serious errors (see analyses of LLM limits and fabrication risks at Intuitive Data Analytics and the practical lawyer's guide to LLMs).

For Italy's IT teams and managers the lesson is practical: treat generative outputs as provisional, embed human‑in‑the‑loop checks, build verification workflows for every citation and statutory cross‑check, and train junior staff in promptcraft, model‑validation and source‑auditing so speed doesn't undercut legal accuracy; Nucamp's overview of using AI in Italy's public sector highlights how these safeguards fit into national implementation plans.

The memorable risk: a model can sound authoritative while inventing a case or clause - so every citation must be confirmed before it goes into a draft that will shape policy.

DO NOT, UNDER ANY CIRCUMSTANCES, RELY ON CASE CITATIONS PROVIDED BY ANY LLM, INCLUDING THE ONES OFFERED BY LEXIS AND WESTLAW, UNLESS YOU HAVE PERSONALLY VERIFIED THAT THE CITED CASE EXISTS AND SAYS EXACTLY WHAT YOU ARE CITING IT FOR.

Routine caseworkers who screen Reddito di cittadinanza and other benefit claims sit at the most fragile intersection of public service and automation: governments are increasingly using algorithmic eligibility checks and fraud‑scoring to speed throughput, yet investigations across Europe show these systems can strip people of essential support - “many people who were already receiving it were suddenly stripped of support, particularly Roma and people with disabilities” - a vivid caution for Italy where ADM is already woven into fiscal and administrative monitoring (see AlgorithmWatch Italy chapter on automated decision-making).

For IT leaders in ministries and local social services the practical rule is simple but hard: insist on human‑in‑the‑loop review queues, clear appeal pathways, DPIAs and model audits before any automated triage touches a claimant's livelihood; pair every rollout with robust pseudonymisation and secure linkage protocols so tools assist investigators without weaponising raw personal data (Nucamp guidance on pseudonymized cross-database analysis - AI Essentials for Work syllabus).

At the EU level watchdogs are urging stronger limits - Human Rights Watch and partners argue social‑scoring must be banned because it systematically undermines access to social security - so Italian IT teams should design systems that augment caseworkers' judgement, not replace it, and prioritize explainability, documentation and speedy human remedies whenever an automated flag affects benefits.

“No government wants to get left behind in the great AI race. So it's being deployed without proper testing, reinforcing systemic issues and bias. And the strong xenophobic narrative – anti-immigrant, anti-foreigner – is helping governments justify the need for fraud detection using AI.”

Practical next steps for Italian public servants and IT managers: treat AI rollouts as programme‑management problems, not just vendor installs - require DPIAs and human‑in‑the‑loop checkpoints before deployment, build prompt‑audit and model‑validation tasks into job descriptions, and insist on strong pseudonymisation and provenance clauses in procurement to protect citizens' privacy and reduce false positives that can strip people of essential support.

Use the national playbook: Italy's AI Strategy for 2024–2026 stresses systematic upskilling and public‑sector training as a core pillar (Italy's AI Strategy for 2024–2026 (DLA Piper analysis)), so pair technical safeguards with training in promptcraft, triage and secure data‑linkage; short, practical courses like the AI Essentials for Work syllabus help non‑technical staff move from policing models to supervising them (AI Essentials for Work syllabus).

Start small, measure user‑level harms as well as efficiency gains, and scale only when audits, appeals processes and staff certification are in place - one verified audit trail is worth a thousand untested automations.