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The Italian transport sector is picking up pace. Operational management must do the same

Il trasporto italiano (Adobe Stock)

3' min read

Translated by AI
Versione italiana

3' min read

Translated by AI
Versione italiana

Italian airports handled 229.7 million passengers in 2025, representing a 5 per cent increase on the previous year. In January 2026, air traffic rose by a further 6.7 per cent, driven mainly by international passenger flows. High-speed rail is also showing positive trends. The Italian transport sector is growing. Yet the financial statements of companies in the sector tell a different story: turnover reached 96 billion euros, but profitability is struggling to keep pace. Between 2019 and 2026, the cost of electricity rose by 73 per cent, diesel by 30 per cent and labour by 18 per cent. Growing volumes, margins under pressure: this is the contradiction that defines the sector today.

Greater demand, greater complexity

Airlines, rail operators, infrastructure managers and public transport companies face a structural challenge: how to scale up operational capacity without increasing costs. The answer does not lie in the incremental optimisation of existing processes; it requires a change of model.

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The 2026–2035 National Airports Plan provides for investment of around 1.2 billion euros in rail links to key airports. By 2031, 26 Italian airports will be integrated with the national rail network. The direction is clear: the mobility of the future is intermodal and integrated, and requires systems capable of coordinating in real time on a much larger scale.

From the digital enterprise to the autonomous enterprise

Many transport companies have already completed their digital transformation: cloud-based management systems, centralised data, standardised processes. It was a necessary step, but it is not the end goal.

The real breakthrough lies in moving from an organisation that uses data to make better decisions to an enterprise capable of acting autonomously, detecting operational variations, triggering real-time responses and managing routine exceptions without human intervention wherever possible. It is not about replacing people: it is about freeing them from high-volume, low-value tasks so they can focus on areas where human judgement is truly crucial. This is the concept of the Autonomous Enterprise.

For an airline, this means independently managing knock-on delays, reallocating ground resources and rebooking passengers. For a rail operator, it means anticipating faults before they lead to service disruptions. For a public transport company, it means optimising service frequencies and routes based on real-time demand, minute by minute.

These scenarios are no longer mere projections: for example, AI agents natively integrated into business processes enable risk management for technical assets by autonomously identifying components that lack a risk score, analysing similar items that have already been assessed and proposing a severity rating, enabling a railway operator to anticipate faults in locomotives or infrastructure before they lead to service disruptions. Furthermore, we have AI agents capable of detecting anomalies in processes, validating cases and proposing corrective actions in seconds rather than hours: for an airline, this helps to improve the handling of passenger complaints and reduce costs associated with flight irregularities.

The foundations that make all this possible

However, an autonomous enterprise is not built on AI agents. It is built on data. For an AI agent to act reliably in a critical process – such as the maintenance of a locomotive, the management of an operational disruption, or the optimisation of a flight plan, it needs accurate, integrated, real-time data: data from operations, maintenance, finance and human resources, all connected within a single technological environment.

This environment enables transport companies to create the information framework within which AI can operate safely. And in a regulated and safety-critical sector such as transport, governance is not an afterthought: every autonomous decision must be traceable, verifiable and compliant with current regulations.

A strategic choice

The journey towards the Autonomous Enterprise is not just an IT project. It is a decision about how to compete over the next five years: with what level of operational efficiency, and with what speed of response. Those who lay the right foundations today – reliable data, intelligent processes, AI integrated into operational decision-making, and robust governance – will be in a fundamentally different position to those who wait. Inaction also comes at a cost. In the transport sector, where margins are measured in minutes and decimal points of a percentage, this difference matters.

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(*) Enzo Pagliaroli, Sales Director, Services & Public Sector at SAP Italia

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