All Intelligence-first rail is shaping the future of the industry

I recently spent a few days at Huawei Connect 2025, attending an event dedicated to the digital transformation of rail. What stood out for me was how quickly cloud architectures, AI‑assisted planning, and mission‑critical wireless are converging into a single operational layer for rail.

The presentations and conversations were pragmatic rather than theoretical: operators want reliability, capacity, and better passenger information, and Huawei’s portfolio is increasingly organised to deliver exactly that.

Huawei’s main rail session introduced two key updates: Intelligent Scheduling of Train Operation and Urban Rail Smart Station, both designed to connect sensing, decision-making, and execution.

On stage and in briefings, Huawei referenced its rail footprint and industry expertise, with more than 300 urban rail lines in over 70 cities and 180,000 km of railways supported across the globe.

Malaysia’s ECRL was highlighted as a flagship FRMCS project, boasting rapid emergency call setup and robust bandwidth for advanced train control and IoT, while China’s 12306 ticketing platform was also showcased as an example of both increased efficiency and better passenger experience.

The clear strategy: “Go Broadband, Go Cloud, Go Intelligence” to boost capacity and revenue as demand grows. All of this reinforces the direction towards broadband connectivity, cloud-based operations, and practical AI shaping the future of rail.

Cloud moves into the operational core

One major theme I took away: “cloud” is no longer just a website for dashboards. In Huawei’s framing, it is the orchestration plane for day‑to‑day operations. Their SMART Logistics & Warehousing Solution uses a “1+N” model with a smart operations cloud coordinating yards, depots, and parks.

Although positioned for logistics, the components map neatly to rail: a trustable data space for multi‑party data sharing, an AI planning and scheduling engine for allocating people and rolling stock, and automation for the repetitive shunting and yard movements that steal time from service.

For passenger operators, that means standardised interfaces and API‑first design, so you can evolve timetables toward responsive plans without tearing up the stack. For freight and intermodal, it supports integrated supply chain models where rail is one node in a near real‑time network. My read is that this is becoming the default architecture for modern operations, not an edge experiment.

Reliability first, then scale

Rail buys on availability and safety. The Astana LRT design reflects a discipline I heard repeatedly at the event: keep safety‑critical communications on a dedicated, interference‑shielded network, then use cloud to augment planning, analytics, and customer experience. Huawei’s LTE‑M approach provides end‑to‑end coverage from tunnels to depots with base stations, core, and onboard devices tuned for train‑to‑ground continuity. That cuts OPEX by reducing handover failures and allows phased capacity upgrades as cities grow, rather than disruptive refits.

Applied AI, not AI for its own sake

Another practical note: AI showed up where it saves time and raises utilisation. The Wukong intelligent recognition model was framed as a way to accelerate document‑heavy workflows—maintenance records, claims, compliance paperwork—freeing teams to focus on operations. More strategically, the AI‑driven planning and scheduling engine points to measurable gains in depot operations, maintenance windows, and rolling‑stock balancing. In conversations with operators, these are the areas where incremental improvements compound into real capacity.

Intermodal lessons that benefit rail

Much of the narrative has obvious rail applications for intermodal transport, particularly at ports and airports. If your corridor competes for time‑sensitive cargo, securely sharing data with terminals, customs, and road partners becomes a performance advantage. The path runs through a cloud platform that enforces data trust, offers fine‑grained access, and coordinates plans across organisations. The promised intermodal outcomes – fewer switching errors, faster turnarounds, more predictable end‑to‑end journeys – are the very outcomes rail leaders target on their own networks.

Cloud as a strategic enabler

All of this underscores the growing importance of cloud infrastructure in rail. By centralising data and enabling real-time analytics, cloud platforms offer operators greater flexibility and scalability – particularly in complex, multi-system environments.

Huawei’s approach appears to prioritise interoperability and modularity, allowing its solutions to be integrated with existing systems. This is a key consideration for operators managing legacy infrastructure, where full system replacements are often impractical.

The use of cloud-native architecture also opens the door to future innovations, such as AI-driven maintenance, dynamic passenger flow modelling and energy optimisation. These capabilities are increasingly seen as essential for building resilient, efficient and customer-focused rail networks.

My takeaways for rail leaders

So, in practical terms for rail leaders, what should you be doing now? I think there are four key areas to focus on today:

• Start with a rail‑grade, dedicated wireless backbone suitable for safety‑critical services, then layer cloud for orchestration and experience.
• Use cloud to standardise data models across operations, maintenance, and commercial. That unlocks AI scheduling and continuous optimisation without fragmenting systems.
• Design for growth. Elastic interfaces and modular upgrades avoid expensive refits as ridership rises or patterns change.
• Treat intermodal partners as users of your platform. Secure data spaces and documented APIs let everyone move faster while staying compliant.

Outlook from the floor

After leaving Shanghai, my impression is that Huawei is bringing cloud‑native thinking to rail while respecting the sector’s reliability bar. Mission‑critical wireless, operational cloud platforms, and applied AI are aligning around real operator priorities: more capacity on existing infrastructure, better passenger information, and lower lifecycle cost. As networks densify and interfaces with ports and airports tighten, coordination in the cloud – without compromising safety – looks like the edge that will separate leaders from laggards. Based on what I saw at Huawei Connect 2025, that future isn’t theoretical. It is arriving, one deployment at a time.