Article | September 15, 2026

How AI technology is driving performance in the transportation industry

The pressure’s the same right across the transport industry. 

Whether you run airlines, rail, transit, logistics or automotive operations, it comes with the territory: improve service, control costs, modernize without disrupting the network… and repeat.

That’s a hard brief in any sector. And harder still in one where assets are forever moving, regulation is tight and margins can stay thin even in better years. The International Air Transport Association (IATA) expects airlines to post a 3.9% net margin in 2026, while return on invested capital is still forecast to remain below the cost of capital, for example. 

Forty-one percent of organizations say they have already implemented AI in at least one business function, according to Gitnux. But the next wave of advantage in transport won’t come from having the most AI projects. It will come from governing AI, data and automation well enough to improve real operating outcomes: more reliable services, better maintenance execution, faster decisions, stronger compliance and safer customer experiences. And for C-suite leaders, that’s what matters.

Transport is a classic execution business.

Value is created or lost in the handoff between plan and reality — in other words, a maintenance job completed late, a route that excludes a passenger, a poor asset record, a missed disruption signal or a validation process that can’t keep pace with safety demands.

That helps explain why so many digital programs disappoint. McKinsey reports that early adopters of AI-enabled supply chain management improved logistics costs by 15%, inventory levels by 35% and service levels by 65% compared to slower-moving competitors. Yet the same research says that more than 60% of recent projects were delivered late or over budget. 

The lesson is simple. The prize is real, but execution discipline is still a scarce asset. 

For transport leaders, that means a workable AI strategy must start with operating pain points, not technology enthusiasm:

• A predictive model is useful only if it changes how work orders are issued.

• A routing engine matters only if it improves reliability, accessibility or asset utilization.

• A digital twin matters only if it helps teams make better decisions at the speed of operations.

In practice, governed execution has three traits. First, it’s anchored to a measurable business result. Second, it’s embedded in frontline workflows instead of sitting in a side lab. Third, it treats security, compliance and trust as design requirements, not cleanup work.

That last point is increasingly important. The World Economic Forum’s 2026 cybersecurity outlook found that 87% of respondents identified AI-related vulnerabilities as the fastest-growing cyber risk in 2025. In transport, where safety, community confidence and functional continuity are inseparable, governance is not an administrative drag. It’s what makes scale possible.

You can see this in public transport, where DXC helped transport operators across sectors successfully deliver governed execution. 

MV Transportation developed an open-source application that collects, processes and distributes pedestrian pathway data, so agencies can improve mobility and availability. The practical outcome is not “better data” in the abstract. It gives a clearer view of sidewalks, curb cuts and pathway conditions, so agencies can plan more accessible fixed-route journeys and reduce dependence on costly paratransit, where appropriate. 

You can see it in rail maintenance, too. Queensland Rail rolled out a mobile work management solution to more than 600 mobile workers across roughly 8,000 kilometers of railway infrastructure. The result was stronger work-order execution, more complete field reporting, better compliance oversight and improved maintenance. For executives, that’s the ideal frame for digital investment: less friction in the field, better visibility for management and stronger control of critical assets. 

You can also see it in automated driving. CARIAD has been using a verification and validation framework that allows teams to create realistic virtual driving cases and test automated functions across traffic, weather and road conditions. The gains described publicly are faster, more collaborative software validation and improved system reliability. That matters because in transport, speed without trust is not progress. 

The executive agenda now is not to greenlight more pilots. It’s to choose a small number of operating workflows where better decisions compound value quickly, then insist on measurable outcomes, cross-functional ownership and disciplined rollout. Maintenance, service recovery, network planning, access and safety validation are all strong candidates because they sit close to cost, resilience and customer confidence.

The wider point is that transport modernization is becoming less about isolated digital tools and more about operational coherence. Leaders who connect data, decisions and frontline action — with AI acting as the bridge across them by transforming information into immediately usable actionable intelligence — will pull ahead. Those who treat AI as a parallel innovation track will keep accumulating experiments without changing performance.

Learn more

• Software solutions defining the future of automotive
• Transform AI potential into real-world performance