The transportation industry is missing a knowledge layer

Despite ongoing innovations in LLM models and agentic AI, today’s transportation companies have not been able to fully leverage AI technologies. And it’s certainly not because they don’t have the data.

Transportation sits in a unique position for AI adoption, generating massive volumes of continuous data through telematics, sensors, transactions, and operational systems. This rich, dynamic, and highly contextual data makes transportation one of the strongest candidates for AI-driven prediction, optimization, and autonomous decision-making. Yet most of this data remains siloed throughout the transportation chain.

This leaves organizations vulnerable to seemingly isolated operational issues that can cascade through scheduling, customer service, maintenance, and financial planning. What starts as a technical event often becomes a human coordination problem involving multiple teams, systems, and decisions.

Linking transport without linked data

Most transportation organizations have invested heavily in collecting and storing data that remains siloed within individual functions. This means valuable data cannot easily be combined into a unified view of what is happening and what actions should be taken next.

Deloitte’s own research confirms that 59% of leaders see data quality as a major problem, with serious implications for seemingly isolated incidents.

One incident, three problems

Imagine a severe thunderstorm grounding several aircraft, closing a highway, or preventing a marine crossing.

Operations teams would assess its impact on aircraft availability, crew assignments, gate capacity, and passenger itineraries. From that point, much of the response would still depend on manual coordination across multiple functions:

• Coordination. Multiple teams coordinate via calls, emails, and dashboards.
• Scheduling. Planners manually decide which flights or shipments to cancel or delay.
• Rebooking. Airline agents manually rebook passengers and arrange hotels and vouchers. Freight agents rebook shipments and arrange fleet, drivers, and carriers. Customers wait for updates and often call the contact centre.

Recovery can take hours and continue to depend on human coordination. And human input is not only inefficient, but it can also be incorrect.

Connected data for faster insights and decisions

By contrast, organizations whose AI agents operate on trusted, connected data can respond to disruptions with greater speed, confidence, and coordination. A connected knowledge layer—also known as an ontology—ensures common definitions and shared understanding.

Using this knowledge layer, AI can understand how operational, customer, workforce, and financial data relate to one another. Then, it can use that knowledge to support decision-making across the enterprise.  

Example: Airline knowledge layer 

One layer, three solutions

With this knowledge layer, AI agents can automate much of the work, automating the response to the previous situation.

• Coordination. This time, AI detects the disruption in real time from weather and operational data and begins immediately coordinating the response.
• Scheduling. AI optimizes the recovery plan, selecting cancellations that minimize the impact and operational cost to passengers, terminals, and shipments.
• Rebooking. AI automatically rebooks shipments or passengers, who require new hotels, meal vouchers, and baggage routing. Passengers can also receive personalized alternatives and digital boarding passes within minutes—often before asking.

With a fully implemented knowledge layer informing AI solutions, recovery is substantially accelerated, and accuracy is substantially higher. This time, humans only need to approve exceptions.

How Deloitte can help

Knowledge layers must represent relationships across the entire enterprise, which is why organizations need a partner that understands both the breadth of transportation operations and the rapidly evolving AI landscape. Deloitte combines deep industry experience with data, cloud, ontology, and AI engineering capabilities to help organizations create the connected knowledge foundation required for agentic AI at scale.

Our experience spans the full transportation ecosystem, including aviation, rail, ports, transit, logistics, customer operations, asset management, workforce optimization, and finance transformation. We understand how operational decisions affect downstream business outcomes and how disconnected information can limit the value of AI investments. We can also help you navigate adoption, governance, and operating model implications for complex operational decision rights.

Ready to begin? Let’s continue the conversation.