AI in Transport Operations: Data Governance Considerations

Artificial intelligence is increasingly being introduced into regulated industries to support decision making, reduce administrative burden, and improve operational visibility. Transport is no exception.AI is already being adopted in transport contexts worldwide. Industry reporting indicates that over 95 percent of transportation firms report increased value from AI related data technologies such as IoT and telematics, even while concerns around privacy and fragmented systems persist.Market forecasts project the global AI in transportation sector to grow from around 3.5 billion dollars in 2023 to nearly 15 billion dollars by 2030. This underlines the rapid uptake of intelligent tools across transport operations.From scheduling and compliance monitoring to incident reporting and performance analysis, AI assisted systems are beginning to play a role in day to day operations. As adoption increases, so does the importance of understanding how these systems handle operational data.In regulated transport environments, data is not simply information. It is evidence.Data as a Regulatory AssetOperational records including driver activity, vehicle status, incident logs, compliance checks, and communications are often subject to regulatory review. These records may be requested during audits, inspections, investigations, or legal proceedings.Any system that processes operational data must therefore be assessed not only for functionality, but for how it manages access, storage, and accountability, particularly when AI is involved.Many modern AI systems are designed for general productivity or analytics use rather than for regulated operational contexts. When applied without adequate safeguards, this can create uncertainty around data ownership, visibility, and long term control.These concerns are neither hypothetical nor unreasonable. They reflect the responsibility operators have to regulators, employees, passengers, and the public.Key Questions to Ask Before Using AI in OperationsTransport sector research consistently highlights fragmentation, data readiness gaps, and integration challenges as key barriers to technology adoption, even where potential benefits are clear.Based on discussions with transport operators and compliance professionals, there are four practical questions that any organisation should be able to answer before introducing AI into operational workflows.1. Who can access my operational data?It should be clear which individuals or entities can view, process, or interact with operational data.This includes not only internal staff, but also software providers, subcontractors, and any third parties involved in system monitoring or support. Access boundaries should be explicit, auditable, and aligned with operational roles.2. Is my data reused or analysed outside my organisation?Operators should understand whether their data is isolated to their own environment or aggregated for purposes such as analytics, benchmarking, or system improvement.In regulated contexts, reuse of operational data beyond its original purpose may introduce unintended risk. Clarity on whether data is sandboxed, shared, or repurposed is essential.3. Where is the data stored and how is it handled in practice?Data location, transport, and storage matter.This includes the jurisdictions in which data is stored, how it is protected in transit and at rest, how access is logged and reviewed, and how records can be retrieved during audits or incidents.Systems used in regulated environments should support transparency and traceability rather than obscure them.4. What is the data lifecycle and retention policy?Operators should know how long data is retained, under what conditions it is deleted, and what happens if they stop using a system.Clear retention policies support both regulatory compliance and operational control, ensuring that records remain available when required and are not held indefinitely without purpose.Designing AI for Regulated OperationsIntroducing AI into transport operations does not remove responsibility. It shifts it.AI is already used in real world transport settings for predictive maintenance, real time optimisation, and driver analytics. These applications demonstrate clear operational value while reinforcing the need for responsible data governance.Systems intended for regulated environments should be designed with the assumption that data may be scrutinised, reconstructed, or relied upon after the fact. This requires clear data boundaries, predictable behaviour, and governance models that prioritise accountability.At Okomfo, these principles inform how operational data is handled. Data is scoped to the operator, access is explicit, and records are treated as compliance assets rather than general purpose inputs.This approach reflects a broader reality. In regulated transport environments, trust is built not through novelty, but through consistency, control, and clarity.Looking AheadAI will continue to play an increasing role in transport operations. The relevant question is not whether these tools will be used, but how they are governed.For operators, regulators, and technology providers alike, ensuring that AI systems respect the role of operational data as evidence is a shared responsibility and a prerequisite for sustainable adoption.