**London**: A recent Deloitte survey reveals the state of AI adoption in the transportation sector, highlighting a broad interest but limited implementation. While many executives report engagement with AI, deep integration remains challenging due to data fragmentation and outdated systems, with significant changes expected over the next few years.
The realm of commercial transportation and logistics finds itself at a critical juncture, as the complexities of moving goods across a myriad of transport modes, routes, and timelines amplify the industry’s need for transformation. A new survey by Deloitte highlights the current landscape of Artificial Intelligence (AI) adoption within the sector, indicating a broad interest yet shallow implementation of AI technologies.
Among the 280 executives surveyed, an encouraging 75% reported that their companies are at least engaging with AI, with around half confirming they have implemented Generative AI (GenAI) solutions beyond initial pilot stages. However, the depth of this adoption appears lacking, with only 20% of respondents indicating a comprehensive integration within their operations. This inconsistency is largely attributed to the fragmented nature of industry data, which often remains unstructured or poorly managed, relying on outdated systems including green screens and, in some cases, paper.
The findings from this Deloitte survey, which includes responses from executives at large firms earning upwards of $10 billion annually, underscore a cautious outlook regarding the timeline for transformative changes via AI. Despite a strong belief in its potential, industry leaders anticipate significant shifts will not materialise for another three years or more—contrasting with sectors like financial services, energy, and healthcare, where shorter timeframes for transformation are expected.
From a functionality standpoint, broad implementations are emerging more in critical transportation areas such as strategy and operations rather than in supporting functions like finance or human resources. The survey indicates that the most pronounced economic impacts and adoption rates relate to route optimisation, asset management—including various forms of transport such as ships and trucks—and warehouse operations.
Companies with GenAI applications are primarily targeting cost reduction and efficiency, with 86% of respondents prioritising these goals in their efforts. They are keen to quantify the financial benefits from their GenAI investments within profit-and-loss assessments. Notably, 75% of those implementing GenAI have reported enhanced traceability in their operations, alongside improvements in dynamic supply chain decision-making (74%), inventory efficiency (67%), and customer relationship management (48%).
However, the data also reveals significant barriers to broader AI deployment. Respondents voiced concerns regarding talent readiness, absence of proper governance structures, and issues surrounding data privacy and security.
Examples of early adopters in the AI space illustrate the potential within the industry. Rail companies have begun utilising AI to provide real-time shipment tracking for their industrial clients, while predictive maintenance powered by AI is being utilised for safer operations—detecting issues in rail-car wheels to prevent larger failures. Another area of opportunity lies in resolving the “bill of lading problem,” where AI could identify revenue losses due to overlooked costs, thus streamlining transactions and reducing the manual effort required for auditing.
As transport organisations look to advance their AI strategies, a structured approach is recommended. They are advised to thoroughly list and analyse existing business problems across various operational areas, consider automating processes, and engage suitable technology partners capable of swiftly transitioning innovative ideas into full-scale implementations. Furthermore, establishing clear governance and risk management structures is deemed vital in navigating the associated challenges of AI use.
Investments in improving data quality are fundamental, as the effectiveness of AI systems is intrinsically linked to the integrity of their input data. The industry is encouraged to publicise their successes in AI adoption as a means of attracting further investment during periods of growth.
In summarising the shifts occurring within this traditional yet evolving industry, it becomes evident that transportation is on a trajectory toward significant transformation. As GenAI continues to reshape operations, the momentum for change is palpable, with the potential for substantial value creation and strengthened supplier relationships at the forefront of strategic initiatives in the sector.
Source: Noah Wire Services
Subscribe to Industry Updates
