Gen AI transforming transportation: Lessons from the frontier of an emerging technology
Gen AI shows early promise in the transportation industry, as leaders chart a path around significant adoption challenges, toward effective implementation
Larry Hitchcock
Mauricio Garza
Eileen Crowley
Kate Ferrara
Maggie Rauch
As leaders in transportation grapple with shifting trade flows, margin pressures and increasing demands from shippers and regulators, generative AI is increasingly among the technologies they employ. Deloitte’s July 2024 survey of over 200 executives indicates activity ranges from pilots and limited implementations to broad, functional, and constant use of gen AI, and industry leaders are looking ahead to how lessons from the incipient period of this technology can help chart the best path forward.
While over half of companies surveyed have active gen AI implementations, they tend to be limited and focused on a few aspects of the business. Core operational areas, such as supply chains and strategy and operations, have received the most attention. Many also report implementations in sales and marketing, but this varies widely across subcategories within transportation (figure 1).
Enough value is being realized to convince leaders of the transformative potential of AI. Nearly all transportation executives surveyed (99%) expect the technology to transform their industry—but more than two-third (71%) expect this transformation to take more than three years, which is slower than several other industries studied.1 The transportation use cases seeing the highest adoption and impact are asset management, route optimization, and warehouse operations (figure 2). While these implementations are mostly limited, more than half of companies surveyed are running gen AI initiatives within each of these use cases, and roughly 80% of adopters report extremely high or high economic value in each use case.
In the race to apply gen AI, transportation companies might be overlooking opportunities to realize efficiency. Nearly all respondents report feeling unprepared to adopt AI from risk, compliance, and talent perspectives. Most lack even limited implementations for back-office functions such as IT, HR, and legal. It may be that ongoing technology transformations are already targeting efficiency in these back-office functions, while gen AI investments are directed toward core business functions.
Significant barriers to adoption include technology infrastructure, risk and governance, and talent. At the same time, data-related pitfalls represent the biggest risks on executives’ minds, as 40% cited misuse of data as their number one gen-AI-associated risk. Remarkably, even the companies identified as gen AI leaders (the top 17% in adoption and value creation) report many of the same challenges as others. The biggest differences observed pertain to their risk-mitigation approaches. Leaders are more likely to have imposed companywide data-governance policies, rather than delegating to individuals and committees.
While it can still be considered early days in gen AI adoption, this technology has the potential to move quickly from novelty to necessity. Leaders will increasingly expect return on their gen AI investments. Understanding where success is emerging and which challenges appear most frequently, and learning how the savviest are navigating, can help chart a course to effective adoption, and to the leading edge of the coming industry transformation.
