Almost all transportation executives (99%) believe that generative AI can transform the industry, according to a recent Deloitte report. However, only one in five amongst those surveyed have stepped up their efforts to broad implementation.
Seventy-one percent of the 200+ survey participants expect the shift to take more than three years, which Deloitte reported is slower than most other industries. Nearly one-third of the executives (30%) represent transportation companies with over $10 billion in revenue. Over half represent companies with $1 billion to $10 billion in revenue (37% with $1 billion to $5 billion, and 15% with $5 billion to $10 billion), and 18% are from companies with revenues under $1 billion.
Though many are adopting gen AI, few are doing so broadly, the report indicated. Fifty-four percent of respondents have at least one gen AI implementation, while another 21% have pilots only.
Logistics providers such as third-party logistics, freight forwarders and brokerages, are reportedly significantly less likely than other transportation entities (trucking, first-and middle-mile providers, and retail and consumer product supply chain/logistics) to have scaled gen AI implementations in all functions aside from strategy and operations.
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When asked on organization's current level of adoption and its economic value, some operational areas attract more attention, while a few early use cases show more early potential.Deloitte
Asset management, route optimization and warehouse operations are seeing the highest adoption rate and economic impact so far. Demand planning, inventory management and fleet management are also seeing high adoption but with less economic value. None of the participants have utilized gen AI for frontline workforce productivity or recruitment and detention.
The Deloitte report indicated finance as a missed opportunity, with only 30% having a mature finance implementation. But for those who do leverage its capability, 93% report high economic impact — the highest of any use case.
For those expecting bottom-line success, most participants expect that gen AI can offer greater efficiency and higher revenue. However, few say they are achieving those goals. Only 13% of 56% who said they wanted to increase revenue through gen AI have succeeded. In comparison, fewer are aiming gen AI at goals such as encouraging innovation, enhancing client relationships and uncovering new insights. More than half with those goals say they’ve succeeded.
Clear challenges
Among larger companies, risk management and governance are noted as barriers, while tech infrastructure and tech talent are less of a concern. Whilst companies are moderately confident about their readiness to implement gen AI in strategy and operations, only half of companies with over $10 billion in revenue are confident in their technology infrastructure.
For the smallest companies surveyed, executive commitment, maturity of AI technology and tech infrastructure are noted as critical hurdles compared to other entities whose revenue is over $1 billion.
Data-related risks are primary concerns among survey participants.Deloitte
Data is another hurdle in its implementation for most companies, with four in 10 respondents noting it as a primary concern. Forty-one percent noted a data-related concern (with 21% noting unauthorized use of organizational data and 20% pointing out misuse of client data). One in five cited unintended outcomes as a critical data-related concern of gen AI’s rapid adoption.
