Few industries are as ripe for change as commercial transportation and logistics given the complexity of moving goods around the world via disparate transport methods, routes and timelines. The AI opportunity in particular is rich, but adoption, though wide, is still shallow.
Adoption is wide in the sense that 3 out of 4 surveyed companies in the industry are at least experimenting with AI. And about half say they have a bona fide Generative AI (GenAI) implementation (vs. pilot). But adoption is shallow in the sense that only 20% have a broad implementation. Too often industry data is not structured for easy use, managed on green screens and, in some instances, even paper.
This is according to a recent Deloitte survey of 280 executives with strategic oversight at transportation and supply chain companies, nearly one-third of whom represent companies with more than $10 billion in revenue.
Seeking Economic Impact
There’s little doubt that AI will eventually produce enormous value. The industry is an outlier, however, in how long respondents think industry transformation will take. Most believe it will occur beyond three years from now, as opposed to financial services, energy, and healthcare. According to the Deloitte survey, executives in those industries expect to transform a year or two earlier.
Broad AI implementations, though relatively scarce, are underway more often in core transportation functions (strategy & operations; supply chain) than in enabling functions like finance, IT, HR and risk. Areas with the highest adoption and economic impact include route optimization, asset (e.g., ships, locomotives, railcars and trucks) management and warehouse operations.
Among respondents with at least one GenAI implementation, nearly 9 in 10 respondents (86%) are prioritizing cost reduction and efficiency improvement in their GenAI efforts. A key goal is attempting to quantify GenAI benefits in their profit-and-loss analyses.
Data Drives Insights
Innovators are realizing other valuable business outcomes from experiments in supply chain operations and customer service. A full 75% of respondents who have at least one GenAI implementation reported improved traceability. Other outcomes include enablement of dynamic supply chain decisions (74%), improved inventory efficiency (67%) better customer relationship management (48%), digital commerce and distribution strategy (46%) and consumer insights (42%)
What’s holding back deeper AI adoption? Although respondents are bullish on AI’s transformative power, the survey revealed concerns about talent readiness, lack of governance and data privacy and security.
Real-World Examples
Nonetheless, some early adopters are seeing real value in GenAI adoption.
Rail companies are using AI to answer similar questions for industrial customers (e.g., where’s my shipment?). AI is also powering predictive maintenance in the sector, where fixed cameras capture images of rolling trains to detect flaws in rail-car wheels, potentially catching problems before they become catastrophes.
One massive cross-industry opportunity is the “bill of lading problem.” Shippers, being buried in work, often forget to bill for all eligible costs. A clerk may leave out, say, a fuel surcharge, thereby sacrificing earned revenue. Shipping customers, similarly deluged, often overpay due to lack of time for scrutiny across all costs. AI, after training on a set of complete and fair bills of lading, will be able to detect needle-in-haystack errors on both sides of the transaction. Along the way, GenAI could reduce manual processing, accelerate trade financing and reduce audit fees.
Your Next Moves
Industry players investing in AI should consider six strategies to make their next leap forward:
- Dive into the deep end. Start by systematically cataloging business problems in your marketing, sales, operations, supply chain, finance, IT, HR and legal/risk/compliance organizations. Then look to automate tasks or to learn about the potential of AI and GenAI and start a pilot. Every challenge is an opportunity to improve.
- Collaborate with partners. Find tech partners and advisors who are steeped in AI and can apply it to diverse business problems. Smaller, more agile companies may be better suited than larger established players to move quickly from idea to full-bore implementation.
- Clarify governance structures. AI’s risks are as significant as its opportunities, so it’s essential to devote resources to areas like workforce education and risk management.
- Measure value holistically. Bottom-line impact may indeed result from your GenAI investments, but it can be difficult to quantify. In addition to new revenue, also consider the value outcomes like improved supply chain decisions, traceability, inventory efficiency, and consumer profiling.
- Improve data. AI is powerful, but the garbage in/out axiom nevertheless applies. Leaders are investing effort in cleaning, organizing, and modeling their data for optimal output quality.
- Trumpet successes. Transportation can be a good bet for investors during high-growth times. To attract investment, publicize your accomplishments, progress and improvements in customer service.
As a long-established industry with complex data and partner ecosystem, transportation is taking prudent steps toward inevitable transformation. Momentum is increasing, hard-dollar value is being created, and customer implementations are providing important lessons for every participant. With GenAI transforming business, this industry is no exception. Its time has arrived.
