Logistics leaders are at the forefront of adopting next-generation digital technologies. Our latest survey of over 260 respondents, encompassing both shippers and providers, uncovers fascinating trends and challenges in this digital era. With high digital adoption rates and robust investment plans, the logistics landscape is undergoing a significant transformation. However, challenges still persist, demanding a strategic approach to navigate through.

Unlock the Potential of Digital in Logistics

Intense Growth in Digital Adoption

Logistics leaders are loading up on next-generation digital technologies, as evidenced by our survey. For larger companies with revenues over $500 million, the growth is intense on top of already-high levels of digital adoption. They are actively exploring a wide range of advanced digital technologies and showing great ambition in their gen AI plans. Fifty-five percent have already implemented at least two gen AI use cases, and the same share expects to have implemented at least seven in three years. This shows their commitment to staying ahead in the digital race.In contrast, lower-revenue companies are generally less mature in their adoption and have deployed fewer digital and AI use cases. But within this group, consumer and healthcare companies have a higher level of digital maturity due to their strong end-customer focus.

Widespread Application of Digital Technologies

Shippers and service providers are applying digital technologies across various activities in the logistics function. From demand forecasting and capacity planning to warehouse automation and asset maintenance, the use cases are diverse. This year's survey included about a dozen gen AI use cases that automate tasks like scenario analysis and documentation generation. Different industries also have varying levels of technology deployment. Shippers in the energy, industrials, and materials sector lead in digital use case adoption, while advanced industry players have a slight lead in gen AI adoption. Healthcare companies, on the other hand, have the fewest current deployments and lowest expectations for new ones.

Challenges in Digital Implementations

Digital implementations are not without their difficulties. More than 40 percent of companies say past digital implementations have taken longer than expected to achieve business goals. Common challenges include technology-related issues like data quality and availability, as well as integration complexity. People issues such as skills shortages and change management challenges are also significant. Data quality challenges were cited more frequently this year, perhaps due to the increased data appetite of advanced digital tools. Process-related barriers like scalability and regulatory compliance are also highlighted, with regulatory compliance being a particular challenge for gen AI use cases.Despite these challenges, respondents indicate that the effort and time have been worthwhile. More than three-quarters say digital deployments have improved their strategic or operational effectiveness, and nearly 90 percent express satisfaction with currently deployed digital use cases.

Planning for Success in Digital Investments

To increase the likelihood of success in digital investments, organizations should take a more systematic and comprehensive approach. This begins with a holistic assessment of current logistics performance, considering end-to-end processes and the role of third-party transportation. By doing so, a value-first approach can be adopted to identify and prioritize high-ROI use cases.Upgrading data infrastructure is also crucial. An appropriate data architecture, including data lakes for centralized data management and streamlined integration, simplifies the deployment and management of advanced digital tools. Adding value-tracking mechanisms allows companies to monitor the impact of initiatives and refine strategies based on real-time insights.Finally, addressing people and process challenges upfront is essential. This may involve redesigning workflows and operating models to capture the full value of technology investments. Systematic upgrading of skills and the application of automation to create additional human capacity are also necessary. As AI-based use cases become the norm, developing a robust pipeline of data science specialists and talent with AI expertise is crucial.