Logistics leaders are loading up on next-generation digital technologies, with most expecting to adopt at least ten additional digital use cases over the next three years. Our latest survey of more than 260 respondents, representing both shippers and providers, reveals high digital adoption rates, robust investment plans, and a growing interest in the opportunities presented by advanced generative artificial intelligence (gen AI) technologies. While momentum is high, this year’s survey also shows that challenges still exist. The logistics technology landscape remains extremely fragmented, often requiring companies to adopt several solutions to execute logistics effectively. This year’s data suggests that companies are largely satisfied with the performance of their digital investments; while more than 85 percent saying digital projects have added value to their organizations, many still cite challenges. Many respondents admit that their digital journey has taken longer than expected to pay off, due to challenges with data quality, systems integration, and change management.
The logistics function has been pursuing digital solutions for its strategic and operational challenges for several years, with these efforts accelerating during the supply chain turbulence that characterized the early 2020s. Our previous survey, conducted in 2023, found that logistics functions had already built foundational technology stacks, with leaders actively exploring a wide range of advanced digital technologies.
However, to successfully capitalize on digital enablement in logistics, it’s critical to understand the value potential and develop a clear vision, including what processes and technology will do what activities to capture what value. This paves the way for a clear view of how and what data to use, how to stitch the data and systems together, and perhaps most importantly, how the organization will need to adapt the ways they work. This means overcoming long-standing and largely analog sources of delay and value erosion, including skills shortages and the difficulty of getting employees to change the way they work.
Digitization in full flow
Shippers and service providers are applying digital technologies across a wide range of activities in the planning, sourcing, execution, and performance management of their logistics function. Our survey examined respondents' adoption of some 28 digital use cases, from demand forecasting and capacity planning to warehouse automation and asset maintenance. In an evolution from last year's survey, this list included about a dozen gen AI use cases, which use large language models and similar technologies to automate tasks such as scenario analysis and documentation generation.
For the larger companies in our survey—those with revenues over $500 million—the headline story is one of intense growth on top of already-high levels of digital adoption (Exhibit 1). Lower-revenue companies are generally less mature in their adoption and have deployed fewer digital and AI use cases.
Large enterprises are even more ambitious in their gen AI plans, with that ambition shared equally across shippers and service providers (Exhibit 2). Fifty-five percent of large enterprise respondents have already implemented at least two gen AI use cases, and the same share of respondents expect to have implemented at least seven in three years.
Technology deployed by shippers varies by their industries. Shippers in the energy, industrials, and materials sector lead in the adoption of digital use cases, while advanced industry players hold a slight lead in gen AI adoption (Exhibit 3). Healthcare companies have the fewest current digital and gen AI deployments and the lowest expectations for new deployments in the next three years.
Smaller companies—those with less than $500 million in revenue—generally lag their larger counterparts in existing and expected use case deployments, often because of resource constraints and a more cautious approach to investment. Within this group, consumer and healthcare companies have the highest level of digital maturity, likely a result of their strong end-customer focus.
No silver bullet
Logistics functions are pursuing a broad range of digital objectives. Most of the use cases in our survey appear on the three-year to-do lists of 43 to 53 percent of large-company respondents or have already been implemented by these organizations (Exhibit 4).
Looking only at digital tools that are already up and running, however, our survey found clear differences in adoption rates and perceived value between traditional digital and gen AI use cases (Exhibit 5). This reinforces the point that it’s still early days for these advanced gen AI technologies.
Roughly half the companies that have already begun gen AI transformations also believe that implementing these tools is fundamentally different from previous digital efforts. Nearly 60 percent say these transformations are also more complex. However, companies expect the extra effort to be worth it, with about 60 percent saying they are more excited about gen AI transformations than conventional digitization, and 65 percent expecting these new technologies to deliver more value.
Are we there yet?
Digital implementations are already difficult, even before the added complexity of gen AI. More than 40 percent of companies in our survey say past digital implementations have taken longer or much longer than expected to achieve their business goals (Exhibit 6).
However, 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. Although our data set has fewer gen AI use case deployments than traditional digital deployments, users’ perceptions of payback time, impact, and overall satisfaction are similar.
Delays getting to the finish line
Digital has been around long enough for most companies to understand that successful implementations don’t rely on technology alone. Effective digital deployments also need skilled people, rigorous change management, and effective mechanisms to deploy, scale, and integrate new ways of working.
Among respondents to our survey, the most common challenges faced in digital projects are technology-related issues, including data quality and availability and integration complexity. People issues, including skills shortages and change management challenges, are a close second.
Data quality challenges were cited much more frequently this year than in our previous survey. This may be because of the greater data appetite of advanced digital and AI tools. Companies may also be dealing with imperfect data more often as they scale new use cases across their organizations.
Process-related barriers, such as scalability and regulatory compliance, are highlighted by many respondents, with the latter seen as a particularly acute challenge for gen AI use cases. The difficulty of achieving a return on digital investment was cited as an issue by only one in eight respondents, but it was the barrier to progress most often cited by the minority of companies that have recently delayed or shelved digitization plans.
Planning for smoother journeys
Research elsewhere has found that three-quarters of digital transformations in logistics fail to achieve all their stated goals. To increase the likelihood of success, organizations should consider a more systematic and comprehensive approach to digital investments.
Such an approach should begin with a holistic assessment of current logistics performance that considers end-to-end processes, including fleets, warehouses, network configuration, and the role of third-party transportation. This assessment enables a value-first approach to identifying and prioritizing high-ROI use cases.
While some digital use cases can be implemented alongside existing systems, companies may also need to upgrade their data infrastructure to simplify the deployment, scaling, and ongoing management of advanced digital tools. An appropriate data architecture might include data lakes for centralized data management and streamlined integration. By adding value-tracking mechanisms, companies can monitor the impact of initiatives and refine strategies based on real-time insights.
Finally, companies should address people and process challenges up front. This may involve redesigning workflows and operating models to capture the full value of technology investments. It will certainly require the systematic upgrading of skills, along with the application of automation to create additional human capacity. As AI-based use cases increasingly become the norm, organizations will need to pay particular attention to developing a robust pipeline of data science specialists and talent with expertise in AI and machine learning technologies.
