Every single day, nearly 60 million packages are brought out of delivery trucks and placed on porches, stoops, and steps across the country. Shippers have never been busier. Keeping pace with the speed of the internet and rising consumer expectations, packages, boxes and bags are being picked, packed, and shipped faster than ever. Despite increased workload on shipments and deliveries, US consumer delivery expectations decreased from 5.7 days just 5 years ago to 2.5 days today and are still dropping dramatically. In some product categories, customers expect next-day—if not same-day—delivery, a demanding standard online retailers and their shippers are feeling firsthand.
Pick up any package delivered to your door in the last 48 hours? Chances are, it traveled thousands of miles to get to you. And to make that happen in the hours between you clicking “buy” and now, it passed through one of the most technologically sophisticated environments on the planet: well-orchestrated global supply chains feeding highly automated warehouses, where advanced storage systems and fleets of autonomous robots work alongside human employees to pick, pack, and fulfill orders—all driven by data and continuous process optimization. But you probably wouldn’t know that watching the box truck full of cardboard boxes drive down the street.
The prevailing vision of logistics is a bit manual, even a little grim: workers in warehouses with boxes to the rafters, scanning barcodes as forklifts scurry back and forth under fluorescent lights. But reality is much different. What’s actually happening inside warehouses and fulfillment centers is closer to a live experiment of the future of industrial AI—towering inventories stacked 30 feet high, fleets of autonomous robots moving with quiet precision, tightly coupled software platforms constantly exchanging terabyte of data, and human decision‑making binding it all together across real‑world constraints. And, in the words of Sally Miller, global chief information officer at DHL Supply Chain, it’s moving at a “breakneck pace.”
Unlike other industries, the logistics sector had a peculiar advantage going into the AI era: decades of experience adapting to structural change and rapidly shifting customer expectations. That experience became a critical asset around 2016 and 2017, when a major wave of disruption hit, driven by the first online mega-retailer to become a household name and the aggressive investment in warehouse robotics and the consumer expectations it created. If one company could tell you your delivery was five stops away, every competitor had to figure out how to meet that bar. That triggered a flood of venture capital into the space. Suddenly, a somewhat thankless and not often thought-of industry unexpectedly became one of the most fertile grounds for technology development.
DHL Supply Chain, one of the world’s leading contract logistics businesses, didn’t respond to the squeeze of moving even more packages faster by simply buying new tools. Instead, working with partners like Boston Consulting Group (BCG), it built the infrastructure needed to evaluate, test, and deploy emerging technologies at scale—helping define how innovation actually reaches the logistics industry.
“Logistics was an industry that was ripe for innovation, and we saw this trend emerging, so we focused on developing a structured program that focused on 12 technology areas that we felt were going to impact the supply chain in the next three to five years,” Miller says. “We have more than 2,800 sites across the globe—that’s why we need technology that can scale. We’re not interested in solutions with such narrow use cases that they only make sense in a handful of locations.”
Instead of betting on niche technologies, Miller’s strategy is to prioritize technologies that can be deployed at global scale—delivering real impact quickly, not hypothetically. So the team built funnels, tracked technology categories, and created internal teams dedicated to scanning the market and engaging with startups and VCs, and running structured proof-of-concept pipelines. It wasn’t glamorous work, but it turned out to be the work that determines whether technology scales or quietly stalls.
When generative AI arrived, DHL was ready in a way others simply weren'’t. They had clean data and data scientists who knew what to do with it, along with governance structures already in place. They had, strategically, been preparing for this development.
“There is definitely an advantage when you’ve gone through that process and developed core functionality and agents in-house that can be used elsewhere,” says Miller. And her team had that.
The contrast with late movers is now visible. BCG, which works across industries, reports on its AI logistics case studies to clients in other sectors. The clients see the work and say, “Wow, you did this with DHL two years ago—we’re just getting there now,” says Markus Weidmann, managing director and partner at BCG.
The gap he sees between leading-edge companies like DHL and those still trying to figure out their AI strategy isn’t widening because of ambition or investment—it’s because of the technological infrastructure needed within a company to be ready for these technologies, and, most importantly, team capabilities. Companies that spent years treating automation deployment at scale and employee knowledge as a strategic priority have a compounding advantage.
When the contract logistics business within DHL Group began its structured Accelerated Digitization program years before generative AI entered the mainstream conversation, the team started with prioritizing technology categories—robotics, data analytics, autonomous handling, AI. Then, they brought in the teams needed to conduct research, create proof of concepts, test products, and support implementation. As is usually the case with technology (and science, for that matter), the methodology proved its value when things went smoothly, and also when they didn’t.
During an AI pilot program with BCG, the team pursued two use cases simultaneously. One was to find what they called the sweet spot of AI-assisted proposal writing, where the system could support drafting responses to complex client RFPs, surface relevant data, and even integrate publicly available news about a client’s business that a human salesperson could easily miss. That use case basically went off without a hitch.
But the second use case was more challenging. DHL hoped to use AI to assist with warehouse design. Its contract logistics arm employs hundreds of expert design engineers with 20-plus years of experience who analyze enormous volumes of client data before recommending a facility layout, staffing model, and cost structure.
It didn’t go well at first, and the early results were unconvincing. Standard large language model architectures, it turned out, weren’t well-suited to the precise numerical reasoning required for warehouse design. The team faced a choice: Abandon the use case or find an alternative approach.
The team pivoted to an agentic model architecture—one built specifically to handle the complex calculations involved. At the same time, luckily, the underlying AI technology itself caught up. The jump from earlier LLM generations to more capable ones came at exactly the right moment.
“If it would have been six months earlier,” says Miller, “I'm not sure the tech would have been fast enough to live up to that level of sophistication.”
Instead of having to learn to use the next generation of an AI tool, together, the DHL and BCG teams were already constructing uses that could accommodate next-gen tools. They were, in effect, waiting for the technology to catch up to them.
The warehouse design tool that eventually emerged does something more interesting than simply speeding up a process. It frees up some of the most experienced people in the organization to do the work that actually requires their expertise—and that they actually enjoy doing. The data crunching, the standard analyses, the repetitive numerical work—the AI handles it. The warehouse designers can then focus on client relationships and creative and strategic work.
That, in itself, is part of the broader lesson the logistics industry can lend, and that applies far beyond any single industry. The companies pulling ahead in AI adoption share a common profile: They looked ahead and prioritized data quality and process documentation with the technologies of the future in mind, then built the organizational muscle to integrate new tools and capabilities quickly and at scale with their team of employees.
The next package you receive will have been touched, in some way, by systems most industries still aren’t using at such large scales. Machine learning models that can predict inventory discrepancies before they happen, AI agents drafting sales proposals, and autonomous systems organizing warehouse inventory. What consumers take for granted when a package arrives at their doorstep is, in reality, an exercise in industrial‑scale automation and AI—coordinating thousands of decisions long before the doorbell rings.
