Arkajit Rakshit

Autonomous tech is reshaping mobility — and its potential in logistics is just as big. The technology works. What takes time is scaling it in a way that builds trust, earns confidence, and fits into the realities of day-to-day operations. The opportunity isn’t replacing humans — it’s empowering them. Imagine autonomous vehicles managing high-volume, repetitive routes while teams focus on exception management, customer relationships, and strategic growth. Early pilots show what’s possible: in some urban test zones, autonomous last-mile deliveries have cut costs by up to 30% while improving on-time performance. But success depends on systems and people working in sync — data, automation, and human oversight aligned.

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As product leads, we live in back-to-back meetings, firefighting, and constant context switching. It’s easy to forget that every shipped feature compounds into real impact. To our tech & product teams : yes, we spend long hours working on something which may seem insignificant. This is just a reminder : Atlas WMS is a foundational tech stack powering Walmart and Sam’s Club’s warehouses. Small features at a scale this big → massive impact. ✨ #WMS #warehousemodernization #productmanagement

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Coming out of multiple conversations with customers and partners this past week at #TPM26, one thing became very clear. Regardless of role, geography, or technology stack, the same fundamental equation continues to drive supply chains: price equals cost plus value. At TPM this year, that equation showed up with renewed recognition. In international supply chains, the real value is not faster transit. It is reliable end to end lead time from PO issuance through production and final delivery. Most companies focus on one node or the other, but the bigger driver of inventory and working capital is variability across the full cycle. When production lead time swings and transit is unpredictable, planners hedge. They order earlier, increase safety stock, and tie up cash. The average lead time may not change, but the range does, and that range is expensive. When PO milestone tracking improves and production and transit become more reliable, safety stock drops, expedites decrease, and working capital frees up. Same freight rate, same average transit, very different financial outcome. The real question is not how fast your freight moves. It is how predictable your PO to delivery cycle is. Predictability is where the value sits. On a more personal note, it was incredibly rewarding to reconnect with customers past, present and future, as well as colleagues. What stands out every time is the shared passion across this community. Different companies, different roles, but the same drive to improve and strengthen global supply chains. That energy is what makes this industry so compelling.

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The 2025 Top Tech list from Food Logistics and Supply & Demand Chain Executive mirrors the levels of optimism over the potential of AI to increase the efficiency and effectiveness of supply chains. In her commentary, Marina Mayer points out that 59% of the winners describe themselves as being in the 'AI' category. I'll leave aside any commentary of the [short term] upside of attaching 'AI' to a company/product description and focus instead on a category I call 'AI Adjacent'. These are companies that either a) make AI models more valuable or b) can significantly increase value to customers by leveraging AI. In noting that you might find different people putting the same company into 'AI', 'AI-powered automation', or 'AI Adjacent' I'll give a few examples from the list... - Tive. I wrote yesterday about the opportunity for dynamic, ground truth data to power automated workflows (think Agentic AI) and how the combination of AI and billions of historical data points will be combined to create new insights. Cargo security* is the example here but lane/network optimization is the big one: https://lnkd.in/eTscGFYC - Voxel. Next to IoT sensors, data from various types of cameras will become a critical input to AI models and a technology that will benefit hugely from AI. If a feature of IoT sensors is 'reach' (anywhere in the world), a feature of Computer Vision is 'coverage' (everything within a field of view). Workforce safety is a good example of quick/clear ROI but, again, optimization is the big one. - Loadsure. If ‘cargo security’ and ‘worker safety’ are two near-term use cases for analytics built on IoT data and Computer Vision then there is also a big opportunity (need) around insurance. Loadsure 'has compressed the insurance stack’ by embedding dynamic pricing for cargo risk in a TMS. - AutoScheduler.AI and Pallet. I'll give credit to Benjamin Gordon for this one who recently called out the news that Lineage believes they will generate $110 million in additional EBITDA over the next 5 years as they deploy their new warehouse software platform, LinOS. He noted that few companies can such tech themselves and that this ROI will come from partnering with companies such as Pallet and AutoScheduler: https://lnkd.in/exCDkGY8 ...food for thought (sorry!)... The full list is here: https://lnkd.in/eWnKVMTH * given rising levels of cargo theft, and that food & beverage is often a target (lots of targets and easy/quick to sell), I was surprised not to see more security tech companies on the list...maybe next year. #AI #logistics #supplychain

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