Rohit Laila has spent decades navigating the intricate web of global supply chains, witnessing firsthand how technology either bridges gaps or creates new hurdles for the movement of goods. As an expert in logistics innovation with a career spanning deep operational roles and high-level strategy, he has guided numerous organizations through the transition from manual entry to sophisticated automation. Today, we sit down with him to discuss why Robotic Process Automation remains a cornerstone of freight operations despite the persistent industry push for standardized modernization. Our conversation explores the structural realities of logistics that make APIs a distant dream for many, the common pitfalls that lead to the phenomenon of “zombie bots,” and how the shift from deterministic to probabilistic AI is changing the stakes for operational accuracy. We delve into the nuances of why logistics functions differ so drastically from finance or HR, the necessity of designing for the “exception tail,” and how to build a resilient automation estate that survives the inevitable decay caused by external interface changes.
Logistics involves fragmented external interfaces like carrier portals and customs systems that often lack standard APIs. How do you decide when to stop waiting for an elegant integration and instead deploy a bot to manage those dozen open browser tabs?
The decision usually boils down to the hard reality of the shipment lifecycle rather than a lack of desire for modern technology. In my experience, a single shipment can cross paths with a carrier portal, a customs broker’s document system, a terminal platform, and a 3PL warehouse management system, all within a matter of days. Most of these parties have zero commercial incentive to build a custom API for one counterparty, and the smaller players simply don’t have the engineering staff to even entertain the idea. When you are looking at a teammate on the operations floor who is manually keying container numbers into a portal because that is the only way to get the data today, you realize the gap isn’t technological—it’s structural. We choose automation because it can ship this quarter against systems that no one in our building controls. It isn’t about being elegant; it’s about the fact that if a partner has been “meaning to upgrade” since 2019 and still sends spreadsheets, we can’t afford to let our operations stall while waiting for a roadmap promise that never arrives.
Among the various processes within freight operations, which specific areas consistently provide the highest return on investment for automation, and why do they stand out?
The real winners in this space are high-volume tasks that interact with external interfaces that change just slowly enough to make encoding worth the effort. Milestone and status consolidation is a classic example because tracking data arrives in every possible format, from EDI 214s to manual portal lookups, and bots are excellent at normalizing this into a single status view. I also see massive value in freight bill audit and reconciliation; since it involves comparing invoiced charges against contracted rates and fuel surcharges, the recovery is measurable in cold, hard cash. Container and equipment availability checks are another area where bots shine, specifically for tracking the last free day or terminal lookups where a miss is incredibly expensive. A bot that checks for changes every hour and escalates a shift in availability beats a person checking twice a day every single time. Finally, the assembly of customs and trade documentation—pulling fields from packing lists and commercial invoices—saves an enormous amount of manual assembly work, even if you keep a human in the loop for the final submission to ensure compliance.
You’ve mentioned that the business case for logistics automation is often priced incorrectly. What are the hidden factors that most companies overlook when calculating their return on investment?
The biggest mistake is treating maintenance as a flat, low percentage of the build cost, which might work for internal finance systems but is a disaster in freight. Because our bots run against interfaces owned by other companies—like a broker changing a document layout or a port adding a bot-check to their lookup—there is a natural decay rate on the automation estate. I always tell my teams to look for the “half-life” of an automated process: how long will this run before an upstream change quietly breaks it or, even worse, changes the data it returns? Another correction needed is how we measure success; most programs report that they have automated ninety percent of tracking updates, yet the operations team feels no relief because the remaining ten percent are the “hard tail” exceptions. These are the short-ships, the customs holds, and the detention disputes that require context a human would have gathered if they had worked the whole batch. If your bot simply dumps these difficult cases into a shared mailbox, you haven’t removed work; you have just moved it around and made it more confusing for the staff.
There is a terrifying concept you call “silent wrongness” where a bot appears to be working but is actually corrupting data. How can logistics leaders protect their operations from these types of failures?
Silent wrongness is the most dangerous failure pattern because the run log shows a successful completion while the bot is actually booking the wrong accessorial code or misinterpreting a status change. To fight this, you have to move beyond monitoring “green” completion statuses and start instrumenting for output correctness. This means sampling outputs against known-good results on a strict schedule and setting tolerance thresholds that trigger a manual review if something looks even slightly off. We also have to break the cycle of “ticket-queue maintenance” where a selector is fixed, the ticket is closed, and the organizational knowledge of why it broke evaporates. If four brokers change their document formats in the same quarter, that is a commercial signal that our automation estate should be capturing and reporting. Without a mechanism to reconcile bot-produced records against downstream systems, you might find yourself with months of suspect data before a human finally notices the error in a monthly report.
When an automation program reaches its fourteenth month, it often faces a crisis of trust or budget. What specific adoption sequence should a company follow to ensure their bots remain durable and effective?
The survival of a program depends on shifting the focus from volume to durability during the process selection stage. You should score every candidate based on how many external interfaces it touches and how often those interfaces have historically changed; building the durable, internal-facing ones first creates the early wins that buy the program time. Most importantly, you must design the exception path before you ever build the “happy path.” This forces you to decide exactly what the bot does when it cannot proceed—what it captures, who it routes to, and what that person sees on their screen—ensuring that the operations team actually trusts the system. We also have to treat maintenance as the core operating model rather than a secondary service line, which means having a standing budget and named ownership from the very first workshop. Finally, you have to be willing to retire bots deliberately; if the underlying process has moved on or the interface has become too unstable, kill the bot rather than letting it become a “zombie” that no one monitors but everyone pays for.
As we integrate more sophisticated AI and document intelligence into these workflows, how does the nature of failure change for logistics automation?
The shift from deterministic, scripted bots to probabilistic AI tools is a double-edged sword for the industry. On one hand, document intelligence has changed the economics of paper-heavy tasks like reading bills of lading or arrival notices, allowing us to process documents that previously required too much manual configuration. However, while a standard bot fails “loudly” with a broken selector or a missing field, AI fails “softly” and plausibly. It can produce an output that looks perfectly reasonable and fits the expected format long after it has stopped being accurate, which makes the discipline of correctness monitoring even more critical. Every point we’ve discussed about exception ownership and logging break causes becomes harder to skip when the automation is making judgment calls rather than just following a script. The programs that will still be standing in the next few years are those that realize the discipline required for a scripted bot fleet is the exact same discipline needed to make an AI-assisted one safe and reliable.
What is your forecast for the evolution of robotic automation within the global supply chain over the next few years?
I believe we are moving away from the era of “set and forget” automation and toward a model of continuous, high-fidelity monitoring where the bot is viewed as a digital teammate rather than a static piece of software. We will see a much tighter integration between commercial relationships and technical performance; when a carrier’s portal changes, it will be seen as a signal of their operational maturity that influences procurement decisions. The organizations that thrive will be those that stop asking “how fast can we build this?” and start asking “how will we know when this stops being right?” We will see a shift in the labor market as well, where the most valuable operations managers are those who can manage a hybrid workforce of humans and bots, knowing exactly when to step in and handle an exception. Ultimately, the future belongs to the pragmatists who accept that the logistics world will always be messy and fragmented, and who build their systems to be resilient in the face of that chaos rather than waiting for a perfect, integrated world that isn’t coming. Do you have any advice for our readers who are currently struggling with manual data entry? My advice is to stop viewing automation as a project with a finish line and start viewing it as a permanent operational capability. If you are still relying on a team to manage twelve open browser tabs, don’t wait for a global API standard to save you; instead, build a process that can handle the messy interfaces of today while ensuring you have a rigorous way to catch the system the moment it stops being right.
