Decisional Awareness: The Next Era of LogisticsTech
Few companies have had as much impact on our daily lives as those in logistics and supply chain. But over the past few years, this industry has faced challenges at every level of the stack: margins are razor-thin, and companies are at the whim of boom-bust cycles layered on top of black-swan events that completely upend the industry. To build a more robust supply chain, we need to make these businesses better, healthier, more resilient, and, of course, more profitable.
From Records to Execution
Logistics technology has evolved through several eras, each one shaped by a recurring cycle: innovations unlock new operational capabilities, those capabilities get deployed by the most forward-thinking companies, and the rest of the industry is forced to adjust to the new standard. The first era was defined by systems of record. In the 1990s, warehouse management systems began tracking inventory and warehouse activity, giving operators visibility, a way to see into their system from above. Over time, this technology became table stakes, and customers started expecting accurate inventory, real-time tracking, and reliable fulfillment at a scale previously unseen. To meet these expectations, systems needed to start evolving from simply recording activity to actually directing the physical execution of work: replenishment, picking, packing, and shipping. This gave rise to a set of systems built around these workflows: TMS for transportation, YMS for yard operations, LMS for labor, OMS for order routing, and WCS/WES for coordinating automation within the warehouse. This transformed logistics technology from a system of record to a proper system of execution. And as time goes on, the cycle repeats. New capability creates new expectations and new pressure to adopt.
The Ceiling of Execution
But there is a clear ceiling to where this cycle has taken us thus far. What we haven't seen made possible yet is a system of decision. Logistics is unique in that day-to-day operational decisions are layered with overlapping, complicated, and sometimes hidden constraints. In a few specific textbook cases, technology does exist to aid decision-making at scale: a TMS can solve routing problems, and a WMS can solve slotting problems. But these individual problems are often well defined and already handled by existing software, with little room for customization or changing conditions. Most decisions can't be made this way. They sit across, between, or adjacent to individual systems, and must account for ill-defined, complex, and constantly shifting demands.
These are the decisions that don't fit neatly into any single system and have historically been left to people under the assumption that they are inherently judgment calls, things that require experience, intuition, and deep familiarity with the operation to navigate. But this same assumption was once true of route planning, where dispatchers built routes by hand, relying on experience and gut feel learned from years on the job. However, as the industry formalized the problem and built the infrastructure to support it, routing became something that algorithms could solve far better than any dispatcher ever could. The same pattern played out with warehouse slotting, labor scheduling, and load planning, and in every case, what used to be guessed at with intuition and tribal knowledge became a solved problem.
The next frontier of technology is to extend this pattern to every decision in every operation, not just the well-scoped problems that already live inside a TMS or WMS, but the messy, cross-functional decisions that people are still making by hand. These are the decisions where a few percentage points of efficiency can be the difference between a losing year and a profitable one, and every one of them has an optimal answer that we haven't yet figured out how to find.
Building a Live Model
The reason is that formalizing these problems is fundamentally harder than formalizing something like route scheduling because we are no longer focused on a single decision process but on coordinating many processes at once, where we may not fully understand how their constraints overlap, or even how the processes actually work end-to-end. This kind of optimization is no longer over the variables of a single process but over the structure of how an entire operation makes decisions.
Doing this in practice requires a live model of the operation itself, one that captures inventory, labor, capacity, transportation, demand, service promises, and exceptions. The challenge is that the data needed to build this model doesn't naturally exist. The data an operation produces typically tells us what happened, not how or why it happened. To formalize these decisions, we need more than outcomes. We need an understanding of the processes themselves, the constraints that shape them, and the tradeoffs that operators navigate every day, none of which have ever been captured in a way that makes formalization possible.
This is the foundational problem, but for the first time, AI makes it solvable. Not because AI is faster or more powerful in a general sense, but because it can do the specific thing that has always been missing, which is to take the messy, unstructured reality of how an operation actually works and turn it into a formal model that technology can act on. AI can process the fragmented data that operations produce, deduce the actual constraints that govern how a business operates, and surface an understanding of processes that has historically lived only in the heads of experienced operators, making it finally possible to formalize the models that optimization requires.
Decisions at Operational Speed
But building the model is only the foundation. The real value is what sits on top of it. Once we have a faithful representation of the operation, including its constraints, dependencies, and current state, we can apply the optimization techniques that have existed for decades. Solvers, operations research, and mathematical programming are powerful, but their reach has always been limited by how narrowly a problem had to be scoped to fit them. With a unified, live model of the operation, that scope expands significantly. Decisions that used to largely be ambiguous or hard to make, handled by whoever happened to be on shift, can now be formulated as proper optimization problems and solved at scale. This is what turns a system of execution into a system of decision.
Consider a scenario that plays out every day at carriers across the country. A driver is mid-haul when traffic delays mean they will run out of Hours of Service before reaching the receiver. The dispatcher now has to decide, in a few minutes, whether to miss the appointment, find a relay driver, reassign the load, or renegotiate the delivery window. Each option touches HOS compliance, driver pay, customer relationships, and the rest of the week's load plan. Today this gets resolved by a dispatcher juggling a phone, a TMS screen, and a mental map of where every driver is. With a live model of the operation and a solver running against it, the same decision can be made in seconds, with full visibility into the downstream effects and consistently across every dispatcher and every shift.
Decisional Awareness
This is what we are building: a platform that unifies fragmented operational data, applies AI to discover the real processes and constraints behind how a business operates, and uses that understanding to formalize and solve the decisions that have never been formalized before. Through this, we give operators a live model of their business, a way to make decisions on top of it at the speed and scale the industry demands, and a feedback loop that gets smarter the longer it runs.
Every previous era of logistics technology was defined by formalizing something that used to be informal, turning visibility into data, turning data into direct execution. This era will be defined by formalizing the decisions themselves, and in doing so, building the kinds of healthier, more resilient businesses that a robust supply chain depends on.