Check out our recent article on Generative AI uses cases in Logistics here, co-authored with Bain Capital Ventures

Generative AI Is Reinventing Logistics Tech

We explore the opportunities startups can take advantage of to revolutionize freight logistics through generative artificial intelligence.

Operator Stack x Bain Capital Ventures
Operator Stack co-authored this article with Zeeza Cole at Bain Capital Ventures.

Vertical SaaS has been a major success story over the last decade, transforming historically manual industries, largely through automation. Companies like ServiceTitan (home services), Toast (dining) and Procore (construction) became multi-billion dollar companies by replacing pen and paper and outdated on-premise solutions with industry-specific, cloud-based software that enabled a step change in end-user productivity and growth. In many ways, we believe generative AI is taking this automation to the next level, especially in historically automation-resistant verticals, such as law (e.g., EvenUp with demand letters) and insurance (e.g., Sixfold with underwriting).

We believe the logistics category is the next frontier, with GenAI applications offering significant workflow improvements. With nearly $1 trillion in spend, the industry is massive and fundamental to how the economy operates. Yet in many areas it remains largely under-supported from a technology perspective and is still one of the most labor-intensive sectors. The industry has adopted some automation to reduce human errors and improve operational efficiency, but there is still significant room for improvement.

Our Thesis: As language models continue to improve, progress toward the “touchless load” will continue to grow. This is the state where each step of the load execution process becomes sufficiently automated, such that the loads — once tendered to a broker — get automatically routed to the optimal carrier, and ultimately paid, with minimal human intervention. We also expect to see increasing horizontal and intermodal expansion over time, as innovative companies expand their product use cases and branch out to adjacent parts of the ecosystem. Many of these AI-first improvements will be built by incumbents, but we also see room for startups that focus on seamlessly integrating GenAI applications into core productivity use cases throughout the execution of a truckload.

Where the opportunity lies

We believe freight market conditions today (unprecedented loose market where supply surpasses demand) significantly elevate the importance of cost management and support our core automation thesis. Although digital brokerages like Convoy helped shine a light on structural issues in the market and innovated on a lot of first-to-market technologies, there are some challenges with the digital freight broker model and these are exacerbated during a loose market. The clear lesson from Convoy for both traditional and digital brokers is that cost structure matters, and this is a key reason why we are optimistic about the potential for GenAI in this category, as we discuss in more detail below.

Why generative AI?

Although predictive AI has improved key logistics workflows, such as load pricing, route optimization and real-time shipment visibility, GenAI’s impact on logistics tech could be equally – if not more profound – than predictive AI, for a couple of key reasons:

1. Language models provide standardization and enable significantly faster system and data integrations.
In the freight world, data integrations between counterparties (e.g., shipper to broker) are critical to ensure consistent tracking and delivery of freight on agreed-upon timelines.

We increasingly see potential for natural language models to understand, map and unify historically unstructured data across traditional enterprise resource planning (ERP), transport management system (TMS) and warehouse management system (WMS) data stores, facilitating seamless data transfer between counterparties.

This high level data mapping process alone could potentially shave weeks off of a typical system integration process, driving faster time to revenue and reducing integration costs.

2. GenAI-powered software can unlock significant efficiency gains.
As in the legal world, where higher caseload volume means higher revenue, it is the same for freight brokers: More loads equals more revenue. For example, if the average truck brokerage employee (or “carrier rep”) could book 8-10 loads per day with various carriers (i.e., entities that transport the load) using pen and paper, and vertical SaaS applications can improve that to 22-25 loads per day, we see a world where about 200 loads booked per carrier rep per day could be possible with GenAI.

To illustrate: a carrier rep today performs countless semi-structured, repetitive tasks, such as sending hundreds of emails per day with similar data fields (e.g., weight, destination, pricing) and spending hours on the phone with potential carriers to schedule loads. LLMs cannot only structure but also automate these processes by sending out requests, tracking follow-ups and managing the process in a single pane of glass.

This type of automation translates into a significant cost reduction in labor — which is one of the largest expense items at a traditional truck brokerage.

Where the innovation is today and where it’s headed

Founders are already building — and we see further potential for — GenAI applications across the entire freight brokerage transport value chain, including data translation and systems integration, load rating (pricing), quoting and order entry, scheduling, carrier procurement, and even payments.

Although our primary focus here is on truck brokerage, we think (and point out below) where many of these GenAI capabilities could be useful for shippers and carriers, as well.

Data translation and integration

In the US alone, thousands of shippers collectively spend nearly $1 trillion per year moving loads across the country – and about 80% is spent on trucking. These loads are distributed across hundreds of thousands of trucking companies (“carriers”), and in the middle are roughly 26,000 truck brokerages who act as intermediaries, matching truck capacity with shipper demand.

These parties all need to communicate with each other, often in real time, to ensure reliable freight delivery. Historically, counterparties have relied on electronic data interchange (EDI) integrations for structured data exchange, but these often take 6-12 weeks (or longer) to implement and require specialized knowledge, delaying time to revenue and increasing costs.

We see increasing potential for natural language models to streamline these complex system integration processes. For instance, it is now possible to use fine-tuned language models to unify data across disparate enterprise data stores (e.g., TMS/WMS), creating a “knowledge graph” that can then be used to automate data modeling and pipelining between counterparties, for tasks such as inventory coordination, scheduling and invoicing.

Companies like AVRL are currently building unique applications in this space.

Load rating (pricing)

Rating, or pricing, a load is a critical task which can make or break the profitability of brokers. Most truck brokerages have historically bid on loads using insider knowledge or aggregated static inputs, such as load boards (DAT), which can lead to lane mispricing that becomes very costly over time.

Although predictive AI techniques can address this pain point (e.g., using machine learning to optimize load pricing) — and companies like are successfully tackling it today — we see interesting potential applications for GenAI in this part of the value chain, as well.

These include:

  • Implementing a conversational, question-answering interface that enables a shipper, broker or carrier to optimize load distribution and pricing using natural language inputs, incorporating their historical pricing behavior along with external market data, and improving the outputs through reinforcement learning over time

  • Rendering external-facing components of the output into email, text or even voice, which can then be used in quoting and negotiation

Quoting/order entry

Order entry today is a “pull-based” process whereby mid-sized and smaller shippers will often invite a broker or carrier to tender and quote loads via highly manual processes such as through a portal, email, phone or text. This is a highly error-prone and time-consuming process.

We see potential for language models to ultimately transform this “pull” order entry process into a “push” process, making it largely automatic and repeatable.

For instance, instead of a broker logging into a shipper’s portal to pull the order information and transfer that data to its own system of record, language models will ultimately do this automatically by reading incoming load data in any format (e.g., PDF, spreadsheet, plain text), extracting the relevant data fields, and pushing that data directly to the broker’s system.

Since many freight brokers handle thousands of loads per year, touchless order entry could drive significant efficiency gains over time. is actively building in this problem space.

Load scheduling

Once a load is acquired from the shipper, interim stops must be scheduled (usually two or more) between the load pickup and dropoff points.

Scheduling methods vary widely but most often involve cumbersome spreadsheets containing hundreds of tabs that list key facilities and contacts that the broker or carrier must call or email to schedule appointments.

Based on our industry conversations, we believe this load scheduling data and manual workflow is perfectly suited for conversational AI, which can take the structured data in spreadsheets and transform it into natural language through context modeling and text generation. This output can then be used in email communication or even to make scheduling calls directly to a warehouse.

Since a meaningful portion of a broker’s workload involves scheduling, this type of automation could be a key accelerant toward achieving a 10x improvement in load volume.

Carrier procurement

Each load accepted by a brokerage must be routed to a carrier. This task is completed by the carrier rep who will call a roster of around 40-50 carriers seeking to maximize margin per load. A single small broker could receive anywhere from five to 10 emails per minute from prospective carriers seeking loads. is developing an AI copilot to automate these carrier conversations and dramatically expedite the email-to-quote process by: 1) processing inbound carrier emails using natural language models and understanding context, 2) starting negotiations with the carrier, and 3) escalating it to a “human in the loop” once the load is ready for booking.

With about 30-40% of a broker’s workload consumed by carrier negotiations, automating this last leg of the load booking process could truly revolutionize throughput for AI-first freight brokers.

Payments and invoicing

Once the load is delivered, both the broker and carrier need to be paid. This is a highly convoluted process today. In addition to the proliferation of invoicing methods and formats used (e.g., EDIs, PDFs, scanned documents, photos), there can be upwards of 30 or more different line items in a single invoice. Invoices tie back to complicated shipper-broker pricing agreements, which often stipulate precise conditions for getting paid. When you’re brokering and hauling thousands of loads per year with hundreds of counterparties, this quickly becomes a back office nightmare.

It is estimated that, on average, 20% of freight invoices have rate errors, and due to this high error rate, many brokers and carriers have come to expect a certain loss on the loads they cover (anywhere from 2-5% of freight managed). On the shipper side, managing this complexity creates additional back office cost, hurts profitability and can damage credibility.

Using AI techniques like natural language processing and artifact extraction, it is increasingly possible to reduce this invoicing complexity by automating it away, significantly reducing aggregate cost and latency across the entire logistics ecosystem.

For instance, Loop is using proprietary, fine-tuned language models to help shippers and brokers automatically extract and structure data from their freight documents, which they can then use to streamline freight audits, accounting or payments, dramatically reducing errors and back-office overhead.

Loop believes 95%+ automation rates are possible in its core data extraction use cases, and its ultimate goal is enabling a fully “touchless experience” from the completion of a load to its payment and reconciliation.

This also highlights the embedded fintech opportunity within the logistics category. By integrating financial functions within day-to-day software, companies like Loop can improve financial access and eliminate unnecessary spend, ultimately providing a greater value proposition.

We’re bullish on this category

We at BCV and Operator Stack have a long history of investing in the logistics category and are bullish on the next wave of founders and startups building in the space.

As language models continue to improve, we are especially excited about future opportunities that leverage this foundational technology to further automate and productize this category.

Thanks for reading, and please reach out ( & if you’re building something transformative in logistics tech!

Special shout-out to Matt McKinney at Loop, Dawn Salvucci-Favier at, Anthony Sutardja at Parade, and Jesse Buckingham and Mike Carter at Vooma for their input on this article.