How Small and Midsize Trucking Companies Can Close the Profitability Gap

TUCKER, Ga. — Trucking is experiencing its AI breakthrough moment. For brokers navigating an increasingly complex market, the tools to optimize operations are finally here. A recent Boston Consulting Group survey found that emerging AI solutions are significantly improving processes for Logistics Service Providers. Nearly half cited plans to deploy AI for more efficient workflows and greater profitability. Because reactive pricing is one of the hardest problems in logistics, it is the one we decided to solve first.

Small and midsize trucking companies represent the backbone of the American freight industry, yet they have historically been underserved by technology solutions. These businesses have struggled to operate profitably for years on instinct and institutional memory alone, no small feat in an industry with margins this thin, without any of the pricing infrastructure larger fleets take for granted.

The Problem: Margin Is Survival

In freight brokerage, margin is not just a financial metric. It is survival. Large brokerages can afford dedicated back-office teams to handle pricing, and even then, achieving margin is brutal. That is where agentic AI changes the equation. It offers smaller companies access to operational capabilities that were once available only to larger enterprises, without enterprise-level overhead and costs, allowing them to protect margins while keeping their teams focused on serving customers.

Most businesses cannot hire a data scientist to comb through years of routes, seasonality, and customer behavior to find dollars left on the table. So we set out to create a pricing engine to take on that work. By harnessing agentic AI, these teams turn operational guesswork into a system that improves with every completed job, without requiring the technology budget or headcount only large companies have enjoyed.

Proven Results: What Happens When AI Meets Trucking

In 2024, Sherpa Auto Transport implemented an AI-powered pricing engine designed to move pricing decisions beyond instinct and spreadsheets. Within a year of deployment, Sherpa experienced measurable improvements across several key business metrics:

These results demonstrate what is possible when AI accessibility is prioritized for smaller players. These solutions are not years away. They are here now.

What Motivated Us to Build a Better Solution

Here are some of the lessons that motivated us to create a better solution accessible to the industry majority:

1. When innovation is siloed to the big players, freight moves across the country less efficiently.
2. The data has always been there, but a lack of timely visibility and manual processes made it unusable in real time.
3. The inability to hire a technical staff should not be a barrier to entry for teams looking to utilize AI.

Our mission is to ease the burden small and midsize companies feel when trying to integrate new tech while also taking care of their day-to-day functions. We built our AI modules to be intuitive, not to require large tech teams and robust back office support, and to help people that are in the same difficult situations in trucking we have experienced.

The advent of agentic AI has fundamentally changed the landscape for lean teams. It is now possible to provide deep operational assistance for brokers where AI agents handle complex tasks with minimal manual intervention, allowing teams to focus on growth rather than data entry.

This is not just about AI or logistics. It is about strengthening the supply chains that keep America moving. When small carriers and brokers have access to the same intelligent tools as the industry's largest players, freight moves more efficiently, businesses become more competitive, costs are reduced, and consumers benefit. We believe innovation should lift the entire industry, not just the few companies large enough to afford it.