How to lead your freight brokerage through an AI tech transformation

Your most experienced reps have built their careers on relationships and gut instinct, perfecting their workflows over decades. That’s why introducing new technology like AI can feel disruptive. In a time-sensitive industry like freight, even small changes to proven routines can feel risky.

But the freight market is moving fast. Competitive teams quote loads in seconds, while others get left behind. AI is no longer optional, but even the best tools won’t stick without effective leadership.

Jay Gustafson, EVP of Brokerage Operations at Echo Global Logistics, has helped hundreds of freight professionals make that shift. His takeaway? Adoption sticks when you prove value with small wins, scaling AI implementation step by step.

In this guide, Jay shares the tactics he used to win over AI skeptics: practical strategies you can implement to help your team win more freight with AI.

Define Rep Problems Before Introducing Your Tool

Your reps have expertise that no algorithm can fully match, including knowledge of subtle seasonal patterns and carrier preferences. When Jay introduced AI pricing at Echo, many reps insisted they could price better than the AI model. And in their world, that rang true. Human judgment was still critical for effective pricing for complex, highly nuanced loads.

The pushback wasn’t really about the math; it was about trust. Too often, leaders roll out new tech without showing how it connects to real problems on the floor. “When it comes to the daily operations that their teams perform,” Jay admits, “Leadership can sometimes be out of touch.”

That’s why Jay recommends starting with data, not assumptions about what reps need. Instead of launching tools based on theory, map out where your reps’ work breaks down: response times that lose deals, error rates that eat into margins, and unrealistic quote volumes that delay service. Then, connect AI solutions directly to those gaps.

At Echo, that’s precisely how Jay course-corrected. He didn't lecture reps on the algorithms or abstract inefficiencies. Instead, he showed reps the specific quotes they missed due to bandwidth limitations. Once reps saw their commission disappearing as opportunities were lost to faster competitors, adopting AI tech felt like a straightforward solution.

Build Helpful Support Systems

AI adoption often demands personalized coaching. When time and money are on the line, reps can’t afford to fumble through new systems. That’s why a one-size-fits-all approach to training often fails.

Jay suggests using change management and one-on-one training to successfully tackle AI tool introductions. Instead of hosting conference room training sessions or recording one-way tutorials, try sitting with reps while they work and walking them through solutions in real time. “We really believe that this white-glove approach is an impactful way to drive change,” Jay explains. “It really helps show people how this new way of doing things is better for them in their day-to-day work.”

And Echo’s experience shows why. They tracked AI adoption closely, circled back to struggling teams, and paired reps with training buddies for at least two weeks. Then, Jay’s team followed up rep training with office hours where team members could ask questions without judgment.

While this high-touch approach might seem expensive, it doesn’t require extensive resources. This approach is also far less expensive than the cost of a failed adoption, including wasted upfront investment, drained team hours, and even customers lost during unsuccessful transitions.

Prove Your Point with Real Numbers

Reps rely on real experiences, not industry benchmarks. No matter how convincing your external research is, reps want proof from their own accounts and books of business.

Jay recommends running volunteer pilot programs to build reps’ trust in new AI solutions. “When our people have rightfully raised tech concerns,” Jay explains, “We use operational data to work through those roadblocks.”

Start by finding the reps eager to test AI solutions. Then, track their improvements across key metrics like win rates. When reps see their early-adopter peers moving more freight with less effort, their skepticism fades and adoption gains momentum.

At Echo, Jay showed individual reps their before-and-after numbers. “We used the data and the outcome of those algorithms to demonstrate rep success and build confidence in AI solutions,” Jay says. Four years ago, Echo priced everything manually. Today, they run 100% of spot quotes through AI algorithms.

Turn Internal Frustrations Into Your Implementation Roadmap

Your team already knows what should be automated; they’ve been hacking together creative workarounds for years. The complaints reps share at happy hour should lay the foundation for your AI implementation roadmap.

Jay recommends reversing the typical tech rollout by taking a bottom-up approach. “First, ask your employees how they use publicly available AI services or LLMs,” he says. “Then, solicit ideas from the reps who do your frontline work.”

Not sure where to start? Try asking your team what processes take too long, where they’ve cobbled together their solutions, and what makes them want to toss their computer out of the window. “Focus less on the details of change,” Jay suggests, “And more on what that change enables.”

At Echo, Jay turned common rep frustrations into solutions while publicly crediting the sources. For example, if a rep suggests automating rate checks, they’re recognized when the solution launches. This strategy creates a positive feedback loop. Reps highlight problems, see helpful solutions built, and naturally watch for more opportunities.

Pick Partners Who Understand the Complexity of Freight

Too many tech vendors try to force reps into workflows that don’t reflect how the industry actually runs. The right technology partner should meet your reps where they are, not force them into rigid processes.

Jay recommends evaluating AI tools through a simple lens: Is the solution flexible enough to handle the daily reality of your freight workflows? Tools should cope with messy workflows, adapt to different customer preferences, and fit directly into the systems your reps already use.

Take Echo’s early AI pricing algorithms, for example. While the tool was a step in the right direction, it left most of the team’s book untouched. “Many of our customers still reach out to Echo via email for quotes,” he explains. “But our first AI pricing tool was portal-based. That algorithm only priced out tech-savvy customers, which covered about 15 to 20% of our base.”

The breakthrough came when Echo partnered with Vooma, an AI automation platform created by people who understand the reality of freight. By extending AI capabilities into email, Vooma fits Echo’s real communication mix and enables reps to bring AI pricing to every customer, not just the digital-savvy few.

How Vooma Accelerates Your AI Success

The question isn't whether or not you should adopt AI. It’s whether you have the right partner to make it work in the fast-moving, unpredictable world of freight.

That’s where Vooma comes in. We help you turn AI into a revenue-driving operation by:

• Proving value with your data: Vooma's analytics dashboard tracks the metrics your reps care about, including response times and win rates, so adoption is driven by real outcomes, not theory.

• Keeping adoption on track: Vooma adapts to your team’s pace and processes, so adoption builds momentum instead of stalling. That’s why companies like DTS can now handle thousands more quotes without increasing headcount.
• Working flexibly with freight’s messy reality: Vooma plugs into the tools and channels you already use, including TMS platforms, email, spreadsheets, and phone calls. No rigid workflows, no complex customization.

Book a demo to see how brokerages like yours are winning more business with Vooma.