Putting AI to Work in B2B – A Case Study

AI has more than proven it can improve individual workflows, and that trend will absolutely continue. But putting AI to work inside a complex B2B operation is a very different challenge.

In real businesses, value isn’t created in isolation. It’s created across marketing, sales, distributors, CRMs, intake forms, internal rules, and human decision-making. Getting AI to operate inside that reality (safely and accurately) requires more than just dropping in a tool that makes one person faster.

At Trellist, we’ve been experimenting with what we call AI transformation: helping organizations embed AI directly into real, cross-functional workstreams. One result of that effort is Trellist's LeadFlow AI, and we recently implemented it for a B2B manufacturing company.

The Problem

This company was dealing with a familiar challenge:

Low-quality and spam-heavy inbound leads
Manual lead review and routing
A complex distributor network based on lead company type, region, and even related product model

Processing and assigning leads correctly took time and errors were hard to detect.

The Solution

LeadFlow AI was implemented to:

Filter and eliminate spam at the point of intake
Instantly process inbound leads via a team of AI Agents trained on a heuristic developed collaboratively with the client
Accurately route each lead through a complex distributor logic tree

The Results

The impact was immediate:

• The Agent's worked 24-hours per day, solving the pervasive issue of handling international leads received overnight
• Lead handling time was reduced by 90%
• Higher-quality leads reached the right distributors faster
• A surprising discovery: the AI was following the routing rules more accurately than the existing manual process

By comparing AI-driven routing with historical human decisions, the company uncovered a major inefficiency, and in some cases incorrect assignments that were previously undetectable.

The Best Part: It's Repeatable

All the time previously spent managing and correcting leads has now been reclaimed and is now being redirected toward higher-value work. Just as importantly, the entire system was built inside the client’s existing infrastructure, making it secure, scalable, and affordable to maintain.

Now that the Agent layer is complete, we can tackle additional challenges and put more Agents to the test, and that is what we plan to do.

The most important takeaway for me is that the state of the art is now production ready for B2B use cases. But are we ready for our first AI team members?