The Problem
A 12-person regional freight broker was spending a disproportionate amount of staff time on work that didn't require human judgment. Three workflows were killing their team:
- Quote generation — Sales reps manually pulling lane rates from multiple carrier portals, building quotes in spreadsheets, then reformatting them into emails. Each quote took 20–35 minutes.
- Status reporting — A dispatcher manually pulling shipment statuses from three different TMS platforms each morning, consolidating into a report, and emailing 14 clients by 9am.
- Invoice reconciliation — A part-time bookkeeper spending 6–8 hours every Friday matching carrier invoices against internal load records, flagging discrepancies manually.
Collectively, these three workflows consumed roughly 18–20 staff hours per week. That's the equivalent of half a full-time employee doing work that adds zero strategic value.
The owner knew AI could help. But every vendor they talked to wanted to sell them a six-figure "AI transformation program." They needed something practical, fast, and measurable.
What We Did
We started with a two-day AI readiness assessment—not a slideshow, an actual audit of their workflows, data sources, and tools. The goal: identify what could be automated with AI in the next 30 days with a clear return.
The three workflows above stood out immediately. They had structured inputs, predictable outputs, and clear success metrics. Perfect candidates.
Week 1–2: Quote Automation
We built an AI pipeline that connects to their carrier rate APIs, accepts a load request (origin, destination, weight, commodity), and generates a formatted quote document in under 90 seconds. The sales rep reviews, adjusts if needed, and sends. What took 30 minutes now takes 3.
Week 2–3: Status Reporting
We built an automated morning report that pulls from all three TMS platforms via API, uses an LLM to summarize exceptions and flag anything needing attention, and emails a clean status report to each client automatically. The dispatcher now handles exceptions only—not routine reporting.
Week 3–4: Invoice Reconciliation
We connected their carrier invoices (via email parsing) to their internal load data in QuickBooks and built an AI reconciliation layer that matches invoices to loads, flags discrepancies above a configurable threshold, and presents the exception list for human review. Friday reconciliation dropped from 7 hours to under 90 minutes.
The Results
At the end of 30 days, the team was running all three automations in production. After the first full quarter:
- 18 hours/week recovered across the team
- Sales response time on quotes dropped from same-day to under 15 minutes
- Client satisfaction improved (faster, more consistent status updates)
- Invoice discrepancy catch rate went up because the AI flags everything, not just what a tired human notices at 4pm on a Friday
- 4.2x ROI in Q1 relative to implementation cost
The owner's comment at our 90-day check-in: "I kept waiting for something to break. It didn't."
What Made This Work
A few things that made this project land cleanly:
We started with the assessment, not the tools. A lot of AI consulting starts with "here's a tool, let's figure out where to use it." We started with the workflows that hurt most, then matched tools to the problem.
We didn't rebuild their stack. All three automations connect to their existing tools—their TMS platforms, QuickBooks, their email. No new platforms, no migration, no rip-and-replace.
We measured from day one. Before we built anything, we agreed on what success looked like in numbers. That made the ROI conversation at 90 days a formality.
The team was trained to own it. By the end of week 4, their dispatcher could add a new TMS integration without our help. That's the goal.
If your business has workflows that feel like they should be automated but you're not sure where to start, the AI readiness assessment is the right first step. It's a real conversation, not a sales pitch.