Try OnsaI spend a lot of time looking for real AI case studies. Not demos. Not pilots. Not “we’re exploring the potential of…” announcements. Actual business results that moved numbers.
Most of what I find is vaporware. Companies announce AI initiatives, the stock bumps 2%, and nothing measurable ever gets reported.
Then I came across C.H. Robinson.
They automated their inbound quote process with AI. The stock jumped 20% in a single day—roughly $3 billion in market cap. And unlike most AI announcements, the results were specific, measurable, and directly tied to revenue.
This is that story, and what I think it means for sales teams.
C.H. Robinson is one of the largest freight brokers in the US. Their core business: connecting shippers (companies that need to move stuff) with carriers (trucks that move stuff).
A huge part of this involves quote requests. A shipper emails: “I need to move 40,000 pounds of widgets from Chicago to Phoenix by Friday. How much?”
Before AI, here’s what happened:
1. Employee reads the email
2. Manually extracts shipment details (weight, origin, destination, timeline)
3. Enters data into their transportation management system
4. Looks up pricing
5. Writes a response email
6. Sends it
Each request took time. And they were getting thousands per day.
The result: C.H. Robinson could only respond to about 60% of inbound requests on time.
In freight, speed matters more than most industries. If you’re a shipper and you need a quote now, you’re emailing multiple brokers. First response often wins. Slow response means lost business.
Their AI system handles the entire inbound quote workflow:
Step 1: Reads the incoming email and extracts shipment details—origin, destination, weight, commodity type, timeline, special requirements.
Step 2: Creates the load in their TMS automatically. No human data entry.
Step 3: Calls their pricing engine to get the rate.
Step 4: Generates and sends a response email with the quote.
End-to-end, no human in the loop for standard requests.
Average response time: 2 minutes and 13 seconds.
Volume: approximately 2,000 quote requests per day.
A few things stand out about their approach:
They didn’t change customer behavior.
Shippers still email quote requests the way they always have. No new portal to learn. No API integration required. No “please use our new AI quote tool” messaging.
The customer experience is identical. They email, they get a fast response. They don’t know or care that AI is involved.
This is underrated. A lot of AI projects fail because they require customers to change how they work. C.H. Robinson’s approach was invisible to customers.
They automated a complete workflow, not a single task.
They didn’t just use AI to “help employees draft responses faster.” They automated intake, data entry, pricing lookup, and response—the entire process.
Partial automation creates friction. Full workflow automation creates speed.
They picked the right process.
Inbound quote requests are:
- High volume (2,000/day)
- Time-sensitive (speed wins)
- Structured (similar format each time)
- Low risk (a wrong quote gets corrected, no catastrophe)
This is the sweet spot for AI automation. Not everything qualifies, but this did.
Response rate: 60% → 100%
Before, staff couldn’t keep up with volume. Now every request gets a response.
Response time: Minutes → 2 minutes 13 seconds
Consistently. Not “sometimes fast, sometimes slow”—consistently under 3 minutes.
Cost impact: 25-30% on spot market
C.H. Robinson cited MIT research suggesting that faster responses lead to better pricing dynamics on the spot freight market. When you respond first, you have more leverage.
Stock impact: +20% in one day
After management disclosed these results on an earnings call, the stock jumped roughly 20%. That’s approximately $3 billion in market cap, directly attributed to this AI implementation.
I’ve tracked a lot of “AI announcements.” This is one of the only ones I’ve seen where the market reaction was this immediate and this large—because the results were specific and credible.
C.H. Robinson is a freight company, not a SaaS startup. But the pattern applies broadly.
The pattern: Automate intake and response. Keep relationships human.
Most sales teams have some version of this problem:
These are high-volume, time-sensitive, somewhat structured—exactly the profile that worked for C.H. Robinson.
What doesn’t transfer:
Complex negotiations. Relationship building. Strategic accounts. High-stakes deals where nuance matters.
C.H. Robinson didn’t automate their enterprise sales team. They automated commodity quote requests. Know the difference.
Building Onsa, I’ve thought a lot about what to automate and what to leave human.
C.H. Robinson’s success reinforced something: the ROI is in boring, repetitive, time-sensitive workflows.
Not flashy demos. Not “AI that thinks strategically.” Just: fast responses to structured requests.
When I look at sales teams, I see the same pattern. The highest-leverage AI applications aren’t the sexy ones. They’re:
None of this is exciting. All of it saves hours and accelerates deals.
C.H. Robinson’s process was obvious in hindsight: high volume, time-sensitive, structured, low-risk. Perfect for automation.
What’s your equivalent?
Most sales teams have at least one workflow that fits this profile. Something that:
- Happens repeatedly (daily or more)
- Benefits from speed
- Follows a similar pattern each time
- Doesn’t require senior judgment for every instance
That’s your starting point. Not “how do we use AI?” but “what process has this profile?”
Find that, automate it properly, and the results follow.
I’m Bayram, founder of Onsa. We’re building AI agents for B2B sales prospecting—automating the research and outreach that fits this pattern. If you want to talk about what might apply to your team, find me on LinkedIn.