Danfoss Cut Order Processing from 42 Hours to Minutes. Here's How.

Forty-two hours. That's how long it took Danfoss—a $10 billion Danish engineering company with 40,000 employees—to process an email order.

Not because they were inefficient. Not because they didn't care. But because email-based order processing is genuinely hard. You get an order in your inbox. It has attachments. Someone needs to read it, extract the data, validate it against ERP, check inventory, confirm pricing, and push it through the system. Multiply that by thousands of orders across multiple countries, and suddenly 42 hours doesn't seem unreasonable.

It seems inevitable.

Until it isn't.

What Danfoss Actually Did

Danfoss partnered with Go Autonomous to deploy AI agents specifically for email-based order intake. Not chatbots. Not copilots. Actual agents that read incoming emails, extract data from both the message and attachments, validate everything against their SAP system, and prepare orders for processing.

The key word there is "autonomous." These agents don't wait for a human to click approve on every step. They handle the routine decisions themselves and only loop in humans when something genuinely requires human judgment.

Here's what that looks like in practice:

1. Order email arrives
2. AI agent reads the email and any attachments (PDFs, spreadsheets, whatever)
3. Agent extracts relevant data (product codes, quantities, customer info)
4. Agent validates against SAP (pricing, inventory, customer history)
5. Agent runs compliance checks
6. If everything checks out: order goes straight to processing
7. If something's off: human gets pulled in with full context

The result? 80% of transactional decisions are now made by AI agents. The remaining 20% still get human attention—but those humans have full context and a single interface instead of jumping between five different systems.

The Numbers That Matter

Let's talk about what actually changed:

Before: 42-hour average turnaround for manual orders.

After: Near real-time for 80% of orders.

Time saved per order: ~5 minutes of human work.

Systems consolidated: From 5 separate interfaces down to 1.

That five minutes per order doesn't sound dramatic until you multiply it across thousands of orders. Danfoss estimates the platform will save millions of euros annually. More importantly, their customer care teams went from doing data entry to doing actual customer care.

Why This Works (And Why Most Attempts Don't)

There's a reason most companies haven't achieved this. Email automation sounds simple in concept but breaks down in practice because:

Emails are messy. No two customers format orders the same way. Some send PDFs, some send spreadsheets, some type everything directly into the email body. Traditional automation chokes on this variability.

Validation is complex. It's not enough to extract data—you need to verify it against live systems. Is that price correct? Is that SKU valid? Does this customer have credit?

Edge cases multiply. What happens when an order is partially valid? When a customer asks for something that doesn't exist? When the email contains both an order and a question?

The Danfoss approach works because the AI agents handle variability natively. Machine learning models don't need a rigid template. They interpret intent, extract structured data from unstructured inputs, and handle edge cases by escalating to humans with full context.

This is the difference between "automation" and "AI employees." Automation follows rules. AI employees make judgment calls.

What This Means for Your Business

You probably don't have Danfoss's scale. You're not processing thousands of email orders across Europe. But the underlying problem is universal:

Every business has email-based workflows that eat up human time.

Maybe it's not orders. Maybe it's:

• Customer inquiries that need to be categorized and routed
• Vendor communications that need to be tracked and responded to
• Internal requests that need to be processed and fulfilled
• Lead qualification that requires reading, scoring, and responding

The pattern is always the same: unstructured input arrives, human reads and interprets it, human performs actions across multiple systems, human responds. Repeat thousands of times.

Danfoss proved that AI agents can handle 80% of this autonomously. Not by replacing humans, but by handling the routine so humans can focus on exceptions—the cases that actually benefit from human judgment.

The Rollout Reality

Here's something Danfoss did right that most companies miss: they started small. Spain, France, and Italy first. Proved it worked. Then expanded globally.

That's the playbook. You don't need to automate everything on day one. You need to automate one workflow, prove the ROI, and expand from there.

Danfoss's AI agents now handle order intake across the organization. But it started with a single use case in a single region. The million-euro savings came from scaling something that worked—not from betting big on something unproven.

The Bottom Line

Danfoss cut order processing from 42 hours to minutes. They automated 80% of transactional decisions. They freed their customer care teams to actually care for customers.

They did it with AI agents that read emails, extract data, validate against enterprise systems, and make routine decisions autonomously.

This isn't future tech. This is happening now, at scale, in production.

The question isn't whether AI agents can handle your email workflows. The question is how long you'll keep doing it the 42-hour way.

Want to see what AI employees can do for your email workflows? Try Geta.Team: https://geta.team