AI for Order Intake

The Case for AI in Order Intake

Many enterprises have EDI connections with strategic customers but they most often receive orders by email with PDFs, spreadsheets or free texts because they don't want to force customers to adjust to an alternative approach like a portal. This persistent challenge in our industry is something that even leaders at global Fortune 500 companies report. For this report I analyzed conference presentations and case studies shared by over 10 global industry leaders - including Pfizer, Siemens, and Carlsberg document exactly how they are successfully using Al to digitize unstructured orders (emails, PDFs, and faxes). to a To bridge the gap between observation and execution, I have also included a case study from Glacis. This section details our work with a $10 billion global manufacturer, offering transparent look at how we implemented Agentic Al to transform a complex, multi-channel order workflow into a streamlined, automated asset.

Automation Use Cases & Business Impact

I compiled this data to showcase detailed breakdown of how leading enterprises are deploying Al to digitize unstructured orders, demonstrating the direct link between automated workflows and measurable operational gains like 80%+ touchless processing.

Company	Industry	Use Case	Impact
Pfizer	Pharma Manufacturing	Captures unstructured orders (email/PDF), validates pricing/codes, creates ERP orders.	Achieved 80% touchless processing; order entry time dropped to <5 minutes; scaled to 10 markets in 6 months.
Thermo Fisher Scientific	Scientific Equipment	Integrated 17 systems. Al digitizes orders from phone/fax/email.	$200-$300M cash flow improvement due to speed; 27% faster call handling; 0.5-1% revenue uplift.
Carlsberg Breweries	Food & Beverage	Automates capture of emailed distributor orders across 150 markets.	92% touchless order processing rate; saved 140+ hours/month; accelerated time to-market.
Siemens	Industrial Manufacturing	Al extracts PO data, suppliers confirm in cloud portal.	Saved 55,000 working hours/year; self-service tools eliminated "Where is my order?" calls.
BMW	Automotive	Al agent analyzes supplier offers; shop-floor robots (STRs) linked to order data.	Rapid "stress testing" of supplier networks during shortages; optimized JIT logistics.
Schneider Electric	Energy Management	Software maps PDFs to EDI; Al agents handle ad-hoc PPE orders.	70% Zero-Touch for non-EDI orders; robots cut PPE order processing from 4 hours to 2 minutes.
Heineken Spain	Food & Beverage	Al splits single PDF orders into multiple ERP Sales Orders based on kegs vs. glass.	500% faster processing; 50% of orders fully touchless.
ABB	Robotics & Automation	Al optimizes distributor ordering patterns; Vision Al robots pick items.	Saved $200M/year via inventory optimization; picking robots achieve 99.5% accuracу.
Tetra Pak	Packaging Solutions	Real-time digital platform visualizes "digital thread" of orders/production stops.	85% reduction in information collection time; shifted to "factbased" decision making.

How we solved this for a $10 Billion Manufacturer

While these giants have vast resources, we wanted to prove this is achievable for mid-to-large enterprises without billion dollar R&D budgets. Below is a case study of how we did it for a $10B manufacturer.

A $10B manufacturer was struggling to keep up with orders from customers who vary greatly in size and technical sophistication.

Their customers use different channels to send orders and to "meet the customer where they are",

the company accepts orders in 6-8 different ways:

EDI
Bulk orders with Spreadsheets
Email with PDFs
Free-text emails
Scans/faxes of handwritten forms
Customer portal

Therefore the internal order management team pays the price:

Teams pull orders from shared inboxes all day.
They re-type Spreadsheet and PDF orders line by line into ERP.
Scans and faxes require manual interpretation and follow-up.
The legacy portal exists, but adoption is low and customers default back to email.

From deciphering varied formats to manual data entry and back-and-forth clarifications, each step introduces delay and the risk of error. Our client highlighted price matching as a key issue - a symptom of this chaotic process where up to one-third of orders can contain exceptions.

The High Cost of Manual Order Intake

The VP of Supply Chain told us that order intake is a significant time sink that pulls skilled customer service and sales staff away from customer-facing and strategic initiatives

The Solution: AI as the "Anti-Portal"

We built an Al agent that doesn't ask customers to log in but can flexibly handle email, CSV files, XML, PDFs, Spreadsheets - whatever they're using to communicate and collaborate. It plugs into the existing group inbox, scans every incoming message, extracts PO details, quantities, delivery dates, prices, and line items, and then syncs directly with the ERP.

Here's how it works:

Step 1 - Customer sends an email with free text and an attached spreadsheet.

Step 2 - Al agent pulls the email from a group order inbox.

Step 3 - Al agent then:

Extracts items, quantities, requested ship dates, and ship-to site.
Maps customer descriptions ("Dark Roast 5lb bag") to internal item codes.
Checks price, terms, and master data against the company's records.

Step 4 - If data is complete and matches rules:

Al agent creates the sales order.
Al agent then syncs the order into ERP.
Al agent sends a clean confirmation email to the customer: items, price, ship date window.

Step 5 - If something is missing (e.g., FedEx account number):

Al agent flags the order as an exception.
Al sends a targeted clarification email asking only for what is missing.
When the customer replies, the Al updates master data and re-runs validation.

All of this is defined in a standard operating procedure (SOP) playbook inside the platform.

The playbook is configurable by the supply chain teams, not just IT.

Al Automation Delivers Transformative Results Across Key Metrics

The most critical ROI is reclaiming opportunity cost. By automating the mundane, you empower your best people to focus on what matters most: enhancing customer experience, strategic planning, and driving revenue.

For this $10B manufacturer, automating order intake with Glacis delivered:

Metric	Manual Process	With Order Intake Al Agent
Processing Time	8-15 Minutes / Order	< 1 Minute / Order
Cost	High ($10-$15 / Order)	Low ($1.77-$5/ Order)
Accuracy	Lower OTIF prone to costly errors (1-4%)	Higher OTIF near-perfect accuracy
Lead Time	Delayed by backlogs & business hours	Respond fast to customers in real time
Win Rate	Lost sales to faster competitors	Turn quotes into orders instantly

Closing Thoughts

Supply chain excellence is ultimately measured by reliability.

By analyzing leaders like Pfizer and Siemens, the answer is clear. The technology to fix the manual order intake finally exists and it no longer requires forcing your customers to change their behavior.

As this report highlights, manual data entry creates upstream errors that directly undermine your OTIF (On-Time In-Full) performance.

You cannot achieve perfect delivery with imperfect data. We built the Order Intake Al Agent to eliminate the manual friction and errors that hold your metrics back. The result is not just faster processing, but the higher OTIF and near-perfect accuracy that industry leaders demand.

Don't just take my word for it. Let's look at your data.

We have proven that a 93% reduction in processing time is replicable. I invite you to a brief consultation to calculate exactly how much opportunity cost you can reclaim this quarter.

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AI for Automating Order Intake