How Agentic AI Transforms Order Processing: The Hyperfox approach

Supply Chain Café by PICS Belgium supported by ABCAL 23.10.2025
Speaker: André Huynen

Backoffice Maturity in 2025: From Manual Order Entry to Autonomous AI Agents

During our recent Supply Chain Café, we welcomed Tom Devos, CEO of Hyperfox and prof. Kris Pattyn from KU Leuven as guest speakers to share insights on the evolution of back-office order processing and the growing role of AI in operational workflows. Their presentation introduced a clear maturity model that helps organisations understand where they stand today — and what steps are needed to reach true end-to-end order automation.

Below is a summary of the key takeaways.

Why Back-Office Maturity Matters

Many organisations still process a large part of their incoming orders manually — often arriving via email, PDF, Excel, phone, or even WhatsApp. This results in error-prone retyping, constant follow-up, and a heavy reliance on implicit knowledge inside the team.

Hyperfox highlighted that the first step towards scalable automation is building structure:

• a maintained product catalogue
• a clean customer list
• defined pricing rules
• a documented order management process

Without this foundation, advanced automation (let alone AI) cannot succeed.

Assisted Automation: The First Breakthrough

Once structure is in place, companies typically introduce assisted automation: B2B portals, EDI, APIs, OCR, and template-based systems.

This often automates 50–80% of incoming orders — but human validation is still required. Why?
Because customer-specific conditions, consigned stock, delivery constraints and price rules remain highly variable and difficult to fully standardise.

Hyperfox emphasised that assisted automation is an important milestone, but not the finish line.

The Rise of AI Agents in Order Processing

The next leap comes from using multiple AI agents, each responsible for understanding different parts of an incoming request. These agents can:

• read emails, PDFs, Excel, images, audio and WhatsApp messages
• detect whether a message contains a new order, a change, a complaint or a status update
• extract ordered products, quantities, units and delivery dates
• validate business rules automatically
• compare incoming data with master data and pricing policies

A demo during the session showed how several agents independently evaluate imperfect inputs and combine insights to produce a correct, validated order — drastically reducing manual intervention

Agentic AI: From Extraction to Decision Making

Hyperfox presented how modern systems go beyond extraction: they reason, validate and apply business rules autonomously.
Typical examples include:

• checking minimum order quantities
• enforcing earliest delivery dates
• detecting mismatched SKUs
• mapping product descriptions to the correct items
• confirming whether a customer is allowed to order a product

Once validated, the order is pushed directly into the ERP, while humans oversee exceptions instead of retyping data.

Towards a Collaborative AI Ecosystem

A key insight was that today, automation mostly stops at the company boundary.
Distributors automate their incoming orders.
Logistics providers automate transport flows.
Suppliers automate invoicing.

Yet they still rely on emails and PDFs to communicate with one another.

Hyperfox shared a forward-looking vision: AI agents from different companies collaborating — exchanging orders, confirmations, transport instructions and invoices directly. Not just data exchange, but coordinated workflows across the supply chain.

This could significantly reduce latency, manual work and operational overhead between partners.

Humans Remain Central

Despite the rise of autonomous agents, Hyperfox stressed that humans remain in control.
People will shift from data entry to:

• supervising the agent ecosystem
• managing exceptions
• focusing on customer satisfaction
• improving processes

The goal is an environment that is effective, easy, enjoyable and ethical — not one that removes humans, but one that removes repetitive work.

Thank you again for being there!