Oiki Acciai Inossidabili SpA: AI-powered order management with watsonx Orchestrate

Oiki Acciai Inossidabili SpA, a stainless steels and special alloys producer and distributor in Italy, modernized order management with an AI-powered workflow. The solution extracts order details from incoming emails in real time, validates extracted data, and triggers downstream workflow actions to reduce manual effort and speed responses.

Location
Italy
Published
March 2026

Reported outcomes

−60%

order processing timeTime & speed

180 minutes per daybid management time saved

Strategic outcomes

Customer experience & trustFaster customer responsesScale & capacityMore scalable order intakeCost efficiencyGreater internal workflow efficiency

Primary read

Use case focus

Showing 3 of 3

  • 1Workflow automation
  • 2Employee productivity
  • 3AI agents
  • Orders arrived mainly by email, requiring sales representatives to manually interpret messages and search internal systems.
  • The manual workflow delayed responses and limited scalability.
  • Oiki partnered with IBM and BlueIT to build an AI-powered order intake workflow using IBM watsonx Orchestrate.
  • Sales reps upload incoming emails to a dedicated interface where the system analyzes messages, identifies intent and extracts product codes and quantities.
  • The solution connects to ERP and document databases and includes governance modules for trustworthy automation.
  • Average order processing time dropped from 30 minutes to 12 minutes.
  • Bid management saved up to 180 minutes per day.
  • The solution improved response times and customer experience.
Architecture

The solution uses IBM watsonx Orchestrate to power an AI Agent that interprets incoming email intent, validates extracted data and triggers downstream actions. It integrates with ERP and document databases through IBM Cloud and includes governance components for trustworthy automation. Sales users upload emails to a dedicated interface and receive extracted order details in real time.

Implementation partners1
Sources & evidence1
Groundedness: 5/5Type: Case StudyPublished: Mar 10, 2026Publisher: IBMEvidence: PrimaryConfidence: High

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