Supply chain · Saudi Arabia · Confidential · Logistics
Logistics operator: AI-assisted support and ticket automation
Practical AI integration: when to automate, how to measure quality, and how we integrated with existing tools without a rip-and-replace.
Challenge
High ticket volume; manual handling was slow, costly, and inconsistent.
Outcome
Custom assistant + workflow automation. Faster resolution and measurable cost reduction.
The organisation’s support team was drowning in repetitive enquiries—shipment status, documentation, and exception handling. Hiring linearly was not sustainable. They needed automation that augmented agents, not a chatbot that frustrated customers.
Problem framing
We categorised ticket types and measured baseline handle time and escalation rates. The highest-volume, lowest-risk categories became candidates for deflection or draft replies. Anything involving liability or account changes stayed with humans, with AI only suggesting text.
Solution design
We implemented a retrieval-assisted workflow: the model could pull from approved knowledge sources and CRM snippets, with guardrails on tone and length. For structured requests, we added deterministic automation (webhooks and status APIs) so the assistant did not “guess” live shipment data.
Quality and governance
Human review loops and logging were built in from the start. We tracked suggestion acceptance rate, edit distance, and escalation triggers. That gave leadership confidence to expand scope gradually instead of betting on a big-bang go-live.
Outcomes
- ~60% improvement in median first-response time for eligible ticket categories.
- Estimated ~35% reduction in cost per ticket for those categories once stable.
- Higher agent satisfaction: less copy-paste, more time on complex cases.
This engagement is a useful pattern for teams asking: “Can AI help our support?” The answer is yes—when scoped to measurable workflows and paired with engineering rigour.