As logistics systems grow increasingly complex, Nash is pioneering a shift from traditional automation to full-context AI — a layered intelligence that understands and responds to real-world operational nuance.

At DELIVER Europe 2025, Tamer Al Ghussein, Chief Strategy Officer at Nash, walked attendees through a vision of AI-enabled logistics that goes far beyond simple decision trees. With the sector still deeply fragmented — across delivery providers, fulfilment points, and internal tech stacks — Nash offers a new foundation: structured, complete, and real-time data to power every choice from dispatch to support.

Context is King: The Role of Data in Decision-Making

Al Ghussein made one point crystal clear: "Simple rules can’t solve complex problems." Rather than reducing complexity, Nash helps retailers manage it more effectively. The first step is establishing a "complete data model" — integrating everything from order context, delivery SLAs, and fleet performance to environmental signals like weather and peak days.

❝ Most systems operate in silos. Full-context AI helps connect and harmonise these silos to drive smarter and faster decisions — from first mile to customer support. ❞
— Tamer Al Ghussein, Nash

Real Use Cases, Real Impact

Nash’s platform isn’t just theoretical. Al Ghussein shared real-world examples:

• Dynamic Dispatch Decisions: Rather than defaulting to the cheapest provider, Nash’s AI weighs cost, fleet reliability, SLAs, customer NPS, and even pre-existing contracts. A retailer using this system saw a 15-point rise in delivery NPS, a 20% drop in late deliveries, and 10% reduction in fulfilment costs — without changing carriers.

• Smart Address Parsing: For one US catering firm, Nash cut address parsing errors by 99%, helping avoid last-mile failures and saving hours of back-and-forth with drivers.

• AI-Powered Customer Service: With real-time access to order and system context, Nash’s AI agents can resolve up to 85% of delivery inquiries, from rescheduling orders to alerting drivers to building access issues.

The Case for AI-Led Human Support

Far from replacing humans, Nash uses AI to free them for high-value work. Routine support tasks are resolved faster — giving both customers and agents better experiences. AI agents can escalate only the most complex issues, ensuring more focused human intervention.

“AI only works if it has both context and the tooling to take action,” explained Al Ghussein. “Otherwise, it’s just another fancy chatbot.”

What's Next?

Looking ahead, Nash sees potential in autonomous delivery fleets and deeper AI-to-AI interactions between systems. But the core message remains grounded: it all starts with data, unified and contextualised, to build the next generation of smart logistics.