Inside the Logistics AI Playbook: Insights from MileNow’s CEO Shawar Javed

Summary
AI adoption in logistics isn’t about racing for automation—it’s about solving the right problems, at the right time, with the right strategy. Shawar Javed, Co-Founder and CEO of MileNow, shares how targeted AI adoption has improved efficiency, reduced returns, and brought measurable value to operations—all without disrupting existing systems.

Introduction

In today’s logistics landscape, AI is often seen as a silver bullet. But according to Shawar Javed, Co-Founder and CEO of MileNow, success depends on how—and where—you apply it. Her approach: start with business value, focus on high-impact use cases, and ensure AI supports teams, not replaces them.

Making AI Practical: Use Cases That Matter

“We’ve seen the most value when AI is deployed to fix specific friction points. For us, that meant things like order confirmations, dispatch accuracy, and reducing customer wait times.”

Rather than chasing flashy solutions, Shawar’s team prioritized use cases that delivered immediate impact. By automating order confirmation processes, clients ensured on-time responses and saw a 25% drop in product returns. On the delivery side, smart dispatching helped match the right drivers to the right jobs, improving overall reliability.

Navigating the AI Rollout Without Disruption

“You can’t just drop AI into your system and expect it to work. We approached it in phases—testing, adjusting, and learning at every step.”

Shawar emphasized the importance of integrating AI in a way that respects the rhythm of existing operations. Their phased approach helped the team avoid common pitfalls. Instead of overhauling everything, they layered AI onto key processes, refining each system as it proved its value.

Balancing Automation and the Human Element

“AI should support people, not sideline them. Our goal was to reduce repetitive work so teams could focus on higher-value decisions.”

While AI took over many routine customer service tasks, the goal was never full replacement. By automating standard queries, two agents could now handle what previously took five. The result? Faster response times, cost savings, and a team empowered to work smarter—not harder.

Designing a Customer-Centric AI Strategy

“We asked ourselves: is this making the experience better for the customer? If not, we didn’t pursue it.”

Whether it was reducing delays, avoiding misrouted packages, or cutting down on errors, every AI initiative was tied back to customer experience. Address accuracy, for instance, was improved through AI-driven extraction and validation—leading to smoother deliveries and fewer returns.

Leading the Change Across the Organization

“AI can’t just live in the tech team. Everyone needs to understand how it impacts the business.”

Shawar made it clear that building AI literacy across departments was critical. From dispatch to customer success, her focus has been on giving every team the tools—and the context—to work alongside AI. This cross-functional understanding helped scale solutions faster and drive adoption across the board.

Frequently Asked Questions

Q: What’s one mistake to avoid when implementing AI in logistics?
A: Trying to do too much, too soon. Focus on solving one clear problem first and expand from there.

Q: How do you balance speed with responsible AI deployment?
A: We move fast, but always validate. Every rollout goes through real-world testing before wider implementation.

Q: What’s the key to long-term AI success in this space?
A: Flexibility. Business needs change, and your AI systems have to evolve with them.