Welcome to this episode of The New Warehouse Podcast. Kevin chats with Bill Pedriana, President of Noblelift North America, live from MODEX 2026. Noblelift, a global manufacturer of lithium-powered lift trucks, is introducing a new wave of innovation.

The conversation centers on the launch of the Omega and Hero platforms, with a strong focus on AI integration and operator enablement. Pedriana shares how Noblelift is rethinking the forklift, not just as equipment, but as an AI forklift platform designed to improve productivity, safety, and workforce performance in modern warehouse environments.

AI Forklift Platform Shifts the Role of Equipment

The biggest shift is not the hardware. It is how the forklift is positioned in the operation. Pedriana explains that Noblelift is moving beyond traditional equipment thinking. “There’s really nothing like this today, so we’re launching the first AI platform on a forklift.”

That shift turns the forklift into a point-of-use technology layer. It is no longer just moving pallets. It collects data, guides operators, and improves decision-making in real time. This aligns with a deeper insight into cost structure. “The human operator is 75% of the cost of the human machine system… Only 25% of the cost is the machine.”

As a result, the strategy becomes clear. Instead of optimizing the machine alone, Noblelift is investing in the operator. The goal is to improve productivity, safety, and skill development through embedded AI and real-time feedback.

Empowering Operators with Real-Time AI Coaching

According to Pedriana, “The traditional forklift is dead.” Enter the Omega. The Omega is “The first electric truck you never have to plug in.” The platform introduces agentic AI directly into the forklift. This allows the system to learn driver behavior and provide ongoing coaching. Pedriana describes how this changes daily operations. “Using agentic AI at the point of use… really drives the productivity, safety, and capability of an operator.”

The platform tracks safety, productivity, and technique. It also introduces gamification, leaderboards, and performance insights. These tools help operators improve continuously, not just during onboarding. The impact compounds quickly. “The productivity benefit pays for everything… including the truck.”

There is also a strong training implication. Instead of waiting years to develop top talent, the system accelerates learning curves. This matters in a market where skilled labor is harder to find and retain.

Human-in-the-Loop Automation Outpaces Full Autonomy

While automation remains a key trend, Noblelift is taking a different stance. The focus is not on removing the operator. It is enhancing them. Pedriana makes this clear. “Full auto is hard.”

Instead, the Omega enables a hybrid model. Operators can command AGVs and AMRs through voice. They act as orchestrators rather than just drivers. This creates a force multiplier effect across the warehouse. Pedriana believes this approach will outperform full automation in many environments. “Keeping the human in the loop and giving the human tools like this agentic AI platform… outperform full automation.”

This also opens the door for new workforce dynamics. As operators gain more control and capability, their role becomes more strategic. That shift could reshape how warehouses think about labor, training, and compensation.

Key Takeaways

• Noblelift is positioning the forklift as an AI-powered platform, not just material handling equipment.
• Human labor represents roughly 75% of total system cost, driving a focus on operator enablement.
• The Omega introduces real-time AI coaching across safety, productivity, and technique.
• Gamification and performance tracking create continuous skill development.
• The platform enables voice-based orchestration of AGVs and AMRs for hybrid automation.
• Battery innovation allows extended runtime without a traditional charging infrastructure.
• Human-in-the-loop automation is positioned to outperform full autonomy in many operations.
• AI acceleration reduces training time from years to significantly shorter timeframes.