Why logistics firms are falling behind on AI ambitions

Logistics firms’ AI ambitions vs. real-world delivery execution

The logistics industry talks a lot about artificial intelligence. Many leaders want to use it to make operations faster and customer experiences better. But the reality on the ground tells a different story. According to research from Zebra Technologies and Oxford Economics, 48% of logistics executives want AI for operational efficiency, and 34% are focused on improving the customer experience. Yet, 62% admit their delivery and field operations still need major improvement. The gap between vision and real-world execution is clear.

What’s happening here is simple: strategy is outrunning capability. AI is often discussed in boardrooms as if it can fix structural issues overnight. It can’t. The real work begins when companies apply it consistently, train teams, and integrate systems that handle data seamlessly. Logistics is under constant pressure, rising eCommerce volumes, complex supply networks, and tighter delivery expectations are stretching operations. Executives need to move past AI as a buzzword and build the architecture that allows it to deliver results day-to-day.

For decision-makers, this moment demands execution discipline. AI without integration is noise. It’s time to match ambition with measured deployment, consistent frontline use, and a focus on operational feedback loops that actually improve performance. The firms that do this will create a measurable advantage.

The critical role of frontline empowerment and enhanced data collection

AI delivers value where data flows are strong and people are empowered to act on them. Phil Sambrook, Transport and Logistics Strategy Director for EMEA at Zebra Technologies, emphasized that improving logistics operations depends on the frontline, the thousands of workers scanning, moving, and recording every product that moves through the system. His argument is straightforward: if AI projects don’t support the people doing the physical work, efficiencies never materialize.

Reliable data collection across every point in the logistics chain is critical. Without this foundation, AI systems can’t identify losses, delays, or theft in time to prevent them. Sambrook pointed to a case where a chocolate manufacturer tracked stolen products by printing unique codes on packages, effectively turning every retail scanner into a checkpoint. This level of visibility demonstrates what’s possible when data is treated as infrastructure.

Zebra’s vision of on-device or “edge” AI moves intelligence directly to the worker’s tools, handheld scanners, cameras, and mobile computers. That means problems can be detected instantly rather than after the fact. Conveyor systems can identify real obstructions instead of false alarms, keeping shipments flowing. Damaged parcels can be flagged and rerouted. Workers can scan multiple barcodes or identify products through visual overlays in seconds.

Executives must understand this: AI succeeds when workers trust it and see its impact in real time. Empowering teams with intuitive, responsive systems builds confidence and accountability. It’s about who uses it and how quickly they can act on it. When that loop closes, efficiency improves and the organization earns agility that competitors can’t easily replicate.

Tangible benefits from AI-driven process improvements

In logistics, speed and accuracy define competitiveness. Artificial intelligence is proving itself in these areas when properly deployed. Zebra Technologies reports that AI-assisted proof-of-delivery tools have reduced workflow time by 55% per stop and brought down claims costs by 10% to 30% annually in some customer operations. These outcomes don’t come from large-scale transformation projects but from focused applications that remove friction from daily tasks.

These systems combine image capture, barcode reading, and automatic removal of personal data into a single process. That level of consolidation cuts wasted motion and reduces errors. Every minute saved on a delivery route adds up to meaningful operational gains. Equally important, improved proof-of-delivery processes increase transparency, shortening the time needed to verify shipments and resolve disputes. The business value becomes direct and quantifiable.

For executives, this demonstrates the kind of incremental yet high-impact improvements AI can deliver today. Many companies overcomplicate AI strategy, expecting broad automation from the start. The firms achieving real results are those introducing targeted AI tools where they immediately drive reliability and lower cost. The focus should remain on building systems that make work faster, data cleaner, and decisions more informed. These small but concrete performance improvements compound into significant competitive strength.

The imperative for organizational change alongside technological upgrades

Phil Sambrook, Transport and Logistics Strategy Director for EMEA at Zebra Technologies, warned that technology alone cannot resolve the delivery performance gap. He underscored the need for cultural and operational support, leadership visibility, employee training, and a commitment to continuous learning. AI becomes valuable only when people adopt it and use it correctly. Without a supportive environment, even the most advanced systems remain idle or underutilized.

Logistics operations run at high speed with tight labor and time constraints. When new tools are introduced without context or guidance, resistance emerges. Workers under pressure can’t afford uncertainty about how technology will affect their workflow. This makes structured onboarding and clear communication essential. Management must explain both the intent and the long-term advantages of AI adoption. When employees understand purpose and benefit, adoption improves, and the transition stabilizes.

For executives, this is a leadership challenge. The most successful AI implementations pair technical investment with people investment. Training cannot be a one-off event but part of ongoing skill development. Leaders should make successful early adopters visible and reward teams that demonstrate measurable performance improvements. In doing so, they signal that AI is not a temporary initiative but a permanent part of the organization’s operating model.

Ultimately, cultural alignment ensures that AI supports and amplifies human capability. It turns technology from a point solution into a sustained operational advantage, driven not just by code and sensors, but by a workforce that understands and embraces it.

Zebra technologies’ strategic positioning in frontline AI solutions

Zebra Technologies is pushing forward with a focused strategy to integrate AI directly into frontline logistics operations. The company has expanded its range of AI products, deploying on-device tools and intelligent software that support real-time decisions on the warehouse floor and during delivery. This approach reflects a clear understanding of how logistics actually functions: success depends on responsiveness, speed, and accurate execution under constant operational pressure.

The company’s recent ranking, 10th among S&P 500 firms for AI readiness by The Wall Street Journal—demonstrates its growing influence in enterprise AI adoption. Zebra aims to meet customers where the operational challenges are most immediate. Its systems enable real-time data analysis through handheld devices, scanners, and sensors, removing delays between identifying and reacting to issues. The result is a simplified workflow that helps logistics companies cut downtime, prevent loss, and improve delivery reliability.

Phil Sambrook, Transport and Logistics Strategy Director for EMEA at Zebra Technologies, noted that bringing AI onto devices used by frontline teams allows repetitive tasks to shift from manual effort to intelligent automation. This doesn’t eliminate jobs, it elevates them. Work becomes more skilled, engaging, and precise, which improves retention and overall performance. It creates a cycle where better tools lead to more motivated staff and improved customer outcomes.

For executives assessing technology partners, Zebra’s direction illustrates the level of integration required for sustainable transformation. The company isn’t selling theoretical automation; it’s refining execution by blending technology, data, and human decision-making at every point in the supply chain. This operational focus ensures that AI isn’t confined to planning systems, it actively enhances how logistics work gets done in real time. The combination of technical capability and practical field application defines Zebra’s leadership position in frontline AI.

Key highlights

• Close the AI ambition–execution gap: Most logistics firms see AI as essential but haven’t achieved meaningful operational progress. Leaders should link AI strategy directly to real delivery processes to turn ambition into measurable results.
• Empower the frontline with real-time data: AI succeeds when frontline workers have access to accurate, timely data. Executives should invest in edge AI and user-friendly tools that help employees identify and resolve issues instantly.
• Focus on practical, high-impact AI use cases: Targeted AI tools, such as proof-of-delivery systems, can deliver quick, tangible ROI. Prioritize implementations that streamline critical tasks and unlock measurable efficiency gains.
• Invest in culture and capability: Successful AI adoption requires continuous training and leadership commitment. Decision-makers must build a supportive culture that encourages learning, transparency, and trust in new systems.
• Align technology and people strategy for long-term gains: Zebra Technologies shows that frontline-focused AI integration drives both performance and engagement. Executives should adopt a similar approach, ensuring technology enhances the workforce.