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AI & Automation

Analysis

AI Cuts Logistics Costs 5%–20%, Inventory 20%–30% in Supply Chains

AI delivers measurable logistics value in demand forecasting, freight matching, warehouse slotting, and shipment visibility—cutting inventory by 20–30%, logistics costs by 5–20%, and procurement spend by 5–15%. A last-mile operator saved $30M–$35M on a $2M AI investment. Meanwhile, 51% of CEOs report AI-driven supplier risk monitoring gains, though data quality (38%), skills gaps (30%), and ROI clarity (29%) remain key barriers. Real-world wins include 100,000+ AI-handled carrier calls and automated processing of 100–120 daily orders.

Original source: Source information pending

AI Cuts Logistics Costs 5%–20%, Inventory 20%–30% in Supply Chains

According to inboundlogistics.com, artificial intelligence delivers measurable operational value in structured logistics domains—including demand forecasting, freight matching, warehouse slotting, and shipment visibility—while falling short in judgment-intensive tasks like exception handling and customs brokerage.

Where AI Delivers Operational Value

Nicolai von Bismarck, Partner at McKinsey & Company, states that AI excels in repeatable, rules-based workflows where outcomes are quantifiable. McKinsey’s research on AI in distribution operations shows 20 to 30% reductions in inventory, 5 to 20% cuts in logistics costs, and 5 to 15% decreases in procurement spend. One last-mile operator with more than 10,000 vehicles achieved $30 million to $35 million in annual savings from AI-powered virtual dispatcher agents—a 15x return on a $2-million investment.

The gap between AI deployment and AI impact remains wide, as many organizations pursue uncoordinated experimentation without clear linkage to economic value. Success hinges on identifying high-leverage use cases where AI creates disproportionate impact—and prioritizing those first.

Supplier Risk Monitoring and Adoption Barriers

According to The Global Supply Chain Resilience Outlook report by Proxima (a procurement and supply chain consultancy part of Bain & Company), 51% of surveyed CEOs say AI is delivering measurable value in supplier risk monitoring. Yet barriers persist: 38% cite data quality issues, 30% point to lack of skills, and 29% note unclear ROI. The report surveyed over 500 CEOs at businesses generating more than $500 million in annual revenue across the United States, UK, Australia, Singapore, and Germany.

These constraints reflect both technological limits—AI struggles with ambiguity and novel scenarios—and human adoption challenges, especially among frontline workers whose decision-making patterns don’t align with AI tool design.

Real-World Gains in Order Entry and Carrier Engagement

Matt Huckeba, Chief Strategy Officer at Evans Transportation, highlights two high-volume applications delivering daily service improvements: order entry and carrier calls. Previously, team members manually keyed 100 to 120 orders per day; now AI agents extract shipment details from emails and PDFs and populate the transportation management system automatically—reducing human touchpoints to just one or two orders daily.

On the carrier side, AI agents have answered more than 100,000 inbound calls over several months—identifying carriers by MC number, confirming safety status, filtering spam, and performing initial rate qualification. Before this, roughly half those calls were missed; today, nearly all are answered, improving capacity coverage, pricing speed, and load retention.

Repetitive Task Automation and Development Acceleration

Milton Feliciano, Vice President, Information Technology at iGPS Logistics, confirms gains in exception handling, status updates, and data entry—tasks once requiring dedicated personnel. He adds:

“For repetitive tasks that used to require a dedicated person, like exception handling, status updates, and data entry, AI just does it, cleanly and consistently. That has been a genuine win.” — Milton Feliciano, Vice President, Information Technology, iGPS Logistics

AI code assistants have also accelerated internal development: tools and dashboards now ship in days instead of weeks. However, Feliciano cautions that AI doesn’t replace domain-specific operational knowledge—such as understanding pallet network dynamics, customer behavior, or recurring exception patterns—which remains foundational to competitive advantage.

Physical AI Partnership Scales Globally

Wiliot, a provider of Physical AI for supply chains, expanded its collaboration with AT&T to scale deployment of its Physical AI platform across enterprise supply chain environments. Since late 2025, the companies have operated under a systems integration model supporting design, installation, asset tagging, and ongoing maintenance. Wiliot’s platform supplies sensing and intelligence via battery-free IoT Pixels, while AT&T provides cellular connectivity, network infrastructure, and field execution.

This partnership responds to rising enterprise demand for continuous, item-level visibility across complex global supply chains—especially as physical operations digitize at scale.

Source: inboundlogistics.com

Compiled from international media by the SCI.AI editorial team.

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AI & Automation

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