The AI Transparency Gap: When Algorithms Make Mistakes, Who Pays?

AI is making supply chain decisions across Australia. Freight brokers are using algorithms to price shipments. Warehouses rely on AI to predict inventory needs. Carriers deploy automated route optimisation to cut costs and improve delivery times.

The promise is efficiency. The reality is more complicated.

When an AI pricing algorithm overcommits capacity, when an inventory prediction system creates massive stockouts, or when automated routing sends freight to the wrong location – who is liable? The software vendor? The company that deployed it? The data provider whose information trained the model?

In 2025, as AI adoption accelerates across Australian supply chains, we’re facing a growing legal and operational grey zone where algorithmic decisions create real-world losses, but accountability remains dangerously unclear.

AI Adoption is Accelerating

The numbers tell a clear story. According to the Australian Industry Group’s 2025 Trade & Supply Chain Survey, 81% of Australian supply chain leaders expect new technologies to reduce freight costs by at least 5% by 2030. Meanwhile, 44% of manufacturers plan to increase their supply chain investment in 2026, with AI-powered solutions ranking as a top priority for 27% of respondents.

This isn’t hypothetical. Major logistics providers are already implementing AI at scale. Nearly 40% of supply chain leaders report measurable improvements in logistics and transportation operations due to AI implementation. The technology is being applied to predictive maintenance, demand forecasting, real-time inventory management, and automated carrier matching.

But here’s what the same data reveals: 60% of organisations have yet to capture measurable AI benefits. More concerning, 47% of industrial businesses reported supply chain disruptions in mid-2025 – many of which involved technology failures or integration issues.

The divide isn’t just between adopters and non-adopters. It’s between those implementing AI responsibly with clear governance structures, and those rushing to deploy without understanding the risks.

When Supply Chain AI Fails, Who Pays?

Research from MIT indicates that 73% of supply chain AI failures stem from incomplete data visibility rather than algorithmic problems. The issue isn’t the technology – it’s how it’s implemented and governed across complex logistics networks.

Consider what happens when freight pricing algorithms get it wrong. A broker’s AI system misprices a shipment, leading to thousands of dollars in losses. The carrier claims they were contracted based on the algorithm’s quote. The shipper demands service at the quoted rate. Who absorbs the cost?

Or when warehouse inventory prediction fails. An AI system forecasts high demand and triggers massive stock orders. The demand doesn’t materialise. Who’s responsible for the excess inventory costs – the warehouse operator running the system, the software vendor, or the data provider whose market intelligence trained the model?

Legal experts at Rigby Cooke Lawyers warn that using incorrect tariff classifications based on AI input could expose importers and licensed customs brokers to penalties or compliance actions. Yet “reliance on AI will not amount to a robust defence to allegations by regulators.”

The Australian Competition and Consumer Commission has stated clearly that using AI doesn’t shield companies from responsibility. But Australia’s October 2025 Treasury review revealed that existing consumer laws struggle with AI supply chain complexity.

The problems are structural for logistics operations:

• Multi-party liability chains: A single AI-powered freight decision might involve a TMS vendor, a data analytics provider, a carrier’s routing system, and the broker’s own customised algorithms. When something goes wrong, attribution becomes nearly impossible.
• Service contracts versus system failures: Is an AI routing error a breach of service contract, a software defect, or operator misuse? The answer determines which remedies apply and who bears liability.
• Cross-border complications: Supply chains are inherently global. When an AI system managing international freight makes an error, which jurisdiction’s laws apply? The software developer’s home country? The operator’s location? Where the loss occurred?

Australia’s regulatory approach – relying on existing laws and voluntary standards rather than AI-specific legislation – leaves supply chain operators navigating uncertainty.

Real-World Supply Chain AI Failures

The failures aren’t theoretical – they’re happening now, with real financial consequences.

A global retailer’s AI-powered inventory tool wildly over-ordered during a demand spike, causing millions in excess stock and markdowns. An e-commerce giant’s delivery bot rollout failed spectacularly when the system couldn’t recognise unexpected road closures, paralysing last-mile logistics.

In manufacturing supply chains, the cascading effect of AI failures is particularly severe. When an AI-powered quality control system used by a component supplier upstream fails to detect a defect, that defect often remains undetected until products reach retailers or customers downstream. The manufacturer faces recalls, reputational damage, and legal consequences – even though the fault originated earlier in the supply chain with a supplier’s AI system.

Conversely, when AI systems become overly conservative, rejecting too many acceptable components to avoid defects, they create artificial shortages that delay production and disrupt delivery schedules. The financial impact is immediate, but the liability remains murky.

Perhaps most dangerous are misaligned AI systems operating independently across the same supply chain. One organisation’s AI forecasts a surge in demand and triggers increased production. Simultaneously, a logistics partner’s AI predicts a slowdown and scales back transportation capacity. The result: excess inventory with no way to move it, or critical stockouts despite ample production capacity.

Industry analysis reveals persistent challenges specific to logistics operations:

• Data quality across fragmented systems: Legacy TMS and ERP systems hold data in silos. When AI tries to optimise across these disconnected sources, decisions can be dangerously flawed.
• Carrier network complexity: With over 700,000 carriers operating across different technology platforms, AI systems struggle to maintain accurate, real-time visibility. Pricing and routing decisions based on incomplete information create operational failures.
• Regulatory compliance gaps: Freight brokers must explain algorithmic decisions to enterprise shippers, but many AI systems operate as “black boxes” where even the operators can’t fully explain how specific decisions were made.

McKinsey’s 2025 Global AI Report found that only 16% of companies have successfully scaled AI in supply chain operations, despite 68% claiming to use it. The failures aren’t just technical – they’re operational, creating real financial losses with unclear accountability.

The Insurance Industry Sees the Risk

In 2025, Marsh launched BrokerSafe, a first-of-its-kind freight broker auto liability insurance facility specifically designed to address AI-related exposures in logistics operations.

The product exists because traditional coverage models don’t adequately address algorithmic decision-making in freight brokerage. When AI systems are pricing loads, matching carriers, and optimising routes, the risk profile changes fundamentally. Rising freight broker auto liability rates and increased “nuclear verdicts” in liability cases signal that insurers recognise risks that regulation hasn’t yet addressed.

New startups are developing product liability policies specifically for AI developers in logistics technology. The emergence of these specialised insurance products tells you everything about the perceived accountability gap in supply chain AI.

What Supply Chain Operators Should Do Now

Waiting for regulatory clarity isn’t a strategy. The first major AI liability case in Australian logistics will set a precedent, and you don’t want your operation to be the test case.

Leading supply chain organisations are taking proactive steps:

• Map accountability across your logistics network: Document every AI system touching your supply chain operations – freight pricing algorithms, inventory prediction tools, warehouse management systems, routing optimisation. Identify who developed each system, who provides the data, who customised it, and who operates it. Make responsibilities explicit in contracts.
• Build oversight into freight operations: For high-stakes decisions – large shipments, critical inventory orders, sensitive routing – implement human verification checkpoints. AI can recommend, but experienced logistics professionals should approve before execution.
• Test your systems against operational failures: Don’t just test for efficiency. Test for what happens when port congestion spikes, when carriers cancel unexpectedly, when demand forecasts are wildly wrong. The failures that damage your business are the scenarios your AI wasn’t trained to handle.
• Establish clear escalation protocols: When AI systems detect defects or anomalies, ensure there are defined processes for human intervention, particularly where multiple organisations in the supply chain are affected. Rapid communication prevents localised AI failures from cascading into network-wide disruptions.
• Review insurance for logistics-specific exposures: Standard commercial policies may not cover losses from algorithmic freight pricing errors, inventory prediction failures, or routing optimisation mistakes. Assess your specific exposures and consider specialised coverage.
• Document your AI governance for clients and regulators: When enterprise shippers or regulators ask how your AI makes decisions, you need clear answers. Implement the National AI Centre’s Guidance for AI Adoption specifically for your supply chain operations – not as a compliance exercise, but as operational protection.

Australia’s approach places the burden on logistics operators to manage AI responsibly. The companies establishing robust governance frameworks now will be positioned as trusted partners when mandatory requirements arrive.

The AI transparency gap won’t close itself. In Australian supply chains, the question is whether your organisation will lead on accountability in freight operations, or learn from costly mistakes.