Logistics: The Real ROI of Ai

The global supply chain is undergoing significant changes. No longer serving as the simple movement of goods, the supply chain comprises intricate networks that rely on real-time coordination, agility and careful planning. However, even with this foresight, 80 per cent of supply chain managers have reported that unforeseen challenges – from geopolitical tensions, natural disasters, extreme weather events, rising costs of raw materials and labor shortages – have forced them to revise their strategies in the past year.

The Australian market is not immune to these challenges, mainly as its e-commerce sector has grown by 105 per cent over the past five years. Many businesses are increasingly adopting Ai tools to augment their supply chain systems to keep up with this demand. Through Ai-powered analytics, Australian supply chain managers aim to have real-time inventory tracking capabilities, optimised shipping routes and implement early warning systems to plan for potential disruptions.

However, with Ai promised to be a game-changer across the industry, leaders face key questions: Can these tools deliver measurable ROI and what tools deliver longer-term value, rather than short-term hype?

Moving From Vision To Reality

When Ai-based supply chain solutions were introduced, many leaders envisioned the technology as a unifying layer that could seamlessly connect subcontractors, delivery partners, distribution centers and other players across the network. However, this vision depends on a fully digitised, interconnected ecosystem – something that remains largely out of reach for most organisations.

Ai adoption has been mostly tactical and isolated, applied in areas like customer service chatbots, order verification and shipment tracking. While these functions provide value, they fail to deliver the envisioned end-to-end operational transformation.

This is due in large part to the fragmented nature of supply chains. Each link – from manufacturers to logistics providers to retailers – operates with different digital maturity levels, tools and data formats. This fragmentation makes it difficult for Ai tools to access the clean, real-time, shareable data necessary to generate meaningful, network-wide insights and actions.

Budget constraints, disparate technology and real-world operational complexities like manual processes or aging assets only exaggerate these challenges. As a result, many organisations find Ai integration complex and investments produce incremental, rather than transformative, returns.

Bridging The Gap

Despite these challenges, Ai is increasingly being leveraged not to replace legacy systems but to augment and integrate with them. Rather than rip-and-replace approaches, the most successful implementations embed Ai within existing workflows and infrastructure, helping companies accelerate processes and improve decision-making.

Ai often acts as an enabler to help bridge fragmented systems by aggregating data from disparate sources and automating workflows across partners. In the logistics industry, machine learning paired with workflow tools can identify, track and recommend resolutions for exceptions based on historical patterns, creating a feedback loop that continuously improves speed and accuracy, which goes a long way toward delivering on consumer expectations.

This practical approach, which focuses on process acceleration and integration, helps companies reduce latency, minimise manual errors and improve responsiveness without demanding full autonomy from day one. Furthermore, the payout is measurable, with fewer disruptions, reduced downtime and stronger overall supply chain performance. These early successes build confidence and provide a foundation for scaling Ai across the network, turning isolated wins into broader operational transformation.

‘Prove It To Me’

As the use of Ai expands along with the technology’s sophistication, Australian supply-chain teams are shifting their mindset. Ai is no longer experimental or “in development.” Supply-chain leaders are unwilling to speculate on innovative tools for their own sake – they want to know their solutions work, and abstract promises no longer cut it. Instead, providers need to prove their products provide tangible and measurable returns.

To demonstrate ROI, companies must establish baseline metrics before Ai deployment and monitor performance continuously after implementation. Real-time dashboards and analytics tools enable teams to quantify benefits and quickly identify areas for improvement.

Proving value isn’t just about technology capabilities; it’s about linking Ai outcomes directly to business goals like cost savings and customer satisfaction. Suppliers that provide transparent data and actionable insights will build greater trust and long-term partnerships.

They’re also looking to the future. Australian businesses see Ai as an integral part of their digital transformation and sustainability efforts. They recognise that Ai isn’t only a differentiator but an increasingly important component of an agile, intelligent and accountable supply chain. By focusing on measurable outcomes and continuous performance tracking, the technology is helping Australian logistics companies lay the groundwork for continued innovation over the long term.

Sean Tinney is Senior Vice President and General Manager of Enterprise Computing Solutions, Uniysys.

Last Updated on August 15, 2025 by Sean Tinney